INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80516, 2017, 'bio', 'Resolution and reconciliation of non-binary gene trees with transfers, duplications and losses', 'Jacox, Edwin|Weller, Mathias|Tannier, Eric|Scornavacca, Celine', 'ISE-M, Université Montpellier, CNRS, IRD, EPHE, Montpellier, France.|Institut de Biologie Computationnelle (IBC), Montpellier, France.|INRIA Rhône-Alpes, LBBE, Université Lyon 1, Lyon, France.|ISE-M, Université Montpellier, CNRS, IRD, EPHE, Montpellier, France.', 'France|France|France|France', 0, 'Algorithms|Computer Simulation|Cyanobacteria|Databases, Genetic|Evolution, Molecular|Extinction, Biological|Gene Duplication|Genes|Genetic Variation|Phylogeny|Proteobacteria', 'Journal Article', '10.1093/bioinformatics/btw778', '28073758', 'Gene trees reconstructed from sequence alignments contain poorly supported branches when the phylogenetic signal in the sequences is insufficient to determine them all. When a species tree is available, the signal of gains and losses of genes can be used to correctly resolve the unsupported parts of the gene history. However finding a most parsimonious binary resolution of a non-binary tree obtained by contracting the unsupported branches is NP-hard if transfer events are considered as possible gene scale events, in addition to gene origination, duplication and loss. We propose an exact, parameterized algorithm to solve this problem in single-exponential time, where the parameter is the number of connected branches of the gene tree that show low support from the sequence alignment or, equivalently, the maximum number of children of any node of the gene tree once the low-support branches have been collapsed. This improves on the best known algorithm by an exponential factor. We propose a way to choose among optimal solutions based on the available information. We show the usability of this principle on several simulated and biological datasets. The results are comparable in quality to several other tested methods having similar goals, but our approach provides a lower running time and a guarantee that the produced solution is optimal.'),
(80517, 2017, 'bio', 'Better diagnostic signatures from RNAseq data through use of auxiliary co-data', 'Novianti, Putri W|Snoek, Barbara C|Wilting, Saskia M|van de Wiel, Mark A', 'Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 4, 'Bayes Theorem|Early Detection of Cancer|Female|Genomics|Humans|Models, Genetic|Molecular Sequence Annotation|Sequence Analysis, RNA|Software|Uterine Cervical Neoplasms', 'Journal Article', '10.1093/bioinformatics/btw837', '28073760', 'Our aim is to improve omics based prediction and feature selection using multiple sources of auxiliary information: co-data. Adaptive group regularized ridge regression (GRridge) was proposed to achieve this by estimating additional group-based penalty parameters through an empirical Bayes method at a low computational cost. We illustrate the GRridge method and software on RNA sequencing datasets. The method boosts the performance of an ordinary ridge regression and outperforms other classifiers. Post-hoc feature selection maintains the predictive ability of the classifier with far fewer markers.'),
(80518, 2017, 'bio', 'Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest', 'Hou, Qingzhen|De Geest, Paul F G|Vranken, Wim F|Heringa, Jaap|Feenstra, K Anton', 'Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.', 'Netherlands|Netherlands|Belgium|Netherlands|Netherlands', 11, 'Algorithms|Computational Biology|Models, Statistical|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Protein Multimerization|ROC Curve|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx005', '28073761', 'Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction. In this paper, we evaluate the importance of various features using Random Forest (RF), and include as a novel feature backbone flexibility predicted from sequences to further optimise protein interface prediction.'),
(80519, 2017, 'bio', 'A DNA intercalation methodology for an efficient prediction of ligand binding pose and energetics', 'Soni, Anjali|Khurana, Pooja|Singh, Tanya|Jayaram, B', 'Department of Chemistry and Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi, India.|Department of Chemistry and Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi, India.|Department of Chemistry and Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi, India.|Department of Chemistry and Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi, India.', 'India|India|India|India', 0, 'Anti-Bacterial Agents|Antineoplastic Agents|Antiviral Agents|Binding Sites|DNA|Intercalating Agents|Molecular Docking Simulation|Molecular Dynamics Simulation|Nucleic Acid Conformation|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx006', '28073762', 'Drug intercalation is an important strategy for DNA inhibition which is often employed in cancer chemotherapy. Despite its high significance, the field is characterized by limited success in identification of novel intercalator molecules and lack of automated and dedicated drug-DNA intercalation methodology.'),
(80520, 2017, 'bio', 'Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies', 'Nikolova, Olga|Moser, Russell|Kemp, Christopher|Gxc3xb6nen, Mehmet|Margolin, Adam A', 'Computational Biology Program, Oregon Health and Science University, Portland, OR 97239, USA.|Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Computational Biology Program, Oregon Health and Science University, Portland, OR 97239, USA.|Computational Biology Program, Oregon Health and Science University, Portland, OR 97239, USA.', 'United States|United States|United States|United States|United States', 4, 'Adenocarcinoma|Antineoplastic Agents|Bayes Theorem|Biomarkers, Pharmacological|Cell Line|Cell Transformation, Neoplastic|Genes, Neoplasm|Genomics|Humans|Lung Neoplasms|Models, Genetic|Neoplasms|Pancreatic Neoplasms|Precision Medicine|Unsupervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btw836', '28082455', 'In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments.'),
(80521, 2017, 'bio', 'H-BLAST: a fast protein sequence alignment toolkit on heterogeneous computers with GPUs', 'Ye, Weicai|Chen, Ying|Zhang, Yongdong|Xu, Yuesheng', 'School of Data and Computer Science, and Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, Peoples Republic of China.|School of Data and Computer Science, and Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, Peoples Republic of China.|School of Data and Computer Science, and Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, Peoples Republic of China.|School of Data and Computer Science, and Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, Peoples Republic of China.', 'China|China|China|China', 9, 'Algorithms|Amino Acid Sequence|Computer Graphics|Computers|Databases, Nucleic Acid|Proteins|Sequence Alignment|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btw769', '28087515', 'The sequence alignment is a fundamental problem in bioinformatics. BLAST is a routinely used tool for this purpose with over 118 000 citations in the past two decades. As the size of bio-sequence databases grows exponentially, the computational speed of alignment softwares must be improved.'),
(80522, 2017, 'bio', 'The super-n-motifs model: a novel alignment-free approach for representing and comparing RNA secondary structures', 'Glouzon, Jean-Pierre Sxc3xa9hi|Perreault, Jean-Pierre|Wang, Shengrui', 'Department of Computer Science, Faculty of Science, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada.|RNA Group, Department of Biochemistry, Faculty of Medicine and Health Sciences, Applied Cancer Research Pavilion, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.|Department of Computer Science, Faculty of Science, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada.', 'Canada|Canada|Canada', 0, 'Algorithms|Computational Biology|Conserved Sequence|G-Quadruplexes|Nucleic Acid Conformation|Nucleotide Motifs|Protein Structure, Secondary|RNA|Sequence Alignment|Time Factors', 'Comparative Study', '10.1093/bioinformatics/btw773', '28088762', 'Comparing ribonucleic acid (RNA) secondary structures of arbitrary size uncovers structural patterns that can provide a better understanding of RNA functions. However, performing fast and accurate secondary structure comparisons is challenging when we take into account the RNA configuration (i.e. linear or circular), the presence of pseudoknot and G-quadruplex (G4) motifs and the increasing number of secondary structures generated by high-throughput probing techniques. To address this challenge, we propose the super-n-motifs model based on a latent analysis of enhanced motifs comprising not only basic motifs but also adjacency relations. The super-n-motifs model computes a vector representation of secondary structures as linear combinations of these motifs.'),
(80523, 2017, 'bio', 'Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R', 'McCarthy, Davis J|Campbell, Kieran R|Lun, Aaron T L|Wills, Quin F', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.|CRUK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 225, 'Cell Line|Humans|Principal Component Analysis|Programming Languages|Quality Control|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw777', '28088763', 'Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization.'),
(80524, 2017, 'bio', 'Development and application of an algorithm to compute weighted multiple glycan alignments', 'Hosoda, Masae|Akune, Yukie|Aoki-Kinoshita, Kiyoko F', 'Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan.|Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan.|Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Databases, Factual|Glycomics|Molecular Structure|Monosaccharides|Polysaccharides', 'Journal Article', '10.1093/bioinformatics/btw827', '28093404', 'A glycan consists of monosaccharides linked by glycosidic bonds, has branches and forms complex molecular structures. Databases have been developed to store large amounts of glycan-binding experiments, including glycan arrays with glycan-binding proteins. However, there are few bioinformatics techniques to analyze large amounts of data for glycans because there are few tools that can handle the complexity of glycan structures. Thus, we have developed the MCAW (Multiple Carbohydrate Alignment with Weights) tool that can align multiple glycan structures, to aid in the understanding of their function as binding recognition molecules.'),
(80525, 2017, 'bio', 'Protein multiple sequence alignment benchmarking through secondary structure prediction', 'Le, Quan|Sievers, Fabian|Higgins, Desmond G', 'Conway Institute, UCD School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Dublin 4, Ireland.|Conway Institute, UCD School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Dublin 4, Ireland.|Conway Institute, UCD School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Dublin 4, Ireland.', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Amino Acid Sequence|Benchmarking|Computational Biology|Data Accuracy|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw840', '28093407', 'Multiple sequence alignment (MSA) is commonly used to analyze sets of homologous protein or DNA sequences. This has lead to the development of many methods and packages for MSA over the past 30 years. Being able to compare different methods has been problematic and has relied on gold standard benchmark datasets of true alignments or on MSA simulations. A number of protein benchmark datasets have been produced which rely on a combination of manual alignment and/or automated superposition of protein structures. These are either restricted to very small MSAs with few sequences or require manual alignment which can be subjective. In both cases, it remains very difficult to properly test MSAs of more than a few dozen sequences. PREFAB and HomFam both rely on using a small subset of sequences of known structure and do not fairly test the quality of a full MSA.'),
(80526, 2017, 'bio', 'VCF-kit: assorted utilities for the variant call format', 'Cook, Daniel E|Andersen, Erik C', 'Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA.|Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA.', 'United States|United States', 19, 'Genetic Variation|Genomics|Information Storage and Retrieval|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx011', '28093408', 'The variant call format (VCF) is a popular standard for storing genetic variation data. As a result, a large collection of tools has been developed that perform diverse analyses using VCF files. However, some tasks common to statistical and population geneticists have not been created yet. To streamline these types of analyses, we created novel tools that analyze or annotate VCF files and organized these tools into a command-line based utility named VCF-kit. VCF-kit adds essential utilities to process and analyze VCF files, including primer generation for variant validation, dendrogram production, genotype imputation from sequence data in linkage studies, and additional tools.'),
(80527, 2017, 'bio', 'DeuteRater: a tool for quantifying peptide isotope precision and kinetic proteomics', 'Naylor, Bradley C|Porter, Michael T|Wilson, Elise|Herring, Adam|Lofthouse, Spencer|Hannemann, Austin|Piccolo, Stephen R|Rockwood, Alan L|Price, John C', 'Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.|Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.|Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.|Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.|Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.|Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Pathology, University of Utah, Salt Lake City, UT, USA.|Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Gene Expression Regulation|Isotopes|Kinetics|Mass Spectrometry|Mice|Peptides|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btx009', '28093409', 'Using mass spectrometry to measure the concentration and turnover of the individual proteins in a proteome, enables the calculation of individual synthesis and degradation rates for each protein. Software to analyze concentration is readily available, but software to analyze turnover is lacking. Data analysis workflows typically dont access the full breadth of information about instrument precision and accuracy that is present in each peptide isotopic envelope measurement. This method utilizes both isotope distribution and changes in neutromer spacing, which benefits the analysis of both concentration and turnover.'),
(80528, 2017, 'bio', 'FASTdoop: a versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applications', 'Ferraro Petrillo, Umberto|Roscigno, Gianluca|Cattaneo, Giuseppe|Giancarlo, Raffaele', 'Dipartimento di Scienze Statistiche, Università di Roma-La Sapienza, Rome, Italy.|Dipartimento di Informatica, Università di Salerno, Fisciano (SA), Italy.|Dipartimento di Informatica, Università di Salerno, Fisciano (SA), Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.', 'Italy|Italy|Italy|Italy', 16, 'Database Management Systems|Gene Library|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btx010', '28093410', 'MapReduce Hadoop bioinformatics applications require the availability of special-purpose routines to manage the input of sequence files. Unfortunately, the Hadoop framework does not provide any built-in support for the most popular sequence file formats like FASTA or BAM. Moreover, the development of these routines is not easy, both because of the diversity of these formats and the need for managing efficiently sequence datasets that may count up to billions of characters. We present FASTdoop, a generic Hadoop library for the management of FASTA and FASTQ files. We show that, with respect to analogous input management routines that have appeared in the Literature, it offers versatility and efficiency. That is, it can handle collections of reads, with or without quality scores, as well as long genomic sequences while the existing routines concentrate mainly on NGS sequence data. Moreover, in the domain where a comparison is possible, the routines proposed here are faster than the available ones. In conclusion, FASTdoop is a much needed addition to Hadoop-BAM.'),
(80529, 2017, 'bio', 'LRSSL: predict and interpret drug-disease associations based on data integration using sparse subspace learning', 'Liang, Xujun|Zhang, Pengfei|Yan, Lu|Fu, Ying|Peng, Fang|Qu, Lingzhi|Shao, Meiying|Chen, Yongheng|Chen, Zhuchu', 'Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Colitis, Ulcerative|Disease|Drug Repositioning|Humans|Pharmaceutical Preparations|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btw770', '28096083', ': Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem.'),
(80530, 2017, 'bio', 'TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes', 'Jung, Inuk|Jo, Kyuri|Kang, Hyejin|Ahn, Hongryul|Yu, Youngjae|Kim, Sun', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.|Department of Computer Science and Engineering.|Department of Applied Biology and chemistry, Seoul National University, Gwanak-Gu, Seoul, 151-744, Republic of Korea.|Department of Computer Science and Engineering.|Department of Computer Science and Engineering.|Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.', '-|-|-|-|-|-', 10, 'Algorithms|Cluster Analysis|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Oligonucleotide Array Sequence Analysis|Phenotype|Reproducibility of Results|Software|Time Factors|Transcriptome', 'Comparative Study', '10.1093/bioinformatics/btw780', '28096084', 'Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions.'),
(80531, 2017, 'bio', 'Improved orthology inference with Hieranoid 2', 'Kaduk, Mateusz|Sonnhammer, Erik', 'Department of Biochemistry and Biophysics, Stockholm University.|Department of Biochemistry and Biophysics, Stockholm University.', '-|-', 0, 'Algorithms|Computational Biology|Sequence Alignment|Sequence Homology|Software', 'Journal Article', '10.1093/bioinformatics/btw774', '28096085', 'The initial step in many orthology inference methods is the computationally demanding establishment of all pairwise protein similarities across all analysed proteomes. The quadratic scaling with proteomes has become a major bottleneck. A remedy is offered by the Hieranoid algorithm which reduces the complexity to linear by hierarchically aggregating ortholog groups from InParanoid along a species tree.'),
(80532, 2017, 'bio', 'Historian: accurate reconstruction of ancestral sequences and evolutionary rates', 'Holmes, Ian H', 'Department of Biochemistry and Biophysics, Stockholm University.', '-', 6, 'Algorithms|Evolution, Molecular|Humans|INDEL Mutation|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btw791', '28104629', 'Reconstruction of ancestral sequence histories, and estimation of parameters like indel rates, are improved by using explicit evolutionary models and summing over uncertain alignments. The previous best tool for this purpose (according to simulation benchmarks) was ProtPal, but this tool was too slow for practical use.'),
(80533, 2017, 'bio', 'Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo', 'Kapli, P|Lutteropp, S|Zhang, J|Kobert, K|Pavlidis, P|Stamatakis, A|Flouri, T', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Foundation for Research and Technology, Hellas Institute of Computer Science GR, Heraklion, Crete, Greece.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Greece|Germany|Germany', 181, 'Algorithms|Animals|Classification|DNA Barcoding, Taxonomic|Electron Transport Complex IV|Genes, Mitochondrial|Markov Chains|Monte Carlo Method|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btx025', '28108445', 'In recent years, molecular species delimitation has become a routine approach for quantifying and classifying biodiversity. Barcoding methods are of particular importance in large-scale surveys as they promote fast species discovery and biodiversity estimates. Among those, distance-based methods are the most common choice as they scale well with large datasets, however, they are sensitive to similarity threshold parameters and they ignore evolutionary relationships. The recently introduced Poisson Tree Processes (PTP) method is a phylogeny-aware approach that does not rely on such thresholds. Yet, two weaknesses of PTP impact its accuracy and practicality when applied to large datasets, it does not account for divergent intraspecific variation and is slow for a large number of sequences.'),
(80534, 2017, 'bio', 'chainCleaner improves genome alignment specificity and sensitivity', 'Suarez, Hernando G|Langer, Bjoern E|Ladde, Pradnya|Hiller, Michael', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Animals|Biological Evolution|Genome|Genomics|Humans|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btx024', '28108446', 'Accurate alignments between entire genomes are crucial for comparative genomics. However, computing sensitive and accurate genome alignments is a challenging problem, complicated by genomic rearrangements.'),
(80535, 2017, 'bio', 'miRmine: a database of human miRNA expression profiles', 'Panwar, Bharat|Omenn, Gilbert S|Guan, Yuanfang', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States', 77, 'Databases, Genetic|Female|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Male|MicroRNAs|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx019', '28108447', 'MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving, there are many factors that affect expression values significantly.'),
(80536, 2017, 'bio', 'aRNApipe: a balanced, efficient and distributed pipeline for processing RNA-seq data in high-performance computing environments', 'Alonso, Arnald|Lasseigne, Brittany N|Williams, Kelly|Nielsen, Josh|Ramaker, Ryne C|Hardigan, Andrew A|Johnston, Bobbi|Roberts, Brian S|Cooper, Sara J|Marsal, Sara|Myers, Richard M', 'HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.|Rheumatology Research Group, Vall dHebron Hospital Research Institute, Barcelona, Spain.|HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|Spain|United States', 0, 'Alternative Splicing|Computing Methodologies|Genotyping Techniques|Humans|RNA|Sequence Analysis, RNA|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx023', '28108448', 'The wide range of RNA-seq applications and their high-computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high-performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high-performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis.'),
(80537, 2017, 'bio', 'Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data', 'Peters, Ruby|Benthem Muñiz, Marta|Griffié, Juliette|Williamson, David J|Ashdown, George W|Lorenz, Christian D|Owen, Dylan M', 'Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.|Department of Physics and Randall Division of Cell and Molecular Biophysics, Kings College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cluster Analysis|Humans|Image Processing, Computer-Assisted|Jurkat Cells|Single Molecule Imaging|T-Lymphocytes', 'Journal Article', '10.1093/bioinformatics/btx026', '28108449', 'Unlike conventional microscopy which produces pixelated images, SMLM produces data in the form of a list of localization coordinates-a spatial point pattern (SPP). Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular clustering within, for example, the plasma membrane. While SMLM cluster analysis is now well developed, techniques for analyzing fibrous structures remain poorly explored.'),
(80538, 2017, 'bio', 'NetLand: quantitative modeling and visualization of Waddingtons epigenetic landscape using probabilistic potential', 'Guo, Jing|Lin, Feng|Zhang, Xiaomeng|Tanavde, Vivek|Zheng, Jie', 'Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore.|Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 9, 'Animals|Cell Differentiation|Computational Biology|Epigenesis, Genetic|Gene Regulatory Networks|Humans|Kinetics|Models, Biological|Software|Stem Cells', 'Journal Article', '10.1093/bioinformatics/btx022', '28108450', 'Waddingtons epigenetic landscape is a powerful metaphor for cellular dynamics driven by gene regulatory networks (GRNs). Its quantitative modeling and visualization, however, remains a challenge, especially when there are more than two genes in the network. A software tool for Waddingtons landscape has not been available in the literature. We present NetLand, an open-source software tool for modeling and simulating the kinetic dynamics of GRNs, and visualizing the corresponding Waddingtons epigenetic landscape in three dimensions without restriction on the number of genes in a GRN. With an interactive and graphical user interface, NetLand can facilitate the knowledge discovery and experimental design in the study of cell fate regulation (e.g. stem cell differentiation and reprogramming).'),
(80539, 2017, 'bio', 'GWAR: robust analysis and meta-analysis of genome-wide association studies', 'Dimou, Niki L|Tsirigos, Konstantinos D|Elofsson, Arne|Bagos, Pantelis G', 'Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Swedish E-Science Research Center, Stockholm University, Solna, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Swedish E-Science Research Center, Stockholm University, Solna, Sweden.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.', 'Greece|Sweden|Sweden|Greece', 0, 'Genetic Predisposition to Disease|Genetics, Population|Genome-Wide Association Study|Genomics|Humans|Hypertension|Meta-Analysis as Topic|Models, Genetic|Polymorphism, Single Nucleotide|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btx008', '28108451', 'In the context of genome-wide association studies (GWAS), there is a variety of statistical techniques in order to conduct the analysis, but, in most cases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran-Armitage trend test (CATT) is suboptimal. Robust procedures that maximize the power and preserve the nominal type I error rate are preferable. Moreover, performing a meta-analysis using robust procedures is of great interest and has never been addressed in the past. The primary goal of this work is to implement several robust methods for analysis and meta-analysis in the statistical package Stata and subsequently to make the software available to the scientific community.'),
(80540, 2017, 'bio', 'Biomartr: genomic data retrieval with R', 'Drost, Hajk-Georg|Paszkowski, Jerzy', 'Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.', 'Greece|Greece', 16, 'Databases, Chemical|Humans|Metagenomics|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btw821', '28110292', 'Retrieval and reproducible functional annotation of genomic data are crucial in biology. However, the current poor usability and transparency of retrieval methods hinders reproducibility. Here we present an open source R package, biomartr , which provides a comprehensive easy-to-use framework for automating data retrieval and functional annotation for meta-genomic approaches. The functions of biomartr achieve a high degree of clarity, transparency and reproducibility of analyses.'),
(80541, 2017, 'bio', 'ASCIIGenome: a command line genome browser for console terminals', 'Beraldi, Dario', 'Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.', 'United Kingdom', 2, 'Genome|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx007', '28119307', 'Current genome browsers are designed to work via graphical user interfaces (GUIs), which, however intuitive, are not amenable to operate within console terminals and therefore are difficult to streamline or integrate in scripts. To circumvent these limitations, ASCIIGenome runs exclusively via command line interface to display genomic data directly in a terminal window. By following the same philosophy of UNIX tools, ASCIIGenome aims to be easily integrated with the command line, including batch processing of data, and therefore enables an effective exploration of the data.'),
(80542, 2017, 'bio', 'QCEWAS: automated quality control of results of epigenome-wide association studies', 'Van der Most, Peter J|Küpers, Leanne K|Snieder, Harold|Nolte, Ilja', 'Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.|Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.|Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.|Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Genome-Wide Association Study|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btw766', '28119308', 'The increasing popularity of epigenome-wide association studies (EWAS) has led to the establishment of several large international meta-analysis consortia. However, when using data originating from multiple sources, a thorough and centralized quality control is essential. To facilitate this, we developed the QCEWAS R package. QCEWAS enables automated quality control of results files of EWAS. QCEWAS produces cohort-specific statistics and graphs to interpret the quality of the results files, graphs comparing results of multiple cohorts, as well as cleaned input files ready for meta-analysis.'),
(80543, 2017, 'bio', 'Introducing COCOS: codon consequence scanner for annotating reading frame changes induced by stop-lost and frame shift variants', 'Butkiewicz, Mariusz|Haines, Jonathan L|Bush, William S', 'Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.', 'United States|United States|United States', 0, 'Codon|Frameshift Mutation|Genomics|Humans|INDEL Mutation|Reading Frames|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw820', '28122779', 'Reading frame altering genomic variants can impact gene expression levels and the structure of protein products, thus potentially inducing disease phenotypes. Current annotation approaches report the impact of such variants in the context of altered DNA sequence only, attributes of the resulting transcript, reading frame and translated protein product are not reported. To remedy this shortcoming, we present a new genetic annotation approach termed Codon Consequence Scanner (COCOS). Implemented as an Ensembl variant effect predictor (VEP) plugin, COCOS captures amino acid sequence alterations stemming from variants that produce an altered reading frame, such as stop-lost variants and small insertions and deletions (InDels). To highlight its significance, COCOS was applied to data from the 1000 Genomes Project. Transcripts affected by stop-lost variants introduce a median of 15 amino acids, while InDels have a more extensive impact with a median of 66 amino acids being incorporated. Captured sequence alterations are written out in FASTA format and can be further analyzed for impact on the underlying protein structure.'),
(80544, 2017, 'bio', 'Unrealistic phylogenetic trees may improve phylogenetic footprinting', 'Nettling, Martin|Treutler, Hendrik|Cerquides, Jesus|Grosse, Ivo', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.|Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.|Institut dInvestigació en Intel ligència Artificial, IIIA-CSIC, Campus UAB, Cerdanyola, Spain.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.', 'Germany|Germany|Spain|Germany', 0, 'Animals|Binding Sites|Computational Biology|DNA-Binding Proteins|Gene Regulatory Networks|Humans|Phylogeny|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx033', '28130227', 'The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.'),
(80545, 2017, 'bio', 'Ligandbook: an online repository for small and drug-like molecule force field parameters', 'Domanski, Jan|Beckstein, Oliver|Iorga, Bogdan I', 'Department of Biochemistry, University of Oxford, Oxford, UK.|Department of Physics, Arizona State University, Tempe, AZ, USA.|Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Université Paris-Saclay, Labex LERMIT, Gif-sur-Yvette, France.', 'United Kingdom|United Kingdom|France', 6, 'Databases, Factual|Ligands|Metadata|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/btx037', '28130228', 'Ligandbook is a public database and archive for force field parameters of small and drug-like molecules. It is a repository for parameter sets that are part of published work but are not easily available to the community otherwise. Parameter sets can be downloaded and immediately used in molecular dynamics simulations. The sets of parameters are versioned with full histories and carry unique identifiers to facilitate reproducible research. Text-based search on rich metadata and chemical substructure search allow precise identification of desired compounds or functional groups. Ligandbook enables the rapid set up of reproducible molecular dynamics simulations of ligands and protein-ligand complexes.'),
(80546, 2017, 'bio', 'Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencing', 'Zhang, Shuai|Qin, Chunxia|Cao, Guoqiong|Guo, Limin|Feng, Chengqiang|Zhang, Wensheng', 'Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, China.|Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, China.|Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, China.|Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, China.|Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, China.|Beijing Area Major Laboratory of Protection and Utilization of Traditional Chinese Medicine, Beijing Normal University, Beijing, China.', 'China|China|China|China|China|China', 15, 'Aging|Alzheimer Disease|Animals|Brain|DNA Methylation|Disease Models, Animal|Male|Mice|Sequence Analysis, RNA|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx040', '28130229', 'The pathogenesis of AD is complex and contributed by both genetic and environmental factors. Recent work revealed a potential link between DNA methylation and AD. However, a genome-wide study to identify potential DNA methylation sites involved in AD is still at an early stage. WGBS, an up-to-date technology, was used in this study. We investigated mouse brain genome-wide DNA methylation profiles between seven-month-old SAMP8 and SAMR1 models through deep WGBS.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80547, 2017, 'bio', 'MetaShot: an accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data', 'Fosso, B|Santamaria, M|DAntonio, M|Lovero, D|Corrado, G|Vizza, E|Passaro, N|Garbuglia, A R|Capobianchi, M R|Crescenzi, M|Valiente, G|Pesole, G', 'Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, Bari, Italy.|Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, Bari, Italy.|CINECA, Rome, Italy.|Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, Bari, Italy.|Department of Oncological Surgery, Gynecologic Oncology Unit, Regina Elena National Cancer Institute, Rome, Italy.|Department of Oncological Surgery, Gynecologic Oncology Unit, Regina Elena National Cancer Institute, Rome, Italy.|Department of Cell Biology and Neurosciences, Italian National Institute of Health, Rome, Italy.|Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy.|Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy.|Department of Cell Biology and Neurosciences, Italian National Institute of Health, Rome, Italy.|Algorithms, Bioinformatics, Complexity and Formal Methods Research Group, Technical University of Catalonia, Barcelona, Spain.|Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Spain|Italy', 0, 'Algorithms|Bacteria|Fungi|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Software|Viruses|Workflow', 'Journal Article', '10.1093/bioinformatics/btx036', '28130230', 'Shotgun metagenomics by high-throughput sequencing may allow deep and accurate characterization of host-associated total microbiomes, including bacteria, viruses, protists and fungi. However, the analysis of such sequencing data is still extremely challenging in terms of both overall accuracy and computational efficiency, and current methodologies show substantial variability in misclassification rate and resolution at lower taxonomic ranks or are limited to specific life domains (e.g. only bacteria). We present here MetaShot, a workflow for assessing the total microbiome composition from host-associated shotgun sequence data, and show its overall optimal accuracy performance by analyzing both simulated and real datasets.'),
(80548, 2017, 'bio', 'Extracting morphologies from third harmonic generation images of structurally normal human brain tissue', 'Zhang, Zhiqing|Kuzmin, Nikolay V|Groot, Marie Louise|de Munck, Jan C', 'LaserLab Amsterdam, Department of Physics, Faculty of Sciences, VU University, HV Amsterdam, The Netherlands.|LaserLab Amsterdam, Department of Physics, Faculty of Sciences, VU University, HV Amsterdam, The Netherlands.|LaserLab Amsterdam, Department of Physics, Faculty of Sciences, VU University, HV Amsterdam, The Netherlands.|Physics and Medical Technology Department, VU University Medical Center, ?HZ Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 9, 'Algorithms|Brain|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Second Harmonic Generation Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btx035', '28130231', 'The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging.'),
(80549, 2017, 'bio', 'ANAQUIN: a software toolkit for the analysis of spike-in controls for next generation sequencing', 'Wong, Ted|Deveson, Ira W|Hardwick, Simon A|Mercer, Tim R', 'Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW, Australia.|Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW, Australia.|Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW, Australia.|Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW, Australia.', 'Australia|Australia|Australia|Australia', 0, 'High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx038', '28130232', 'Spike-in controls are synthetic nucleic-acid sequences that are added to a users sample and constitute internal standards for subsequent steps in the next generation sequencing workflow.'),
(80550, 2017, 'bio', 'A comprehensive quality control workflow for paired tumor-normal NGS experiments', 'Schroeder, Christopher M|Hilke, Franz J|Lxc3xb6ffler, Markus W|Bitzer, Michael|Lenz, Florian|Sturm, Marc', 'Institute of Medical Genetics and Applied Genomics, University of Txc3xbcbingen, Txc3xbcbingen, Germany.|Institute of Medical Genetics and Applied Genomics, University of Txc3xbcbingen, Txc3xbcbingen, Germany.|Department of General, Visceral and Transplant Surgery, University Hospital Txc3xbcbingen, Txc3xbcbingen, Germany.|Department of Internal Medicine I, University Hospital Txc3xbcbingen, Txc3xbcbingen, Germany.|Institute of Medical Genetics and Applied Genomics, University of Txc3xbcbingen, Txc3xbcbingen, Germany.|Institute of Medical Genetics and Applied Genomics, University of Txc3xbcbingen, Txc3xbcbingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 4, 'High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Quality Control|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx032', '28130233', 'Quality control (QC) is an important part of all NGS data analysis stages. Many available tools calculate QC metrics from different analysis steps of single sample experiments (raw reads, mapped reads and variant lists). Multi-sample experiments, as sequencing of tumor-normal pairs, require additional QC metrics to ensure validity of results. These multi-sample QC metrics still lack standardization. We therefore suggest a new workflow for QC of DNA sequencing of tumor-normal pairs. With this workflow well-known single-sample QC metrics and additional metrics specific for tumor-normal pairs can be calculated. The segmentation into different tools offers a high flexibility and allows reuse for other purposes. All tools produce qcML, a generic XML format for QC of -omics experiments. qcML uses quality metrics defined in an ontology, which was adapted for NGS.'),
