INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69179, 2018, 'bib', 'Molecular dynamics recipes for genome research', 'Biagini, Tommaso|Chillemi, Giovanni|Mazzoccoli, Gianluigi|Grottesi, Alessandro|Fusilli, Caterina|Capocefalo, Daniele|Castellana, Stefano|Vescovi, Angelo Luigi|Mazza, Tommaso', 'Cellular and Molecular Biology.|Biochemistry and Molecular Biology.|American Physiological Society.|Cineca Specialistic User Support group.|Statistics for Biomedicine.|Life Sciences, `La Sapienza University of Rome.|Cellular and Molecular Biology.|Bicocca University of Milan.|IRCCS Casa Sollievo della Sofferenza, Bioinformatics unit, viale Regina Margherita, Rome, Italy.', '-|-|-|-|-|-|-|-|Italy', 10, 'Algorithms|Computational Biology|Databases, Genetic|Genomics|Humans|Molecular Dynamics Simulation|Polymorphism, Single Nucleotide|Protein Conformation|Proteins|Software|Software Design', 'Journal Article', '10.1093/bib/bbx006', '28334084', 'Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in sorting pathogenic from neutral genomic mutations. Considering their computational requirements, simulations are commonly performed on HPC computing devices, which are generally expensive and hard to administer. However, variables like the software tool used for modeling and simulation or the size of the molecule under investigation might make one hardware type or configuration more advantageous than another or even make the commodity hardware definitely suitable for MD studies. This work aims to shed lights on this aspect.'),
(69180, 2018, 'bib', 'Individualized analysis of differentially expressed miRNAs with application to the identification of miRNAs deregulated commonly in lung cancer tissues', 'Yan, Haidan|Cai, Hao|Guan, Qingzhou|He, Jun|Zhang, Juan|Guo, You|Huang, Haiyan|Li, Xiangyu|Li, Yawei|Gu, Yunyan|Qi, Lishuang|Guo, Zheng', 'Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Preventive Medicine, Gannan Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Harbin Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.|Department of Bioinformatics, Fujian Medical University, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 4, 'Algorithms|Biomarkers, Tumor|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Lung Neoplasms|MicroRNAs|Reference Values', 'Evaluation Studies', '10.1093/bib/bbx015', '28334118', 'Identifying differentially expressed microRNAs (DE miRNAs) between cancer samples and normal controls is a common way to investigate carcinogenesis mechanisms. However, for a DE miRNA detected at the population-level, we do not know whether it is DE in a particular cancer sample. Here, based on the finding that the within-sample relative expression orderings of miRNA pairs are highly stable in a particular type of normal tissues but widely disrupted in the corresponding cancer tissues, we proposed a method, called RankMiRNA, to identify DE miRNAs in each cancer tissue compared with its own normal state. Evaluated with pair-matched miRNA expression profiles of cancer tissues and adjacent normal tissues for lung and liver cancers, RankMiRNA exhibited excellent performance. Finally, we exemplified an application of the individual-level differential expression analysis by finding miRNAs DE in at least 90% lung cancer tissues, defined as common DE miRNAs of lung cancer. After identifying DE miRNAs for each of 991 lung cancer samples from The Cancer Genome Atlas with RankMiRNA, we found that hsa-mir-210 was upregulated, while hsa-mir-490 and hsa-mir-486 were downregulated inxe2x80x89>xe2x80x8990% of the 991 lung cancer samples. These common DE miRNAs were validated in independent pair-matched samples of cancer tissues and adjacent normal tissues measured with different platforms. In conclusion, RankMiRNA provides us a novel tool to find common and subtype-specific miRNAs for a type of cancer, allowing us to study cancer mechanisms in a novel way.'),
(69181, 2018, 'bib', 'A review of network-based approaches to drug repositioning', 'Lotfi Shahreza, Maryam|Ghadiri, Nasser|Mousavi, Sayed Rasoul|Varshosaz, Jaleh|Green, James R', 'Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.|Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.|Imperial College London.|Drug Delivery Systems Research Center of Isfahan University of Medical Sciences.|Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.', '-|-|-|-|-', 47, 'Computational Biology|Databases, Pharmaceutical|Drug Interactions|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions|Gene Regulatory Networks|Humans|Machine Learning|Metabolic Networks and Pathways|Molecular Docking Simulation|Protein Interaction Maps', 'Journal Article', '10.1093/bib/bbx017', '28334136', 'Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.'),