(80551, 2017, 'bio', 'Evolutionary design of multiple genes encoding the same protein', 'Terai, Goro|Kamegai, Satoshi|Taneda, Akito|Asai, Kiyoshi', 'Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan.|Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan.|Graduate School of Science and Technology, Hirosaki University, Hirosaki, Aomori, Japan.|Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Biological Evolution|Codon|Homologous Recombination|Open Reading Frames|Proteins|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx030', '28130234', 'Enhancing expression levels of a target protein is an important goal in synthetic biology. A widely used strategy is to integrate multiple copies of genes encoding a target protein into a host organism genome. Integrating highly similar sequences, however, can induce homologous recombination between them, resulting in the ultimate reduction of the number of integrated genes.'),
(80552, 2017, 'bio', 'ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model', 'Savojardo, Castrense|Fariselli, Piero|Martelli, Pier Luigi|Casadio, Rita', 'Biocomputing Group, Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.|Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Padova, Italy.|Biocomputing Group, Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.|Biocomputing Group, Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.', 'Italy|Italy|Italy|Italy', 9, 'Computational Biology|Machine Learning|Protein Conformation|Protein Interaction Domains and Motifs|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx044', '28130235', 'The identification of protein-protein interaction (PPI) sites is an important step towards the characterization of protein functional integration in the cell complexity. Experimental methods are costly and time-consuming and computational tools for predicting PPI sites can fill the gaps of PPI present knowledge.'),
(80553, 2017, 'bio', 'H(O)TA: estimation of DNA methylation and hydroxylation levels and efficiencies from time course data', 'Kyriakopoulos, Charalampos|Giehr, Pascal|Wolf, Verena', 'Computer Science Department, Saarland University, Saarbruecken, Germany.|Department of Biological Sciences, Saarland University, Saarbruecken, Germany.|Computer Science Department, Saarland University, Saarbruecken, Germany.', 'Germany|Germany|Germany', 4, '5-Methylcytosine|CpG Islands|DNA|DNA Methylation|Epigenesis, Genetic|Hydroxylation|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx042', '28130236', 'Methylation and hydroxylation of cytosines to form 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) belong to the most important epigenetic modifications and their vital role in the regulation of gene expression has been widely recognized. Recent experimental techniques allow to infer methylation and hydroxylation levels at CpG dinucleotides but require a sophisticated statistical analysis to achieve accurate estimates.'),
(80554, 2017, 'bio', 'Identification of protein complexes by integrating multiple alignment of protein interaction networks', 'Ma, Cheng-Yu|Chen, Yi-Ping Phoebe|Berger, Bonnie|Liao, Chung-Shou', 'Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.|Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Vic, Australia.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan.', 'United States|Australia|United States|United States', 25, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Drosophila melanogaster|Humans|Multiprotein Complexes|Protein Interaction Maps|Protein Multimerization|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btx043', '28130237', 'Protein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes.'),
(80555, 2017, 'bio', 'The JWS online simulation database', 'Peters, Martin|Eicher, Johann J|van Niekerk, David D|Waltemath, Dagmar|Snoep, Jacky L', 'Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.|Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa.|Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.|Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa.', 'Germany|South Africa|South Africa|Germany|South Africa', 11, 'Computational Biology|Computer Simulation|Databases, Factual|Models, Biological|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btw831', '28130238', 'JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments.'),
(80556, 2017, 'bio', 'LOCUST: a custom sequence locus typer for classifying microbial isolates', 'Brinkac, Lauren M|Beck, Erin|Inman, Jason|Venepally, Pratap|Fouts, Derrick E|Sutton, Granger', 'J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.|J. Craig Venter Institute, Rockville, MD, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Bacteria|Genome, Bacterial|Genomics|Molecular Typing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx045', '28130240', 'LOCUST is a custom sequence locus typer tool for classifying microbial genomes. It provides a fully automated opportunity to customize the classification of genome-wide nucleotide variant data most relevant to biological research.'),
(80557, 2017, 'bio', 'Classification of RNA structure change by gazing at experimental data', 'Woods, Chanin Tolson|Laederach, Alain', 'Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States', 8, 'Classification|High-Throughput Nucleotide Sequencing|Humans|Mutation|Nucleic Acid Conformation|Polymorphism, Single Nucleotide|RNA|Sequence Analysis, RNA|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx041', '28130241', 'Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2 Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by gel gazing. SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human gazing by identifying features of the SHAPE profile that human experts agree looks like a riboSNitch.'),
(80558, 2017, 'bio', 'Hierarchical sets: analyzing pangenome structure through scalable set visualizations', 'Pedersen, Thomas Lin', 'Department of Systems Biology, Center for Biological Sequence Analysis, The Technical University of Denmark, Building 208, Lyngby, Denmark.', 'Denmark', 3, 'Algorithms|Bacteria|Cluster Analysis|Escherichia|Genome, Bacterial|Genomics|Sequence Analysis, DNA|Shigella|Software', 'Journal Article', '10.1093/bioinformatics/btx034', '28130242', 'The increase in available microbial genome sequences has resulted in an increase in the size of the pangenomes being analyzed. Current pangenome visualizations are not intended for the pangenome sizes possible today and new approaches are necessary in order to convert the increase in available information to increase in knowledge. As the pangenome data structure is essentially a collection of sets we explore the potential for scalable set visualization as a tool for pangenome analysis.'),
(80559, 2017, 'bio', 'Dscam1 web server: online prediction of Dscam1 self- and hetero-affinity', 'Marini, Simone|Nazzicari, Nelson|Biscarini, Filippo|Wang, Guang-Zhong', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, Lodi, Italy.|Department of Bioinformatics and Biostatistics, PTP Science Park, Lodi, Italy.|Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.', 'Japan|Italy|Italy|China', 0, 'Animals|Computational Biology|Drosophila|Drosophila Proteins|Neural Cell Adhesion Molecules|Neurons|Protein Multimerization|Software', 'Journal Article', '10.1093/bioinformatics/btx039', '28137710', 'Formation of homodimers by identical Dscam1 protein isomers on cell surface is the key factor for the self-avoidance of growing neurites. Dscam1 immense diversity has a critical role in the formation of arthropod neuronal circuit, showing unique evolutionary properties when compared to other cell surface proteins. Experimental measures are available for 89 self-binding and 1722 hetero-binding protein samples, out of more than 19 thousands (self-binding) and 350 millions (hetero-binding) possible isomer combinations.'),
(80560, 2017, 'bio', 'COVCOP allows to detect CNVs responsible for inherited diseases among amplicons sequencing data', 'Derouault, P|Parfait, B|Moulinas, R|Barrot, C-C|Sturtz, F|Merillou, S|Lia, A-S', 'GEIST, Université de Limoges, Limoges, France.|Génétique et Biologie Moléculaire, Hôpital Cochin, Paris, France.|GENOLIM, CHU de Limoges, Limoges, France.|Biochimie et Génétique Moléculaire, CHU de Limoges, Limoges, France.|Biochimie et Génétique Moléculaire, CHU de Limoges, Limoges, France.|UMR7252-XLIM, Université de Limoges, Limoges, France.|Biochimie et Génétique Moléculaire, CHU de Limoges, Limoges, France.', 'France|France|France|France|France|France|France', 8, 'Algorithms|DNA Copy Number Variations|DNA Mutational Analysis|Genetic Diseases, Inborn|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx017', '28137711', 'In order to help molecular geneticists to rapidly identify CNVs responsible for inherited diseases among amplicons sequencing data generated by NGS, we designed a user-friendly tool  CovCop . Using the runs coverage file provided by the sequencer, CovCop simultaneously analyzes all the patients of the run using a two-stage algorithm containing correction and normalization levels and provides an easily understandable output, showing with various colors, potentially deleted and duplicated amplicons.'),
(80561, 2017, 'bio', 'Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data', 'Basu, Sumanta|Duren, William|Evans, Charles R|Burant, Charles F|Michailidis, George|Karnovsky, Alla', 'Department of Statistics, University of California, Berkeley, CA, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Statistics, University of Florida, Gainesville, FL, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.', 'United States|United States|United States|United States|United States|United States', 34, 'Adult|Algorithms|Female|Humans|Mass Spectrometry|Metabolic Networks and Pathways|Metabolomics|Middle Aged|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btx012', '28137712', 'Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data.'),
(80562, 2017, 'bio', 'HIPred: an integrative approach to predicting haploinsufficient genes', 'Shihab, Hashem A|Rogers, Mark F|Campbell, Colin|Gaunt, Tom R', 'MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol, UK.|MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Chromatin|Epigenesis, Genetic|Genome, Human|Genomics|Haploinsufficiency|Histones|Humans|Machine Learning|Protein Interaction Maps|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx028', '28137713', 'A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein-protein interaction networks that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well-studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1000 genomes project, NHLBI Exome Sequencing Project and the Exome Aggregation Consortium creates an urgent need for unbiased haploinsufficiency prediction methods.'),
(80563, 2017, 'bio', 'MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites', 'Olarerin-George, Anthony O|Jaffrey, Samie R', 'Department of Pharmacology, Weill Medical College, Cornell University, New York, NY, USA.|Department of Pharmacology, Weill Medical College, Cornell University, New York, NY, USA.', 'United States|United States', 28, 'Protein Binding|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx002', '28158328', 'An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.'),
(80564, 2017, 'bio', 'Riborex: fast and flexible identification of differential translation from Ribo-seq data', 'Li, Wenzheng|Wang, Weili|Uren, Philip J|Penalva, Luiz O F|Smith, Andrew D', 'Molecular and Computational Biology, Division of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Molecular and Computational Biology, Division of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Molecular and Computational Biology, Division of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Molecular and Computational Biology, Division of Biological Sciences, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States', 29, 'Animals|Computational Biology|Humans|Mice|Protein Biosynthesis|Ribosomes|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx047', '28158331', 'Global analysis of translation regulation has recently been enabled by the development of Ribosome Profiling, or Ribo-seq, technology. This approach provides maps of ribosome activity for each expressed gene in a given biological sample. Measurements of translation efficiency are generated when Ribo-seq data is analyzed in combination with matched RNA-seq gene expression profiles. Existing computational methods for identifying genes with differential translation across samples are based on sound principles, but require users to choose between accuracy and speed.'),
(80565, 2017, 'bio', 'admixturegraph: an R package for admixture graph manipulation and fitting', 'Leppälä, Kalle|Nielsen, Svend V|Mailund, Thomas', 'Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.|Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.|Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.', 'Denmark|Denmark|Denmark', 17, 'Animals|Genetics, Population|Humans|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx048', '28158333', 'Admixture graphs generalize phylogenetic trees by allowing genetic lineages to merge as well as split. In this paper we present the R package admixturegraph containing tools for building and visualizing admixture graphs, for fitting graph parameters to genetic data, for visualizing goodness of fit and for evaluating the relative goodness of fit between different graphs.'),
(80566, 2017, 'bio', 'CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models', 'Haraldsdóttir, Hulda S|Cousins, Ben|Thiele, Ines|Fleming, Ronan M T|Vempala, Santosh', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.|School of Computer Science, Algorithms and Randomness Center, Georgia Institute of Technology, Atlanta, GA, USA.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.|School of Computer Science, Algorithms and Randomness Center, Georgia Institute of Technology, Atlanta, GA, USA.', 'Luxembourg|Georgia|Luxembourg|Luxembourg|Georgia', 17, 'Algorithms|Computational Biology|Humans|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btx052', '28158334', 'In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.'),
(80567, 2017, 'bio', 'Large-scale 16S gene assembly using metagenomics shotgun sequences', 'Zeng, Feng|Wang, Zicheng|Wang, Ying|Zhou, Jizhong|Chen, Ting', 'Department of Automation, Xiamen University, Xiamen, Fujian, China.|Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing, China.|Department of Automation, Xiamen University, Xiamen, Fujian, China.|Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA.|Bioinformatics Division, TNLIST, and Department of Computer Science and Technology, Tsinghua University, Beijing, China.', 'China|China|China|United States|China', 4, 'Bacteria|Cluster Analysis|Genes, Bacterial|Metagenomics|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx018', '28158392', 'Combining a 16S rRNA (16S) gene database with metagenomic shotgun sequences promises unbiased identification of known and novel microbes.'),
(80568, 2017, 'bio', 'FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions', 'Iwayama, Koji|Aisaka, Yuri|Kutsuna, Natsumaro|Nagano, Atsushi J', 'Research Institute for Food and Agriculture, Ryukoku University, Otsu, Shiga, Japan.|LPixel Inc, Hongo, Bunkyo-ku, Tokyo, Japan.|LPixel Inc, Hongo, Bunkyo-ku, Tokyo, Japan.|Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga, Japan.', 'Japan|Japan|Japan|Japan', 1, 'Computational Biology|Gene Expression Regulation, Plant|Models, Genetic|Models, Statistical|Oryza|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx049', '28158396', 'Considerable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the capability to predict transcriptome variation in rice plants. However, the computational cost of parameter optimization has prevented its wide application.'),
(80569, 2017, 'bio', 'NSSRF: global network similarity search with subgraph signatures and its applications', 'Zhang, Jiao|Kwong, Sam|Jia, Yuheng|Wong, Ka-Chun', 'Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 2, 'Animals|Computational Biology|Humans|Metabolic Networks and Pathways|Models, Theoretical|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx051', '28158419', 'The exponential growth of biological network database has increasingly rendered the global network similarity search (NSS) computationally intensive. Given a query network and a network database, it aims to find out the top similar networks in the database against the query network based on a topological similarity measure of interest. With the advent of big network data, the existing search methods may become unsuitable since some of them could render queries unsuccessful by returning empty answers or arbitrary query restrictions. Therefore, the design of NSS algorithm remains challenging under the dilemma between accuracy and efficiency.'),
(80570, 2017, 'bio', 'LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data', 'Kyle, Jennifer E|Crowell, Kevin L|Casey, Cameron P|Fujimoto, Grant M|Kim, Sangtae|Dautel, Sydney E|Smith, Richard D|Payne, Samuel H|Metz, Thomas O', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 35, 'Chromatography, Liquid|Lipids|Plants|Software|Tandem Mass Spectrometry|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx046', '28158427', 'We introduce an open-source software, LIQUID, for semi-automated processing and visualization of LC-MS/MS-based lipidomics data. LIQUID provides users with the capability to process high throughput data and contains a customizable target library and scoring model per project needs. The graphical user interface provides visualization of multiple lines of spectral evidence for each lipid identification, allowing rapid examination of data for making confident identifications of lipid molecular species. LIQUID was compared to other freely available software commonly used to identify lipids and other small molecules (e.g. CFM-ID, MetFrag, GNPS, LipidBlast and MS-DIAL), and was found to have a faster processing time to arrive at a higher number of validated lipid identifications.'),
(80571, 2017, 'bio', 'Automated cell type discovery and classification through knowledge transfer', 'Lee, Hao-Chih|Kosoy, Roman|Becker, Christine E|Dudley, Joel T|Kidd, Brian A', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY, USA.', 'United States|United States|United States|United States|United States', 14, 'Animals|Biomarkers|Cluster Analysis|Computational Biology|Cytophotometry|Humans|Leukocytes|Machine Learning|Reproducibility of Results|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/btx054', '28158442', 'Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis.'),
(80572, 2017, 'bio', 'Efficient computation of transfer free energies of amino acids in beta-barrel membrane proteins', 'Tian, Wei|Lin, Meishan|Naveed, Hammad|Liang, Jie', 'Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.|Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA.|Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Bacterial Proteins|Computational Biology|Gram-Negative Bacteria|Membrane Proteins|Protein Structure, Secondary|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx053', '28158457', 'Transmembrane beta-barrel proteins (TMBs) serve a multitude of essential cellular functions in Gram-negative bacteria, mitochondria and chloroplasts. Transfer free energies (TFEs) of residues in the transmembrane (TM) region provides fundamental quantifications of thermodynamic stabilities of TMBs, which are important for the folding and the membrane insertion processes, and may help in understanding the structure-function relationship. However, experimental measurement of TFEs of TMBs is challenging. Although a recent computational method can be used to calculate TFEs, the results of which are in excellent agreement with experimentally measured values, this method does not scale up, and is limited to small TMBs.'),
(80573, 2017, 'bio', 'A Cytoscape app for motif enumeration with ISMAGS', 'Van Parys, Thomas|Melckenbeeck, Ine|Houbraken, Maarten|Audenaert, Pieter|Colle, Didier|Pickavet, Mario|Demeester, Piet|Van de Peer, Yves', 'Department of Plant Systems Biology, VIB, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University - iMinds, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University - iMinds, Ghent, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.|Department of Plant Systems Biology, VIB, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 10, 'Algorithms|Computational Biology|Computer Graphics|Software', 'Journal Article', '10.1093/bioinformatics/btw626', '28158465', 'We present a Cytoscape app for the ISMAGS algorithm, which can enumerate all instances of a motif in a graph, making optimal use of the motifs symmetries to make the search more efficient. The Cytoscape app provides a handy interface for this algorithm, which allows more efficient network analysis.'),
(80574, 2017, 'bio', 'Image-based surrogate biomarkers for molecular subtypes of colorectal cancer', 'Popovici, Vlad|Budinská, Eva|Dušek, Ladislav|Kozubek, Michal|Bosman, Fred', 'Faculty of Medicine, Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic.|Faculty of Science, Research Centre for Toxic Compounds in the Environment, Masaryk University, Brno, Czech Republic.|Faculty of Medicine, Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic.|Faculty of Informatics, Masaryk University, Brno, Czech Republic.|University Institute of Pathology, University of Lausanne, Switzerland.', '-|-|-|-|Switzerland', 2, 'Biomarkers, Tumor|Colorectal Neoplasms|Gene Expression Regulation, Neoplastic|Humans|Image Processing, Computer-Assisted|Neural Networks (Computer)|Prognosis|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btx027', '28158480', 'Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the case of colorectal cancer (CRC) where several such categorizations have been proposed. Despite recent developments, the problem of subtype definition and recognition remains open, one of the causes being the intrinsic heterogeneity of each tumor, which is difficult to estimate from gene expression profiles. However, one of the observations of these studies indicates that there may be links between the dominant tumor morphology characteristics and the molecular subtypes. Benefiting from a large collection of CRC samples, comprising both gene expression and histopathology images, we investigated the possibility of building image-based classifiers able to predict the molecular subtypes. We employed deep convolutional neural networks for extracting local descriptors which were then used for constructing a dictionary-based representation of each tumor sample. A set of support vector machine classifiers were trained to solve different binary decision problems, their combined outputs being used to predict one of the five molecular subtypes.'),
(80575, 2017, 'bio', 'Supporting precision medicine by data mining across multi-disciplines: an integrative approach for generating comprehensive linkages between single nucleotide variants (SNVs) and drug-binding sites', 'Roy Choudhury, Amrita|Cheng, Tiejun|Phan, Lon|Bryant, Stephen H|Wang, Yanli', 'National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.', 'United States|United States|United States|United States|United States', 4, 'Binding Sites|Data Mining|Gene Frequency|Humans|Polymorphism, Single Nucleotide|Precision Medicine|Protein Binding|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btx031', '28158543', 'Genetic variants in drug targets and metabolizing enzymes often have important functional implications, including altering the efficacy and toxicity of drugs. Identifying single nucleotide variants (SNVs) that contribute to differences in drug response and understanding their underlying mechanisms are fundamental to successful implementation of the precision medicine model. This work reports an effort to collect, classify and analyze SNVs that may affect the optimal response to currently approved drugs.'),
(80576, 2017, 'bio', 'Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms', 'Bianco, Luca|Riccadonna, Samantha|Lavezzo, Enrico|Falda, Marco|Formentin, Elide|Cavalieri, Duccio|Toppo, Stefano|Fontana, Paolo', 'Research and Innovation Center, Edmund Mach Foundation, 38010xe2x80x89S. Michele allAdige, Italy.|Research and Innovation Center, Edmund Mach Foundation, 38010xe2x80x89S. Michele allAdige, Italy.|Department of Molecular Medicine.|Department of Molecular Medicine.|Department of Biology, University of Padova, 35131 Padova, Italy.|Research and Innovation Center, Edmund Mach Foundation, 38010xe2x80x89S. Michele allAdige, Italy.|Department of Molecular Medicine.|Research and Innovation Center, Edmund Mach Foundation, 38010xe2x80x89S. Michele allAdige, Italy.', 'Italy|Italy|-|-|Italy|Italy|-|Italy', 3, 'Computational Biology|Gene Expression Profiling|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw636', '28158604', 'Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology.'),
(80577, 2017, 'bio', 'Accurate prediction of human essential genes using only nucleotide composition and association information', 'Guo, Feng-Biao|Dong, Chuan|Hua, Hong-Li|Liu, Shuo|Luo, Hao|Zhang, Hong-Wan|Jin, Yan-Ting|Zhang, Kai-Yue', 'School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.|Department of Physics, Tianjin University, Tianjin, China.|School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China|China|China', 20, 'Animals|Base Composition|Eukaryota|Genes, Essential|Humans|Mice|Models, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx055', '28158612', 'Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the xcexbb-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model.'),
(80578, 2017, 'bio', 'PALADIN: protein alignment for functional profiling whole metagenome shotgun data', 'Westbrook, Anthony|Ramsdell, Jordan|Schuelke, Taruna|Normington, Louisa|Bergeron, R Daniel|Thomas, W Kelley|MacManes, Matthew D', 'Department of Computer Science, University of New Hampshire, Durham, NH, USA.|Department of Computer Science, University of New Hampshire, Durham, NH, USA.|Department of Molecular Cellular and Biomedical Sciences, University of New Hampshire, Durham, NH, USA.|Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH, USA.|Department of Computer Science, University of New Hampshire, Durham, NH, USA.|Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH, USA.|Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH, USA.', 'United States|United States|United States|United States|United States|United States|United States', 10, 'Algorithms|Bacteria|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx021', '28158639', 'Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN-a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space.'),
(80579, 2017, 'bio', 'PyMod 20: improvements in protein sequence-structure analysis and homology modeling within PyMOL', 'Janson, Giacomo|Zhang, Chengxin|Prado, Maria Giulia|Paiardini, Alessandro', 'Department of Biochemical Sciences A. Rossi Fanelli, Sapienza Università di Roma, Rome, Italy.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Department of Biochemical Sciences A. Rossi Fanelli, Sapienza Università di Roma, Rome, Italy.|Department of Biology and Biotechnology Charles Darwin, Sapienza Università di Roma, Rome, Italy.', 'Italy|United States|Italy|Italy', 53, 'Computational Biology|Models, Molecular|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw638', '28158668', 'The recently released PyMod GUI integrates many of the individual steps required for protein sequence-structure analysis and homology modeling within the interactive visualization capabilities of PyMOL. Here we describe the improvements introduced into the version 2.0 of PyMod.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80580, 2017, 'bio', 'cyvcf2: fast, flexible variant analysis with Python', 'Pedersen, Brent S|Quinlan, Aaron R', 'Department of Human Genetics, Department of Biomedical Informatics, and USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.|Department of Human Genetics, Department of Biomedical Informatics, and USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.', 'United States|United States', 7, 'Genetic Variation|Genotyping Techniques|Humans|Metadata|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx057', '28165109', 'Variant call format (VCF) files document the genetic variation observed after DNA sequencing, alignment and variant calling of a sample cohort. Given the complexity of the VCF format as well as the diverse variant annotations and genotype metadata, there is a need for fast, flexible methods enabling intuitive analysis of the variant data within VCF and BCF files.'),
(80581, 2017, 'bio', 'MotifNet: a web-server for network motif analysis', 'Smoly, Ilan Y|Lerman, Eugene|Ziv-Ukelson, Michal|Yeger-Lotem, Esti', 'Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.', 'Israel|Israel|Israel|Israel', 8, 'Computational Biology|Databases, Factual|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btx056', '28165111', 'Network motifs are small topological patterns that recur in a network significantly more often than expected by chance. Their identification emerged as a powerful approach for uncovering the design principles underlying complex networks. However, available tools for network motif analysis typically require download and execution of computationally intensive software on a local computer. We present MotifNet, the first open-access web-server for network motif analysis. MotifNet allows researchers to analyze integrated networks, where nodes and edges may be labeled, and to search for motifs of up to eight nodes. The output motifs are presented graphically and the user can interactively filter them by their significance, number of instances, node and edge labels, and node identities, and view their instances. MotifNet also allows the user to distinguish between motifs that are centered on specific nodes and motifs that recur in distinct parts of the network.'),
(80582, 2017, 'bio', 'Bayesian network-response regression', 'Wang, Lu|Durante, Daniele|Jung, Rex E|Dunson, David B', 'Department of Statistical Science, Duke University, Durham, NC, USA.|Department of Statistical Sciences, University of Padova, Padova, Italy.|Department of Psychology, University of New Mexico, Albuquerque, NM, USA.|Department of Statistical Science, Duke University, Durham, NC, USA.', 'United States|Italy|Mexico|United States', 2, 'Algorithms|Bayes Theorem|Brain|Computational Biology|Computer Simulation|Humans|Models, Biological|Nerve Net|Software', 'Journal Article', '10.1093/bioinformatics/btx050', '28165112', 'There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects.'),
(80583, 2017, 'bio', 'RelExplain-integrating data and networks to explain biological processes', 'Berchtold, Evi|Csaba, Gergely|Zimmer, Ralf', 'Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, Amalienstraße 17, München, Germany.|Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, Amalienstraße 17, München, Germany.|Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, Amalienstraße 17, München, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Biological Phenomena|Breast Neoplasms|Computational Biology|Humans|Metabolic Networks and Pathways|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btx060', '28165113', 'The goal of many genome-wide experiments is to explain the changes between the analyzed conditions. Typically, the analysis is started with a set of differential genes DG and the first step is to identify the set of relevant biological processes BP . Current enrichment methods identify the involved biological process via statistically significant overrepresentation of differential genes in predefined sets, but do not further explain how the differential genes interact with each other or which other genes might be important for the enriched process. Other network-based methods determine subnetworks of interacting genes containing many differential genes, but do not employ process knowledge for a more focused analysis.'),
(80584, 2017, 'bio', 'pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data', 'Sun, Hokeun|Wang, Ya|Chen, Yong|Li, Yun|Wang, Shuang', 'Department of Statistics, Pusan National University, Busan, Korea.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.|Division of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.', 'United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|CpG Islands|DNA Methylation|Female|Genomics|Humans|Logistic Models|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx064', '28165116', 'DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network-based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time.'),
(80585, 2017, 'bio', 'Genotyping inversions and tandem duplications', 'Ebler, Jana|Schxc3xb6nhuth, Alexander|Marschall, Tobias', 'Center for Bioinformatics, Saarland University, Saarbrxc3xbccken, Germany.|Life Sciences Group, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands.|Center for Bioinformatics, Saarland University, Saarbrxc3xbccken, Germany.', 'Germany|Netherlands|Germany', 0, 'Chromosome Duplication|Chromosome Inversion|Databases, Nucleic Acid|Genomic Structural Variation|Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btx020', '28169394', 'Next Generation Sequencing (NGS) has enabled studying structural genomic variants (SVs) such as duplications and inversions in large cohorts. SVs have been shown to play important roles in multiple diseases, including cancer. As costs for NGS continue to decline and variant databases become ever more complete, the relevance of genotyping also SVs from NGS data increases steadily, which is in stark contrast to the lack of tools to do so.'),
(80586, 2017, 'bio', 'annoPeak: a web application to annotate and visualize peaks from ChIP-seq/ChIP-exo-seq', 'Tang, Xing|Srivastava, Arunima|Liu, Huayang|Machiraju, Raghu|Huang, Kun|Leone, Gustavo', 'Department of Molecular Virology, Immunology and Medical Genetics College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Molecular Virology, Immunology and Medical Genetics College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Molecular Virology, Immunology and Medical Genetics College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.|Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Molecular Virology, Immunology and Medical Genetics College of Medicine, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Animals|Binding Sites|Chromatin Immunoprecipitation|DNA|High-Throughput Nucleotide Sequencing|Humans|Mice|Promoter Regions, Genetic|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx016', '28169395', 'We developed annoPeak, a web application to annotate, visualize and compare predicted protein-binding regions derived from ChIP-seq/ChIP-exo-seq experiments using human and mouse cells. Users can upload peak regions from multiple experiments onto the annoPeak server to annotate them with biological context, identify associated target genes and categorize binding sites with respect to gene structure. Users can also compare multiple binding profiles intuitively with the help of visualization tools and tables provided by annoPeak. In general, annoPeak will help users identify patterns of genome wide transcription factor binding profiles, assess binding profiles in different biological contexts and generate new hypotheses.'),
(80587, 2017, 'bio', 'DEF: an automated dead-end filling approach based on quasi-endosymbiosis', 'Liu, Lili|Zhang, Zijun|Sheng, Taotao|Chen, Ming', 'Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.', 'China|China|China|China', 1, 'Computational Biology|Escherichia coli|Metabolic Networks and Pathways|Models, Biological|Software|Symbiosis', 'Journal Article', '10.1093/bioinformatics/btw604', '28171511', 'Gap filling for the reconstruction of metabolic networks is to restore the connectivity of metabolites via finding high-confidence reactions that could be missed in target organism. Current methods for gap filling either fall into the network topology or have limited capability in finding missing reactions that are indirectly related to dead-end metabolites but of biological importance to the target model.'),
(80588, 2017, 'bio', 'Pro54DB: a database for experimentally verified sigma-54 promoters', 'Liang, Zhi-Yong|Lai, Hong-Yan|Yang, Huan|Zhang, Chang-Jian|Yang, Hui|Wei, Huan-Huan|Chen, Xin-Xin|Zhao, Ya-Wei|Su, Zhen-Dong|Li, Wen-Chao|Deng, En-Ze|Tang, Hua|Chen, Wei|Lin, Hao', 'Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Department of Pathophysiology, Southwest Medical University, Luzhou, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China', 74, 'Bacteria|Databases, Genetic|Promoter Regions, Genetic|RNA Polymerase Sigma 54', 'Journal Article', '10.1093/bioinformatics/btw630', '28171531', 'In prokaryotes, the xcfx8354 promoters are unique regulatory elements and have attracted much attention because they are in charge of the transcription of carbon and nitrogen-related genes and participate in numerous ancillary processes and environmental responses. All findings on xcfx8354 promoters are favorable for a better understanding of their regulatory mechanisms in gene transcription and an accurate discovery of genes missed by the wet experimental evidences. In order to provide an up-to-date, interactive and extensible database for xcfx8354 promoter, a free and easy accessed database called Pro54DB (xcfx8354 promoter database) was built to collect information of xcfx8354 promoter. In the current version, it has stored 210 experimental-confirmed xcfx8354 promoters with 297 regulated genes in 43 species manually extracted from 133 publications, which is helpful for researchers in fields of bioinformatics and molecular biology.'),
(80589, 2017, 'bio', 'Comparing co-evolution methods and their application to template-free protein structure prediction', 'de Oliveira, Saulo Henrique Pires|Shi, Jiye|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.|Department of Informatics, UCB Pharma, Slough SL1 3WE, UK|Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.', 'United Kingdom|United Kingdom|United Kingdom', 17, 'Algorithms|Computational Biology|Models, Molecular|Molecular Conformation|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btw618', '28171606', 'Co-evolution methods have been used as contact predictors to identify pairs of residues that share spatial proximity. Such contact predictors have been compared in terms of the precision of their predictions, but there is no study that compares their usefulness to model generation.'),
(80590, 2017, 'bio', 'Granular clustering of de novo protein models', 'Guzenko, Dmytro|Strelkov, Sergei V', 'Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.|Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.', 'Belgium|Belgium', 0, 'Algorithms|Cluster Analysis|Computational Biology|Models, Molecular|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw628', '28171609', 'Modern algorithms for de novo prediction of protein structures typically output multiple full-length models (decoys) rather than a single solution. Subsequent clustering of such decoys is used both to gauge the success of the modelling and to decide on the most native-like conformation. At the same time, partial protein models are sufficient for some applications such as crystallographic phasing by molecular replacement (MR) in particular, provided these models represent a certain part of the target structure with reasonable accuracy.'),
(80591, 2017, 'bio', 'The START App: a web-based RNAseq analysis and visualization resource', 'Nelson, Jonathan W|Sklenar, Jiri|Barnes, Anthony P|Minnier, Jessica', 'The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.|The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.|Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA.|The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.', 'United States|United States|United States|United States', 30, 'Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw624', '28171615', 'Transcriptional profiling using RNA sequencing (RNAseq) has emerged as a powerful methodology to quantify global gene expression patterns in various contexts from single cells to whole tissues. The tremendous amount of data generated by this profiling technology presents a daunting challenge in terms of effectively visualizing and interpreting results. Convenient and intuitive data interfaces are critical for researchers to easily upload, analyze and visualize their RNAseq data. We designed the START (Shiny Transcriptome Analysis Resource Tool) App with these requirements in mind. This application has the power and flexibility to be resident on a local computer or serve as a web-based environment, enabling easy sharing of data between researchers and collaborators.'),
(80592, 2017, 'bio', 'Sequence-structure relations of biopolymers', 'Barrett, Christopher|Huang, Fenix W|Reidys, Christian M', 'Biocomplexity Institute of Virginia Tech, Virginia Tech University, Blacksburg, VA, USA.|Biocomplexity Institute of Virginia Tech, Virginia Tech University, Blacksburg, VA, USA.|Biocomplexity Institute of Virginia Tech, Virginia Tech University, Blacksburg, VA, USA.', 'United States|United States|United States', 5, 'Algorithms|Computational Biology|Nucleic Acid Conformation|Probability|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw621', '28171628', 'DNA data is transcribed into single-stranded RNA, which folds into specific molecular structures. In this paper we pose the question to what extent sequence- and structure-information correlate. We view this correlation as structural semantics of sequence data that allows for a different interpretation than conventional sequence alignment. Structural semantics could enable us to identify more general embedded patterns in DNA and RNA sequences.'),
(80593, 2017, 'bio', 'ARResT/Interrogate: an interactive immunoprofiler for IG/TR NGS data', 'Bystry, Vojtech|Reigl, Tomas|Krejci, Adam|Demko, Martin|Hanakova, Barbora|Grioni, Andrea|Knecht, Henrik|Schlitt, Max|Dreger, Peter|Sellner, Leopold|Herrmann, Dietrich|Pingeon, Marine|Boudjoghra, Myriam|Rijntjes, Jos|Pott, Christiane|Langerak, Anton W|Groenen, Patricia J T A|Davi, Frederic|Brüggemann, Monika|Darzentas, Nikos|EuroClonality-NGS', 'CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Department of Hematology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.|Department of Hematology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.|Department of Medicine V, University Hospital Heidelberg, Heidelberg, Germany.|Department of Medicine V, University Hospital Heidelberg, Heidelberg, Germany.|Department of Hematology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.|Department of Hematology, Hopital Pitié-Salpêtrière and Pierre et Marie Curie University, Paris, France.|Department of Hematology, Hopital Pitié-Salpêtrière and Pierre et Marie Curie University, Paris, France.|Department of Pathology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.|Department of Hematology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.|Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.|Department of Pathology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.|Department of Hematology, Hopital Pitié-Salpêtrière and Pierre et Marie Curie University, Paris, France.|Department of Hematology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.', '-|-|-|-|-|-|Germany|Germany|Germany|Germany|Germany|France|France|Netherlands|Germany|Netherlands|Ne', 0, 'Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Immunogenetics|Immunoglobulins|Receptors, Antigen, T-Cell|Software', 'Journal Article', '10.1093/bioinformatics/btw634', '28172348', 'The study of immunoglobulins and T cell receptors using next-generation sequencing has finally allowed exploring immune repertoires and responses in their immense variability and complexity. Unsurprisingly, their analysis and interpretation is a highly convoluted task.'),
(80594, 2017, 'bio', 'CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data', 'Russo, Daniel P|Kim, Marlene T|Wang, Wenyi|Pinolini, Daniel|Shende, Sunil|Strickland, Judy|Hartung, Thomas|Zhu, Hao', 'The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.|The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.|The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.|The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.|The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.|ILS, Research Triangle Park, NC, USA.|Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.|The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 16, 'Animals|Biosimilar Pharmaceuticals|Computational Biology|Risk Assessment|Software|Toxicology', 'Journal Article', '10.1093/bioinformatics/btw640', '28172359', 'We have developed a public Chemical In vitro–In vivo Profiling (CIIPro) portal, which can automatically extract in vitro biological data from public resources (i.e. PubChem) for user-supplied compounds. For compounds with in vivo target activity data (e.g. animal toxicity testing results), the integrated cheminformatics algorithm will optimize the extracted biological data using in vitro–in vivo correlations. The resulting in vitro biological data for target compounds can be used for read-across risk assessment of target compounds. Additionally, the CIIPro portal can identify the most similar compounds based on their optimized bioprofiles. The CIIPro portal provides new powerful assessment capabilities to the scientific community and can be easily integrated with other cheminformatics tools.'),
(80595, 2017, 'bio', 'simPATHy: a new method for simulating data from perturbed biological PATHways', 'Salviato, Elisa|Djordjilovic, Vera|Chiogna, Monica|Romualdi, Chiara', 'Department of Biology, University of Padova, Padova, Italy.|Department of Statistical Sciences, University of Padova, Padova, Italy.|Department of Statistical Sciences, University of Padova, Padova, Italy.|Department of Biology, University of Padova, Padova, Italy.', 'Italy|Italy|Italy|Italy', 2, 'Computational Biology|Computer Simulation|Gene Expression Regulation|Models, Biological|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btw642', '28172414', 'In the omic era, one of the main aims is to discover groups of functionally related genes that drive the difference between different conditions. To this end, a plethora of potentially useful multivariate statistical approaches has been proposed, but their evaluation is hindered by the absence of a gold standard. Here, we propose a method for simulating biological data – gene expression, RPKM/FPKM or protein abundances – from two conditions, namely, a reference condition and a perturbation of it. Our approach is built upon probabilistic graphical models and is thus especially suited for testing topological approaches.'),
(80596, 2017, 'bio', 'BASIC: BCR assembly from single cells', 'Canzar, Stefan|Neu, Karlynn E|Tang, Qingming|Wilson, Patrick C|Khan, Aly A', 'Toyota Technological Institute at Chicago, Chicago, IL, USA|Committee on Immunology, The Knapp Center of Lupus and Immunology Research, The University of Chicago, Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA|Committee on Immunology, The Knapp Center of Lupus and Immunology Research, The University of Chicago, Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA', 'United States|United States|United States|United States|United States', 30, 'Gene Expression Profiling|Gene Expression Regulation|Humans|Receptors, Antigen, B-Cell|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw631', '28172415', 'The B-cell receptor enables individual B cells to identify diverse antigens, including bacterial and viral proteins. While advances in RNA-sequencing (RNA-seq) have enabled high throughput profiling of transcript expression in single cells, the unique task of assembling the full-length heavy and light chain sequences from single cell RNA-seq (scRNA-seq) in B cells has been largely unstudied.'),
(80597, 2017, 'bio', 'Powerful differential expression analysis incorporating network topology for next-generation sequencing data', 'Dona, Malathi S I|Prendergast, Luke A|Mathivanan, Suresh|Keerthikumar, Shivakumar|Salim, Agus', 'Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.|Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.|Department of Biochemistry and Genetics, La Trobe Institute of Molecular Science, La Trobe University, Melbourne, VIC, Australia.|Department of Biochemistry and Genetics, La Trobe Institute of Molecular Science, La Trobe University, Melbourne, VIC, Australia.|Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.', 'Australia|Australia|Australia|Australia|Australia', 9, 'Carcinoma, Hepatocellular|Colorectal Neoplasms|Gene Expression Regulation, Neoplastic|High-Throughput Nucleotide Sequencing|Humans|Liver Neoplasms|Sample Size|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw833', '28172447', 'RNA-seq has become the technology of choice for interrogating the transcriptome. However, most methods for RNA-seq differential expression (DE) analysis do not utilize prior knowledge of biological networks to detect DE genes. With the increased availability and quality of biological network databases, methods that can utilize this prior knowledge are needed and will offer biologists with a viable, more powerful alternative when analyzing RNA-seq data.'),
(80598, 2017, 'bio', 'OMBlast: alignment tool for optical mapping using a seed-and-extend approach', 'Leung, Alden King-Yung|Kwok, Tsz-Piu|Wan, Raymond|Xiao, Ming|Kwok, Pui-Yan|Yip, Kevin Y|Chan, Ting-Fung', 'School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China|School of Biomedical Engineering, Science and Health System, Drexel University, Philadelphia, PA, USA.|Institute for Human Genetics.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.|School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China', 'China|China|China|United States|United States|China|China', 15, 'Animals|Caenorhabditis elegans|Escherichia coli|Genome|Genomics|Humans|Optical Restriction Mapping|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw620', '28172448', 'Optical mapping is a technique for capturing fluorescent signal patterns of long DNA molecules (in the range of 0.1–1 Mbp). Recently, it has been complementing the widely used short-read sequencing technology by assisting with scaffolding and detecting large and complex structural variations (SVs). Here, we introduce a fast, robust and accurate tool called OMBlast for aligning optical maps, the set of signal locations on the molecules generated from optical mapping. Our method is based on the seed-and-extend approach from sequence alignment, with modifications specific to optical mapping.'),
(80599, 2017, 'bio', 'QUBIC: a bioconductor package for qualitative biclustering analysis of gene co-expression data', 'Zhang, Yu|Xie, Juan|Yang, Jinyu|Fennell, Anne|Zhang, Chi|Ma, Qin', 'College of Computer Science and Technology, Jilin University, Changchun, China.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.|Center for Computational Biology and Bioinformatics and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.', 'China|United States|United States|United States|India|United States', 31, 'Algorithms|Cluster Analysis|Computational Biology|Escherichia coli|Gene Expression Profiling|RNA, Bacterial|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw635', '28172469', 'Biclustering is widely used to identify co-expressed genes under subsets of all the conditions in a large-scale transcriptomic dataset. The program, QUBIC, is recognized as one of the most efficient and effective biclustering methods for biological data interpretation. However, its availability is limited to a C implementation and to a low-throughput web interface.'),
(80600, 2017, 'bio', 'LDAP: a web server for lncRNA-disease association prediction', 'Lan, Wei|Li, Min|Zhao, Kaijie|Liu, Jin|Wu, Fang-Xiang|Pan, Yi|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.|Department of Computer Science, Georgia State University, Atlanta, GA, USA.|School of Information Science and Engineering, Central South University, Changsha, China.', 'China|China|China|China|Canada|Georgia|China', 68, 'Computational Biology|Disease|Humans|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bioinformatics/btw639', '28172495', 'Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource.'),
(80601, 2017, 'bio', 'P3BSseq: parallel processing pipeline software for automatic analysis of bisulfite sequencing data', 'Luu, Phuc-Loi|Gerovska, Daniela|Arrospide-Elgarresta, Mikel|Retegi-Carrión, Sugoi|Schöler, Hans R|Araúzo-Bravo, Marcos J', 'Computational Biology and Bioinformatics, Max Planck Institute for Molecular Biomedicine, Münster, Germany.|Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, Spain.|Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, Spain.|Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, Spain.|Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany.|Computational Biology and Bioinformatics, Max Planck Institute for Molecular Biomedicine, Münster, Germany.', 'Germany|Spain|Spain|Spain|Germany|Germany', 9, 'DNA Methylation|Electronic Data Processing|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btw633', '28172520', 'Bisulfite sequencing (BSseq) processing is among the most cumbersome next generation sequencing (NGS) applications. Though some BSseq processing tools are available, they are scattered, require puzzling parameters and are running-time and memory-usage demanding.'),
(80602, 2017, 'bio', 'Assocplots: a Python package for static and interactive visualization of multiple-group GWAS results', 'Khramtsova, Ekaterina A|Stranger, Barbara E', 'Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA.', 'United States|United States', 7, 'Genetic Variation|Genome-Wide Association Study|Software', 'Journal Article', '10.1093/bioinformatics/btw641', '28172529', 'Over the last decade, genome-wide association studies (GWAS) have generated vast amounts of analysis results, requiring development of novel tools for data visualization. Quantile–quantile (QQ) plots and Manhattan plots are classical tools which have been utilized to visually summarize GWAS results and identify genetic variants significantly associated with traits of interest. However, static visualizations are limiting in the information that can be shown. Here, we present Assocplots, a Python package for viewing and exploring GWAS results not only using classic static Manhattan and QQ plots, but also through a dynamic extension which allows to interactively visualize the relationships between GWAS results from multiple cohorts or studies.'),
(80603, 2017, 'bio', 'State aggregation for fast likelihood computations in molecular evolution', 'Davydov, Iakov I|Robinson-Rechavi, Marc|Salamin, Nicolas', 'Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, Switzerland|Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, Switzerland|Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, Switzerland', 'Switzerland|Switzerland|Switzerland', 2, 'Algorithms|Computational Biology|Evolution, Molecular|Markov Chains|Models, Genetic|Probability|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btw632', '28172542', 'Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models.'),
(80604, 2017, 'bio', 'CloudPhylo: a fast and scalable tool for phylogeny reconstruction', 'Xu, Xingjian|Ji, Zhaohua|Zhang, Zhang', 'CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS), Beijing, China.|College of Computer and Information Engineering, Inner Mongolia Normal University, Hohhot, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS), Beijing, China.', 'China|China|China', 5, 'Bacteria|Evolution, Molecular|Genomics|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw645', '28172557', 'Phylogeny reconstruction is fundamentally crucial for molecular evolutionary studies but remains computationally challenging. Here we present CloudPhylo, a tool built on Spark that is capable of processing large-scale datasets for phylogeny reconstruction. As testified on empirical data, CloudPhylo is well suited for big data analysis, achieving high efficiency and good scalability on phylogenetic tree inference.'),
(80605, 2017, 'bio', 'SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments', 'Savojardo, Castrense|Martelli, Pier Luigi|Fariselli, Piero|Casadio, Rita', 'Biocomputing Group, BiGeA - CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.|Biocomputing Group, BiGeA - CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.|Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Padova, Italy.|Biocomputing Group, BiGeA - CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.', 'Italy|Italy|Italy|Italy', 5, 'Chloroplasts|Computational Biology|Machine Learning|Membrane Proteins|Plant Proteins|Protein Transport|Sequence Analysis, Protein|Software|Viridiplantae', 'Journal Article', '10.1093/bioinformatics/btw656', '28172591', 'Chloroplasts are organelles found in plants and involved in several important cell processes. Similarly to other compartments in the cell, chloroplasts have an internal structure comprising several sub-compartments, where different proteins are targeted to perform their functions. Given the relation between protein function and localization, the availability of effective computational tools to predict protein sub-organelle localizations is crucial for large-scale functional studies.'),
(80606, 2017, 'bio', 'iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals', 'Cheng, Xiang|Zhao, Shu-Guang|Xiao, Xuan|Chou, Kuo-Chen', 'College of Information Science and Technology, Donghua University, Shanghai, China.|College of Information Science and Technology, Donghua University, Shanghai, China.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|Gordon Life Science Institute, Boston, MA, USA.', 'China|China|China|United States', 177, 'Computational Biology|Drug Discovery|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btw644', '28172617', 'Given a compound, can we predict which anatomical therapeutic chemical (ATC) class/classes it belongs to? It is a challenging problem since the information thus obtained can be used to deduce its possible active ingredients, as well as its therapeutic, pharmacological and chemical properties. And hence the pace of drug development could be substantially expedited. But this problem is by no means an easy one. Particularly, some drugs or compounds may belong to two or more ATC classes.'),
(80607, 2017, 'bio', 'SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data', 'Choudhary, Krishna|Ruan, Luyao|Deng, Fei|Shih, Nathan|Aviran, Sharon', 'Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.|Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA.', 'United States|United States|United States|United States|United States', 6, 'High-Throughput Nucleotide Sequencing|Quality Control|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw627', '28172632', 'To serve numerous functional roles, RNA must fold into specific structures. Determining these structures is thus of paramount importance. The recent advent of high-throughput sequencing-based structure profiling experiments has provided important insights into RNA structure and widened the scope of RNA studies. However, as a broad range of approaches continues to emerge, a universal framework is needed to quantitatively ensure consistent and high-quality data. We present SEQualyzer, a visual and interactive application that makes it easy and efficient to gauge data quality, screen for transcripts with high-quality information and identify discordant replicates in structure profiling experiments. Our methods rely on features common to a wide range of protocols and can serve as standards for quality control and analyses.'),
(80608, 2017, 'bio', 'Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis', 'Wang, Sufang|Gribskov, Michael', 'Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.|Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.', 'United States|United States', 28, 'Arabidopsis|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bioinformatics/btw625', '28172640', 'With the decreased cost of RNA-Seq, an increasing number of non-model organisms have been sequenced. Due to the lack of reference genomes, de novo transcriptome assembly is required. However, there is limited systematic research evaluating the quality of de novo transcriptome assemblies and how the assembly quality influences downstream analysis.'),
(80609, 2017, 'bio', 'cooccurNet: an R package for co-occurrence network construction and analysis', 'Zou, Yuanqiang|Wu, Zhiqiang|Deng, Lizong|Wu, Aiping|Wu, Fan|Li, Kenli|Jiang, Taijiao|Peng, Yousong', 'College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.|College of Biology, Hunan University, Changsha, China.', 'China|China|China|China|China|China|China|China', 2, 'Computer Simulation|Evolution, Molecular|Genome, Viral|Genomics|Orthomyxoviridae|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx062', '28174895', 'Previously, we developed a computational model to identify genomic co-occurrence networks that was applied to capture the coevolution patterns within genomes of influenza viruses. To facilitate easy public use of this model, an R package cooccurNet is presented here.'),
(80610, 2017, 'bio', 'PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF', 'Stein-OBrien, Genevieve L|Carey, Jacob L|Lee, Wai Shing|Considine, Michael|Favorov, Alexander V|Flam, Emily|Guo, Theresa|Li, Sijia|Marchionni, Luigi|Sherman, Thomas|Sivy, Shawn|Gaykalova, Daria A|McKay, Ronald D|Ochs, Michael F|Colantuoni, Carlo|Fertig, Elana J', 'McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Oncology and Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Oncology and Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Oncology and Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Oncology and Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Mathematics and Statistics, The College of New Jersey, Ewing Township, NJ, USA.|Department of Mathematics and Statistics, The College of New Jersey, Ewing Township, NJ, USA.|Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Lieber Institute for Brain Development, Baltimore, MD, USA.|Department of Mathematics and Statistics, The College of New Jersey, Ewing Township, NJ, USA.|Department of Neurology and Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Oncology and Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|Jersey|Jersey|United States|United States|Jersey|United States|United States', 8, 'Algorithms|Bayes Theorem|Biomarkers|Gene Expression Profiling|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx058', '28174896', 'Non-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g. time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel patternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with patternMarkers requires whole-genome data. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. Additionally, a manual version of the GWCoGAPS algorithm contains analytic and visualization tools including patternMatcher, a Shiny web application. The decomposition in the manual pipeline can be replaced with any NMF algorithm, for further generalization of the software. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTEx data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80611, 2017, 'bio', 'An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets', 'Lai, Yinglei|Zhang, Fanni|Nayak, Tapan K|Modarres, Reza|Lee, Norman H|McCaffrey, Timothy A', 'Department of Statistics, The George Washington University, Washington, DC 20052, USA.|Department of Statistics, The George Washington University, Washington, DC 20052, USA.|Department of Statistics, The George Washington University, Washington, DC 20052, USA.|Department of Statistics, The George Washington University, Washington, DC 20052, USA.|Department of Pharmacology and Physiology.|Division of Genomic Medicine, Department of Medicine, The George Washington University Medical Center, Washington, DC 20037, USA.', 'United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Genetic Association Studies|Genome, Human|Humans|Lung Neoplasms|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA', 'Comparative Study', '10.1093/bioinformatics/btx061', '28174897', 'We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets.'),
(80612, 2017, 'bio', 'Phyx: phylogenetic tools for unix', 'Brown, Joseph W|Walker, Joseph F|Smith, Stephen A', 'Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.|Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.|Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States', 35, 'Genomics|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btx063', '28174903', 'The ease with which phylogenomic data can be generated has drastically escalated the computational burden for even routine phylogenetic investigations. To address this, we present phyx : a collection of programs written in Cxe2x80x89++xe2x80x89to explore, manipulate, analyze and simulate phylogenetic objects (alignments, trees and MCMC logs). Modelled after Unix/GNU/Linux command line tools, individual programs perform a single task and operate on standard I/O streams that can be piped to quickly and easily form complex analytical pipelines. Because of the stream-centric paradigm, memory requirements are minimized (often only a single tree or sequence in memory at any instance), and hence phyx is capable of efficiently processing very large datasets.'),
(80613, 2017, 'bio', 'CPSS 20: a computational platform update for the analysis of small RNA sequencing data', 'Wan, Changlin|Gao, Jianing|Zhang, Huan|Jiang, Xiaohua|Zang, Qiguang|Ban, Rongjun|Zhang, Yuanwei|Shi, Qinghua', 'Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Collaborative Innovation Center of Genetics and Development, Collaborative Innovation Center for Cancer Medicine, Hefei 230027, China.', 'China|China|China|China|China|China|China|China', 8, 'Animals|Computational Biology|Eukaryota|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|RNA, Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx066', '28177064', 'Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48, (ii) improved strategies applied to detect ncRNAs, (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA, (iv) identification of differentially expressed ncRNAs among multiple samples, (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research.'),
(80614, 2017, 'bio', 'GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data', 'Rahmani, Elior|Yedidim, Reut|Shenhav, Liat|Schweiger, Regev|Weissbrod, Omer|Zaitlen, Noah|Halperin, Eran', 'Blavatnik School of Computer Science, Tel Aviv University, Ramat Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Ramat Aviv, Israel.|Statistics and Operations Research Department, Tel Aviv University, Ramat Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Ramat Aviv, Israel.|Statistics and Operations Research Department, Tel Aviv University, Ramat Aviv, Israel.|Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Computer Science Department, University of California Los Angeles, Los Angeles, CA, USA.', 'Israel|Israel|Israel|Israel|Israel|United States|United States', 12, 'DNA Methylation|Epigenomics|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx059', '28177067', 'GLINT is a user-friendly command-line toolset for fast analysis of genome-wide DNA methylation data generated using the Illumina human methylation arrays. GLINT, which does not require any programming proficiency, allows an easy execution of Epigenome-Wide Association Study analysis pipeline under different models while accounting for known confounders in methylation data.'),
(80615, 2017, 'bio', 'STELLS2: fast and accurate coalescent-based maximum likelihood inference of species trees from gene tree topologies', 'Pei, Jingwen|Wu, Yufeng', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.', 'United States|United States', 2, 'Algorithms|Alleles|Computational Biology|Computer Simulation|Genetics, Population|Likelihood Functions|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx079', '28186220', 'It is well known that gene trees and species trees may have different topologies. One explanation is incomplete lineage sorting, which is commonly modeled by the coalescent process. In multispecies coalescent, a gene tree topology is observed with some probability (called the gene tree probability) for a given species tree. Gene tree probability is the main tool for the program STELLS, which finds the maximum likelihood estimate of the species tree from the given gene tree topologies. However, STELLS becomes slow when data size increases. Recently, several fast species tree inference methods have been developed, which can handle large data. However, these methods often do not fully utilize the information in the gene trees.'),
(80616, 2017, 'bio', 'Kollector: transcript-informed, targeted de novo assembly of gene loci', 'Kucuk, Erdi|Chu, Justin|Vandervalk, Benjamin P|Hammond, S Austin|Warren, René L|Birol, Inanc', 'University of British Columbia, Vancouver, BC, Canada.|University of British Columbia, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada.|University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 4, 'Animals|Caenorhabditis elegans|Eukaryota|Genetic Loci|Genomics|Humans|Pediculus|Picea|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx078', '28186221', 'Despite considerable advancements in sequencing and computing technologies, de novo assembly of whole eukaryotic genomes is still a time-consuming task that requires a significant amount of computational resources and expertise. A targeted assembly approach to perform local assembly of sequences of interest remains a valuable option for some applications. This is especially true for gene-centric assemblies, whose resulting sequence can be readily utilized for more focused biological research. Here we describe Kollector, an alignment-free targeted assembly pipeline that uses thousands of transcript sequences concurrently to inform the localized assembly of corresponding gene loci. Kollector robustly reconstructs introns and novel sequences within these loci, and scales well to large genomes-properties that makes it especially useful for researchers working on non-model eukaryotic organisms.'),
(80617, 2017, 'bio', 'VALORATE: fast and accurate log-rank test in balanced and unbalanced comparisons of survival curves and cancer genomics', 'Treviño, Victor|Tamez-Pena, Jose', 'Cátedra de Bioinformática, Escuela de Medicina, Tecnológico de Monterrey, Monterrey N.L., México.|Cátedra de Bioinformática, Escuela de Medicina, Tecnológico de Monterrey, Monterrey N.L., México.', '-|-', 5, 'Genomics|Humans|Mutation|Neoplasms|Software|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btx080', '28186225', 'The association of genomic alterations to outcomes in cancer is affected by a problem of unbalanced groups generated by the low frequency of alterations. For this, an R package (VALORATE) that estimates the null distribution and the P -value of the log-rank based on a recent reformulation is presented. For a given number of alterations that define the size of survival groups, the log-rank density is estimated by a weighted sum of conditional distributions depending on a co-occurrence term of mutations and events. The estimations are accurately accelerated by sampling across co-occurrences allowing the analysis of large genomic datasets in few minutes. In conclusion, the proposed VALORATE R package is a valuable tool for survival analysis.'),
(80618, 2017, 'bio', 'Binning_refiner: improving genome bins through the combination of different binning programs', 'Song, Wei-Zhi|Thomas, Torsten', 'School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia.|Centre for Marine Bio-Innovation, University of New South Wales, NSW, Australia.', 'Australia|Australia', 13, 'Algorithms|Genome, Microbial|Metagenomics|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx086', '28186226', 'Microbial genomes have recently been reconstructed from metagenomic datasets using binning approaches. Inconsistent binning results are however often observed between different binning programs, likely due to the different algorithms or statistical models used. We present Binning_refiner, a pipeline that merges the results of different binning programs. Our results demonstrated that Binning_refiner can significantly reduce the contamination level of genome bins and increase the total size of contamination-free and good-quality genome bins. Binning_refiner is thus an useful tool to improve the quality of genome bins derived from metagenomic data.'),
(80619, 2017, 'bio', 'OncoSimulR: genetic simulation with arbitrary epistasis and mutator genes in asexual populations', 'Diaz-Uriarte, Ramon', 'Department of Biochemistry, Universidad Autxc3xb3noma de Madrid, Instituto de Investigaciones Biomxc3xa9dicas Alberto Sols (UAM-CSIC), Madrid, Spain.', 'Spain', 7, 'Computational Biology|Computer Simulation|Epistasis, Genetic|Mutation|Neoplasms|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx077', '28186227', 'OncoSimulR implements forward-time genetic simulations of biallelic loci in asexual populations with special focus on cancer progression. Fitness can be defined as an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, restrictions in the order of accumulation of mutations, and order effects. Mutation rates can differ among genes, and can be affected by (anti)mutator genes. Also available are sampling from simulations (including single-cell sampling), plotting the genealogical relationships of clones and generating and plotting fitness landscapes.'),
(80620, 2017, 'bio', 'PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation', 'Patrick, Ralph|Kobe, Bostjan|Lê Cao, Kim-Anh|Bodén, Mikael', 'School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Australia.|School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Australia.|The University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD, Australia.|School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Amino Acid Sequence|Computational Biology|Humans|Phosphorylation|Phosphotransferases|Polymorphism, Single Nucleotide|Protein Binding|Protein Processing, Post-Translational|Software', 'Journal Article', '10.1093/bioinformatics/btx072', '28186228', 'Genome-wide association studies are identifying single nucleotide variants (SNVs) linked to various diseases, however the functional effect caused by these variants is often unknown. One potential functional effect, the loss or gain of protein phosphorylation sites, can be induced through variations in key amino acids that disrupt or introduce valid kinase binding patterns. Current methods for predicting the effect of SNVs on phosphorylation operate on the sequence content of reference and variant proteins. However, consideration of the amino acid sequence alone is insufficient for predicting phosphorylation change, as context factors determine kinase-substrate selection.'),
(80621, 2017, 'bio', 'DiagnoProt: a tool for discovery of new molecules by mass spectrometry', 'Silva, André R F|Lima, Diogo B|Leyva, Alejandro|Duran, Rosario|Batthyany, Carlos|Aquino, Priscila F|Leal, Juliana C|Rodriguez, Jimmy E|Domont, Gilberto B|Santos, Marlon D M|Chamot-Rooke, Julia|Barbosa, Valmir C|Carvalho, Paulo C', 'Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.|Institut Pasteur, Structural Mass Spectrometry and Proteomics Unit, Paris, France.|Analytical Biochemistry and Proteomics Unit, Institut Pasteur de Montevideo/IIBCE, Montevideo, Uruguay.|Analytical Biochemistry and Proteomics Unit, Institut Pasteur de Montevideo/IIBCE, Montevideo, Uruguay.|Analytical Biochemistry and Proteomics Unit, Institut Pasteur de Montevideo/IIBCE, Montevideo, Uruguay.|Leônidas and Maria Deane Institute, Fiocruz, Amazonas, Brazil.|Leônidas and Maria Deane Institute, Fiocruz, Amazonas, Brazil.|Proteomics Unit, Chemistry Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.|Proteomics Unit, Chemistry Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.|Carlos Chagas Institute, Computational Mass Spectrometry & Proteomics Group, Fiocruz, Paraná, Brazil.|Institut Pasteur, Structural Mass Spectrometry and Proteomics Unit, Paris, France.|Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.|Carlos Chagas Institute, Computational Mass Spectrometry & Proteomics Group, Fiocruz, Paraná, Brazil.', 'Brazil|France|Uruguay|Uruguay|Uruguay|Brazil|Brazil|Brazil|Brazil|Brazil|France|Brazil|Brazil', 3, 'Aspergillus|Fungal Proteins|Machine Learning|Proteomics|Sequence Analysis, Protein|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btx093', '28186229', 'Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortlist high-quality unidentified mass spectra that are discriminative of different biological conditions.'),
(80622, 2017, 'bio', 'Simulome: a genome sequence and variant simulator', 'Price, Adam|Gibas, Cynthia', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, 28223-0001, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, 28223-0001, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx091', '28186240', '-'),