(69182, 2018, 'bib', 'Circular RNA identification based on multiple seed matching', 'Gao, Yuan|Zhang, Jinyang|Zhao, Fangqing', 'University of Chinese Academy of Sciences.|University of Chinese Academy of Sciences.|Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.', '-|-|China', 96, 'Algorithms|Computational Biology|Humans|Likelihood Functions|RNA|RNA Splicing|Reproducibility of Results|Sequence Alignment|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bib/bbx014', '28334140', 'Computational detection methods have been widely used in studies on the biogenesis and the function of circular RNAs (circRNAs). However, all of the existing tools showed disadvantages on certain aspects of circRNA detection. Here, we propose an improved multithreading detection tool, CIRI2, which used an adapted maximum likelihood estimation based on multiple seed matching to identify back-spliced junction reads and to filter false positives derived from repetitive sequences and mapping errors. We established objective assessment criteria based on real data from RNase R-treated samples and systematically compared 10 circular detection tools, which demonstrated that CIRI2 outperformed its previous version CIRI and all other widely used tools, featured with remarkably balanced sensitivity, reliability, duration and RAM usage.'),
(69183, 2018, 'bib', 'Strategies for processing and quality control of Illumina genotyping arrays', 'Zhao, Shilin|Jing, Wang|Samuels, David C|Sheng, Quanghu|Shyr, Yu|Guo, Yan', 'Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Molecular Physics and Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Biostatistics, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Cluster Analysis|Computational Biology|Continental Population Groups|Female|Gene Frequency|Genome-Wide Association Study|Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Male|Models, Genetic|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Quality Control|Software', 'Journal Article', '10.1093/bib/bbx012', '28334151', 'Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data onxe2x80x89>xe2x80x89100xc2xa0000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.'),
(69184, 2017, 'bib', 'How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications', 'Khomtchouk, Bohdan B|Weitz, Edmund|Karp, Peter D|Wahlestedt, Claes', 'Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbx016', '28334156', '-'),
(69185, 2017, 'bib', 'A review of connectivity map and computational approaches in pharmacogenomics', 'Musa, Aliyu|Ghoraie, Laleh Soltan|Zhang, Shu-Dong|Glazko, Galina|Yli-Harja, Olli|Dehmer, Matthias|Haibe-Kains, Benjamin|Emmert-Streib, Frank', 'Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 14, '-', 'Journal Article', '10.1093/bib/bbx023', '28334173', '-'),
(69186, 2017, 'bib', 'miRandb: a resource of online services for miRNA research', 'Aghaee-Bakhtiari, Seyed Hamid|Arefian, Ehsan|Lau, Pierre', 'Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.|Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bib/bbx027', '28334200', '-'),
(69187, 2018, 'bib', 'Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity', 'Wang, Huilin|Feng, Liubin|Webb, Geoffrey I|Kurgan, Lukasz|Song, Jiangning|Lin, Donghai', 'Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China.|NMR Center, Xiamen University, China.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Australia.|Department of Computer Science, Virginia Commonwealth University, USA.|Department of Biochemistry and Molecular Biology, Monash University, Australia.|Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China.', 'China|China|Australia|United States|Australia|China', 8, 'Algorithms|Amino Acid Sequence|Computational Biology|Crystallization|Crystallography, X-Ray|Databases, Protein|Humans|Proteins|Software', 'Comparative Study', '10.1093/bib/bbx018', '28334201', 'X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms.'),
(69188, 2018, 'bib', 'Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions', 'Evans, Ciaran|Hardin, Johanna|Stoebel, Daniel M', 'Department of Statistics, Baker Hall, Carnegie Mellon University, Pittsburgh, PA, USA.|Pomona College.|Harvey Mudd College.', 'United States|United States|United States', 40, 'Computational Biology|Computer Simulation|Databases, Genetic|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbx008', '28334202', 'RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An underemphasized feature of normalization is the assumptions on which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this article, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.'),
(69189, 2018, 'bib', 'A survey of the approaches for identifying differential methylation using bisulfite sequencing data', 'Shafi, Adib|Mitrea, Cristina|Nguyen, Tin|Draghici, Sorin', 'Department of Computer Science, Wayne State University, USA.|Department of Computer Science, Wayne State University, USA.|Department of Computer Science, Wayne State University, USA.|Department of Computer Science, Wayne State University, USA.', 'United States|United States|United States|United States', 19, 'Computational Biology|CpG Islands|DNA Methylation|Epigenesis, Genetic|High-Throughput Nucleotide Sequencing|Humans|Logistic Models|Markov Chains|Sequence Analysis, DNA|Sulfites', 'Journal Article', '10.1093/bib/bbx013', '28334228', 'DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data, however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.'),
(69190, 2018, 'bib', 'Comparison of the general co-expression landscapes between human and mouse', 'Liu, Di|Zhao, Linna|Chen, Yang|Wang, Zhaoyang|Xu, Jing|Li, Ying|Lei, Changgui|Hu, Simeng|Niu, Miaomiao|Jiang, Yongshuai', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|China', 0, 'Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Humans|Mice|Species Specificity', 'Comparative Study', '10.1093/bib/bbx024', '28334239', 'The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse. The differences and similarities of the general co-expression maps between the two species were compared in detail. The results showed low similarities in the human and mouse, with only about 36.54% of the co-expression relationships conserved between the two species. These results indicate that researchers should pay attention to these differences when performing research using the expression data of human and mouse. To facilitate use of this information, we also developed the human-mouse general co-expression difference database (coexpressMAP) to search differences in co-expression between human and mouse. This database is freely available at http://www.bioapp.org/coexpressMAP.'),
(69191, 2018, 'bib', 'Review and comparative assessment of sequence-based predictors of protein-binding residues', 'Zhang, Jian|Kurgan, Lukasz', 'School of Computer and Information Technology, Xinyang Normal University.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.', 'United States|United States', 12, 'Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|Ligands|Nucleic Acids|Protein Binding|Protein Interaction Domains and Motifs|Proteins|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Comparative Study', '10.1093/bib/bbx022', '28334258', 'Understanding of molecular mechanisms that govern protein-protein interactions and accurate modeling of protein-protein docking rely on accurate identification and prediction of protein-binding partners and protein-binding residues. We review over 40 methods that predict protein-protein interactions from protein sequences including methods that predict interacting protein pairs, protein-binding residues for a pair of interacting sequences and protein-binding residues in a single protein chain. We focus on the latter methods that provide residue-level annotations and that can be broadly applied to all protein sequences. We compare their architectures, inputs and outputs, and we discuss aspects related to their assessment and availability. We also perform first-of-its-kind comprehensive empirical comparison of representative predictors of protein-binding residues using a novel and high-quality benchmark data set. We show that the selected predictors accurately discriminate protein-binding and non-binding residues and that newer methods outperform older designs. However, these methods are unable to accurately separate residues that bind other molecules, such as DNA, RNA and small ligands, from the protein-binding residues. This cross-prediction, defined as the incorrect prediction of nucleic-acid- and small-ligand-binding residues as protein binding, is substantial for all evaluated methods and is not driven by the proximity to the native protein-binding residues. We discuss reasons for this drawback and we offer several recommendations. In particular, we postulate the need for a new generation of more accurate predictors and data sets, inclusion of a comprehensive assessment of the cross-predictions in future studies and higher standards of availability of the published methods.'),
(69192, 2018, 'bib', 'An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data', 'Liu, Bingqiang|Yang, Jinyu|Li, Yang|McDermaid, Adam|Ma, Qin', 'School of Mathematics, Shandong University, Jinan Shandong, P. R. China.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|School of Mathematics, Shandong University, Jinan Shandong, P. R. China.|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.', 'China|United States|China|United States|United States', 11, 'Algorithms|Base Sequence|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA|Gene Regulatory Networks|Humans|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bib/bbx026', '28334268', 'Transcription factors are proteins that bind to specific DNA sequences and play important roles in controlling the expression levels of their target genes. Hence, prediction of transcription factor binding sites (TFBSs) provides a solid foundation for inferring gene regulatory mechanisms and building regulatory networks for a genome. Chromatin immunoprecipitation sequencing (ChIP-seq) technology can generate large-scale experimental data for such protein-DNA interactions, providing an unprecedented opportunity to identify TFBSs (a.k.a. cis-regulatory motifs). The bottleneck, however, is the lack of robust mathematical models, as well as efficient computational methods for TFBS prediction to make effective use of massive ChIP-seq data sets in the public domain. The purpose of this study is to review existing motif-finding methods for ChIP-seq data from an algorithmic perspective and provide new computational insight into this field. The state-of-the-art methods were shown through summarizing eight representative motif-finding algorithms along with corresponding challenges, and introducing some important relative functions according to specific biological demands, including discriminative motif finding and cofactor motifs analysis. Finally, potential directions and plans for ChIP-seq-based motif-finding tools were showcased in support of future algorithm development.'),