(80623, 2017, 'bio', 'FELLS: fast estimator of latent local structure', 'Piovesan, Damiano|Walsh, Ian|Minervini, Giovanni|Tosatto, Silvio C E', 'Department of Biomedical Sciences, University of Padua, Padova, Italy.|Department of Biomedical Sciences, University of Padua, Padova, Italy.|Department of Biomedical Sciences, University of Padua, Padova, Italy.|Department of Biomedical Sciences, University of Padua, Padova, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Computational Biology|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx085', '28186245', 'The behavior of a protein is encoded in its sequence, which can be used to predict distinct features such as secondary structure, intrinsic disorder or amphipathicity. Integrating these and other features can help explain the context-dependent behavior of proteins. However, most tools focus on a single aspect, hampering a holistic understanding of protein structure. Here, we present Fast Estimator of Latent Local Structure (FELLS) to visualize structural features from the protein sequence. FELLS provides disorder, aggregation and low complexity predictions as well as estimated local propensities including amphipathicity. A novel fast estimator of secondary structure (FESS) is also trained to provide a fast response. The calculations required for FELLS are extremely fast and suited for large-scale analysis while providing a detailed analysis of difficult cases.'),
(80624, 2017, 'bio', 'sfinx: an R package for the elimination of false positives from affinity purification-mass spectrometry datasets', 'Titeca, Kevin|Meysman, Pieter|Laukens, Kris|Martens, Lennart|Tavernier, Jan|Eyckerman, Sven', 'VIB Medical Biotechnology Center, A. Baertsoenkaai 3, Ghent, Belgium.|Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.|Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.|VIB Medical Biotechnology Center, A. Baertsoenkaai 3, Ghent, Belgium.|VIB Medical Biotechnology Center, A. Baertsoenkaai 3, Ghent, Belgium.|VIB Medical Biotechnology Center, A. Baertsoenkaai 3, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 2, 'Mass Spectrometry|Proteomics|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx076', '28186257', 'We describe sfinx, an R package providing access to the straightforward filtering index (SFINX) for the separation of true positive from false positive protein interactions in affinity purification - mass spectrometry datasets. This package maintains the reliability and user-friendliness of the SFINX web site interface but is faster, unlimited in input size, and can be run locally within R.'),
(80625, 2017, 'bio', 'MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets', 'Fort, Alexandre|Panousis, Nikolaos I|Garieri, Marco|Antonarakis, Stylianos E|Lappalainen, Tuuli|Dermitzakis, Emmanouil T|Delaneau, Olivier', 'Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.|New York Genome Center, New York, USA.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|United States|Switzerland|Switzerland', 0, 'Bias|Genomics|Genotyping Techniques|Humans|Quantitative Trait Loci|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx074', '28186259', 'Large genomic datasets combining genotype and sequence data, such as for expression quantitative trait loci (eQTL) detection, require perfect matching between both data types.'),
(80626, 2017, 'bio', 'Succinct colored de Bruijn graphs', 'Muggli, Martin D|Bowe, Alexander|Noyes, Noelle R|Morley, Paul S|Belk, Keith E|Raymond, Robert|Gagie, Travis|Puglisi, Simon J|Boucher, Christina', 'Department of Computer Science, Colorado State University, Fort Collins, CO, USA.|Department of Informatics, National Institute of Informatics, Chiyoda-ku, Tokyo, Japan.|Department of Clinical Sciences.|Department of Clinical Sciences.|Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA.|Department of Computer Science, Colorado State University, Fort Collins, CO, USA.|School of Computer Science and Telecommunications, Diego Portales University and CEBIB, Santiago, Chile.|Department of Computer Science, University of Helsinki, Helsinki, Finland.|Department of Computer Science, Colorado State University, Fort Collins, CO, USA.', 'United States|Japan|United States|United States|United States|United States|Chile|Finland|United States', 0, 'Algorithms|Bacteria|Eukaryota|Genotyping Techniques|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx067', '28200001', 'In 2012, Iqbal et al. introduced the colored de Bruijn graph, a variant of the classic de Bruijn graph, which is aimed at detecting and genotyping simple and complex genetic variants in an individual or population. Because they are intended to be applied to massive population level data, it is essential that the graphs be represented efficiently. Unfortunately, current succinct de Bruijn graph representations are not directly applicable to the colored de Bruijn graph, which requires additional information to be succinctly encoded as well as support for non-standard traversal operations.'),
(80627, 2017, 'bio', 'IRaPPA: information retrieval based integration of biophysical models for protein assembly selection', 'Moal, Iain H|Barradas-Bautista, Didier|Jimxc3xa9nez-Garcxc3xada, Brian|Torchala, Mieczyslaw|van der Velde, Arjan|Vreven, Thom|Weng, Zhiping|Bates, Paul A|Fernxc3xa1ndez-Recio, Juan', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.|Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA.|Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK.|Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.', 'United Kingdom|Spain|Spain|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Spain', 13, 'Information Storage and Retrieval|Internet|Molecular Docking Simulation|Protein Conformation|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btx068', '28200016', 'In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction.'),
(80628, 2017, 'bio', 'Statistical database analysis of the role of loop dynamics for protein-protein complex formation and allostery', 'Gu, Yina|Li, Da-Wei|Brxc3xbcschweiler, Rafael', 'Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA.|Campus Chemical Instrument Center, The Ohio State University, Columbus, OH, USA.|Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States', 2, 'Allosteric Site|Animals|Computational Biology|Humans|Mice|Models, Molecular|Protein Binding|Protein Interaction Domains and Motifs|Protein Multimerization|Software', 'Journal Article', '10.1093/bioinformatics/btx070', '28200021', 'Protein loops show rich conformational dynamics properties on a wide range of timescales as they play an essential role for many cellular functions during protein-protein interactions and recognition processes. However, little is known about the detail behavior of loops upon protein binding including allostery.'),
(80629, 2017, 'bio', 'Abstracting the dynamics of biological pathways using information theory: a case study of apoptosis pathway', 'Palaniappan, Sucheendra K|Bertaux, François|Pichené, Matthieu|Fabre, Eric|Batt, Gregory|Genest, Blaise', 'INRIA, Rennes, France.|INRIA, Paris-Saclay, France.|INRIA, Rennes, France.|INRIA, Rennes, France.|INRIA, Paris-Saclay, France.|CNRS, IRISA, Rennes, France.', 'France|France|France|France|France|France', 8, 'Algorithms|Apoptosis|Entropy|HeLa Cells|Humans|Information Theory|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btx095', '28200026', 'Quantitative models are increasingly used in systems biology. Usually, these quantitative models involve many molecular species and their associated reactions. When simulating a tissue with thousands of cells, using these large models becomes computationally and time limiting.'),
(80630, 2017, 'bio', 'Genome-wide genetic heterogeneity discovery with categorical covariates', 'Llinares-López, Felipe|Papaxanthos, Laetitia|Bodenham, Dean|Roqueiro, Damian|COPDGene Investigators|Borgwardt, Karsten', 'Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Algorithms|Arabidopsis|Female|Genetic Heterogeneity|Genetics, Population|Genomics|Humans|Male|Software', 'Journal Article', '10.1093/bioinformatics/btx071', '28200033', 'Genetic heterogeneity is the phenomenon that distinct genetic variants may give rise to the same phenotype. The recently introduced algorithm Fast Automatic Interval Search ( FAIS ) enables the genome-wide search of candidate regions for genetic heterogeneity in the form of any contiguous sequence of variants, and achieves high computational efficiency and statistical power. Although FAIS can test all possible genomic regions for association with a phenotype, a key limitation is its inability to correct for confounders such as gender or population structure, which may lead to numerous false-positive associations.'),
(80631, 2017, 'bio', 'LASER server: ancestry tracing with genotypes or sequence reads', 'Taliun, Daniel|Chothani, Sonia P|Schönherr, Sebastian|Forer, Lukas|Boehnke, Michael|Abecasis, Gonçalo R|Wang, Chaolong', 'Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.|Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck 6020, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck 6020, Austria.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.|Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore.', 'United States|Singapore|Austria|Austria|United States|United States|Singapore', 0, 'Genetic Variation|Humans|Phylogeography|Population Groups|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx075', '28200055', 'To enable direct comparison of ancestry background in different studies, we developed LASER to estimate individual ancestry by placing either sezquenced or genotyped samples in a common ancestry space, regardless of the sequencing strategy or genotyping array used to characterize each sample. Here we describe the LASER server to facilitate application of the method to a wide range of genetic studies. The server provides genetic ancestry estimation for different geographic regions and user-friendly interactive visualization of the results.'),
(80632, 2017, 'bio', 'Adaptive MCMC in Bayesian phylogenetics: an application to analyzing partitioned data in BEAST', 'Baele, Guy|Lemey, Philippe|Rambaut, Andrew|Suchard, Marc A', 'Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.|Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.|Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.|Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.', 'Belgium|Belgium|United Kingdom|United Kingdom', 12, 'Bayes Theorem|Computational Biology|Evolution, Molecular|Markov Chains|Monte Carlo Method|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx088', '28200071', 'Advances in sequencing technology continue to deliver increasingly large molecular sequence datasets that are often heavily partitioned in order to accurately model the underlying evolutionary processes. In phylogenetic analyses, partitioning strategies involve estimating conditionally independent models of molecular evolution for different genes and different positions within those genes, requiring a large number of evolutionary parameters that have to be estimated, leading to an increased computational burden for such analyses. The past two decades have also seen the rise of multi-core processors, both in the central processing unit (CPU) and Graphics processing unit processor markets, enabling massively parallel computations that are not yet fully exploited by many software packages for multipartite analyses.'),
(80633, 2017, 'bio', 'AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs', 'Jiang, Biaobin|Kloster, Kyle|Gleich, David F|Gribskov, Michael', 'Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.|Department of Mathematics, Purdue University, West Lafayette, IN, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.', 'United States|United States|United States|United States', 11, 'Algorithms|Animals|Computational Biology|Drosophila|Gene Ontology|Humans|Proteins|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btx029', '28200073', 'Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model.'),
(80634, 2017, 'bio', 'An approach to infer putative disease-specific mechanisms using neighboring gene networks', 'Ansari, Sahar|Donato, Michele|Saberian, Nafiseh|Draghici, Sorin', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Department of Computer Science.', '-|-|-|-', 4, 'Algorithms|Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Phenotype|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btx097', '28200075', 'The ultimate goal of any experiment is to understand the biological phenomena underlying the condition investigated. This process often results in genes network through which a certain biological mechanism is explained. Such networks have been proven to be extremely useful, for the prediction of mechanisms of action of drugs or the responses of an organism to a specific impact (e.g. a disease, a treatment, etc.). Here, we introduce an approach able to build a network that captures the putative mechanisms at play in the given condition, by using datasets from multiple experiments studying the same phenotype. This method takes advantage of known interactions extracted from multiple sources such as protein-protein interactions and curated biological pathways. Based on such prior knowledge, we overcome the drawbacks of snap-shot data by considering the possible effects of each gene on its neighbors.'),
(80635, 2017, 'bio', 'PMAnalyzer: a new web interface for bacterial growth curve analysis', 'Cuevas, Daniel A|Edwards, Robert A', 'Computational Science Research Center, San Diego State University, San Diego, CA, USA.|Computational Science Research Center, San Diego State University, San Diego, CA, USA.', 'United States|United States', 5, 'Bacteria|Computational Biology|Microbiological Techniques|Software', 'Journal Article', '10.1093/bioinformatics/btx084', '28200078', 'Bacterial growth curves are essential representations for characterizing bacteria metabolism within a variety of media compositions. Using high-throughput, spectrophotometers capable of processing tens of 96-well plates, quantitative phenotypic information can be easily integrated into the current data structures that describe a bacterial organism. The PMAnalyzer pipeline performs a growth curve analysis to parameterize the unique features occurring within microtiter wells containing specific growth media sources. We have expanded the pipeline capabilities and provide a user-friendly, online implementation of this automated pipeline. PMAnalyzer version 2.0 provides fast automatic growth curve parameter analysis, growth identification and high resolution figures of sample-replicate growth curves and several statistical analyses.'),
(80636, 2017, 'bio', 'Benchmarking substrate-based kinase activity inference using phosphoproteomic data', 'Hernandez-Armenta, Claudia|Ochoa, David|Gonçalves, Emanuel|Saez-Rodriguez, Julio|Beltrao, Pedro', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Computer Simulation|Humans|Phosphoproteins|Phosphotransferases|Proteomics|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx082', '28200105', 'Phosphoproteomic experiments are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies.'),
(80637, 2017, 'bio', 'Changes associated with Ebola virus adaptation to novel species', 'Pappalardo, Morena|Reddin, Ian G|Cantoni, Diego|Rossman, Jeremy S|Michaelis, Martin|Wass, Mark N', 'School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK.|School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK.|School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK.|School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK.|School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK.|School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Animals|Cricetinae|Ebolavirus|Evolution, Molecular|Guinea Pigs|Humans|Mice|Mutation|Protein Conformation|Rodentia|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btx065', '28200119', 'Ebola viruses are not pathogenic but can be adapted to replicate and cause disease in rodents. Here, we used a structural bioinformatics approach to analyze the mutations associated with Ebola virus adaptation to rodents to elucidate the determinants of host-specific Ebola virus pathogenicity.'),
(80638, 2017, 'bio', 'nala: text mining natural language mutation mentions', 'Cejuela, Juan Miguel|Bojchevski, Aleksandar|Uhlig, Carsten|Bekmukhametov, Rustem|Kumar Karn, Sanjeev|Mahmuti, Shpend|Baghudana, Ashish|Dubey, Ankit|Satagopam, Venkata P|Rost, Burkhard', 'TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Luxembourg|Germany', 7, 'Data Mining|Humans|Mutation|Natural Language Processing|PubMed|Software|Unsupervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx083', '28200120', 'The extraction of sequence variants from the literature remains an important task. Existing methods primarily target standard (ST) mutation mentions (e.g. E6V), leaving relevant mentions natural language (NL) largely untapped (e.g. glutamic acid was substituted by valine at residue 6).'),
(80639, 2017, 'bio', 'Threshold-seq: a tool for determining the threshold in short RNA-seq datasets', 'Magee, Rogan|Loher, Phillipe|Londin, Eric|Rigoutsos, Isidore', 'Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19107, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19107, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19107, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19107, USA.', 'United States|United States|United States|United States', 8, 'High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx073', '28203700', 'We present Threshold-seq, a new approach for determining thresholds in deep-sequencing datasets of short RNA transcripts. Threshold-seq addresses the critical question of how many reads need to support a short RNA molecule in a given dataset before it can be considered different from background. The proposed scheme is easy to implement and incorporate into existing pipelines.'),
(80640, 2017, 'bio', 'Integrative analysis identifies co-dependent gene expression regulation of BRG1 and CHD7 at distal regulatory sites in embryonic stem cells', 'Yang, Pengyi|Oldfield, Andrew|Kim, Taiyun|Yang, Andrian|Yang, Jean Yee Hwa|Ho, Joshua W K', 'Charles Perkins Centre and School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia.|Systems Biology Group, Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, RTP, NC 27709, USA.|Victor Chang Cardiac Research Institute.|Victor Chang Cardiac Research Institute.|Charles Perkins Centre and School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia.|Victor Chang Cardiac Research Institute.', 'Australia|United States|United States|United States|Australia|United States', 8, 'Animals|Cell Line|Chromatin Immunoprecipitation|DNA Helicases|DNA-Binding Proteins|Embryonic Stem Cells|Gene Expression Regulation, Developmental|Mice|Nuclear Proteins|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx092', '28203701', 'DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co-factors often bind cooperatively to activate or repress their target genes in a cell type-specific manner. Nonetheless, the precise role of cooperative binding in defining cell-type identity is still largely uncharacterized.'),
(80641, 2017, 'bio', 'Pln24NT: a web resource for plant 24-nt siRNA producing loci', 'Liu, Qi|Ding, Changjun|Chu, Yanguang|Zhang, Weixi|Guo, Ganggang|Chen, Jiafei|Su, Xiaohua', 'State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing, China.|State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing, China.|State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing, China.|State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing, China.|Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.|State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing, China.|State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing, China.', 'China|China|China|China|China|China|China', 4, 'DNA Methylation|Gene Expression Regulation, Plant|Genes, Plant|Genome, Plant|Genomics|High-Throughput Nucleotide Sequencing|Plants|RNA, Plant|RNA, Small Interfering|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx096', '28203705', 'In plants, 24 nucleotide small interfering RNAs (24-nt siRNAs) account for a large percentage of the total siRNA pool, and they play an important role in guiding plant-specific RNA-directed DNA methylation (RdDM), which transcriptionally silences transposon elements, transgenes, repetitive sequences and some endogenous genes. Several loci in plant genomes produce clusters of 24-nt RNAs, and these loci are receiving increasing attention from the research community. However, at present there is no bioinformatics resource dedicated to 24-nt siRNA loci and their derived 24-nt siRNAs. Thus, in this study, Pln24NT, a freely available web resource, was created to centralize 24-nt siRNA loci and 24-nt siRNA information, including fundamental locus information, expression profiles and annotation of transposon elements, from next-generation sequencing (NGS) data for 10 popular plant species. An intuitive web interface was also developed for convenient searching and browsing, and analytical tools were included to help users flexibly analyze their own siRNA NGS data. Pln24NT will help the plant research community to discover and characterize 24-nt siRNAs, and may prove useful for studying the roles of siRNA in RNA-directed DNA methylation in plants.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80642, 2017, 'bio', 'Correlated mutations select misfolded from properly folded proteins', 'Wozniak, P P|Vriend, G|Kotulska, M', 'Faculty of Fundamental Problems of Technology, Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland.|Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Nijmegen, The Netherlands.|Faculty of Fundamental Problems of Technology, Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland.', 'Poland|Netherlands|Poland', 2, 'Algorithms|Computational Biology|Mutation|Protein Conformation|Protein Folding|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx013', '28203707', 'The recently developed direct coupling analysis (DCA) method has greatly improved the accuracy with which residue-residue contacts can be predicted from multiple sequence alignments. Contact prediction accuracy, though, is still often not sufficient for complete ab initio protein structure prediction. DCA can, however, support protein structure studies in several ways.'),
(80643, 2017, 'bio', 'SANA: simulated annealing far outperforms many other search algorithms for biological network alignment', 'Mamano, Nil|Hayes, Wayne B', 'Department of Computer Science, University of California, Irvine, CA, USA.|Department of Computer Science, University of California, Irvine, CA, USA.', 'United States|United States', 30, 'Algorithms|Humans|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btx090', '28203713', 'Every alignment algorithm consists of two orthogonal components: an objective function M measuring the quality of an alignment, and a search algorithm that explores the space of alignments looking for ones scoring well according to M . We introduce a new search algorithm called SANA (Simulated Annealing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the topological measure. Compared against 12 recent algorithms, SANA produces 5-10 times as many correct node pairings as the others when the correct answer is known. We expose an anti-correlation in many existing aligners between their ability to produce good topological vs. functional similarity scores, whereas SANA usually outscores other methods in both measures. If given the perfect objective function encoding the identity mapping, SANA quickly converges to the perfect solution while many other algorithms falter. We observe that when aligning networks with a known mapping and optimizing only S 3 , SANA creates alignments that are not perfect and yet whose S 3 scores match that of the perfect alignment. We call this phenomenon saturation of the topological score . Saturation implies that a measures correlation with alignment correctness falters before the perfect alignment is reached. This, combined with SANAs ability to produce the perfect alignment if given the perfect objective function, suggests that better objective functions may lead to dramatically better alignments. We conclude that future work should focus on finding better objective functions, and offer SANA as the search algorithm of choice.'),
(80644, 2017, 'bio', 'Glimma: interactive graphics for gene expression analysis', 'Su, Shian|Law, Charity W|Ah-Cann, Casey|Asselin-Labat, Marie-Liesse|Blewitt, Marnie E|Ritchie, Matthew E', 'Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia.|Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia.|Department of Medical Biology, The University of Melbourne, Parkville 3010, Australia.|Department of Medical Biology, The University of Melbourne, Parkville 3010, Australia.|Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia.|Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 26, 'Animals|Gene Expression Profiling|Mice|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx094', '28203714', 'graphics for RNA-sequencing and microarray gene expression analyses may contain upwards of tens of thousands of points. Details about certain genes or samples of interest are easily obscured in such dense summary displays. Incorporating interactivity into summary plots would enable additional information to be displayed on demand and facilitate intuitive data exploration.'),
(80645, 2017, 'bio', 'Empirical comparison of web-based antimicrobial peptide prediction tools', 'Gabere, Musa Nur|Noble, William Stafford', 'Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.|Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.', 'Saudi Arabia|United States', 28, 'Antimicrobial Cationic Peptides|Bacteriocins|Internet|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btx081', '28203715', 'Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify.'),
(80646, 2017, 'bio', 'A multi-scale convolutional neural network for phenotyping high-content cellular images', 'Godinez, William J|Hossain, Imtiaz|Lazic, Stanley E|Davies, John W|Zhang, Xian', 'Novartis Institutes for BioMedical Research Inc., Basel, Switzerland.|Novartis Institutes for BioMedical Research Inc., Basel, Switzerland.|Novartis Institutes for BioMedical Research Inc., Basel, Switzerland.|Novartis Institutes for BioMedical Research Inc., Cambridge, MA, USA.|Novartis Institutes for BioMedical Research Inc., Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|United States|Switzerland', 42, 'Cell Line, Tumor|Humans|Image Processing, Computer-Assisted|Microscopy|Neural Networks (Computer)|Software', 'Journal Article', '10.1093/bioinformatics/btx069', '28203779', 'Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters.'),
(80647, 2017, 'bio', 'MapReduce for accurate error correction of next-generation sequencing data', 'Zhao, Liang|Chen, Qingfeng|Li, Wencui|Jiang, Peng|Wong, Limsoon|Li, Jinyan', 'School of Computing and Electronic Information, Guangxi University, Nanning 530004, China.|School of Computing and Electronic Information, Guangxi University, Nanning 530004, China.|Taihe Hospital, Hubei University of Medicine, Hubei 442000, China.|School of Computing and Electronic Information, Guangxi University, Nanning 530004, China.|School of Computing, National University of Singapore, Singapore 117417, Singapore.|Advanced Analytics Institute and Centre for Health Technologies, University of Technology Sydney, Broadway, NSW 2007, Australia.', 'China|China|China|China|Singapore|Australia', 9, 'Algorithms|Base Sequence|High-Throughput Nucleotide Sequencing|Reproducibility of Results|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btx089', '28205674', 'Next-generation sequencing platforms have produced huge amounts of sequence data. This is revolutionizing every aspect of genetic and genomic research. However, these sequence datasets contain quite a number of machine-induced errors-e.g. errors due to substitution can be as high as 2.5%. Existing error-correction methods are still far from perfect. In fact, more errors are sometimes introduced than correct corrections, especially by the prevalent k-mer based methods. The existing methods have also made limited exploitation of on-demand cloud computing.'),
(80648, 2017, 'bio', 'BCFtools/csq: haplotype-aware variant consequences', 'Danecek, Petr|McCarthy, Shane A', 'Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK.', 'United Kingdom|United Kingdom', 37, 'Algorithms|Genetic Variation|Genome, Human|Genomics|Haplotypes|Humans|INDEL Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx100', '28205675', 'Prediction of functional variant consequences is an important part of sequencing pipelines, allowing the categorization and prioritization of genetic variants for follow up analysis. However, current predictors analyze variants as isolated events, which can lead to incorrect predictions when adjacent variants alter the same codon, or when a frame-shifting indel is followed by a frame-restoring indel. Exploiting known haplotype information when making consequence predictions can resolve these issues.'),
(80649, 2017, 'bio', 'GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification', 'Szpiech, Zachary A|Blant, Alexandra|Pemberton, Trevor J', 'Department of Bioengineering and Therapeutic Sciences, University of California - San Francisco, San Francisco, CA 94158, USA.|Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB R3E 0J9, Canada.|Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB R3E 0J9, Canada.', 'United States|Canada|Canada', 9, 'Computer Simulation|Genomics|Haplotypes|Homozygote|Humans|Likelihood Functions|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx102', '28205676', 'Runs of homozygosity (ROH) are important genomic features that manifest when identical-by-descent haplotypes are inherited from parents. Their length distributions and genomic locations are informative about population history and they are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. Here, we present software implementing a model-based method ( Pemberton et al., 2012 ) for inferring ROH in genome-wide SNP datasets that incorporates population-specific parameters and a genotyping error rate as well as provides a length-based classification module to identify biologically interesting classes of ROH. Using simulations, we evaluate the performance of this method.'),
(80650, 2017, 'bio', 'Pseudoalignment for metagenomic read assignment', 'Schaeffer, L|Pimentel, H|Bray, N|Melsted, P|Pachter, L', 'Department of Molecular and Cell Biology, UC Berkeley, Berkeley, CA, USA.|Department of Genetics, Stanford University, Stanford, CA, USA.|Department of Molecular and Cell Biology and Innovative Genomics Institute, UC Berkeley, Berkeley, CA, USA.|Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland.|Department of Molecular and Cell Biology, UC Berkeley, Berkeley, CA, USA.', 'United States|United States|United States|Iceland|United States', 20, 'Algorithms|Bacteria|Genome, Bacterial|Metagenomics|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx106', '28334086', 'Read assignment is an important first step in many metagenomic analysis workflows, providing the basis for identification and quantification of species. However ambiguity among the sequences of many strains makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to taxonomic levels where they are unambiguous. We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data in order to develop novel methods for rapid and accurate quantification of metagenomic strains.'),
(80651, 2017, 'bio', 'FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis', 'Zhang, Yun|Topham, David J|Thakar, Juilee|Qiu, Xing', 'Department of Biostatistics and Computational Biology.|Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, USA.|Department of Biostatistics and Computational Biology.|Department of Biostatistics and Computational Biology.', 'United States|United States|United States|United States', 0, 'Gene Expression Profiling|Gene Expression Regulation|Gene Ontology|Humans|Influenza, Human|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx104', '28334094', 'Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements, and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing.'),
(80652, 2017, 'bio', 'RefBool: a reference-based algorithm for discretizing gene expression data', 'Jung, Sascha|Hartmann, Andras|Del Sol, Antonio', 'Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg', 2, 'Embryonic Stem Cells|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx111', '28334101', 'The identification of genes or molecular regulatory mechanisms implicated in biological processes often requires the discretization, and in particular booleanization, of gene expression measurements. However, currently used methods mostly classify each measurement into an active or inactive state regardless of its statistical support possibly leading to downstream analysis conclusions based on spurious booleanization results.'),
(80653, 2017, 'bio', 'BioJava-ModFinder: identification of protein modifications in 3D structures from the Protein Data Bank', 'Gao, Jianjiong|Prlic, Andreas|Bi, Chunxiao|Bluhm, Wolfgang F|Dimitropoulos, Dimitris|Xu, Dong|Bourne, Philip E|Rose, Peter W', 'Department of Computer Science and C.S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|RCSB Protein Data Bank, San Diego Supercomputer Center.|RCSB Protein Data Bank, San Diego Supercomputer Center.|RCSB Protein Data Bank, San Diego Supercomputer Center.|RCSB Protein Data Bank, San Diego Supercomputer Center.|Department of Computer Science and C.S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|RCSB Protein Data Bank, San Diego Supercomputer Center.|RCSB Protein Data Bank, San Diego Supercomputer Center.', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Computational Biology|Databases, Protein|Internet|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx101', '28334105', 'We developed a new software tool, BioJava-ModFinder, for identifying protein modifications observed in 3D structures archived in the Protein Data Bank (PDB). Information on more than 400 types of protein modifications were collected and curated from annotations in PDB, RESID, and PSI-MOD. We divided these modifications into three categories: modified residues, attachment modifications, and cross-links. We have developed a systematic method to identify these modifications in 3D protein structures. We have integrated this package with the RCSB PDB web application and added protein modification annotations to the sequence diagram and structure display. By scanning all 3D structures in the PDB using BioJava-ModFinder, we identified more than 30 000 structures with protein modifications, which can be searched, browsed, and visualized on the RCSB PDB website.'),
(80654, 2017, 'bio', 'Fast admixture analysis and population tree estimation for SNP and NGS data', 'Cheng, Jade Yu|Mailund, Thomas|Nielsen, Rasmus', 'Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.|Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.|Departments of Integrative Biology and Statistics, University of California, Berkeley, Berkeley, CA, USA.', 'Denmark|Denmark|United States', 8, 'Algorithms|Genetics, Population|High-Throughput Nucleotide Sequencing|Humans|Phylogeny|Population Groups|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx098', '28334108', 'Structure methods are highly used population genetic methods for classifying individuals in a sample fractionally into discrete ancestry components.'),
(80655, 2017, 'bio', 'BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone', 'Yang, Bite|Liu, Feng|Ren, Chao|Ouyang, Zhangyi|Xie, Ziwei|Bo, Xiaochen|Shu, Wenjie', 'Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850.|Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850.|Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850.|Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850.|Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.|Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850.|Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850.', '-|-|-|-|China|-|-', 0, 'Animals|Enhancer Elements, Genetic|Gene Expression Regulation|Genomics|Humans|Mice|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx105', '28334114', 'Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent.'),
(80656, 2017, 'bio', 'fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy', 'Hilsenbeck, Oliver|Schwarzfischer, Michael|Loeffler, Dirk|Dimopoulos, Sotiris|Hastreiter, Simon|Marr, Carsten|Theis, Fabian J|Schroeder, Timm', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen, Neuherberg, Germany.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum Mxc3xbcnchen, Neuherberg, Germany.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Germany|Switzerland|Switzerland|Switzerland|Germany|Germany|Switzerland', 20, 'Algorithms|HeLa Cells|Humans|Image Processing, Computer-Assisted|Microscopy', 'Journal Article', '10.1093/bioinformatics/btx107', '28334115', 'Quantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.g. high cell densities, cell-to-cell variability and low signal-to-noise ratios.'),
(80657, 2017, 'bio', 'A generalized association test based on U statistics', 'Wei, Changshuai|Lu, Qing', 'Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX 76107.|Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 4, 'Genetic Association Studies|Humans|Models, Genetic|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx103', '28334117', 'Second generation sequencing technologies are being increasingly used for genetic association studies, where the main research interest is to identify sets of genetic variants that contribute to various phenotypes. The phenotype can be univariate disease status, multivariate responses and even high-dimensional outcomes. Considering the genotype and phenotype as two complex objects, this also poses a general statistical problem of testing association between complex objects.'),
(80658, 2017, 'bio', 'SVPV: a structural variant prediction viewer for paired-end sequencing datasets', 'Munro, Jacob E|Dunwoodie, Sally L|Giannoulatou, Eleni', 'Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.|Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.|Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Gene Frequency|Genome, Human|Genomic Structural Variation|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx117', '28334120', 'A wide range of algorithms exist for the prediction of structural variants (SVs) from paired-end whole genome sequencing (WGS) alignments. It is essential for the purpose of quality control to be able to visualize, compare and contrast the data underlying the predictions across multiple different algorithms.'),
(80659, 2017, 'bio', 'AQUA-DUCT: a ligands tracking tool', 'Magdziarz, Tomasz|Mitusinska, Karolina|Goldowska, Sandra|Pluciennik, Alicja|Stolarczyk, Michal|Lugowska, Magdalena|Góra, Artur', 'Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Catalytic Domain|Computational Biology|Computer Simulation|Ligands|Models, Molecular|Software', 'Journal Article', '10.1093/bioinformatics/btx125', '28334160', 'The identification and tracking of molecules which enter active site cavity requires screening the positions of thousands of single molecules along several thousand molecular dynamic steps. To fill the existing gap between tools searching for tunnels and pathways and advanced tools employed for accelerated water flux investigations, we have developed AQUA-DUCT.'),