(69193, 2018, 'bib', 'GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures', 'Fan, Qianrui|Zhang, Feng|Wang, Wenyu|Xu, Jiawen|Hao, Jingcan|He, Awen|Wen, Yan|Li, Ping|Liang, Xiao|Du, Yanan|Liu, Li|Wu, Cuiyan|Wang, Sen|Wang, Xi|Ning, Yujie|Guo, Xiong', 'Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.|Health Science Center, Xian Jiaotong University, Xian, Shaanxi, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|China', 1, 'Cardiovascular Diseases|Computational Biology|Computer Simulation|Environmental Exposure|Genetic Variation|Genome-Wide Association Study|Humans|Models, Genetic|Multifactorial Inheritance|Polymorphism, Single Nucleotide|Risk Factors|Smoking', 'Journal Article', '10.1093/bib/bbx025', '28334273', 'Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.'),
(69194, 2018, 'bib', 'Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges', 'Huang, Sheng-You', 'School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.', 'China', 3, 'Algorithms|Binding Sites|Computational Biology|Drug Discovery|Drug Evaluation, Preclinical|Entropy|Humans|Ligands|Molecular Docking Simulation|Protein Binding|Protein Conformation|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bib/bbx030', '28334282', 'Protein-ligand docking has been playing an important role in modern drug discovery. To model drug-target binding in real systems, a number of flexible-ligand docking algorithms with different sampling strategies and scoring methods have been subsequently developed over the past three decades, while rigid-ligand docking is still being used because of its compelling computational efficiency. Here, a comprehensive assessment has been conducted to investigate the effectiveness of flexible-ligand docking versus rigid-ligand docking for three representative docking algorithms (global optimization, incremental construction and multi-conformer docking) in virtual screening and pose prediction on the Directory of Useful Decoys. It was found that overall flexible-ligand docking did not achieve a statistically significant improvement in enrichments over rigid-ligand docking in virtual screening, but all docking programs significantly improved the success rates when considering ligand flexibility in pose prediction. The worse effectiveness of flexible-ligand docking in virtual screening than in pose prediction suggests that the challenges of current docking algorithms exist in ranking more than docking, although the use of flexible-ligand docking in virtual screening was justified by its better effectiveness for more flexible ligand in virtual screening. Challenges for scoring, including internal energy, charge polarization, entropy and flexibility, were investigated and discussed. An empirical way was also proposed to consider loss of ligand conformational entropy for virtual screening.'),
(69195, 2017, 'bib', 'Methodological implementation of mixed linear models in multi-locus genome-wide association studies', 'Wen, Yang-Jun|Zhang, Hanwen|Ni, Yuan-Li|Huang, Bo|Zhang, Jin|Feng, Jian-Ying|Wang, Shi-Bo|Dunwell, Jim M|Zhang, Yuan-Ming|Wu, Rongling', 'School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.', 'China|China|China|China|China|China|China|China|China|China', 9, '-', 'Journal Article', '10.1093/bib/bbx028', '28334354', '-'),
(69196, 2018, 'bib', 'Context-based retrieval of functional modules in protein-protein interaction networks', 'Dobay, Maria Pamela|Stertz, Silke|Delorenzi, Mauro', 'Swiss Institute of Bioinformatics, Quartier Sorge, bâtiment Génopode - Lausanne.|University of Zurich.|Bioinformatics Core Facility at the Swiss Institute of Bioinformatics (SIB).', '-|-|-', 0, 'Algorithms|Computational Biology|Data Mining|Gene Regulatory Networks|Genome-Wide Association Study|Host Microbial Interactions|Humans|Influenza A virus|Protein Interaction Mapping|Protein Interaction Maps|RNA, Small Interfering', 'Journal Article', '10.1093/bib/bbx029', '28369159', 'Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference.'),