(80660, 2017, 'bio', 'ctsGE-clustering subgroups of expression data', 'Sharabi-Schwager, Michal|Or, Etti|Ophir, Ron', 'Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.|Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.|Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.', 'Israel|Israel|Israel', 2, 'Adenocarcinoma|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx116', '28334165', 'A pre-requisite to clustering noisy data, such as gene-expression data, is the filtering step. As an alternative to this step, the ctsGE R-package applies a sorting step in which all of the data are divided into small groups. The groups are divided according to how the time points are related to the time-series median. Then clustering is performed separately on each group. Thus, the clustering is done in two steps. First, an expression index (i.e. a sequence of 1, -1 and 0) is defined and genes with the same index are grouped together, and then each group of genes is clustered by k-means to create subgroups. The ctsGE package also provides an interactive tool to visualize and explore the gene-expression patterns and their subclusters. ctsGE proposes a way of organizing and exploring expression data without eliminating valuable information.'),
(80661, 2017, 'bio', 'Identification of genetic outliers due to sub-structure and cryptic relationships', 'Schlauch, Daniel|Fier, Heide|Lange, Christoph', 'Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.|Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.', 'United States|United States|United States', 3, 'Genetic Association Studies|Genetics, Population|Genome, Human|Humans|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx109', '28334167', 'In order to minimize the effects of genetic confounding on the analysis of high-throughput genetic association studies, e.g. (whole-genome) sequencing (WGS) studies, genome-wide association studies (GWAS), etc., we propose a general framework to assess and to test formally for genetic heterogeneity among study subjects. As the approach fully utilizes the recent ancestor information captured by rare variants, it is especially powerful in WGS studies. Even for relatively moderate sample sizes, the proposed testing framework is able to identify study subjects that are genetically too similar, e.g. cryptic relationships, or that are genetically too different, e.g. population substructure. The approach is computationally fast, enabling the application to whole-genome sequencing data, and straightforward to implement.'),
(80662, 2017, 'bio', 'RNAscClust: clustering RNA sequences using structure conservation and graph based motifs', 'Miladi, Milad|Junge, Alexander|Costa, Fabrizio|Seemann, Stefan E|Havgaard, Jakob Hull|Gorodkin, Jan|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.|Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.|Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.|Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.|Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.', 'Germany|Denmark|Germany|Denmark|Denmark|Denmark|Germany', 0, 'Algorithms|Cluster Analysis|Humans|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx114', '28334186', 'Clustering RNA sequences with common secondary structure is an essential step towards studying RNA function. Whereas structural RNA alignment strategies typically identify common structure for orthologous structured RNAs, clustering seeks to group paralogous RNAs based on structural similarities. However, existing approaches for clustering paralogous RNAs, do not take the compensatory base pair changes obtained from structure conservation in orthologous sequences into account.'),
(80663, 2017, 'bio', 'swga: a primer design toolkit for selective whole genome amplification', 'Clarke, Erik L|Sundararaman, Sesh A|Seifert, Stephanie N|Bushman, Frederic D|Hahn, Beatrice H|Brisson, Dustin', 'Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|DNA Primers|Drosophila melanogaster|Genetics, Population|Genome, Bacterial|Genomics|Humans|Mycobacterium tuberculosis|Sequence Analysis, DNA|Software|Wolbachia', 'Journal Article', '10.1093/bioinformatics/btx118', '28334194', 'Population genomic analyses are often hindered by difficulties in obtaining sufficient numbers of genomes for analysis by DNA sequencing. Selective whole-genome amplification (SWGA) provides an efficient approach to amplify microbial genomes from complex backgrounds for sequence acquisition. However, the process of designing sets of primers for this method has many degrees of freedom and would benefit from an automated process to evaluate the vast number of potential primer sets.'),
(80664, 2017, 'bio', 'Stepwise inference of likely dynamic flux distributions from metabolic time series data', 'Faraji, Mojdeh|Voit, Eberhard O', 'Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.', 'Georgia|Georgia', 0, 'Computational Biology|Computer Simulation|Lignin|Metabolic Networks and Pathways|Models, Biological|Panicum|Software', 'Journal Article', '10.1093/bioinformatics/btx126', '28334199', 'Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution.'),
(80665, 2017, 'bio', 'Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy', 'Megjhani, Murad|Correa de Sampaio, Pedro|Leigh Carstens, Julienne|Kalluri, Raghu|Roysam, Badrinath', 'Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.|Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Animals|Fluorescent Dyes|Humans|Image Processing, Computer-Assisted|Mice|Microscopy, Fluorescence|Software', 'Journal Article', '10.1093/bioinformatics/btx108', '28334208', 'Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub-cellular structures, and their local spatial context.'),
(80666, 2017, 'bio', 'Systematic inference of functional phosphorylation events in yeast metabolism', 'Chen, Yu|Wang, Yonghong|Nielsen, Jens', 'State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.|State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.|Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden.', 'China|China|Sweden', 3, 'Algorithms|Computational Biology|Phosphorylation|Protein Processing, Post-Translational|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx110', '28334215', 'Protein phosphorylation is a post-translational modification that affects proteins by changing their structure and conformation in a rapid and reversible way, and it is an important mechanism for metabolic regulation in cells. Phosphoproteomics enables high-throughput identification of phosphorylation events on metabolic enzymes, but identifying functional phosphorylation events still requires more detailed biochemical characterization. Therefore, development of computational methods for investigating unknown functions of a large number of phosphorylation events identified by phosphoproteomics has received increased attention.'),
(80667, 2017, 'bio', 'A network model predicts the intensity of residue-protein thermal coupling', 'Censoni, Luciano|Dos Santos Muniz, Heloisa|Martínez, Leandro', 'Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.|Institute of Physics of São Carlos, University of São Paulo, São Carlos, SP, Brazil.|Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.', 'Brazil|Brazil|Brazil', 0, 'Computational Biology|Molecular Dynamics Simulation|Protein Conformation|Proteins|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btx124', '28334219', 'The flow of vibrational energy in proteins has been shown not to obey expectations for isotropic media. The existence of preferential pathways for energy transport, with probable connections to allostery mechanisms, has been repeatedly demonstrated. Here, we investigate whether, by representing a set of protein structures as networks of interacting amino acid residues, we are able to model heat diffusion and predict residue-protein vibrational couplings, as measured by the Anisotropic Thermal Diffusion (ATD) computational protocol of modified molecular dynamics simulations.'),
(80668, 2017, 'bio', 'QRank: a novel quantile regression tool for eQTL discovery', 'Song, Xiaoyu|Li, Gen|Zhou, Zhenwei|Wang, Xianling|Ionita-Laza, Iuliana|Wei, Ying', 'Heilbrunn Department of Population & Family Health, Columbia University, New York, NY, USA.|Department of Biostatistics, Columbia University, New York, NY, USA.|Department of Biostatistics, Columbia University, New York, NY, USA.|Department of Biostatistics, Columbia University, New York, NY, USA.|Department of Biostatistics, Columbia University, New York, NY, USA.|Department of Biostatistics, Columbia University, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Computational Biology|Computer Simulation|Gene Expression Profiling|Genetic Variation|Genome-Wide Association Study|Humans|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btx119', '28334222', 'Over the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Motivated by the potential higher order associations, several studies investigated variance eQTLs.'),
(80669, 2017, 'bio', 'Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning', 'Gao, Zhen|Ruan, Jianhua', 'Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA.|Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA.', 'United States|United States', 7, 'Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|DNA|Humans|Protein Binding|Supervised Machine Learning|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx115', '28334224', 'The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance.'),
(80670, 2017, 'bio', 'ProtVista: visualization of protein sequence annotations', 'Watkins, Xavier|Garcia, Leyla J|Pundir, Sangya|Martin, Maria J|UniProt Consortium', 'EMBL-EBI, Hinxton, UK.|EMBL-EBI, Hinxton, UK.|EMBL-EBI, Hinxton, UK.|EMBL-EBI, Hinxton, UK.|EMBL-EBI, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Humans|Molecular Sequence Annotation|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx120', '28334231', 'ProtVista is a comprehensive visualization tool for the graphical representation of protein sequence features in the UniProt Knowledgebase, experimental proteomics and variation public datasets. The complexity and relationships in this wealth of data pose a challenge in interpretation. Integrative visualization approaches such as provided by ProtVista are thus essential for researchers to understand the data and, for instance, discover patterns affecting function and disease associations.'),
(80671, 2017, 'bio', 'DNA Compass: a secure, client-side site for navigating personal genetic information', 'Curnin, Charles|Gordon, Assaf|Erlich, Yaniv', 'New York Genome Center, New York, NY, USA.|New York Genome Center, New York, NY, USA.|New York Genome Center, New York, NY, USA.', 'United States|United States|United States', 3, 'Genetic Privacy|Genome, Human|Humans|Information Storage and Retrieval|Polymorphism, Single Nucleotide|Precision Medicine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx135', '28334237', 'Millions of individuals have access to raw genomic data using direct-to-consumer companies. The advent of large-scale sequencing projects, such as the Precision Medicine Initiative, will further increase the number of individuals with access to their own genomic information. However, querying genomic data requires a computer terminal and computational skill to analyze the data-an impediment for the general public.'),
(80672, 2017, 'bio', 'rMATS-DVR: rMATS discovery of differential variants in RNA', 'Wang, Jinkai|Pan, Yang|Shen, Shihao|Lin, Lan|Xing, Yi', 'Department of Microbiology, Immunology, & Molecular Genetics, University of California Los Angeles, Los Angeles, CA, USA.|Department of Microbiology, Immunology, & Molecular Genetics, University of California Los Angeles, Los Angeles, CA, USA.|Department of Microbiology, Immunology, & Molecular Genetics, University of California Los Angeles, Los Angeles, CA, USA.|Department of Microbiology, Immunology, & Molecular Genetics, University of California Los Angeles, Los Angeles, CA, USA.|Department of Microbiology, Immunology, & Molecular Genetics, University of California Los Angeles, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States', 8, 'Adenosine Deaminase|Gene Knockdown Techniques|Humans|K562 Cells|Polymorphism, Single Nucleotide|RNA|RNA Editing|RNA-Binding Proteins|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx128', '28334241', 'RNA sequences of a gene can have single nucleotide variants (SNVs) due to single nucleotide polymorphisms (SNPs) in the genome, or RNA editing events within the RNA. By comparing RNA-seq data of a given cell type before and after a specific perturbation, we can detect and quantify SNVs in the RNA and discover SNVs with altered frequencies between distinct cellular states. Such differential variants in RNA (DVRs) may reflect allele-specific changes in gene expression or RNA processing, as well as changes in RNA editing in response to cellular perturbations or stimuli.'),
(80673, 2017, 'bio', 'Fast, clash-free RNA conformational morphing using molecular junctions', 'Héliou, Amélie|Budday, Dominik|Fonseca, Rasmus|van den Bedem, Henry', 'LIX, Ecole Polytechnique, CNRS, Inria, Université Paris-Saclay, Palaiseau, France.|Chair of Applied Dynamics, University of Erlangen-Nuremberg, Erlangen, Germany.|Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA.|Biosciences Division, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA.', 'France|Germany|United States|United States', 2, 'Algorithms|Computational Biology|Computer Simulation|Models, Molecular|Nucleic Acid Conformation|RNA, Untranslated|Software', 'Journal Article', '10.1093/bioinformatics/btx127', '28334257', 'Non-coding ribonucleic acids (ncRNA) are functional RNA molecules that are not translated into protein. They are extremely dynamic, adopting diverse conformational substates, which enables them to modulate their interaction with a large number of other molecules. The flexibility of ncRNA provides a challenge for probing their complex 3D conformational landscape, both experimentally and computationally.'),
(80674, 2017, 'bio', 'Discriminating the reaction types of plant type III polyketide synthases', 'Shimizu, Yugo|Ogata, Hiroyuki|Goto, Susumu', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan', 1, 'Acyltransferases|Computational Biology|Plant Proteins|Plants|Polyketide Synthases|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx112', '28334262', 'Functional prediction of paralogs is challenging in bioinformatics because of rapid functional diversification after gene duplication events combined with parallel acquisitions of similar functions by different paralogs. Plant type III polyketide synthases (PKSs), producing various secondary metabolites, represent a paralogous family that has undergone gene duplication and functional alteration. Currently, there is no computational method available for the functional prediction of type III PKSs.'),
(80675, 2017, 'bio', 'Phenopolis: an open platform for harmonization and analysis of genetic and phenotypic data', 'Pontikos, Nikolas|Yu, Jing|Moghul, Ismail|Withington, Lucy|Blanco-Kelly, Fiona|Vulliamy, Tom|Wong, Tsz Lun Ernest|Murphy, Cian|Cipriani, Valentina|Fiorentino, Alessia|Arno, Gavin|Greene, Daniel|Jacobsen, Julius O B|Clark, Tristan|Gregory, David S|Nemeth, Andrea M|Halford, Stephanie|Inglehearn, Chris F|Downes, Susan|Black, Graeme C|Webster, Andrew R|Hardcastle, Alison J|UKIRDC|Plagnol, Vincent', 'UCL Genetics Institute, University College London, London WC1E 6BT, UK.|Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|UCL Cancer Institute, University College London, London WC1E 6DD, UK.|RP Fighting Blindness, PO Box 350, Buckingham, MK18 1GZ, UK.|Institute of Ophthalmology, University College London, London EC1V 9EL, UK.|Barts and The London School of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London E1 2AT, UK.|Barts and The London School of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London E1 2AT, UK.|UCL Genetics Institute, University College London, London WC1E 6BT, UK.|UCL Genetics Institute, University College London, London WC1E 6BT, UK.|Institute of Ophthalmology, University College London, London EC1V 9EL, UK.|Institute of Ophthalmology, University College London, London EC1V 9EL, UK.|Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK.|Barts and The London School of Medicine and Dentistry, William Harvey Research Institute, Queen Mary University of London, John Vane Building, Charterhouse Square, London EC1M 6BQ, UK.|Computer Science Department, University College London, London WC1E 6BT, UK.|Computer Science Department, University College London, London WC1E 6BT, UK.|Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Nuffield Laboratory of Ophthalmology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Leeds Institute of Biomedical and Clinical Sciences, University of Leeds, St Jamess University Hospital, Leeds LS9 7TF, UK.|Oxford Eye Hospital, John Radcliffe Hospital, Oxford OX3 9DU, UK.|Manchester Royal Eye Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester M13 9WL, UK.|Institute of Ophthalmology, University College London, London EC1V 9EL, UK.|Institute of Ophthalmology, University College London, London EC1V 9EL, UK.|Institute of Ophthalmology, University College London, London EC1V 9EL, UK.|UCL Genetics Institute, University College London, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 18, 'Computational Biology|Databases, Factual|Genetic Diseases, Inborn|Humans|Phenotype|Rare Diseases|Software', 'Journal Article', '10.1093/bioinformatics/btx147', '28334266', 'Phenopolis is an open-source web server providing an intuitive interface to genetic and phenotypic databases. It integrates analysis tools such as variant filtering and gene prioritization based on phenotype. The Phenopolis platform will accelerate clinical diagnosis, gene discovery and encourage wider adoption of the Human Phenotype Ontology in the study of rare genetic diseases.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80676, 2017, 'bio', 'ResistoMap-online visualization of human gut microbiota antibiotic resistome', 'Yarygin, Konstantin S|Kovarsky, Boris A|Bibikova, Tatyana S|Melnikov, Damir S|Tyakht, Alexander V|Alexeev, Dmitry G', 'Moscow Institute of Physics and Technology (MIPT), 9 Institutskiy per, Dolgoprudny, Moscow Region, Russian Federation.|Federal Research and Clinical Centre of Physical-Chemical Medicine (FRCC PCM), Malaya Pirogovskaya 1a, Moscow, Russian Federation.|Data Laboratory, Kostycheva 64-12, Bryansk, Russian Federation.|Data Laboratory, Kostycheva 64-12, Bryansk, Russian Federation.|Moscow Institute of Physics and Technology (MIPT), 9 Institutskiy per, Dolgoprudny, Moscow Region, Russian Federation.|Moscow Institute of Physics and Technology (MIPT), 9 Institutskiy per, Dolgoprudny, Moscow Region, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russi', 12, 'Anti-Bacterial Agents|Drug Resistance, Bacterial|Gastrointestinal Microbiome|Humans|Metagenome|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btx134', '28334267', 'We created ResistoMap—a Web-based interactive visualization of the presence of genetic determinants conferring resistance to antibiotics, biocides and heavy metals in human gut microbiota. ResistoMap displays the data on more than 1500 published gut metagenomes of world populations including both healthy subjects and patients. Multiparameter display filters allow visual assessment of the associations between the meta-data and proportions of resistome. The geographic map navigation layer allows to state hypotheses regarding the global trends of antibiotic resistance and correlates the gut resistome variations with the national clinical guidelines on antibiotics application.'),
(80677, 2017, 'bio', 'IntegratedMRF: random forest-based framework for integrating prediction from different data types', 'Rahman, Raziur|Otridge, John|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.|Leidos Biomedical Research Inc, Frederick, MD 21701, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.', 'United States|United States|United States', 26, 'Antineoplastic Agents|Biomarkers, Pharmacological|DNA Methylation|Gene Expression Regulation|Genomics|Humans|Models, Genetic|Neoplasms|Polymorphism, Single Nucleotide|Precision Medicine|Software|Statistics as Topic|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btw765', '28334269', 'IntegratedMRF is an open-source R implementation for integrating drug response predictions from various genomic characterizations using univariate or multivariate random forests that includes various options for error estimation techniques. The integrated framework was developed following superior performance of random forest based methods in NCI-DREAM drug sensitivity prediction challenge. The computational framework can be applied to estimate mean and confidence interval of drug response prediction errors based on ensemble approaches with various combinations of genetic and epigenetic characterizations as inputs. The multivariate random forest implementation included in the package incorporates the correlations between output responses in the modeling and has been shown to perform better than existing approaches when the drug responses are correlated. Detailed analysis of the provided features is included in the Supplementary Material .'),
(80678, 2017, 'bio', 'MotifMap-RNA: a genome-wide map of RBP binding sites', 'Liu, Yu|Sun, Sha|Bredy, Timothy|Wood, Marcelo|Spitale, Robert C|Baldi, Pierre', 'Department of Computer Science and Institute for Genomics and Bioinformatics.|Department of Developmental and Cell Biology.|Department of Neurobiology and Behavior.|Department of Neurobiology and Behavior.|Department of Pharmaceutical Sciences and the Chao Family Comprehensive Cancer Center, University of California, Irvine, CA 92697, USA.|Department of Computer Science and Institute for Genomics and Bioinformatics.', 'United States|United States|United States|United States|United States|United States', 6, 'Animals|Binding Sites|DNA|Gene Expression Regulation|Genome|Genomics|Humans|Mice|RNA-Binding Proteins|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx087', '28334276', 'RNA plays a critical role in gene expression and its regulation. RNA binding proteins (RBPs), in turn, are important regulators of RNA. Thanks to the availability of large scale data for RBP binding motifs and in vivo binding sites results in the form of eCLIP experiments, it is now possible to computationally predict RBP binding sites across the whole genome.'),
(80679, 2017, 'bio', 'SmartR: an open-source platform for interactive visual analytics for translational research data', 'Herzinger, Sascha|Gu, Wei|Satagopam, Venkata|Eifes, Serge|Rege, Kavita|Barbosa-Silva, Adriano|Schneider, Reinhard|eTRIKS Consortium', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch/Belval, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg', 12, 'Breast Neoplasms|Female|Gene Expression Regulation, Neoplastic|Humans|Software|Translational Medical Research', 'Journal Article', '10.1093/bioinformatics/btx137', '28334291', 'In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements.'),
(80680, 2017, 'bio', 'MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics', 'Zhou, Zhiwei|Xiong, Xin|Zhu, Zheng-Jiang', 'Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, Peoples Republic of China.|Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, Peoples Republic of China.|Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, Peoples Republic of China.', 'China|China|China', 31, 'Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btx140', '28334295', 'In metabolomics, rigorous structural identification of metabolites presents a challenge for bioinformatics. The use of collision cross-section (CCS) values of metabolites derived from ion mobility-mass spectrometry effectively increases the confidence of metabolite identification, but this technique suffers from the limit number of available CCS values. Currently, there is no software available for rapidly generating the metabolites CCS values. Here, we developed the first web server, namely, MetCCS Predictor, for predicting CCS values. It can predict the CCS values of metabolites using molecular descriptors within a few seconds. Common users with limited background on bioinformatics can benefit from this software and effectively improve the metabolite identification in metabolomics.'),
(80681, 2017, 'bio', 'Dynamix: dynamic visualization by automatic selection of informative tracks from hundreds of genomic datasets', 'Monfort, Matthias|Furlong, Eileen E M|Girardot, Charles', 'European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.', 'Germany|Germany|Germany', 1, 'Datasets as Topic|Genomics|Humans|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btx141', '28334301', 'Visualization of genomic data is fundamental for gaining insights into genome function. Yet, co-visualization of a large number of datasets remains a challenge in all popular genome browsers and the development of new visualization methods is needed to improve the usability and user experience of genome browsers.'),
(80682, 2017, 'bio', 'PeptideMapper: efficient and versatile amino acid sequence and tag mapping', 'Kopczynski, Dominik|Barsnes, Harald|Njølstad, Pål R|Sickmann, Albert|Vaudel, Marc|Ahrends, Robert', 'Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, Dortmund 44227, Germany.|Proteomics Unit, Department of Biomedicine.|KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.|Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, Dortmund 44227, Germany.|KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.|Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, Dortmund 44227, Germany.', 'Germany|-|Norway|Germany|Norway|Germany', 4, 'Animals|Humans|Mice|Sequence Analysis, Protein|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx122', '28334306', 'PeptideMapper is implemented in the open source Java CompOmics framework under the permissive Apache 2.0 license https://github.com/compomics/compomics-utilities .'),
(80683, 2017, 'bio', 'SynMap2 and SynMap3D: web-based whole-genome synteny browsers', 'Haug-Baltzell, Asher|Stephens, Sean A|Davey, Sean|Scheidegger, Carlos E|Lyons, Eric', 'Genetics GIDP, University of Arizona, Tucson AZ, USA.|Department of Computer Science, University of Arizona, Tucson AZ, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson AZ, USA.|Department of Computer Science, University of Arizona, Tucson AZ, USA.|Genetics GIDP, University of Arizona, Tucson AZ, USA.', 'United States|United States|United States|United States|United States', 31, 'Genome|Genomics|Software|Synteny|Web Browser|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx144', '28334338', 'Current synteny visualization tools either focus on small regions of sequence and do not illustrate genome-wide trends, or are complicated to use and create visualizations that are difficult to interpret. To address this challenge, The Comparative Genomics Platform (CoGe) has developed two web-based tools to visualize synteny across whole genomes. SynMap2 and SynMap3D allow researchers to explore whole genome synteny patterns (across two or three genomes, respectively) in responsive, web-based visualization and virtual reality environments. Both tools have access to the extensive CoGe genome database (containing over 30 000 genomes) as well as the option for users to upload their own data. By leveraging modern web technologies there is no installation required, making the tools widely accessible and easy to use.'),
(80684, 2017, 'bio', 'Meta-analytic framework for liquid association', 'Wang, Lin|Liu, Silvia|Ding, Ying|Yuan, Shin-Sheng|Ho, Yen-Yi|Tseng, George C', 'School of Statistics, Capital University of Economics and Business, Fengtai, Beijing, China.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.|Institute of Statistical Science, Academia Sinica, Nankang, Taipei, Taiwan.|Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.', 'China|United States|United States|United States|United States|United States', 6, 'Algorithms|Computational Biology|Epistasis, Genetic|Gene Expression Profiling|Saccharomyces cerevisiae|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx138', '28334340', 'Although coexpression analysis via pair-wise expression correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many complicated multi-gene regulations require more advanced detection methods. Liquid association (LA) is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). LA detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation and limited sample size. With the rapid development of microarray and NGS technology, LA analysis combining multiple gene expression studies can provide more accurate and stable results.'),
(80685, 2017, 'bio', 'Tagging SNP-set selection with maximum information based on linkage disequilibrium structure in genome-wide association studies', 'Wang, Shudong|He, Sicheng|Yuan, Fayou|Zhu, Xinjie', 'College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao Shandong, China.|College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao Shandong, China.|College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao Shandong, China.|College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao Shandong, China.', 'China|China|China|China', 6, 'Algorithms|Computer Simulation|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btx151', '28334342', 'Effective tagging single-nucleotide polymorphism (SNP)-set selection is crucial to SNP-set analysis in genome-wide association studies (GWAS). Most of the existing tagging SNP-set selection methods cannot make full use of the information hidden in common or rare variants associated diseases. It is noticed that some SNPs have overlapping genetic information owing to linkage disequilibrium (LD) structure between SNPs. Therefore, when testing the association between SNPs and disease susceptibility, it is sufficient to elect the representative SNPs (called tag SNP-set or tagSNP-set) with maximum information.'),
(80686, 2017, 'bio', 'PathwayMapper: a collaborative visual web editor for cancer pathways and genomic data', 'Bahceci, Istemi|Dogrusoz, Ugur|La, Konnor C|Babur, Özgün|Gao, Jianjiong|Schultz, Nikolaus', 'Department of Computer Engineering, Bilkent University, Ankara, Turkey.|Department of Computer Engineering, Bilkent University, Ankara, Turkey.|Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.|Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.|Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.|Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.', 'Turkey|Turkey|United States|United States|United States|United States', 0, 'Genomics|Humans|Metabolic Networks and Pathways|Neoplasms|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx149', '28334343', 'While existing network visualization tools enable the exploration of cancer genomics data, most biologists prefer simplified, curated pathway diagrams, such as those featured in many manuscripts from The Cancer Genome Atlas (TCGA). These pathway diagrams typically summarize how a pathway is altered in individual cancer types, including alteration frequencies for each gene.'),
(80687, 2017, 'bio', 'Estimating gene regulatory networks with pandaR', 'Schlauch, Daniel|Paulson, Joseph N|Young, Albert|Glass, Kimberly|Quackenbush, John', 'Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Womens Hospital, Boston, MA, USA.|Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Womens Hospital, Boston, MA, USA.|Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Womens Hospital, Boston, MA, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Boston, MA, USA.|Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Womens Hospital, Boston, MA, USA.', 'United States|United States|United States|United States|United States', 3, 'Computational Biology|Gene Regulatory Networks|Humans|Models, Biological|Protein Interaction Maps|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx139', '28334344', 'johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu.'),
(80688, 2017, 'bio', 'DeepBlueR: large-scale epigenomic analysis in R', 'Albrecht, Felipe|List, Markus|Bock, Christoph|Lengauer, Thomas', 'Max Planck Institute for Informatics.|Max Planck Institute for Informatics.|Max Planck Institute for Informatics.|Max Planck Institute for Informatics.', '-|-|-|-', 5, 'Epigenomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx099', '28334349', 'While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. The DeepBlue Epigenomic Data Server provides a powerful interface and API for filtering, transforming, aggregating and downloading data from several epigenomic consortia.'),
(80689, 2017, 'bio', 'A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests', 'Wang, Maggie Haitian|Weng, Haoyi|Sun, Rui|Lee, Jack|Wu, William Ka Kei|Chong, Ka Chun|Zee, Benny Chung-Ying', 'Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.|Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.|Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.|Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.|Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR.|Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.|Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 2, 'Algorithms|Computer Simulation|Exome|Gene Frequency|Genetic Association Studies|Genetic Markers|Genetic Predisposition to Disease|Genetic Variation|Genomics|Humans|Hypertension|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx130', '28334355', 'Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power.'),
(80690, 2017, 'bio', 'HLA-MA: simple yet powerful matching of samples using HLA typing results', 'Messerschmidt, Clemens|Holtgrewe, Manuel|Beule, Dieter', 'Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany.|Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany.|Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany.', 'Germany|Germany|Germany', 0, 'Histocompatibility Testing|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx132', '28334360', 'We propose the simple method HLA-MA for consistency checking in pipelines operating on human HTS data. The method is based on the HLA typing result of the state-of-the-art method OptiType. Provided that there is sufficient coverage of the HLA loci, comparing HLA types allows for simple, fast and robust matching of samples from whole genome, exome and RNA-seq data. Our approach uses information from small but genetically highly variable regions and thus complements approaches that rely on genome or exon-wide variant profiles.'),
(80691, 2017, 'bio', 'VCNet: vector-based gene co-expression network construction and its application to RNA-seq data', 'Wang, Zengmiao|Fang, Huaying|Tang, Nelson Leung-Sang|Deng, Minghua', 'Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.|Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.|Department of Chemical Pathology and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.|Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.', 'China|China|China|China', 1, 'Algorithms|Breast|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Kidney Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx131', '28334366', 'Building gene co-expression network (GCN) from gene expression data is an important field of bioinformatic research. Nowadays, RNA-seq data provides high dimensional information to quantify gene expressions in term of read counts for individual exons of genes. Such an increase in the dimension of expression data during the transition from microarray to RNA-seq era made many previous co-expression analysis algorithms based on simple univariate correlation no longer applicable. Recently, two vector-based methods, SpliceNet and RNASeqNet, have been proposed to build GCN. However, they failed to work when sample size is less than the number of exons.'),
(80692, 2017, 'bio', 'Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions', 'Wu, Steven H|Schwartz, Rachel S|Winter, David J|Conrad, Donald F|Cartwright, Reed A', 'The Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.|The Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.|The Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.|Department of Genetics, Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, USA.|The Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.', 'United States|United States|United States|United States|United States', 4, 'DNA Copy Number Variations|Genome, Human|Genomics|Genotyping Techniques|Humans|Models, Statistical|Sensitivity and Specificity|Statistical Distributions|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx133', '28334373', 'Accurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others.'),
(80693, 2017, 'bio', 'SeqArray-a storage-efficient high-performance data format for WGS variant calls', 'Zheng, Xiuwen|Gogarten, Stephanie M|Lawrence, Michael|Stilp, Adrienne|Conomos, Matthew P|Weir, Bruce S|Laurie, Cathy|Levine, David', 'Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Bioinformatics and Computational Biology, Genentech, Inc, South San Francisco, CA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 26, 'Data Compression|Genetic Variation|Genome, Human|Genomics|Humans|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx145', '28334390', 'Whole-genome sequencing (WGS) data are being generated at an unprecedented rate. Analysis of WGS data requires a flexible data format to store the different types of DNA variation. Variant call format (VCF) is a general text-based format developed to store variant genotypes and their annotations. However, VCF files are large and data retrieval is relatively slow. Here we introduce a new WGS variant data format implemented in the R/Bioconductor package SeqArray for storing variant calls in an array-oriented manner which provides the same capabilities as VCF, but with multiple high compression options and data access using high-performance parallel computing.'),
(80694, 2017, 'bio', 'poRe GUIs for parallel and real-time processing of MinION sequence data', 'Stewart, Robert D|Watson, Mick', 'Department of Genetics and Genomics, University of Edinburgh, Easter Bush, UK.|Department of Genetics and Genomics, University of Edinburgh, Easter Bush, UK.', 'United Kingdom|United Kingdom', 3, 'Computer Graphics|Nanopores|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx136', '28334395', 'Oxford Nanopores MinION device has matured rapidly and is now capable of producing over one million reads and several gigabases of sequence data per run. The nature of the MinION output requires new tools that are easy to use by scientists with a range of computational skills and which enable quick and simple QC and data extraction from MinION runs.'),
(80695, 2017, 'bio', 'Quantifying circular RNA expression from RNA-seq data using model-based framework', 'Li, Musheng|Xie, Xueying|Zhou, Jing|Sheng, Mengying|Yin, Xiaofeng|Ko, Eun-A|Zhou, Tong|Gu, Wanjun', 'State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.|State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.|Research Center for Learning Sciences, Southeast University, Nanjing, Jiangsu, China.|State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.|Department of Orthopedics and Trauma, Peking University Peoples Hospital, Beijing, China.|Department of Physiology and Cell Biology, The University of Nevada School of Medicine, Reno, NV, USA.|Department of Physiology and Cell Biology, The University of Nevada School of Medicine, Reno, NV, USA.|State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.', 'China|China|China|China|China|United States|United States|China', 19, 'Algorithms|Computer Simulation|Gene Expression|Humans|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx129', '28334396', 'Circular RNAs (circRNAs) are a class of non-coding RNAs that are widely expressed in various cell lines and tissues of many organisms. Although the exact function of many circRNAs is largely unknown, the cell type-and tissue-specific circRNA expression has implicated their crucial functions in many biological processes. Hence, the quantification of circRNA expression from high-throughput RNA-seq data is becoming important to ascertain. Although many model-based methods have been developed to quantify linear RNA expression from RNA-seq data, these methods are not applicable to circRNA quantification.'),
(80696, 2017, 'bio', 'SIApopr: a computational method to simulate evolutionary branching trees for analysis of tumor clonal evolution', 'McDonald, Thomas O|Michor, Franziska', 'Department of Biostatistics and Computational Biology, Center for Cancer Evolution, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Center for Cancer Evolution, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.', 'United States|United States', 4, 'Algorithms|Clonal Evolution|Computational Biology|Computer Simulation|Humans|Neoplasms|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btx146', '28334409', 'SIApopr (Simulating Infinite-Allele populations) is an R package to simulate time-homogeneous and inhomogeneous stochastic branching processes under a very flexible set of assumptions using the speed of Cxe2x80x89++. The software simulates clonal evolution with the emergence of driver and passenger mutations under the infinite-allele assumption. The software is an application of the Gillespie Stochastic Simulation Algorithm expanded to a large number of cell types and scenarios, with the intention of allowing users to easily modify existing models or create their own.'),
(80697, 2017, 'bio', 'nodedating: dating ancestors in phylogenetic trees in R', 'Jones, Bradley R|Poon, Art F Y', 'Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada.|BC Centre for Excellence in HIV/AIDS, Vancouver, Canada.', 'Canada|Canada', 3, 'Genomics|HIV|Humans|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btw744', '28365756', 'Phylogenetic trees encode the evolutionary distances between species or populations. With sufficient information, these evolutionary distances can be rescaled over time to provide estimates of the dates of the most recent ancestors of the species. Here we present the R program node.dating, divergence-time analysis software, which uses a maximum-likelihood method to estimate the dates of the internal nodes of a phylogenetic tree.'),
(80698, 2017, 'bio', 'Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction and a library interface', 'Andrews, Steven S', 'Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.', 'United States', 32, 'Algorithms|Computer Simulation|Diffusion|Libraries, Digital|Models, Chemical|Models, Molecular|Software', 'Journal Article', '10.1093/bioinformatics/btw700', '28365760', 'Smoldyn is a spatial and stochastic biochemical simulator. It treats each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions, all with good accuracy. This article presents several new features.'),
(80699, 2017, 'bio', 'PhagesDB: the actinobacteriophage database', 'Russell, Daniel A|Hatfull, Graham F', 'Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.|Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.', 'United States|United States', 66, 'Actinobacteria|Bacteriophages|Databases, Genetic|Genome, Viral|Genomics', 'Journal Article', '10.1093/bioinformatics/btw711', '28365761', 'The Actinobacteriophage Database (PhagesDB) is a comprehensive, interactive, database-backed website that collects and shares information related to the discovery, characterization and genomics of viruses that infect Actinobacterial hosts. To date, more than 8000 bacteriophages-including over 1600 with sequenced genomes-have been entered into the database. PhagesDB plays a crucial role in organizing the discoveries of phage biologists around the world-including students in the SEA-PHAGES program-and has been cited in over 50 peer-reviewed articles.'),
(80700, 2017, 'bio', 'Enhanced methods to detect haplotypic effects on gene expression', 'Brown, Robert|Kichaev, Gleb|Mancuso, Nicholas|Boocock, James|Pasaniuc, Bogdan', 'Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA, USA.|Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA, USA.|Department of Pathology and Laboratory Medicine.|Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA, USA.|Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States', 0, 'Gene Expression Regulation|Genetic Association Studies|Haplotypes|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btx142', '28369161', 'Expression quantitative trait loci (eQTLs), genetic variants associated with gene expression levels, are identified in eQTL mapping studies. Such studies typically test for an association between single nucleotide polymorphisms (SNPs) and expression under an additive model, which ignores interaction and haplotypic effects. Mismatches between the model tested and the underlying genetic architecture can lead to a loss of association power. Here we introduce a new haplotype-based test for eQTL studies that looks for haplotypic effects on expression levels. Our test is motivated by compound heterozygous architectures, a common disease model for recessive monogenic disorders, where two different alleles can have the same effect on a genes function.'),
(80701, 2017, 'bio', 'biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements', 'Pirinen, Matti|Benner, Christian|Marttinen, Pekka|Järvelin, Marjo-Riitta|Rivas, Manuel A|Ripatti, Samuli', 'Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.|Biocenter Oulu, University of Oulu, Oulu, Finland.|Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|United States|Finland', 2, 'Genetic Variation|Genetics, Population|Genomics|Humans|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btx166', '28369165', 'Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals.'),
(80702, 2017, 'bio', 'Cpipe: a comprehensive computational platform for sequence and structure-based analyses of Cysteine residues', 'Soylu, Inanc|Marino, Stefano M', 'Department of Agricultural Biotechnology, Akdeniz University, Antalya, 07058, Turkey.|R&D Department, Explora Biotech Srl., Venice, 30172, Italy.', 'Turkey|Italy', 2, 'Computational Biology|Cysteine|Models, Chemical|Models, Molecular|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx181', '28369166', 'Due to their chemical plasticity, Cysteine residues (Cys) can serve many different functions. Identification and classification of reactive Cys isnt a trivial job: currently, no available tool exists for an all-round, comprehensive (inclusive of all different functional types) analysis of Cys, herein we present a computational platform called Cp i pe, dedicated to this task: it implements state-of-the art protocols, elaborating and displaying a wealth of information, sufficiently orthogonal to allow a thorough evaluation of all major aspects of Cys reactivity.'),
(80703, 2017, 'bio', 'ConKit: a python interface to contact predictions', 'Simkovic, Felix|Thomas, Jens M H|Rigden, Daniel J', 'Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, UK.|Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, UK.|Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, UK.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Amino Acid Sequence|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btx148', '28369168', 'Recent advances in protein residue contact prediction algorithms have led to the emergence of many new methods and a variety of file formats. We present ConKit , an open source, modular and extensible Python interface which allows facile conversion between formats and provides an interface to analyses of sequence alignments and sets of contact predictions.'),
(80704, 2017, 'bio', 'Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification', 'Arganda-Carreras, Ignacio|Kaynig, Verena|Rueden, Curtis|Eliceiri, Kevin W|Schindelin, Johannes|Cardona, Albert|Sebastian Seung, H', 'Ikerbasque, Basque Foundation for Science, Bilbao 48013, Spain.|Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.|Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA.|Neuroscience Institute and Computer Science Department, Princeton University, NJ 08544, USA.', 'Spain|United States|United States|United States|United States|United States|United States', 0, 'Animals|Drosophila|Image Processing, Computer-Assisted|Machine Learning|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btx180', '28369169', 'State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.'),
(80705, 2017, 'bio', 'A transition-based joint model for disease named entity recognition and normalization', 'Lou, Yinxia|Zhang, Yue|Qian, Tao|Li, Fei|Xiong, Shufeng|Ji, Donghong', 'Computer School, Wuhan University, Wuhan, 430072, China.|Singapore University of Technology and Design.|College of Computer Science and Technology,Hubei University of Science and Technology, Xianning, 437000, China.|Computer School, Wuhan University, Wuhan, 430072, China.|Pingdingshan University, Pingdingshan, 467000, China.|Computer School, Wuhan University, Wuhan, 430072, China.', 'China|Singapore|China|China|China|China', 20, 'Data Mining|Disease|Humans|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btx172', '28369171', 'Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this.'),
(80706, 2017, 'bio', 'The RNASeq-er API-a gateway to systematically updated analysis of public RNA-seq data', 'Petryszak, Robert|Fonseca, Nuno A|Füllgrabe, Anja|Huerta, Laura|Keays, Maria|Tang, Y Amy|Brazma, Alvis', 'Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.|Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.|Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.|Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.|Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.|Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.|Functional Genomics Group, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Computational Biology|Databases, Genetic|Eukaryota|Gene Expression|Gene Ontology|Humans|Internet|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx143', '28369191', 'The exponential growth of publicly available RNA-sequencing (RNA-Seq) data poses an increasing challenge to researchers wishing to discover, analyse and store such data, particularly those based in institutions with limited computational resources. EMBL-EBI is in an ideal position to address these challenges and to allow the scientific community easy access to not just raw, but also processed RNA-Seq data. We present a Web service to access the results of a systematically and continually updated standardized alignment as well as gene and exon expression quantification of all public bulk (and in the near future also single-cell) RNA-Seq runs in 264 species in European Nucleotide Archive, using Representational State Transfer.'),
(80707, 2017, 'bio', 'Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming', 'Song, Hyun-Seob|Goldberg, Noam|Mahajan, Ashutosh|Ramkrishna, Doraiswami', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Department of Management, Bar-Ilan University, Ramat Gan 52900, Israel.|Industrial Engineering and Operations Research, IIT Bombay, Powai, Mumbai 400076, India.|School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.', 'United States|Israel|India|United States', 0, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Biological|Programming, Linear|Software', 'Journal Article', '10.1093/bioinformatics/btx171', '28369193', 'Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP).'),
(80708, 2017, 'bio', 'Mining heterogeneous causal effects for personalized cancer treatment', 'Zhang, Weijia|Le, Thuc Duy|Liu, Lin|Zhou, Zhi-Hua|Li, Jiuyong', 'School of Information Technology and Mathematical Sciences.|School of Information Technology and Mathematical Sciences.|School of Information Technology and Mathematical Sciences.|National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.|School of Information Technology and Mathematical Sciences.', '-|-|-|China|-', 9, 'Adult|Antineoplastic Agents|Breast Neoplasms|Computational Biology|Data Mining|Disease-Free Survival|Female|Glioma|Humans|Precision Medicine|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/btx174', '28369195', 'Cancer is not a single disease and involves different subtypes characterized by different sets of molecules. Patients with different subtypes of cancer often react heterogeneously towards the same treatment. Currently, clinical diagnoses rather than molecular profiles are used to determine the most suitable treatment. A molecular level approach will allow a more precise and informed way for making treatment decisions, leading to a better survival chance and less suffering of patients. Although many computational methods have been proposed to identify cancer subtypes at molecular level, to the best of our knowledge none of them are designed to discover subtypes with heterogeneous treatment responses.'),
(80709, 2017, 'bio', 'GenomeScope: fast reference-free genome profiling from short reads', 'Vurture, Gregory W|Sedlazeck, Fritz J|Nattestad, Maria|Underwood, Charles J|Fang, Han|Gurtowski, James|Schatz, Michael C', 'Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA.|Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Evolution, Molecular|Genome|Genome Size|Heterozygote|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx153', '28369201', 'GenomeScope is an open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate and repeat content from unprocessed short reads. These features are essential for studying genome evolution, and help to choose parameters for downstream analysis. We demonstrate its accuracy on 324 simulated and 16 real datasets with a wide range in genome sizes, heterozygosity levels and error rates.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80710, 2017, 'bio', 'Constructing linkage maps in the genomics era with MapDisto 20', 'Heffelfinger, Christopher|Fragoso, Christopher A|Lorieux, Mathias', 'Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA.|Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA.|DIADE Research Unit, Institut de Recherche pour le Développement (IRD), Montpellier, France.', 'United States|United States|France', 10, 'Chromosome Mapping|Genetic Linkage|Genomics|Genotyping Techniques|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx177', '28369214', 'Genotyping by sequencing (GBS) generates datasets that are challenging to handle by current genetic mapping software with graphical interface. Geneticists need new user-friendly computer programs that can analyze GBS data on desktop computers. This requires improvements in computation efficiency, both in terms of speed and use of random-access memory (RAM).'),
(80711, 2017, 'bio', 'EthSEQ: ethnicity annotation from whole exome sequencing data', 'Romanel, Alessandro|Zhang, Tuo|Elemento, Olivier|Demichelis, Francesca', 'CIBIO, University of Trento, Trento, Italy.|Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, USA.|Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, USA.|CIBIO, University of Trento, Trento, Italy.', 'Italy|Israel|Israel|Italy', 0, 'Genetics, Population|Genomics|Humans|Molecular Sequence Annotation|Population Groups|Software|Whole Exome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx165', '28369222', 'Whole exome sequencing (WES) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Stratification of individuals ethnicity is fundamental for the correct interpretation of personal genomic variation impact. We implemented EthSEQ to provide reliable and rapid ethnicity annotation from whole exome sequencing individuals data, validated it on 1000 Genome Project and TCGA data (2700 samples) demonstrating high precision, and finally assessed computational performances compared to other tools. EthSEQ can be integrated into any WES based processing pipeline and exploits multi-core capabilities.'),
(80712, 2017, 'bio', 'Aozan: an automated post-sequencing data-processing pipeline', 'Perrin, Sandrine|Firmo, Cyril|Lemoine, Sophie|Le Crom, Stéphane|Jourdren, Laurent', 'École Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de lÉcole Normale Supérieure (IBENS), Plateforme Génomique, Paris, France.|École Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de lÉcole Normale Supérieure (IBENS), Plateforme Génomique, Paris, France.|École Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de lÉcole Normale Supérieure (IBENS), Plateforme Génomique, Paris, France.|École Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de lÉcole Normale Supérieure (IBENS), Plateforme Génomique, Paris, France.|École Normale Supérieure, PSL Research University, CNRS, Inserm, Institut de Biologie de lÉcole Normale Supérieure (IBENS), Plateforme Génomique, Paris, France.', 'France|France|France|France|France', 1, 'Humans|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx154', '28369225', 'Data management and quality control of output from Illumina sequencers is a disk space- and time-consuming task. Thus, we developed Aozan to automatically handle data transfer, demultiplexing, conversion and quality control once a run has finished. This software greatly improves run data management and the monitoring of run statistics via automatic emails and HTML web reports.'),
(80713, 2017, 'bio', 'PARTIE: a partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive', 'Torres, Pedro J|Edwards, Robert A|McNair, Katelyn A', 'Department of Biology.|Department of Biology.|Computational Science Research Center.', '-|-|-', 0, 'Bacteria|Humans|Metagenome|Metagenomics|Microbiota|Molecular Sequence Annotation|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btx184', '28369246', 'The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (10 16 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets.'),
(80714, 2017, 'bio', 'rMSI: an R package for MS imaging data handling and visualization', 'Ràfols, Pere|Torres, Sònia|Ramírez, Noelia|Del Castillo, Esteban|Yanes, Oscar|Brezmes, Jesús|Correig, Xavier', 'Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.|Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.|Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.|Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.|Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.|Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.|Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona 43007, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 10, 'Animals|Brain|Image Processing, Computer-Assisted|Mass Spectrometry|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btx182', '28369250', 'R platform provides some packages that are useful to process mass spectrometry imaging (MSI) data, however, none of them provide an easy to use graphical user interface (GUI). Here, we introduce rMSI, an R package for MSI data analysis focused on providing an efficient way to manage MSI data together with a GUI integrated in R environment. MS data is loaded in rMSI custom format optimized to minimize the memory footprint yet maintaining a fast spectra access. The rMSI GUI is designed for simple and effective data exploration and visualization. Moreover, rMSI is designed to be integrated in the R environment through a library of functions that can be used to share MS data across others R packages. The release of rMSI for R environment establishes a novel and flexible platform for MSI data analysis, completely free and open-source.'),
(80715, 2017, 'bio', 'Entropy-based consensus clustering for patient stratification', 'Liu, Hongfu|Zhao, Rui|Fang, Hongsheng|Cheng, Feixiong|Fu, Yun|Liu, Yang-Yu', 'Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.|Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115, USA.|Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Cluster Analysis|Computational Biology|Entropy|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx167', '28369256', 'Patient stratification or disease subtyping is crucial for precision medicine and personalized treatment of complex diseases. The increasing availability of high-throughput molecular data provides a great opportunity for patient stratification. Many clustering methods have been employed to tackle this problem in a purely data-driven manner. Yet, existing methods leveraging high-throughput molecular data often suffers from various limitations, e.g. noise, data heterogeneity, high dimensionality or poor interpretability.'),
(80716, 2017, 'bio', 'DLTree: efficient and accurate phylogeny reconstruction using the dynamical language method', 'Wu, Qi|Yu, Zu-Guo|Yang, Jianyi', 'Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, China.|Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|China', 3, 'Algorithms|Phylogeny|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx158', '28369270', 'A number of alignment-free methods have been proposed for phylogeny reconstruction over the past two decades. But there are some long-standing challenges in these methods, including requirement of huge computer memory and CPU time, and existence of duplicate computations. In this article, we address these challenges with the idea of compressed vector, fingerprint and scalable memory management. With these ideas we developed the DLTree algorithm for efficient implementation of the dynamical language model and whole genome-based phylogenetic analysis. The DLTree algorithm was compared with other alignment-free tools, demonstrating that it is more efficient and accurate for phylogeny reconstruction.'),
(80717, 2017, 'bio', 'GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis', 'Stricker, Georg|Engelhardt, Alexander|Schulz, Daniel|Schmid, Matthias|Tresch, Achim|Gagneur, Julien', 'Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.|Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.|Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.|Institut für Medizinische Biometrie, Informatik und Epidemiologie, University Hospital Bonn, 53105 Bonn, Germany.|Institute for Genetics, University of Cologne, 50647 Cologne, Germany.|Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Animals|Chromatin Immunoprecipitation|DNA Methylation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mice|Models, Biological|Models, Statistical|Sequence Analysis, DNA|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx150', '28369277', 'Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-Seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective.'),
(80718, 2017, 'bio', 'Spresso: an ultrafast compound pre-screening method based on compound decomposition', 'Yanagisawa, Keisuke|Komine, Shunta|Suzuki, Shogo D|Ohue, Masahito|Ishida, Takashi|Akiyama, Yutaka', 'Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.|Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa 226-8501, Japan.|Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa 226-8501, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Molecular Docking Simulation|Protein Structure, Tertiary|Proteins|Software', 'Comparative Study', '10.1093/bioinformatics/btx178', '28369284', 'Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluatexe2x80x89xe2x89xa5xe2x80x8910 million compounds.'),
(80719, 2017, 'bio', 'annotatr: genomic regions in context', 'Cavalcante, Raymond G|Sartor, Maureen A', 'Department of Computational Medicine and Bioinformatics.|Department of Computational Medicine and Bioinformatics.', '-|-', 39, 'Chromatin|Exons|Genomics|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx183', '28369316', 'Analysis of next-generation sequencing data often results in a list of genomic regions. These may include differentially methylated CpGs/regions, transcription factor binding sites, interacting chromatin regions, or GWAS-associated SNPs, among others. A common analysis step is to annotate such genomic regions to genomic annotations (promoters, exons, enhancers, etc.). Existing tools are limited by a lack of annotation sources and flexible options, the time it takes to annotate regions, an artificial one-to-one region-to-annotation mapping, a lack of visualization options to easily summarize data, or some combination thereof.'),
(80720, 2017, 'bio', 'NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers', 'He, Baoji|Mortuza, S M|Wang, Yanting|Shen, Hong-Bin|Zhang, Yang', 'Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.', 'China|United States|China|United States|United States', 30, 'Bayes Theorem|Computational Biology|Machine Learning|Models, Molecular|Neural Networks (Computer)|Protein Conformation|Protein Folding', 'Journal Article', '10.1093/bioinformatics/btx164', '28369334', 'Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.'),
(80721, 2017, 'bio', 'HiC-spector: a matrix library for spectral and reproducibility analysis of Hi-C contact maps', 'Yan, Koon-Kiu|Yardimci, Galip Gürkan|Yan, Chengfei|Noble, William S|Gerstein, Mark', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.', 'United States|United States|United States|United States|United States', 0, 'Biotinylation|Chromosomes|DNA|Gene Library|Genetic Techniques|Genome|Humans|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btx152', '28369339', 'Genome-wide proximity ligation based assays like Hi-C have opened a window to the 3D organization of the genome. In so doing, they present data structures that are different from conventional 1D signal tracks. To exploit the 2D nature of Hi-C contact maps, matrix techniques like spectral analysis are particularly useful. Here, we present HiC-spector, a collection of matrix-related functions for analyzing Hi-C contact maps. In particular, we introduce a novel reproducibility metric for quantifying the similarity between contact maps based on spectral decomposition. The metric successfully separates contact maps mapped from Hi-C data coming from biological replicates, pseudo-replicates and different cell types.'),
(80722, 2017, 'bio', 'EUPAN enables pan-genome studies of a large number of eukaryotic genomes', 'Hu, Zhiqiang|Sun, Chen|Lu, Kuang-Chen|Chu, Xixia|Zhao, Yue|Lu, Jinyuan|Shi, Jianxin|Wei, Chaochun', 'Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Genetic and Developmental Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.', 'China|China|China|China|China|China|China|China', 0, 'Eukaryota|Genetics, Population|Genome|Genomics|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx170', '28369371', 'Pan-genome analyses are routinely carried out for bacteria to interpret the within-species gene presence/absence variations (PAVs). However, pan-genome analyses are rare for eukaryotes due to the large sizes and higher complexities of their genomes. Here we proposed EUPAN, a eukaryotic pan-genome analysis toolkit, enabling automatic large-scale eukaryotic pan-genome analyses and detection of gene PAVs at a relatively low sequencing depth. In the previous studies, we demonstrated the effectiveness and high accuracy of EUPAN in the pan-genome analysis of 453 rice genomes, in which we also revealed widespread gene PAVs among individual rice genomes. Moreover, EUPAN can be directly applied to the current re-sequencing projects primarily focusing on single nucleotide polymorphisms.'),
(80723, 2017, 'bio', 'ViPTree: the viral proteomic tree server', 'Nishimura, Yosuke|Yoshida, Takashi|Kuronishi, Megumi|Uehara, Hideya|Ogata, Hiroyuki|Goto, Susumu', 'Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake, Sakyo-ku, Kyoto 606-8502, Japan.|Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|SGI Japan, Ltd., Yebisu Garden Place Tower 31F, 4-20-3 Ebisu, Shibuya-ku, Tokyo 150-6031, Japan.|Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 33, 'Genome, Viral|Genomics|Metagenome|Metagenomics|Molecular Sequence Annotation|Proteomics|Software|Viral Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/btx157', '28379287', 'ViPTree is a web server provided through GenomeNet to generate viral proteomic trees for classification of viruses based on genome-wide similarities. Users can upload viral genomes sequenced either by genomics or metagenomics. ViPTree generates proteomic trees for the uploaded genomes together with flexibly selected reference viral genomes. ViPTree also serves as a platform to visually investigate genomic alignments and automatically annotated gene functions for the uploaded viral genomes, thus providing virus researchers the first choice for classifying and understanding newly sequenced viral genomes.'),
(80724, 2017, 'bio', 'Kart: a divide-and-conquer algorithm for NGS read alignment', 'Lin, Hsin-Nan|Hsu, Wen-Lian', 'Institute of Information Science, Academia Sinica, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, Taipei, Taiwan.', '-|-', 16, 'Algorithms|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx189', '28379292', 'Next-generation sequencing (NGS) provides a great opportunity to investigate genome-wide variation at nucleotide resolution. Due to the huge amount of data, NGS applications require very fast and accurate alignment algorithms. Most existing algorithms for read mapping basically adopt seed-and-extend strategy, which is sequential in nature and takes much longer time on longer reads.'),
(80725, 2017, 'bio', 'Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data', 'Alzahrani, Majed|Kuwahara, Hiroyuki|Wang, Wei|Gao, Xin', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMCE) Division, Thuwal, 23955-6900, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMCE) Division, Thuwal, 23955-6900, Saudi Arabia.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMCE) Division, Thuwal, 23955-6900, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|United States|Saudi Arabia', 0, 'Algorithms|Cell Growth Processes|Cluster Analysis|Computational Biology|Data Mining|Escherichia coli|Metabolic Networks and Pathways|Mutation|Phenotype|Proteobacteria|Software|Stress, Physiological|Yeasts', 'Journal Article', '10.1093/bioinformatics/btx199', '28379298', 'Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.'),
(80726, 2017, 'bio', 'Fuento: functional enrichment for bioinformatics', 'Weichselbaum, David|Zagrovic, Bojan|Polyansky, Anton A', 'Department of Structural and Computational Biology, Max F. Perutz Laboratories & University of Vienna, Campus Vienna Biocenter 5, Vienna A-1030, Austria.|Department of Structural and Computational Biology, Max F. Perutz Laboratories & University of Vienna, Campus Vienna Biocenter 5, Vienna A-1030, Austria.|Department of Structural and Computational Biology, Max F. Perutz Laboratories & University of Vienna, Campus Vienna Biocenter 5, Vienna A-1030, Austria.', 'Austria|Austria|Austria', 2, 'Computational Biology|Humans|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx179', '28379299', 'The currently available functional enrichment software focuses mostly on gene expression analysis, whereby server- and graphical-user-interface-based tools with specific scope dominate the field. Here we present an efficient, user-friendly, multifunctional command-line-based functional enrichment tool (fu-en-to), tailored for the bioinformatics researcher.'),
(80727, 2017, 'bio', 'cnAnalysis450k: an R package for comparative analysis of 450k/EPIC Illumina methylation array derived copy number data', 'Knoll, Maximilian|Debus, Jxc3xbcrgen|Abdollahi, Amir', 'Department of Radiation Oncology, University Hospital of Heidelberg, Heidelberg 69120, Germany.|Department of Radiation Oncology, University Hospital of Heidelberg, Heidelberg 69120, Germany.|Department of Radiation Oncology, University Hospital of Heidelberg, Heidelberg 69120, Germany.', 'Germany|Germany|Germany', 0, 'DNA Copy Number Variations|DNA Methylation|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx156', '28379302', 'Detailed copy number (CN) variation data can be obtained from 450k or EPIC Illumina methylation assays. However, the effects of different preprocessing strategies (normalization, transformation and selection of gain/loss cutoff values) on variant calling have not been evaluated systematically.'),
(80728, 2017, 'bio', 'The Tomato Expression Atlas', 'Fernandez-Pozo, Noe|Zheng, Yi|Snyder, Stephen I|Nicolas, Philippe|Shinozaki, Yoshihito|Fei, Zhangjun|Catala, Carmen|Giovannoni, James J|Rose, Jocelyn K C|Mueller, Lukas A', 'Boyce Thompson Institute, Ithaca, NY 14853, USA.|Boyce Thompson Institute, Ithaca, NY 14853, USA.|Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.|Boyce Thompson Institute, Ithaca, NY 14853, USA.|Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.|Boyce Thompson Institute, Ithaca, NY 14853, USA.|Boyce Thompson Institute, Ithaca, NY 14853, USA.|Boyce Thompson Institute, Ithaca, NY 14853, USA.|Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.|Boyce Thompson Institute, Ithaca, NY 14853, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Nucleic Acid|Gene Expression Regulation, Plant|High-Throughput Nucleotide Sequencing|Lycopersicon esculentum|Organ Specificity|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx190', '28379331', 'With the development of new high-throughput DNA sequencing technologies and decreasing costs, large gene expression datasets are being generated at an accelerating rate, but can be complex to visualize. New, more interactive and intuitive tools are needed to visualize the spatiotemporal context of expression data and help elucidate gene function. Using tomato fruit as a model, we have developed the Tomato Expression Atlas to facilitate effective data analysis, allowing the simultaneous visualization of groups of genes at a cell/tissue level of resolution within an organ, enhancing hypothesis development and testing in addition to candidate gene identification. This atlas can be adapted to different types of expression data from diverse multicellular species.'),
(80729, 2017, 'bio', 'SieveSifter: a web-based tool for visualizing the sieve analyses of HIV-1 vaccine efficacy trials', 'Fiore-Gartland, Andrew|Kullman, Nicholas|deCamp, Allan C|Clenaghan, Graham|Yang, Wayne|Magaret, Craig A|Edlefsen, Paul T|Gilbert, Peter B', 'Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|eScience Institute, University of Washington, Seattle, WA 98195-1570, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|eScience Institute, University of Washington, Seattle, WA 98195-1570, USA.|eScience Institute, University of Washington, Seattle, WA 98195-1570, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 3, 'AIDS Vaccines|Computational Biology|HIV Infections|HIV-1|Humans|Internet|Randomized Controlled Trials as Topic|Software', 'Journal Article', '10.1093/bioinformatics/btx168', '28379332', 'Analysis of HIV-1 virions from participants infected in a randomized controlled preventive HIV-1 vaccine efficacy trial can help elucidate mechanisms of partial protection. By comparing the genetic sequence of viruses from vaccine and placebo recipients to the sequence of the vaccine itself, a technique called sieve analysis, one can identify functional specificities of vaccine-induced immune responses. We have created an interactive web-based visualization and data access tool for exploring the results of sieve analyses performed on four major preventive HIV-1 vaccine efficacy trials: (i) the HIV Vaccine Trial Network (HVTN) 502/Step trial, (ii) the RV144/Thai trial, (iii) the HVTN 503/Phambili trial and (iv) the HVTN 505 trial. The tool acts simultaneously as a platform for rapid reinterpretation of sieve effects and as a portal for organizing and sharing the viral sequence data. Access to these valuable datasets also enables the development of novel methodology for future sieve analyses.'),
(80730, 2017, 'bio', 'CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies', 'Yang, Cheng-Hong|Chuang, Li-Yeh|Lin, Yu-Da', 'Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan.|Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84004, Taiwan.|Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan.', '-|-|-', 26, 'Algorithms|Epistasis, Genetic|Genome-Wide Association Study|Humans|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btx163', '28379338', 'Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in large-scale SNP datasets.'),
(80731, 2017, 'bio', 'SynergyFinder: a web application for analyzing drug combination dose-response matrix data', 'Ianevski, Aleksandr|He, Liye|Aittokallio, Tero|Tang, Jing', 'Institute for Molecular Medicine Finland (FIMM), University of Helsinki, FI-00290 Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, FI-00290 Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, FI-00290 Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, FI-00290 Helsinki, Finland.', 'Finland|Finland|Finland|Finland', 0, 'Computational Biology|Drug Combinations|Drug Synergism|Humans|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btx162', '28379339', 'Rational design of drug combinations has become a promising strategy to tackle the drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the pre-clinical significance of pairwise drug combinations, functional screening assays that probe combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such drug combination experiments, we implemented a web application that uses key functions of R-package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the drug combination landscapes in an interactive manner.'),
(80732, 2017, 'bio', 'BioContainers: an open-source and community-driven framework for software standardization', 'da Veiga Leprevost, Felipe|Grüning, Björn A|Alves Aflitos, Saulo|Röst, Hannes L|Uszkoreit, Julian|Barsnes, Harald|Vaudel, Marc|Moreno, Pablo|Gatto, Laurent|Weber, Jonas|Bai, Mingze|Jimenez, Rafael C|Sachsenberg, Timo|Pfeuffer, Julianus|Vera Alvarez, Roberto|Griss, Johannes|Nesvizhskii, Alexey I|Perez-Riverol, Yasset', 'Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany.|Wageningen Plant Research, Cluster Bioinformatics, Wageningen, 6700 AD, Gelderland, Netherlands.|Department of Genetics, Stanford University, USA.|Medizinisches Proteom-Center, Ruhr-University Bochum, Germany, 44801.|Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.|KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway.|EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK.|Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany.|EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|Universität Tübingen, Wilhelm Schickard Institut für Informatik, Applied Bioinformatics Group,D-72076 Tübingen, Germany.|Eberhard-Karls-Universität Tübingen, Department of Computer Science, Applied bioinformatics, 72076 Tübingen.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA.|EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|Germany|Netherlands|United Kingdom|Germany|Norway|Norway|United Kingdom|United Kingdom|Germany|United Kingdom|United Kingdom|Germany|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Genomics|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btx192', '28379341', 'BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters).'),