(69197, 2018, 'bib', 'Identification of differentially expressed peptides in high-throughput proteomics data', 'van Ooijen, Michiel P|Jong, Victor L|Eijkemans, Marinus J C|Heck, Albert J R|Andeweg, Arno C|Binai, Nadine A|van den Ham, Henk-Jan', 'Department of Viroscience, Erasmus MC, CA Rotterdam, Netherlands.|Department of Biostatistics and Research Support, Julius Center, UMC Utrecht, Netherlands.|Julius Center for Health Sciences and Primary Care of the University Medical Center Utrecht, Netherlands.|Biomolecular Mass Spectrometry and Proteomics, Utrecht University, Netherlands.|Department of Viroscience, Erasmus MC, CA Rotterdam, Netherlands.|Biomolecular Mass Spectrometry Group, Utrecht University, Netherlands.|Department of Viroscience, Erasmus MC, CA Rotterdam, Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 7, 'Bayes Theorem|Chromatography, Liquid|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|Humans|Peptides|Protein Array Analysis|Proteomics|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bib/bbx031', '28369175', 'With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level.'),
(69198, 2018, 'bib', 'Row versus column correlations: avoiding the ecological fallacy in RNA/protein expression studies', 'OBrien, Jonathon J|Gunawardena, Harsha P|Qaqish, Bahjat F', 'Department of Cell Biology, Harvard Medical School, Boston, MA, USA.|Analytical Chemistry from Purdue University.|Department of Biostatistics, UNC.', 'United States|United States|United States', 0, 'Bias|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Humans|Models, Biological|Models, Statistical|Proteins|Proteomics|RNA|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbx021', '28369202', 'Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the ecological fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearsons coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the ecological fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.'),
(69199, 2018, 'bib', 'Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?', 'Muth, Thilo|Renard, Bernhard Y', 'Research Group Bioinformatics, Robert Koch Institute, Berlin, Germany.|Research Group Bioinformatics, Robert Koch Institute, Berlin, Germany.', 'Germany|Germany', 18, 'Algorithms|Amino Acid Sequence|Animals|Computational Biology|Computer Simulation|Databases, Protein|Humans|Mice|Peptides|Proteomics|Pyrococcus furiosus|Saccharomyces cerevisiae|Sequence Analysis, Protein|Sequence Tagged Sites|Software|Tandem Mass Spectrometry', 'Evaluation Studies', '10.1093/bib/bbx033', '28369237', 'While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.'),
(69200, 2018, 'bib', 'Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets', 'Tichy, Diana|Pickl, Julia Maria Anna|Benner, Axel|Sültmann, Holger', 'Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.|German Cancer Research Center, Heidelberg, Germany.|Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.|German Cancer Research Center, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 1, 'Algorithms|Biomarkers, Tumor|Computational Biology|Computer Simulation|Data Interpretation, Statistical|High-Throughput Nucleotide Sequencing|Humans|Immunoprecipitation|Linear Models|Male|MicroRNAs|PC-3 Cells|Prostatic Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbx032', '28379479', 'The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years, however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable.'),
(69201, 2018, 'bib', 'CASH: a constructing comprehensive splice site method for detecting alternative splicing events', 'Wu, Wenwu|Zong, Jie|Wei, Ning|Cheng, Jian|Zhou, Xuexia|Cheng, Yuanming|Chen, Dai|Guo, Qinghua|Zhang, Bo|Feng, Ying', 'The State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Linan, Hangzhou, China.|Novel Bioinformatics Co., Ltd, Shanghai, China.|Institute for Nutritional Sciences, Chinese Academy of Sciences (CAS), Shanghai, China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS), Shanghai, China.|Tianjin Medical University General Hospital, China.|Institute for Nutritional Sciences, Chinese Academy of Sciences (CAS), Shanghai, China.|Novel Bioinformatics Co., Ltd, Shanghai, China.|Novel Bioinformatics Co., Ltd, Shanghai, China.|Novel Bioinformatics Co., Ltd, Shanghai, China.|Institute for Nutritional Sciences, Chinese Academy of Sciences (CAS), Shanghai, China.', 'China|China|China|China|China|China|China|China|China|China', 11, 'Alternative Splicing|Animals|Cell Cycle Proteins|Chickens|Computational Biology|Computer Simulation|Exons|High-Throughput Nucleotide Sequencing|Humans|Mice|Models, Genetic|RNA Splice Sites|Repressor Proteins|Sequence Analysis, RNA|Serine-Arginine Splicing Factors|Software', 'Journal Article', '10.1093/bib/bbx034', '28387786', 'RNA-sequencing (RNA-seq) can generate millions of reads to provide clues for analyzing novel or abnormal alternative splicing (AS) events in cells. However, current methods for exploring AS events are still far from being satisfactory. Here, we present Comprehensive AS Hunting (CASH), which constructs comprehensive splice sites including known and novel AS sites in cells, and identifies differentially AS events between cells. We illuminated the versatility of CASH on RNA-seq data from a wide range of species and also on simulated in silico data, validated the advantages of CASH over other AS predictors and exhibited novel differentially AS events. Moreover, we used CASH to identify SRSF10-regulated AS events and investigated the evolution of SRSF10-regulated splicing. The results showed that SRSF10-regulated splicing events are highly evolvable from chickens, mice to humans. However, SRSF10-regulated splicing model was observed to be immutable, in which SRSF10 binding to cassette exon promotes exon inclusion while binding to downstream exon induces exon skipping. Altogether, CASH can significantly improve the detection of AS events and facilitate the study of AS regulation and function in cells, the SRSF10 data first demonstrate a flexibility of SRSF10 with their regulated splicing events but an immutability of SRSF10-regulated splicing model to produce opposite AS outcomes in vertebrates.'),
(69202, 2018, 'bib', 'The anatomy of phenotype ontologies: principles, properties and applications', 'Gkoutos, Georgios V|Schofield, Paul N|Hoehndorf, Robert', 'University of Birmingham Medical School.|University of Cambridge.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, King Abdullah University of Science and Technology, Thuwal.', '-|-|-', 14, 'Animals|Animals, Domestic|Biodiversity|Biological Evolution|Biological Ontologies|Computational Biology|Ecology|Gene Ontology|Humans|Phenotype|Semantic Web', 'Journal Article', '10.1093/bib/bbx035', '28387809', 'The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69203, 2018, 'bib', 'Bioinformatics tools for the identification of gene clusters that biosynthesize specialized metabolites', 'Chavali, Arvind K|Rhee, Seung Y', 'Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.|Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.', 'United States|United States', 5, 'Algorithms|Animals|Bacteria|Biosynthetic Pathways|Computational Biology|Fungi|Humans|Models, Genetic|Multigene Family|Plants|Software', 'Journal Article', '10.1093/bib/bbx020', '28398567', 'Specialized metabolites (also called natural products or secondary metabolites) derived from bacteria, fungi, marine organisms and plants constitute an important source of antibiotics, anti-cancer agents, insecticides, immunosuppressants and herbicides. Many specialized metabolites in bacteria and fungi are biosynthesized via metabolic pathways whose enzymes are encoded by clustered genes on a chromosome. Metabolic gene clusters comprise a group of physically co-localized genes that together encode enzymes for the biosynthesis of a specific metabolite. Although metabolic gene clusters are generally not known to occur outside of microbes, several plant metabolic gene clusters have been discovered in recent years. The discovery of novel metabolic pathways is being enabled by the increasing availability of high-quality genome sequencing coupled with the development of powerful computational toolkits to identify metabolic gene clusters. To provide a comprehensive overview of various bioinformatics methods for detecting gene clusters, we compare and contrast key aspects of algorithmic logic behind several computational tools, including NP.searcher, ClustScan, CLUSEAN, antiSMASH, SMURF, MIDDAS-M, ClusterFinder, CASSIS/SMIPS and C-Hunter among others. We also review additional tools such as NRPSpredictor and SBSPKS that can infer substrate specificity for previously identified gene clusters. The continual development of bioinformatics methods to predict gene clusters will help shed light on how organisms assemble multi-step metabolic pathways for adaptation to various ecological niches.'),
(69204, 2018, 'bib', 'Comparative analysis of de novo assemblers for variation discovery in personal genomes', 'Tian, Shulan|Yan, Huihuang|Klee, Eric W|Kalmbach, Michael|Slager, Susan L', 'Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Division of Information Management and Analytics, Department of Information Technology, Mayo Clinic, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.', 'United States|United States|United States|United States|United States', 0, 'Chromosome Mapping|Chromosomes, Human, Pair 6|Computational Biology|Computer Simulation|Female|Genetic Variation|Genome, Human|HLA Antigens|Haplotypes|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Pregnancy|Whole Exome Sequencing|Whole Genome Sequencing', 'Comparative Study', '10.1093/bib/bbx037', '28407084', 'Current variant discovery approaches often rely on an initial read mapping to the reference sequence. Their effectiveness is limited by the presence of gaps, potential misassemblies, regions of duplicates with a high-sequence similarity and regions of high-sequence divergence in the reference. Also, mapping-based approaches are less sensitive to large INDELs and complex variations and provide little phase information in personal genomes. A few de novo assemblers have been developed to identify variants through direct variant calling from the assembly graph, micro-assembly and whole-genome assembly, but mainly for whole-genome sequencing (WGS) data. We developed SGVar, a de novo assembly workflow for haplotype-based variant discovery from whole-exome sequencing (WES) data. Using simulated human exome data, we compared SGVar with five variation-aware de novo assemblers and with BWA-MEM together with three haplotype- or local de novo assembly-based callers. SGVar outperforms the other assemblers in sensitivity and tolerance of sequencing errors. We recapitulated the findings on whole-genome and exome data from a Utah residents with Northern and Western European ancestry (CEU) trio, showing that SGVar had high sensitivity both in the highly divergent human leukocyte antigen (HLA) region and in non-HLA regions of chromosome 6. In particular, SGVar is robust to sequencing error, k-mer selection, divergence level and coverage depth. Unlike mapping-based approaches, SGVar is capable of resolving long-range phase and identifying large INDELs from WES, more prominently from WGS. We conclude that SGVar represents an ideal platform for WES-based variant discovery in highly divergent regions and across the whole genome.'),