(80733, 2017, 'bio', 'A penalized regression approach to haplotype reconstruction of viral populations arising in early HIV/SIV infection', 'Leviyang, Sivan|Griva, Igor|Ita, Sergio|Johnson, Welkin E', 'Department of Mathematics and Statistics, Georgetown University, Washington DC, 20057, USA.|Department of Mathematics, George Mason University, Fairfax, VA 22030, USA.|Department of Medicine, University of California - San Diego, La Jolla, CA 92093, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Animals|Genome, Viral|Genomics|HIV|HIV Infections|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Retroviridae Infections|Sequence Analysis, RNA|Simian Immunodeficiency Virus|Software', 'Journal Article', '10.1093/bioinformatics/btx187', '28379346', 'Next generation sequencing (NGS) has been increasingly applied to characterize viral evolution during HIV and SIV infections. In particular, NGS datasets sampled during the initial months of infection are characterized by relatively low levels of diversity as well as convergent evolution at multiple loci dispersed across the viral genome. Consequently, fully characterizing viral evolution from NGS datasets requires haplotype reconstruction across large regions of the viral genome. Existing haplotype reconstruction algorithms have not been developed with the particular characteristics of early HIV/SIV infection in mind, raising the possibility that better performance could be achieved through a specifically designed algorithm.'),
(80734, 2017, 'bio', 'BEESEM: estimation of binding energy models using HT-SELEX data', 'Ruan, Shuxiang|Swamidass, S Joshua|Stormo, Gary D', 'Department of Genetics.|Department of Pathology and Immunology, Washington University School of Medicine, St. Louis 63110, USA.|Department of Genetics.', 'United States|United States|United States', 16, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|DNA|Humans|Mice|Position-Specific Scoring Matrices|Protein Array Analysis|Protein Binding|Sequence Analysis, DNA|Software|Thermodynamics|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx191', '28379348', 'Characterizing the binding specificities of transcription factors (TFs) is crucial to the study of gene expression regulation. Recently developed high-throughput experimental methods, including protein binding microarrays (PBM) and high-throughput SELEX (HT-SELEX), have enabled rapid measurements of the specificities for hundreds of TFs. However, few studies have developed efficient algorithms for estimating binding motifs based on HT-SELEX data. Also the simple method of constructing a position weight matrix (PWM) by comparing the frequency of the preferred sequence with single-nucleotide variants has the risk of generating motifs with higher information content than the true binding specificity.'),
(80735, 2017, 'bio', 'SQC: secure quality control for meta-analysis of genome-wide association studies', 'Huang, Zhicong|Lin, Huang|Fellay, Jacques|Kutalik, Zoltán|Hubaux, Jean-Pierre', 'School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.|School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.|Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Confidentiality|Genome-Wide Association Study|Humans|Meta-Analysis as Topic|Quality Control', 'Journal Article', '10.1093/bioinformatics/btx193', '28379351', 'Due to the limited power of small-scale genome-wide association studies (GWAS), researchers tend to collaborate and establish a larger consortium in order to perform large-scale GWAS. Genome-wide association meta-analysis (GWAMA) is a statistical tool that aims to synthesize results from multiple independent studies to increase the statistical power and reduce false-positive findings of GWAS. However, it has been demonstrated that the aggregate data of individual studies are subject to inference attacks, hence privacy concerns arise when researchers share study data in GWAMA.'),
(80736, 2017, 'bio', 'SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation', 'Matsumoto, Hirotaka|Kiryu, Hisanori|Furusawa, Chikara|Ko, Minoru S H|Ko, Shigeru B H|Gouda, Norio|Hayashi, Tetsutaro|Nikaido, Itoshi', 'Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Wako, Saitama 351-0198, Japan.|Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan.|Quantitative Biology Center (QBiC), RIKEN, Suita, Osaka 565-0874, Japan.|Department of Systems Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan.|Department of Systems Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan.|Department of Systems Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan.|Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Wako, Saitama 351-0198, Japan.|Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Wako, Saitama 351-0198, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Animals|Cell Differentiation|Gene Regulatory Networks|Humans|Mice|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx194', '28379368', 'The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.'),
(80737, 2017, 'bio', 'DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation', 'Zeng, Tao|Wu, Bian|Ji, Shuiwang', 'School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.', 'United States|United States|United States', 17, 'Algorithms|Animals|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Electron|Neurites|Neurosciences|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btx188', '28379412', 'Progress in 3D electron microscopy (EM) imaging has greatly facilitated neuroscience research in high-throughput data acquisition. Correspondingly, high-throughput automated image analysis methods are necessary to work on par with the speed of data being produced. One such example is the need for automated EM image segmentation for neurite reconstruction. However, the efficiency and reliability of current methods are still lagging far behind human performance.'),
(80738, 2017, 'bio', 'Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models', 'Mundy, Michael|Mendes-Soares, Helena|Chia, Nicholas', 'Center for Individualized Medicine.|Center for Individualized Medicine.|Center for Individualized Medicine.', '-|-|-', 0, 'Bacteria|Computational Biology|Genome|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btx185', '28379466', 'Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities, however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEEDs ability to automatically reconstruct metabolic models with COBRApys advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms.'),
(80739, 2017, 'bio', 'TASIC: determining branching models from time series single cell data', 'Rashid, Sabrina|Kotton, Darrell N|Bar-Joseph, Ziv', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Department of Medicine, Department of Pathology and Laboratory Medicine, Center for Regenerative Medicine (CReM) of Boston University and Boston Medical Center, Boston, MA 02118, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 19, 'Cell Differentiation|Gene Expression Profiling|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Models, Statistical|Myoblasts|Sequence Analysis, RNA|Signal Transduction|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btx173', '28379537', 'Single cell RNA-Seq analysis holds great promise for elucidating the networks and pathways controlling cellular differentiation and disease. However, the analysis of time series single cell RNA-Seq data raises several new computational challenges. Cells at each time point are often sampled from a mixture of cell types, each of which may be a progenitor of one, or several, specific fates making it hard to determine which cells should be used to reconstruct temporal trajectories. In addition, cells, even from the same time point, may be unsynchronized making it hard to rely on the measured time for determining these trajectories.'),
(80740, 2017, 'bio', 'PreMosa: extracting 2D surfaces from 3D microscopy mosaics', 'Blasse, Corinna|Saalfeld, Stephan|Etournay, Raphaël|Sagner, Andreas|Eaton, Suzanne|Myers, Eugene W', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany.', 'Germany|United States|Germany|Germany|Germany|Germany', 8, 'Algorithms|Animals|Artifacts|Cilia|Drosophila melanogaster|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy|Platyhelminths|Software|Wings, Animal', 'Journal Article', '10.1093/bioinformatics/btx195', '28383656', 'A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (i) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (ii) correcting uneven illumination artifacts, (iii) stitching the mosaic planes into a single, large 2D image and (iv) adjusting the contrast.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80741, 2017, 'bio', 'Use of AAScatterPlot tool for monitoring the evolution of the hemagglutinin cleavage site in H9 avian influenza viruses', 'Lee, Donald W|Whittaker, Gary R', 'School of Chemical and Biomolecular Engineering.|Department of Microbiology and Immunology, Cornell University, Ithaca, NY, USA.', 'United States|United States', 2, 'Animals|Birds|Evolution, Molecular|Genomics|Hemagglutinin Glycoproteins, Influenza Virus|Influenza A virus|Influenza in Birds|Software', 'Journal Article', '10.1093/bioinformatics/btx203', '28383669', 'Viruses rapidly evolve due to their error-prone genome replication, and identifying which mutations are selected for during evolution is critical for virus surveillance efforts. Here we introduce a scatter plot tool (AAScatterPlot) that easily shows the selection and avoidance of certain protein mutations based on biochemical properties. We demonstrate its utility for monitoring the evolution of H9 avian influenza viruses from China between 2005 and 2015, particularly at the hemagglutinin (HA) proteolytic cleavage site (PCS) that can affect virus activation and pathogenicity.'),
(80742, 2017, 'bio', 'EzMap: a simple pipeline for reproducible analysis of the human virome', 'Czeczko, Patrick|Greenway, Steven C|de Koning, A P Jason', 'Bioinformatics Bachelor of Health Sciences Program.|Department of Pediatrics, Cardiac Sciences, Biochemistry & Molecular Biology, Alberta Childrens Hospital Research Institute, Libin Cardiovascular Institute of Alberta.|Department of Biochemistry and Molecular Biology, Department of Medical Genetics, Alberta Childrens Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada.', '-|-|Canada', 2, 'Computational Biology|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btx202', '28383679', 'In solid-organ transplant recipients, a delicate balance between immunosuppression and immunocompetence must be achieved, which can be difficult to monitor in real-time. Shotgun sequencing of cell-free DNA (cfDNA) has been recently proposed as a new way to indirectly assess immune function in transplant recipients through analysis of the status of the human virome. To facilitate exploration of the utility of the human virome as an indicator of immune status, and to enable rapid, straightforward analyses by clinicians, we developed a fully automated computational pipeline, EzMap, for performing metagenomic analysis of the human virome. EzMap combines a number of tools to clean, filter, and subtract WGS reads by mapping to a reference human assembly. The relative abundance of each virus present is estimated using a maximum likelihood approach that accounts for genome size, and results are presented with interactive visualizations and taxonomy-based summaries that enable rapid insights. The pipeline is automated to run on both workstations and computing clusters for all steps. EzMap automates an otherwise tedious and time-consuming protocol and aims to facilitate rapid and reproducible insights from cfDNA.'),
(80743, 2017, 'bio', 'ploidyNGS: visually exploring ploidy with Next Generation Sequencing data', 'Augusto Corrêa Dos Santos, Renato|Goldman, Gustavo Henrique|Riaño-Pachón, Diego Mauricio', 'Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas.|Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto.|Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas.', '-|-|-', 12, 'Genome, Fungal|High-Throughput Nucleotide Sequencing|Ploidies|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx204', '28383704', 'ploidyNGS is a model-free, open source tool to visualize and explore ploidy levels in a newly sequenced genome, exploiting short read data. We tested ploidyNGS using both simulated and real NGS data of the model yeast Saccharomyces cerevisiae. ploidyNGS allows the identification of the ploidy level of a newly sequenced genome in a visual way.'),
(80744, 2017, 'bio', 'GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization', 'Boudard, Mélanie|Barth, Dominique|Bernauer, Julie|Denise, Alain|Cohen, Johanne', 'DAVID, Université de Versailles-St-Quentin-en-Yvelines, Université Paris-Saclay, France.|DAVID, Université de Versailles-St-Quentin-en-Yvelines, Université Paris-Saclay, France.|LIX, Ecole Polytechnique, CNRS, Université Paris-Saclay, France.|LRI, Université Paris-Sud, CNRS, Université Paris-Saclay, France.|LRI, Université Paris-Sud, CNRS, Université Paris-Saclay, France.', 'France|France|France|France|France', 0, 'Algorithms|Computational Biology|Models, Molecular|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx175', '28398456', 'Predicting the 3D structure of RNA molecules is a key feature towards predicting their functions. Methods which work at atomic or nucleotide level are not suitable for large molecules. In these cases, coarse-grained prediction methods aim to predict a shape which could be refined later by using more precise methods on smaller parts of the molecule.'),
(80745, 2017, 'bio', 'MISA-web: a web server for microsatellite prediction', 'Beier, Sebastian|Thiel, Thomas|Münch, Thomas|Scholz, Uwe|Mascher, Martin', 'Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.|KWS Saat SE, Grimsehlstr. 31, 37555 Einbeck, Germany.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, 06466 Seeland, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Genome, Plant|Genomics|Internet|Microsatellite Repeats|Plants|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx198', '28398459', 'Microsatellites are a widely-used marker system in plant genetics and forensics. The development of reliable microsatellite markers from resequencing data is challenging.'),
(80746, 2017, 'bio', 'TIN-X: target importance and novelty explorer', 'Cannon, Daniel C|Yang, Jeremy J|Mathias, Stephen L|Ursu, Oleg|Mani, Subramani|Waller, Anna|Schürer, Stephan C|Jensen, Lars Juhl|Sklar, Larry A|Bologa, Cristian G|Oprea, Tudor I', 'Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.|Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.|Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.|Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.|Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.|UNM Center for Molecular Discovery, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N 2200, Denmark.|UNM Center for Molecular Discovery, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131, USA.|Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.|Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.', 'Mexico|Mexico|Mexico|Mexico|Mexico|Mexico|United States|Denmark|Mexico|Mexico|Mexico', 6, 'Biological Ontologies|Computer Graphics|Data Mining|Disease|Humans|Ion Channels|Phosphotransferases|Receptors, Cytoplasmic and Nuclear|Receptors, G-Protein-Coupled|Software', 'Journal Article', '10.1093/bioinformatics/btx200', '28398460', 'The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins.'),
(80747, 2017, 'bio', 'Machine learning in computational biology to accelerate high-throughput protein expression', 'Sastry, Anand|Monk, Jonathan|Tegel, Hanna|Uhlen, Mathias|Palsson, Bernhard O|Rockberg, Johan|Brunk, Elizabeth', 'Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.|KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.|Department of Bioengineering, University of California, San Diego, CA, USA.|KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.|Department of Bioengineering, University of California, San Diego, CA, USA.', 'United States|United States|Sweden|Sweden|United States|Sweden|United States', 0, 'Computational Biology|Escherichia coli|Gene Expression Regulation|Humans|Machine Learning|Organ Specificity|Proteome|Solubility', 'Journal Article', '10.1093/bioinformatics/btx207', '28398465', 'The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40xe2x80x89000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.'),
(80748, 2017, 'bio', 'ST Pipeline: an automated pipeline for spatial mapping of unique transcripts', 'Navarro, José Fernández|Sjöstrand, Joel|Salmén, Fredrik|Lundeberg, Joakim|Ståhl, Patrik L', 'Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Science for Life Laboratory, Solna, Sweden.|Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Science for Life Laboratory, Solna, Sweden.|Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Science for Life Laboratory, Solna, Sweden.|Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Science for Life Laboratory, Solna, Sweden.|Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Science for Life Laboratory, Solna, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Gene Expression Profiling|Organ Specificity|Sequence Analysis, RNA|Software|Spatial Analysis', 'Journal Article', '10.1093/bioinformatics/btx211', '28398467', 'In recent years we have witnessed an increase in novel RNA-seq based techniques for transcriptomics analysis. Spatial transcriptomics is a novel RNA-seq based technique that allows spatial mapping of transcripts in tissue sections. The spatial resolution adds an extra level of complexity, which requires the development of new tools and algorithms for efficient and accurate data processing.'),
(80749, 2017, 'bio', 'RTK: efficient rarefaction analysis of large datasets', 'Saary, Paul|Forslund, Kristoffer|Bork, Peer|Hildebrand, Falk', 'Structural & Computational Biology Unit, EMBL, 69117 Heidelberg, Germany.|Structural & Computational Biology Unit, EMBL, 69117 Heidelberg, Germany.|Structural & Computational Biology Unit, EMBL, 69117 Heidelberg, Germany.|Structural & Computational Biology Unit, EMBL, 69117 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 17, 'Computational Biology|Environment|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/btx206', '28398468', 'The rapidly expanding microbiomics field is generating increasingly larger datasets, characterizing the microbiota in diverse environments. Although classical numerical ecology methods provide a robust statistical framework for their analysis, software currently available is inadequate for large datasets and some computationally intensive tasks, like rarefaction and associated analysis.'),
(80750, 2017, 'bio', 'PyGOLD: a python based API for docking based virtual screening workflow generation', 'Patel, Hitesh|Brinkjost, Tobias|Koch, Oliver', 'Faculty of Chemistry and Chemical Biology.|Faculty of Chemistry and Chemical Biology.|Faculty of Chemistry and Chemical Biology.', '-|-|-', 3, 'Computational Biology|Molecular Docking Simulation|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btx197', '28398502', 'Molecular docking is one of the successful approaches in structure based discovery and development of bioactive molecules in chemical biology and medicinal chemistry. Due to the huge amount of computational time that is still required, docking is often the last step in a virtual screening approach. Such screenings are set as workflows spanned over many steps, each aiming at different filtering task. These workflows can be automatized in large parts using python based toolkits except for docking using the docking software GOLD. However, within an automated virtual screening workflow it is not feasible to use the GUI in between every step to change the GOLD configuration file. Thus, a python module called PyGOLD was developed, to parse, edit and write the GOLD configuration file and to automate docking based virtual screening workflows.'),
(80751, 2017, 'bio', 'Systemic evaluation of cellular reprogramming processes exploiting a novel R-tool: eegc', 'Zhou, Xiaoyuan|Meng, Guofeng|Nardini, Christine|Mei, Hongkang', 'CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China and University of Chinese Academy of Sciences, Beijing, China.|Computational and Modeling Sciences, Platform Technologies and Science China, Shanghai, GSK.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China and University of Chinese Academy of Sciences, Beijing, China.|Computational and Modeling Sciences, Platform Technologies and Science China, Shanghai, GSK.', 'China|China|China|China', 2, 'Cellular Reprogramming|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Humans|Induced Pluripotent Stem Cells|Molecular Medicine|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx205', '28398503', 'Cells derived by cellular engineering, i.e. differentiation of induced pluripotent stem cells and direct lineage reprogramming, carry a tremendous potential for medical applications and in particular for regenerative therapies. These approaches consist in the definition of lineage-specific experimental protocols that, by manipulation of a limited number of biological cues-niche mimicking factors, (in)activation of transcription factors, to name a few-enforce the final expression of cell-specific (marker) molecules. To date, given the intricate complexity of biological pathways, these approaches still present imperfect reprogramming fidelity, with uncertain consequences on the functional properties of the resulting cells.'),
(80752, 2017, 'bio', 'FunChIP: an R/Bioconductor package for functional classification of ChIP-seq shapes', 'Parodi, Alice C L|Sangalli, Laura M|Vantini, Simone|Amati, Bruno|Secchi, Piercesare|Morelli, Marco J', 'MOX-Department of Mathematics, Politecnico di Milano, Milan 20133, Italy.|MOX-Department of Mathematics, Politecnico di Milano, Milan 20133, Italy.|MOX-Department of Mathematics, Politecnico di Milano, Milan 20133, Italy.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milan 20139, Italy.|MOX-Department of Mathematics, Politecnico di Milano, Milan 20133, Italy.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milan 20139, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Animals|Chromatin Immunoprecipitation|Cluster Analysis|Fibroblasts|Genomics|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btx201', '28398543', 'Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) generates local accumulations of sequencing reads on the genome (peaks), which correspond to specific protein-DNA interactions or chromatin modifications. Peaks are detected by considering their total area above a background signal, usually neglecting their shapes, which instead may convey additional biological information. We present FunChIP, an R/Bioconductor package for clustering peaks according to a functional representation of their shapes: after approximating their profiles with cubic B-splines, FunChIP minimizes their functional distance and classifies the peaks applying a k-mean alignment and clustering algorithm. The whole pipeline is user-friendly and provides visualization functions for a quick inspection of the results. An application to the transcription factor Myc in 3T9 murine fibroblasts shows that clusters of peaks with different shapes are associated with different genomic locations and different transcriptional regulatory activity.'),
(80753, 2017, 'bio', 'StrandScript: evaluation of Illumina genotyping array design and strand correction', 'Wang, Jing|Samuels, David C|Shyr, Yu|Guo, Yan', 'Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN, USA 37232.|Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville TN, USA, 37232.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville TN, USA 37232.|Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN, USA 37232.', '-|-|-|-', 0, 'Genotyping Techniques|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx186', '28402386', 'After the introduction of high-throughput sequencing, genotyping arrays continue to be a viable source for conducting large-scale genetic studies. Currently, Illumina is one of the largest genotyping array manufacturers. One technical issue that has always plagued the post-processing of Illumina genotyping array data is the strand definition. Against convention, Illumina uses their own definition of strand, which is inconsistent with the standard reference forward and reverse definition. This issue has been a major obstacle in the consistency of reporting, meta-analysis and correct interpretation of phenotype association results. To date, the strand issue has not been adequately addressed, prompting us to develop StrandScript, a tool that can convert all genotyping data generated from Illumina genotyping arrays to the reference forward strand. StrandScript works independently of the Illumina array version and is future proof for newer Illumina array designs. Furthermore, StrandScript can examine an Illumina genotyping array manifest file and can detect all problematic SNPs, including SNPs with wrong RS ID and SNPs with mismatched probe sequences. Here, we introduce StrandScripts design and development, and demonstrate its effectiveness using real genotyping data.'),
(80754, 2017, 'bio', 'MetCirc: navigating mass spectral similarity in high-resolution MS/MS metabolomics data', 'Naake, Thomas|Gaquerel, Emmanuel', 'Centre for Organismal Studies, University of Heidelberg, Heidelberg 69120, Germany.|Centre for Organismal Studies, University of Heidelberg, Heidelberg 69120, Germany.', 'Germany|Germany', 12, 'Flowers|Metabolomics|Software|Tandem Mass Spectrometry|Tobacco', 'Journal Article', '10.1093/bioinformatics/btx159', '28402393', 'Among the main challenges in metabolomics are the rapid dereplication of previously characterized metabolites across a range of biological samples and the structural prediction of unknowns from MS/MS data. Here, we developed MetCirc to comprehensively align and calculate pairwise similarity scores among MS/MS spectral data and visualize these across a range of biological samples. MetCirc comprises functionalities to interactively organize these data according to compound familial groupings and to accelerate the discovery of shared metabolites and hypothesis formulation for unknowns. As such, MetCirc provides a significant advance to address biological questions in areas where chemodiversity plays a role.'),
(80755, 2017, 'bio', 'mzML2ISA & nmrML2ISA: generating enriched ISA-Tab metadata files from metabolomics XML data', 'Larralde, Martin|Lawson, Thomas N|Weber, Ralf J M|Moreno, Pablo|Haug, Kenneth|Rocca-Serra, Philippe|Viant, Mark R|Steinbeck, Christoph|Salek, Reza M', 'xc3x89cole Normale Supxc3xa9rieure de Cachan, 94230 Cachan, France.|School of Biosciences.|School of Biosciences.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.|University of Oxford e-Research Centre, Oxford OX1xe2x80x893QG, UK.|School of Biosciences.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.', 'France|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Data Mining|Information Storage and Retrieval|Metabolomics|Metadata|Software', 'Journal Article', '10.1093/bioinformatics/btx169', '28402395', 'Submission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically generate ISA-Tab metadata file stubs from raw XML metabolomics data files. The parsing packages are separated into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). Overall, the use of mzML2ISA & nmrML2ISA reduces the time needed to capture metadata substantially (capturing 90% of metadata on assay and sample levels), is much less prone to user input errors, improves compliance with minimum information reporting guidelines and facilitates more finely grained data exploration and querying of datasets.'),
(80756, 2017, 'bio', 'BioCIDER: a Contextualisation InDEx for biological Resources discovery', 'Horro, Carlos|Cook, Martin|Attwood, Teresa K|Brazas, Michelle D|Hancock, John M|Palagi, Patricia|Corpas, Manuel|Jimenez, Rafael', 'Elixir Department, Earlham Institute, Norwich Research Park Innovation Centre, Norwich NR4 7UH, UK.|ELIXIR Hub, The European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.|School of Computer Science, The University of Manchester, Manchester M13 9PL, UK.|Informatics and Bio-computing, Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada.|Elixir Department, Earlham Institute, Norwich Research Park Innovation Centre, Norwich NR4 7UH, UK.|SIB Training Group, SIB Swiss Institute of Bioinformatics, Lausanne 1005, Switzerland.|Repositive, Future Business Centre, Kings Hedges Road, Cambridge CB4 2HY, UK.|ELIXIR Hub, The European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|Canada|United Kingdom|Switzerland|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Factual|Software', 'Journal Article', '10.1093/bioinformatics/btx213', '28407033', 'The vast, uncoordinated proliferation of bioinformatics resources (databases, software tools, training materials etc.) makes it difficult for users to find them. To facilitate their discovery, various services are being developed to collect such resources into registries. We have developed BioCIDER, which, rather like online shopping recommendations, provides a contextualization index to help identify biological resources relevant to the content of the sites in which it is embedded.'),
(80757, 2017, 'bio', 'Network module-based model in the differential expression analysis for RNA-seq', 'Lei, Mingli|Xu, Jia|Huang, Li-Ching|Wang, Lily|Li, Jing', 'Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, Peoples Republic of China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, Peoples Republic of China.|Center for Quantitative Sciences, Vanderbilt University, Nashville, TN 37232, USA.|Department of Public Health Sciences, Division of Biostatistics, University of Miami Miller School of Medicine, Miami, FL 33136, USA.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, Peoples Republic of China.', 'China|China|United States|United States|China', 0, 'Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Kidney Neoplasms|Liver Neoplasms|Models, Genetic|Sequence Analysis, RNA|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btx214', '28407034', 'RNA-seq has emerged as a powerful technology for the detection of differential gene expression in the transcriptome. The commonly used statistical methods for RNA-seq differential expression analysis were designed for individual genes, which may detect too many irrelevant significantly genes or too few genes to interpret the phenotypic changes. Recently network module-based methods have been proposed as a powerful approach to analyze and interpret expression data in microarray and shotgun proteomics. But the module-based statistical model has not been adequately addressed for RNA-seq data.'),
(80758, 2017, 'bio', 'IcyTree: rapid browser-based visualization for phylogenetic trees and networks', 'Vaughan, Timothy G', 'Department of Computer Science, University of Auckland, Auckland 1010, New Zealand.', 'New Zealand', 0, 'Genomics|Internet|Orthomyxoviridae|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btx155', '28407035', 'IcyTree is an easy-to-use application which can be used to visualize a wide variety of phylogenetic trees and networks. While numerous phylogenetic tree viewers exist already, IcyTree distinguishes itself by being a purely online tool, having a responsive user interface, supporting phylogenetic networks (ancestral recombination graphs in particular), and efficiently drawing trees that include information such as ancestral locations or trait values. IcyTree also provides intuitive panning and zooming utilities that make exploring large phylogenetic trees of many thousands of taxa feasible.'),
(80759, 2017, 'bio', 'emMAW: computing minimal absent words in external memory', 'Hxc3xa9liou, Alice|Pissis, Solon P|Puglisi, Simon J', 'LIX, xc3x89cole Polytechnique, CNRS, INRIA, Universitxc3xa9 Paris-Saclay, Palaiseau, France.|Department of Informatics, Kings College London, London WC2R 2LS, UK.|Department of Computer Science, University of Helsinki, Helsinki FI-00014, Finland.', 'France|United Kingdom|Finland', 0, 'Algorithms|Genome, Human|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx209', '28407038', 'The biological significance of minimal absent words has been investigated in genomes of organisms from all domains of life. For instance, three minimal absent words of the human genome were found in Ebola virus genomes. There exists an O(n) -time and O(n) -space algorithm for computing all minimal absent words of a sequence of length n on a fixed-sized alphabet based on suffix arrays. A standard implementation of this algorithm, when applied to a large sequence of length n , requires more than 20 n xc2xa0bytes of RAM. Such memory requirements are a significant hurdle to the computation of minimal absent words in large datasets.'),
(80760, 2017, 'bio', 'Incorporating prior information into differential network analysis using non-paranormal graphical models', 'Zhang, Xiao-Fei|Ou-Yang, Le|Yan, Hong', 'Department of Statistics, School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan 430079, China.|Department of Electronic Engineering, College of Information Engineering, Shenzhen University, Shenzhen 518060, China.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong 999077, China.', 'China|China|China', 0, 'Algorithms|Computational Biology|Computer Simulation|Female|Gene Regulatory Networks|Glioblastoma|Humans|Normal Distribution|Ovarian Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btx208', '28407042', 'Understanding how gene regulatory networks change under different cellular states is important for revealing insights into network dynamics. Gaussian graphical models, which assume that the data follow a joint normal distribution, have been used recently to infer differential networks. However, the distributions of the omics data are non-normal in general. Furthermore, although much biological knowledge (or prior information) has been accumulated, most existing methods ignore the valuable prior information. Therefore, new statistical methods are needed to relax the normality assumption and make full use of prior information.'),
(80761, 2017, 'bio', 'SubCons: a new ensemble method for improved human subcellular localization predictions', 'Salvatore, M|Warholm, P|Shu, N|Basile, W|Elofsson, A', 'Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Computational Biology|Humans|Protein Transport|Software', 'Journal Article', '10.1093/bioinformatics/btx219', '28407043', 'Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein. Unfortunately large-scale experimental studies are limited in their accuracy. Therefore, the development of prediction methods has been limited by the amount of accurate experimental data. However, recently large-scale experimental studies have provided new data that can be used to evaluate the accuracy of subcellular predictions in human cells. Using this data we examined the performance of state of the art methods and developed SubCons, an ensemble method that combines four predictors using a Random Forest classifier.'),
(80762, 2017, 'bio', 'miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets', 'Paicu, Claudia|Mohorianu, Irina|Stocks, Matthew|Xu, Ping|Coince, Aurore|Billmeier, Martina|Dalmay, Tamas|Moulton, Vincent|Moxon, Simon', 'The Earlham Institute, Norwich Research Park, Norwich NR4xe2x80x897UG, UK.|School of Computing Sciences.|School of Computing Sciences.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich NR4xe2x80x897TJ, UK.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich NR4xe2x80x897TJ, UK.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich NR4xe2x80x897TJ, UK.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich NR4xe2x80x897TJ, UK.|School of Computing Sciences.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich NR4xe2x80x897TJ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Computational Biology|Entropy|Genetic Loci|High-Throughput Nucleotide Sequencing|MicroRNAs|Plants|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx210', '28407097', 'MicroRNAs are a class of xe2x88xbc21-22xe2x80x89nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure.'),
(80763, 2017, 'bio', 'Domain prediction with probabilistic directional context', 'Ochoa, Alejandro|Singh, Mona', 'Lewis-Sigler Institute for Integrative Genomics.|Lewis-Sigler Institute for Integrative Genomics.', '-|-', 1, 'Algorithms|Computational Biology|Humans|Models, Molecular|Models, Statistical|Protein Domains|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx221', '28407137', 'Protein domain prediction is one of the most powerful approaches for sequence-based function prediction. Although domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically.'),
(80764, 2017, 'bio', 'HaploMerger2: rebuilding both haploid sub-assemblies from high-heterozygosity diploid genome assembly', 'Huang, Shengfeng|Kang, Mingjing|Xu, Anlong', 'State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, Peoples Republic of China.|State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, Peoples Republic of China.|State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, Peoples Republic of China.', 'China|China|China', 0, 'Diploidy|Genomics|Haploidy|Heterozygote|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx220', '28407147', 'De novo assembly is a difficult issue for heterozygous diploid genomes. The advent of high-throughput short-read and long-read sequencing technologies provides both new challenges and potential solutions to the issue. Here, we present HaploMerger2 (HM2), an automated pipeline for rebuilding both haploid sub-assemblies from the polymorphic diploid genome assembly. It is designed to work on pre-existing diploid assemblies, which are typically created by using de novo assemblers. HM2 can process any diploid assemblies, but it is especially suitable for diploid assemblies with high heterozygosity (xe2x89xa53%), which can be difficult for other tools. This pipeline also implements flexible and sensitive assembly error detection, a hierarchical scaffolding procedure and a reliable gap-closing method for haploid sub-assemblies. Using HM2, we demonstrate that two haploid sub-assemblies reconstructed from a real, highly-polymorphic diploid assembly show greatly improved continuity.'),
(80765, 2017, 'bio', 'Green listed-a CRISPR screen tool', 'Panda, Sudeepta Kumar|Boddul, Sanjay V|Jiménez-Andrade, Guillermina Yanek|Jiang, Long|Kasza, Zsolt|Fernandez-Ricaud, Luciano|Wermeling, Fredrik', 'Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Marine Sciences, Lundberg Laboratory, University of Gothenburg, SE-413 90 Gothenburg, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'CRISPR-Cas Systems|Gene Editing|Software', 'Journal Article', '10.1093/bioinformatics/btw739', '28414855', 'Genome editing using versions of the bacterial CRISPR/Cas9 system can be used to probe the function of selected genes in any organism. Green Listed is a web-based tool that rapidly designs custom CRISPR screens targeting sets of genes defined by the user. It could thus be used to design screens targeting for example all genes differentially expressed during a specific stimuli or all genes related to a specific pathway or function, as well as to generate targeted secondary screens following a large-scale screen.'),