(69205, 2018, 'bib', 'Improving data workflow systems with cloud services and use of open data for bioinformatics research', 'Karim, Md Rezaul|Michel, Audrey|Zappa, Achille|Baranov, Pavel|Sahay, Ratnesh|Rebholz-Schuhmann, Dietrich', 'Semantics in eHealth and Life Sciences (SeLS), Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland.|School of Biochemistry and Cell Biology, University College Cork, Ireland.|Insight Centre for Data Analytics, National University of Ireland Galway, Dangan, Galway, Ireland.|School of Biochemistry and Cell Biology, University College Cork, Ireland.|Semantics in eHealth and Life Sciences (SeLS), Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland.|Insight Centre for Data Analytics, National University of Ireland Galway, Dangan, Galway, Ireland.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 6, 'Big Data|Cloud Computing|Computational Biology|Data Interpretation, Statistical|Database Management Systems|Drug Discovery|Genomics|Humans|Information Dissemination|Knowledge Bases|Semantic Web|User-Computer Interface|Workflow', 'Journal Article', '10.1093/bib/bbx039', '28419324', 'Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200xe2x80x89GB each), public fact repositories (about 100xc2xa0TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.'),
(69206, 2018, 'bib', 'Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications', 'Liang, Yulan|Kelemen, Arpad', 'Department of Family and Community Health, University of Maryland, Baltimore, MD, USA.|Department of Family and Community Health, University of Maryland, Baltimore, MD, USA.', 'United States|United States', 6, 'Computational Biology|Gene Regulatory Networks|Genomics|Humans|Interatrial Block|Machine Learning|Models, Biological|Models, Statistical|Precision Medicine|Protein Interaction Maps|Proteomics|Software|Stochastic Processes|Systems Biology', 'Journal Article', '10.1093/bib/bbx036', '28430854', 'Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.'),
(69207, 2019, 'bib', 'Bioinformatics in Germany: toward a national-level infrastructure', 'Tauch, Andreas|Al-Dilaimi, Arwa', 'Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße, Bielefeld, Germany.|Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße, Bielefeld, Germany.', 'Germany|Germany', 1, 'Algorithms|Biomedical Research|Computational Biology|Data Curation|Germany|Humans|Microbiota|Plants', 'Journal Article', '10.1093/bib/bbx040', '28430873', 'The German Network for Bioinformatics Infrastructure (de.NBI) is a national initiative funded by the German Federal Ministry of Education and Research (BMBF). The mission of de.NBI is (i) to provide high-quality bioinformatics services to users in basic and applied life sciences research from academia, industry and biomedicine, (ii) to offer bioinformatics training to users in Germany and Europe through a wide range of workshops and courses, and (iii) to foster the cooperation of the German bioinformatics community with international network structures such as the European life-sciences Infrastructure for biological Information (ELIXIR). The network was launched by the BMBF in March 2015 and now includes 40 service projects operated by 30 project partners that are organized in eight service centers. The de.NBI staff develops further and maintains almost 100 bioinformatics services for the human, plant and microbial research fields and provides comprehensive training courses to support users with different expertise levels in bioinformatics. In the future, de.NBI will expand its activities to the European level, as the de.NBI consortium was assigned by the BMBF to establish and run the German node of ELIXIR.'),