(80766, 2017, 'bio', 'Chainy: an universal tool for standardized relative quantification in real-time PCR', 'Mallona, Izaskun|Díez-Villanueva, Anna|Martín, Berta|Peinado, Miguel A', 'Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.|Department of Medicine, Solna, Karolinska Institutet, and Karolinska University Hospital, SE-171 76 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx113', '28419190', '-'),
(80767, 2017, 'bio', 'DMINDA 20: integrated and systematic views of regulatory DNA motif identification and analyses', 'Yang, Jinyu|Chen, Xin|McDermaid, Adam|Ma, Qin', 'Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science.|Center for Applied Mathematics, Tianjin University, Tianjin 300000, China.|Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science.|Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science.', '-|China|-|-', 0, 'Algorithms|Bacteria|DNA|Eukaryota|Genome|Genomics|Nucleotide Motifs|Phylogeny|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx223', '28419194', 'Motif identification and analyses are important and have been long-standing computational problems in bioinformatics. Substantial efforts have been made in this field during the past several decades. However, the lack of intuitive and integrative web servers impedes the progress of making effective use of emerging algorithms and tools.'),
(80768, 2017, 'bio', 'Removal of batch effects using distribution-matching residual networks', 'Shaham, Uri|Stanton, Kelly P|Zhao, Jun|Li, Huamin|Raddassi, Khadir|Montgomery, Ruth|Kluger, Yuval', 'Department of Statistics, Yale University, New Haven, CT 06511, USA.|Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA.|Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Applied Mathematics Program, Yale University, New Haven, CT 06511, USA.|Departments of Neurology and Immunobiology.|Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA.|Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA.', 'United States|United States|United States|United States|United States|United States|United States', 34, 'Computational Biology|Cytophotometry|Data Accuracy|Humans|Machine Learning|Sequence Analysis, RNA|Single-Cell Analysis|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btx196', '28419223', 'Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated.'),
(80769, 2017, 'bio', 'A Bayesian group sparse multi-task regression model for imaging genetics', 'Greenlaw, Keelin|Szefer, Elena|Graham, Jinko|Lesperance, Mary|Nathoo, Farouk S|Alzheimer’s Disease Neuroimaging Initiative', 'Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.|Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.|Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.|Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Alzheimer Disease|Bayes Theorem|Brain|Genomics|Genotyping Techniques|Humans|Models, Statistical|Neuroimaging|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btx215', '28419235', 'Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. have developed an approach for the analysis of imaging genomic studies using penalized multi-task regression with regularization based on a novel group l2,1-norm penalty which encourages structured sparsity at both the gene level and SNP level. While incorporating a number of useful features, the proposed method only furnishes a point estimate of the regression coefficients, techniques for conducting statistical inference are not provided. A new Bayesian method is proposed here to overcome this limitation.'),
(80770, 2017, 'bio', 'EigenTHREADER: analogous protein fold recognition by efficient contact map threading', 'Buchan, Daniel W A|Jones, David T', 'Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.|Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.', 'United Kingdom|United Kingdom', 18, 'Algorithms|Computational Biology|Models, Molecular|Protein Folding|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx217', '28419258', 'Protein fold recognition when appropriate, evolutionarily-related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem ( Moult et al., 2014 ). Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra-residue contacts, and then searches a library of contact maps of known structures. To allow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigenvectors these can then be aligned using standard dynamic programming algorithms. The approach is similar to the Al-Eigen approach of Di Lena et al. (2010) , but with improvements made both to speed and accuracy. With this search strategy, EigenTHREADER does not depend directly on sequence homology between the target protein and entries in the fold library to generate models. This in turn enables EigenTHREADER to correctly identify analogous folds where little or no sequence homology information is.'),
(80771, 2017, 'bio', 'SVMQA: support-vector-machine-based protein single-model quality assessment', 'Manavalan, Balachandran|Lee, Jooyoung', 'Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea.|Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea.', '-|-', 0, 'Caspase 10|Caspase 8|Caspase 9|Computational Biology|Models, Molecular|Protein Conformation|Quality Control|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btx222', '28419290', 'The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool. Conversely, single-model methods do not suffer from this drawback and are better suited for real-life applications where many models from various sources may not be readily available.'),
(80772, 2017, 'bio', 'Mapping genes for calcium signaling and their associated human genetic disorders', 'Hxc3xb6rtenhuber, Matthias|Toledo, Enrique M|Smedler, Erik|Arenas, Ernest|Malmersjxc3xb6, Seth|Louhivuori, Lauri|Uhlxc3xa9n, Per', 'Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171xe2x80x8977 Stockholm, Sweden.|Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171xe2x80x8977 Stockholm, Sweden.|Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171xe2x80x8977 Stockholm, Sweden.|Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171xe2x80x8977 Stockholm, Sweden.|Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171xe2x80x8977 Stockholm, Sweden.|Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171xe2x80x8977 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|United States|Sweden|Sweden', 0, 'Calcium Signaling|Chromosome Mapping|Databases, Genetic|Genome, Human|Genomics|Human Genetics|Humans', 'Journal Article', '10.1093/bioinformatics/btx225', '28430858', 'Signal transduction via calcium ions (Ca2+) represents a fundamental signaling pathway in all eukaryotic cells. A large portion of the human genome encodes proteins used to assemble signaling systems that can transduce signals with diverse spatial and temporal dynamics.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(80773, 2017, 'bio', 'MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks', 'Kelley, James J|Maor, Shay|Kim, Min Kyung|Lane, Anatoliy|Lun, Desmond S', 'Department of Computer Science, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.|Department of Computer Science, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.|Department of Computer Science, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.|Department of Computer Science, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.|Department of Computer Science, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.', 'United States|United States|United States|United States|United States', 2, 'Computational Biology|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btx240', '28430868', 'Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large, (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites, and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii).'),
(80774, 2017, 'bio', 'ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT', 'Porter, Kathryn A|Xia, Bing|Beglov, Dmitri|Bohnuud, Tanggis|Alam, Nawsad|Schueler-Furman, Ora|Kozakov, Dima', 'Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Microbiology, Hebrew University, Jerusalem 91120, Israel.|Department of Microbiology, Hebrew University, Jerusalem 91120, Israel.|Department of Applied Mathematics and Statistics.', 'United States|United States|United States|United States|Israel|Israel|United States', 0, 'Algorithms|Computational Biology|Cyclins|Databases, Protein|Fourier Analysis|Molecular Docking Simulation|Peptides|Protein Conformation|Protein Interaction Domains and Motifs|Software', 'Journal Article', '10.1093/bioinformatics/btx216', '28430871', 'We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptides final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions.'),
(80775, 2017, 'bio', 'Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility', 'Heffernan, Rhys|Yang, Yuedong|Paliwal, Kuldip|Zhou, Yaoqi', 'Signal Processing Laboratory, Griffith University, Brisbane, QLD 4111, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia.|Signal Processing Laboratory, Griffith University, Brisbane, QLD 4111, Australia.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia.', 'Australia|Australia|Australia|Australia', 87, 'Computational Biology|Machine Learning|Models, Molecular|Neural Networks (Computer)|Protein Structure, Secondary|Proteins|Solvents', 'Journal Article', '10.1093/bioinformatics/btx218', '28430949', 'The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some short to intermediate non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window.'),
(80776, 2017, 'bio', 'Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations', 'Zong, Nansu|Kim, Hyeoneui|Ngo, Victoria|Harismendy, Olivier', 'Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA 92093, USA.|Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA 92093, USA.|Betty Irene Moore School of Nursing, UC Davis, Sacramento, CA 95817, USA.|Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA 92093, USA.', 'United States|United States|United States|United States', 51, 'Computational Biology|Data Mining|Humans|Machine Learning|Pharmacology|Semantic Web|Software', 'Journal Article', '10.1093/bioinformatics/btx160', '28430977', 'A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.'),
(80777, 2017, 'bio', 'A method for learning a sparse classifier in the presence of missing data for high-dimensional biological datasets', 'Severson, Kristen A|Monian, Brinda|Love, J Christopher|Braatz, Richard D', 'Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States', 7, 'Bacterial Infections|Classification|Computational Biology|Genes|Humans|Leukemia|Machine Learning|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btx224', '28431087', 'This work addresses two common issues in building classification models for biological or medical studies: learning a sparse model, where only a subset of a large number of possible predictors is used, and training in the presence of missing data. This work focuses on supervised generative binary classification models, specifically linear discriminant analysis (LDA). The parameters are determined using an expectation maximization algorithm to both address missing data and introduce priors to promote sparsity. The proposed algorithm, expectation-maximization sparse discriminant analysis (EM-SDA), produces a sparse LDA model for datasets with and without missing data.'),
(80778, 2017, 'bio', 'CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets', 'Flobak, Åsmund|Vazquez, Miguel|Lægreid, Astrid|Valencia, Alfonso', 'Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway.|Structural Computational Biology Group, Structural Biology and Biocomputing Programme, CNIO (Spanish National Cancer Research Centre), Madrid, Spain.', 'Norway|Norway|Norway|Spain', 3, 'Computational Biology|Drug Combinations|Drug Synergism|Humans|Internet|Models, Biological|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btx161', '28444126', 'Drug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required.'),
(80779, 2017, 'bio', 'HLA class I binding prediction via convolutional neural networks', 'Vang, Yeeleng S|Xie, Xiaohui', 'Department of Computer Science, University of California, Irvine, CA 92697, USA.|Department of Computer Science, University of California, Irvine, CA 92697, USA.', 'United States|United States', 29, 'Epitopes|HLA Antigens|Histocompatibility Antigens Class I|Humans|Machine Learning|Neural Networks (Computer)|Peptides|Protein Binding|Proteomics', 'Journal Article', '10.1093/bioinformatics/btx264', '28444127', 'Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases.'),
(80780, 2017, 'bio', 'Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound', 'Nanni, Loris|Brahnam, Sheryl', 'Department of Information Engineering, University of Padua, Padova 35131, Italy.|Department of Computer Information Systems, Missouri State University, Springfield, MO 65804, USA.', 'Italy|United States', 8, 'Computational Biology|Drug Interactions|Machine Learning|Software', 'Journal Article', '10.1093/bioinformatics/btx278', '28444139', 'Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compounds possible active ingredients but also its therapeutic, pharmacological and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult.'),
(80781, 2017, 'bio', 'RNAblueprint: flexible multiple target nucleic acid sequence design', 'Hammer, Stefan|Tschiatschek, Birgit|Flamm, Christoph|Hofacker, Ivo L|Findeixc3x9f, Sven', 'Faculty of Chemistry, Department of Theoretical Chemistry.|Faculty of Computer Science, Research Group Bioinformatics and Computational Biology.|Faculty of Chemistry, Department of Theoretical Chemistry.|Faculty of Chemistry, Department of Theoretical Chemistry.|Faculty of Chemistry, Department of Theoretical Chemistry.', '-|-|-|-|-', 7, 'Models, Molecular|Nucleic Acid Conformation|RNA|Software|Synthetic Biology', 'Journal Article', '10.1093/bioinformatics/btx263', '28449031', 'Realizing the value of synthetic biology in biotechnology and medicine requires the design of molecules with specialized functions. Due to its close structure to function relationship, and the availability of good structure prediction methods and energy models, RNA is perfectly suited to be synthetically engineered with predefined properties. However, currently available RNA design tools cannot be easily adapted to accommodate new design specifications. Furthermore, complicated sampling and optimization methods are often developed to suit a specific RNA design goal, adding to their inflexibility.'),
(80782, 2017, 'bio', 'veqtl-mapper: variance association mapping for molecular phenotypes', 'Brown, Andrew Anand', 'Department of Genetic Medicine and Development.', '-', 2, 'Computational Biology|Epistasis, Genetic|Gene Expression Regulation|Gene-Environment Interaction|Genetic Variation|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx273', '28449110', 'Genetic loci associated with the variance of phenotypic traits have been of recent interest as they can be signatures of genetic interactions, gene by environment interactions, parent of origin effects and canalization. We present a fast efficient tool to map loci affecting variance of gene expression and other molecular phenotypes in cis. Results: Applied to the publicly available Geuvadis gene expression dataset, we identify 816 loci associated with variance of gene expression using an additive model, and 32 showing differences in variance between homozygous and heterozygous alleles, signatures of parent of origin effects.'),
(80783, 2017, 'bio', 'Neuro-symbolic representation learning on biological knowledge graphs', 'Alshahrani, Mona|Khan, Mohammad Asif|Maddouri, Omar|Kinjo, Akira R|Queralt-Rosinach, Núria|Hoehndorf, Robert', 'Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.|Institute for Protein Research, Osaka University 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA.|Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Japan|United States|Saudi Arabia', 32, 'Computational Biology|Humans|Knowledge Bases|Machine Learning|Neural Networks (Computer)', 'Journal Article', '10.1093/bioinformatics/btx275', '28449114', 'Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics.'),
(80784, 2017, 'bio', 'SigSeeker: a peak-calling ensemble approach for constructing epigenetic signatures', 'Lichtenberg, Jens|Elnitski, Laura|Bodine, David M', 'Genetics and Molecular Biology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Genetics and Molecular Biology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.', 'United States|United States|United States', 4, 'Algorithms|Cell Line|Chromatin Immunoprecipitation|Epigenomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx276', '28449120', 'Epigenetic data are invaluable when determining the regulatory programs governing a cell. Based on use of next-generation sequencing data for characterizing epigenetic marks and transcription factor binding, numerous peak-calling approaches have been developed to determine sites of genomic significance in these data. Such analyses can produce a large number of false positive predictions, suggesting that sites supported by multiple algorithms provide a stronger foundation for inferring and characterizing regulatory programs associated with the epigenetic data. Few methodologies integrate epigenetic based predictions of multiple approaches when combining profiles generated by different tools.'),
(80785, 2017, 'bio', 'modSaRa: a computationally efficient R package for CNV identification', 'Xiao, Feifei|Niu, Yue|Hao, Ning|Xu, Yanxun|Jin, Zhilin|Zhang, Heping', 'Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29201, USA.|Department of Mathematics, University of Arizona, Tucson, AZ 85721, USA.|Department of Mathematics, University of Arizona, Tucson, AZ 85721, USA.|Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Algorithms|DNA Copy Number Variations|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx212', '28453611', 'Chromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps.'),
(80786, 2017, 'bio', 'Linearity of network proximity measures: implications for set-based queries and significance testing', 'Maxwell, Sean|Chance, Mark R|Koyutürk, Mehmet', 'Center for Proteomics and Bioinformatics.|Center for Proteomics and Bioinformatics.|Center for Proteomics and Bioinformatics.', '-|-|-', 0, 'Computational Biology|Computer Simulation|Humans|Monte Carlo Method', 'Journal Article', '10.1093/bioinformatics/btw733', '28453667', 'In recent years, various network proximity measures have been proposed to facilitate the use of biomolecular interaction data in a broad range of applications. These applications include functional annotation, disease gene prioritization, comparative analysis of biological systems and prediction of new interactions. In such applications, a major task is the scoring or ranking of the nodes in the network in terms of their proximity to a given set of seed nodes (e.g. a group of proteins that are identified to be associated with a disease, or are deferentially expressed in a certain condition). Many different network proximity measures are utilized for this purpose, and these measures are quite diverse in terms of the benefits they offer.'),
(80787, 2017, 'bio', 'A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra', 'Kou, Qiang|Wu, Si|Tolic, Nikola|Paša-Tolic, Ljiljana|Liu, Yunlong|Liu, Xiaowen', 'Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.|Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA.|Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.', 'India|United States|United States|United States|India|India', 0, 'Algorithms|Alternative Splicing|Molecular Weight|Mutation|Protein Processing, Post-Translational|Proteome|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btw806', '28453668', 'Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a birds eye view of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem.'),
(80788, 2017, 'bio', 'Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy', 'Hanslovsky, Philipp|Bogovic, John A|Saalfeld, Stephan', 'HHMI Janelia Research Campus, Ashburn, VA 20147, USA.|HHMI Janelia Research Campus, Ashburn, VA 20147, USA.|HHMI Janelia Research Campus, Ashburn, VA 20147, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Central Nervous System|Computing Methodologies|Drosophila melanogaster|Image Interpretation, Computer-Assisted|Microscopy, Electron|Microtomy', 'Journal Article', '10.1093/bioinformatics/btw794', '28453669', 'Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.'),
(80789, 2017, 'bio', 'GppFst: genomic posterior predictive simulations of FST and dXY for identifying outlier loci from population genomic data', 'Adams, Richard H|Schield, Drew R|Card, Daren C|Blackmon, Heath|Castoe, Todd A', 'Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.|Department of Ecology, Evolution & Behavior, University of Minnesota, Saint Paul, MN 55108, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX 76019, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Crotalus|Genetic Loci|Genetics, Population|Genome|Genomics|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btw795', '28453670', 'We introduce GppFst, an open source R package that generates posterior predictive distributions of FST and dx under a neutral coalescent model to identify putative targets of selection from genomic data.'),
(80790, 2017, 'bio', 'Wright-Fisher exact solver (WFES): scalable analysis of population genetic models without simulation or diffusion theory', 'Krukov, Ivan|de Sanctis, Bianca|de Koning, A P Jason', 'Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada.|Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada.|Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada.', 'Canada|Canada|Canada', 3, 'Alleles|Animals|Genetics, Population|Humans|Markov Chains|Models, Genetic|Population Density|Software', 'Journal Article', '10.1093/bioinformatics/btw802', '28453671', 'The simplifying assumptions that are used widely in theoretical population genetics may not always be appropriate for empirical population genetics. General computational approaches that do not require the assumptions of classical theory are therefore quite desirable. One such general approach is provided by the theory of absorbing Markov chains, which can be used to obtain exact results by directly analyzing population genetic Markov models, such as the classic bi-allelic Wright-Fisher model. Although these approaches are sometimes used, they are usually forgone in favor of simulation methods, due to the perception that they are too computationally burdensome. Here we show that, surprisingly, direct analysis of virtually any Markov chain model in population genetics can be made quite efficient by exploiting transition matrix sparsity and by solving restricted systems of linear equations, allowing a wide variety of exact calculations (within machine precision) to be easily and rapidly made on modern workstation computers.'),
(80791, 2017, 'bio', 'Primerize-2D: automated primer design for RNA multidimensional chemical mapping', 'Tian, Siqi|Das, Rhiju', 'Department of Biochemistry.|Department of Biochemistry.', '-|-', 0, 'DNA Primers|Mutation|Nucleic Acid Conformation|Polymerase Chain Reaction|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btw814', '28453672', 'Rapid RNA synthesis of comprehensive single mutant libraries and targeted multiple mutant libraries is enabling new multidimensional chemical approaches to solve RNA structures. PCR assembly of DNA templates and in vitro transcription allow synthesis and purification of hundreds of RNA mutants in a cost-effective manner, with sharing of primers across constructs allowing significant reductions in expense. However, these protocols require organization of primer locations across numerous 96 well plates and guidance for pipetting, non-trivial tasks for which informatics and visualization tools can prevent costly errors. We report here an online tool to accelerate synthesis of large libraries of desired mutants through design and efficient organization of primers. The underlying program and graphical interface have been experimentally tested in our laboratory for RNA domains with lengths up to 300 nucleotides and libraries encompassing up to 960 variants. In addition to the freely available Primerize-2D server, the primer design code is available as a stand-alone Python package for broader applications.'),
(80792, 2017, 'bio', 'VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis', 'Lemaçon, Audrey|Joly Beauparlant, Charles|Soucy, Penny|Allen, Jamie|Easton, Douglas|Kraft, Peter|Simard, Jacques|Droit, Arnaud', 'Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Quebec, Canada.|Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Quebec, Canada.|Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Quebec, Canada.|Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.|Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.|Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Quebec, Canada.|Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Quebec, Canada.', 'Canada|Canada|Canada|United Kingdom|United Kingdom|United Kingdom|Canada|Canada', 1, 'Genome, Human|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw826', '28453673', 'The identification of the functional variants responsible for observed genome-wide association studies (GWAS) signals is one of the most challenging tasks of the post-GWAS research era. Several tools have been developed to annotate genetic variants by their genomic location and potential functional implications. Each of these tools has its own requirements and internal logic, which forces the user to become acquainted with each interface.'),
(80793, 2017, 'bio', 'ntCard: a streaming algorithm for cardinality estimation in genomics data', 'Mohamadi, Hamid|Khan, Hamza|Birol, Inanc', 'Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada.', 'Canada|Canada|Canada', 18, 'Algorithms|Genome Size|Genome, Human|Genome, Plant|Genomics|Humans|Models, Statistical|Picea|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw832', '28453674', 'Many bioinformatics algorithms are designed for the analysis of sequences of some uniform length, conventionally referred to as k -mers. These include de Bruijn graph assembly methods and sequence alignment tools. An efficient algorithm to enumerate the number of unique k -mers, or even better, to build a histogram of k -mer frequencies would be desirable for these tools and their downstream analysis pipelines. Among other applications, estimated frequencies can be used to predict genome sizes, measure sequencing error rates, and tune runtime parameters for analysis tools. However, calculating a k -mer histogram from large volumes of sequencing data is a challenging task.'),
(80794, 2017, 'bio', 'CircosVCF: circos visualization of whole-genome sequence variations stored in VCF files', 'Drori, E|Levy, D|Smirin-Yosef, P|Rahimi, O|Salmon-Divon, M', 'Agriculture and Oenology Research Department, Eastern R&D Center, 40700 Ariel, Israel.|Department of Molecular Biology, Ariel University, 40700 Ariel, Israel.|Department of Molecular Biology, Ariel University, 40700 Ariel, Israel.|Agriculture and Oenology Research Department, Eastern R&D Center, 40700 Ariel, Israel.|Department of Molecular Biology, Ariel University, 40700 Ariel, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Genome, Plant|Genomics|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|Vitis', 'Journal Article', '10.1093/bioinformatics/btw834', '28453675', 'Visualization of whole-genomic variations in a meaningful manner assists researchers in gaining new insights into the underlying data, especially when it comes in the context of whole genome comparisons. CircosVCF is a web based visualization tool for genome-wide variant data described in VCF files, using circos plots. The user friendly interface of CircosVCF supports an interactive design of the circles in the plot, and the integration of additional information such as experimental data or annotations. The provided visualization capabilities give a broad overview of the genomic relationships between genomes, and allow identification of specific meaningful SNPs regions.'),
(80795, 2017, 'bio', 'fastMitoCalc: an ultra-fast program to estimate mitochondrial DNA copy number from whole-genome sequences', 'Qian, Yong|Butler, Thomas J|Opsahl-Ong, Krista|Giroux, Nicholas S|Sidore, Carlo|Nagaraja, Ramaiah|Cucca, Francesco|Ferrucci, Luigi|Abecasis, Gonçalo R|Schlessinger, David|Ding, Jun', 'Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.|Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.|Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.|Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.|Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy.|Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.|Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy.|Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.|Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.|Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA.', 'United States|United States|United States|United States|Italy|United States|Italy|United States|United States|United States|United States', 6, 'Gene Dosage|Genome, Human|Genome, Mitochondrial|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw835', '28453676', 'fastMitoCalc is available at https://lgsun.irp.nia.nih.gov/hsgu/software/mitoAnalyzer/index.html.'),
(80796, 2017, 'bio', 'AFS: identification and quantification of species composition by metagenomic sequencing', 'Liu, Yongchao|Ripp, Fabian|Koeppel, Rene|Schmidt, Hanno|Hellmann, Sören Lukas|Weber, Mathias|Krombholz, Christopher Felix|Schmidt, Bertil|Hankeln, Thomas', 'Institute of Computer Science, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Institute of Molecular Genetics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Official Food Control Authority Canton Zürich, 8032 Zürich, Switzerland.|Institute of Molecular Genetics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Institute of Molecular Genetics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Institute of Molecular Genetics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Institute of Molecular Genetics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Institute of Computer Science, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.|Institute of Molecular Genetics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany.', 'Germany|Germany|Switzerland|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Food Microbiology|High-Throughput Nucleotide Sequencing|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btw822', '28453677', 'DNA-based methods to detect and quantify taxon composition in biological materials are often based on species-specific polymerase chain reaction, limited to detecting species targeted by the assay. Next-generation sequencing overcomes this drawback by untargeted shotgun sequencing of whole metagenomes at affordable cost. Here we present AFS, a software pipeline for quantification of species composition in food. AFS uses metagenomic shotgun sequencing and sequence read counting to infer species proportions. Using Illumina data from a reference sausage comprising four species, we reveal that AFS is independent of the sequencing assay and library preparation protocol. Cost-saving short (50-bp) single-end reads and Nextera ® library preparation yield reliable results.'),
(80797, 2017, 'bio', 'Chainy: an universal tool for standardized relative quantification in real-time PCR', 'Mallona, Izaskun|Díez-Villanueva, Anna|Martín, Berta|Peinado, Miguel A', 'Germans Trias i Pujol Health Science Research Institute (IGTP), Program for Predictive and Personalized Medicine of Cancer (IMPPC), Badalona 08916, Catalonia, Spain.|Germans Trias i Pujol Health Science Research Institute (IGTP), Program for Predictive and Personalized Medicine of Cancer (IMPPC), Badalona 08916, Catalonia, Spain.|Germans Trias i Pujol Health Science Research Institute (IGTP), Program for Predictive and Personalized Medicine of Cancer (IMPPC), Badalona 08916, Catalonia, Spain.|Germans Trias i Pujol Health Science Research Institute (IGTP), Program for Predictive and Personalized Medicine of Cancer (IMPPC), Badalona 08916, Catalonia, Spain.', 'Spain|Spain|Spain|Spain', 6, 'Chromatin Immunoprecipitation|Real-Time Polymerase Chain Reaction|Reference Standards|Software', 'Journal Article', '10.1093/bioinformatics/btw839', '28453678', 'Chainy is a cross-platform web tool providing systematic pipelines and steady criteria to process real-time PCR data, including the calculation of efficiencies from raw data by kinetic methods, evaluation of the suitability of multiple references, standardized normalization using one or more references, and group-wise relative quantification statistical testing. We illustrate the utility of Chainy for differential expression and chromatin immunoprecipitation enrichment (ChIP-QPCR) analysis.'),
(80798, 2017, 'bio', 'BioPAXViz: a cytoscape application for the visual exploration of metabolic pathway evolution', 'Psomopoulos, Fotis E|Vitsios, Dimitrios M|Baichoo, Shakuntala|Ouzounis, Christos A', 'Computational Genomics Unit, Institute of Applied Biosciences, Center for Research & Technology Hellas (CERTH), GR-57001 Thessalonica, Greece.|Computational Genomics Unit, Institute of Applied Biosciences, Center for Research & Technology Hellas (CERTH), GR-57001 Thessalonica, Greece.|Department of Computer Science & Engineering, Faculty of Engineering, University of Mauritius, Reduit 80837, Mauritius.|Computational Genomics Unit, Institute of Applied Biosciences, Center for Research & Technology Hellas (CERTH), GR-57001 Thessalonica, Greece.', 'Greece|Greece|Mauritius|Greece', 0, 'Computational Biology|Evolution, Molecular|Metabolic Networks and Pathways|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btw813', '28453679', 'BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering.'),
(80799, 2017, 'bio', 'The genetic map comparator: a user-friendly application to display and compare genetic maps', 'Holtz, Yan|David, Jacques Léon|Ranwez, Vincent', 'Montpellier Supagro, Department BE, UMR AGAP, 34060 Montpellier, France.|Montpellier Supagro, Department BE, UMR AGAP, 34060 Montpellier, France.|Montpellier Supagro, Department BE, UMR AGAP, 34060 Montpellier, France.', 'France|France|France', 5, 'Disease Resistance|Genes, Plant|Genomics|Plant Diseases|Quantitative Trait Loci|Sequence Analysis, DNA|Software|Triticum|Virus Diseases', 'Journal Article', '10.1093/bioinformatics/btw816', '28453680', 'Marker-assisted selection strongly relies on genetic maps to accelerate breeding programs. High-density maps are now available for numerous species. Dedicated tools are required to compare several high-density maps on the basis of their key characteristics, while pinpointing their differences and similarities.'),
(80800, 2017, 'bio', 'Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction', 'Marks, Claire|Nowak, Jaroslaw|Klostermann, Stefan|Georges, Guy|Dunbar, James|Shi, Jiye|Kelm, Sebastian|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford, UK.|Department of Statistics, University of Oxford, Oxford, UK.|Pharma Research and Early Development, Informatics.|Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany.|Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany.|Department of Informatics, UCB Pharma, Slough, UK.|Department of Informatics, UCB Pharma, Slough, UK.|Department of Statistics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|Germany|Germany|United Kingdom|United Kingdom|United Kingdom', 19, 'Algorithms|Animals|Antibodies|Computational Biology|Knowledge Bases|Models, Molecular|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btw823', '28453681', 'Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction.'),
(80801, 2017, 'bio', 'DistributedFBAjl: high-level, high-performance flux balance analysis in Julia', 'Heirendt, Laurent|Thiele, Ines|Fleming, Ronan M T', 'University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg', 0, 'Computational Biology|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btw838', '28453682', 'Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.'),
(80802, 2017, 'bio', 'MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins', 'Necci, Marco|Piovesan, Damiano|Dosztányi, Zsuzsanna|Tosatto, Silvio C E', 'Department of Biomedical Sciences, University of Padua, 35121 Padova, Italy.|Department of Biomedical Sciences, University of Padua, 35121 Padova, Italy.|MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary.|Department of Biomedical Sciences, University of Padua, 35121 Padova, Italy.', 'Italy|Italy|Hungary|Italy', 48, 'Consensus|Humans|Intrinsically Disordered Proteins|Proteomics|Sensitivity and Specificity|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx015', '28453683', 'Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains.'),
(80803, 2017, 'bio', 'MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform', 'Gupta, Surya|De Puysseleyr, Veronic|Van der Heyden, José|Maddelein, Davy|Lemmens, Irma|Lievens, Sam|Degroeve, Sven|Tavernier, Jan|Martens, Lennart', 'Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Animals|High-Throughput Screening Assays|Humans|Mammals|Protein Array Analysis|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btx014', '28453684', 'Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments.'),
(80804, 2017, 'bio', 'PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data', 'Yang, Yifan|Xu, Lei|Feng, Zheyun|Cruz, Jeffrey A|Savage, Linda J|Kramer, David M|Chen, Jin', 'Department of Epidemiology and Biostatistics.|Department of Computer Science and Engineering.|Department of Computer Science and Engineering.|Department of Energy Plant Research Laboratory.|Department of Energy Plant Research Laboratory.|Department of Energy Plant Research Laboratory.|Institute of Biomedical Informatics, University of Kentucky, Lexington, KY 40536, USA.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Computational Biology|Gene-Environment Interaction|Photosynthesis|Plants|Software', 'Journal Article', '10.1093/bioinformatics/btw673', '28453685', 'Phenomics is essential for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments.'),
(80805, 2017, 'bio', 'The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis', 'Debrabant, Birgit', 'Department of Epidemiology, Biostatistics and Biodemography, University of Southern Denmark, 5000 Odense C, Denmark.', 'Denmark', 0, 'Gene Expression Profiling|Genetic Association Studies|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btw803', '28453686', 'Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis. This is a major handicap to the interpretation of results obtained from a gene set analysis.');