(69208, 2018, 'bib', 'Long noncoding RNA: a crosslink in biological regulatory network', 'Zhang, Yuwei|Tao, Yang|Liao, Qi', 'Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.', 'China|China|China', 21, 'Algorithms|Animals|Binding Sites|Chromatin|Chromatin Immunoprecipitation|Computational Biology|Databases, Genetic|Enhancer Elements, Genetic|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Neoplasms|RNA, Long Noncoding|RNA-Binding Proteins|Sequence Analysis, RNA|Transcription Factors', 'Journal Article', '10.1093/bib/bbx042', '28449042', 'Long noncoding RNAs (lncRNAs) had been defined as a novel class of functional RNAs longer than 200 nucleotides around a decade ago. It is widely acknowledged that lncRNAs play a significant role in regulation of gene expression, but the biological and molecular mechanisms are diverse and complex, and remain to be determined. Especially, the regulatory network of lncRNAs associated with other biological molecules is still a controversial matter, thus becoming a new frontier of the studies on transcriptome. Recent advance in high-throughput sequencing technologies and bioinformatics approaches may be an accelerator to lift the mysterious veil. In this review, we will outline well-known associations between lncRNAs and other biological molecules, demonstrate the diverse bioinformatics approaches applied in prediction and analysis of lncRNA interaction and perform a case study for lncRNA linc00460 to concretely decipher the lncRNA regulatory network.'),
(69209, 2018, 'bib', 'Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory', 'Durán, Claudio|Daminelli, Simone|Thomas, Josephine M|Haupt, V Joachim|Schroeder, Michael|Cannistraci, Carlo Vittorio', 'Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.', 'China|China|China|China|China|China', 19, 'Algorithms|Brain|Computational Biology|Drug Delivery Systems|Drug Discovery|Drug Interactions|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbx041', '28453640', 'The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions, hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.'),
(69210, 2018, 'bib', 'In silico polypharmacology of natural products', 'Fang, Jiansong|Liu, Chuang|Wang, Qi|Lin, Ping|Cheng, Feixiong', 'Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China.|Alibaba Research Center for Complexity Sciences at the Hangzhou Normal University, Hangzhou, China.|Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China.|National Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.|Department of Biomedical Informatics, Vanderbilt University Medical Center in Nashville (United States).', 'China|China|China|China|United States', 43, 'Biological Products|Computational Biology|Computer Simulation|Drug Delivery Systems|Humans|Neoplasms|Polypharmacology|Systems Biology', 'Journal Article', '10.1093/bib/bbx045', '28460068', 'Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or selfish sub-clones) via exploiting the immunological and inflammatory side effects of natural products in the cancer post-genomics era.'),
(69211, 2017, 'bib', 'Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations', 'Luo, Yuan|Uzuner, Özlem|Szolovits, Peter', 'Department of Biomedical Informatics, Vanderbilt University Medical Center in Nashville (United States).|Department of Biomedical Informatics, Vanderbilt University Medical Center in Nashville (United States).|Department of Biomedical Informatics, Vanderbilt University Medical Center in Nashville (United States).', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bbx048', '28472242', '-'),
(69212, 2018, 'bib', 'Advances in computational approaches in identifying synergistic drug combinations', 'Sheng, Zhen|Sun, Yi|Yin, Zuojing|Tang, Kailin|Cao, Zhiwei', 'School of Life Sciences and Technology, Tongji University.|School of Life Sciences and Technology, Tongji University.|School of Life Sciences and Technology, Tongji University.|Advanced Institute of Translational Medicine, Tongji University.|School of Life Sciences and Technology, Tongji University.', '-|-|-|-|-', 14, 'Computer Simulation|Drug Combinations|Drug Synergism|Models, Molecular|Pattern Recognition, Automated', 'Journal Article', '10.1093/bib/bbx047', '28475767', 'Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.'),
(69213, 2018, 'bib', 'Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes', 'Niu, Sheng-Yong|Yang, Jinyu|McDermaid, Adam|Zhao, Jing|Kang, Yu|Ma, Qin', 'Department of Biochemical Science and Technology, National Taiwan University, Taiwan.|Department of Mathematics and Statistics at South Dakota State University, Brookings, SD, USA.|Department of Mathematics and Statistics at South Dakota State University, Brookings, SD, USA.|Department of Internal Medicine at University of South Dakota Sanford School of Medicine.|Beijing Institute of Genomics of Chinese Academy of Sciences.|Department of Mathematics and Statistics at South Dakota State University and BioSNTR, SD, USA.', 'United States|United States|United States|United States|United States|United States', 7, 'Computational Biology|Metagenome|Microbiota|RNA, Ribosomal, 16S|Transcriptome', 'Journal Article', '10.1093/bib/bbx051', '28481971', 'Metagenomic and metatranscriptomic sequencing approaches are more frequently being used to link microbiota to important diseases and ecological changes. Many analyses have been used to compare the taxonomic and functional profiles of microbiota across habitats or individuals. While a large portion of metagenomic analyses focus on species-level profiling, some studies use strain-level metagenomic analyses to investigate the relationship between specific strains and certain circumstances. Metatranscriptomic analysis provides another important insight into activities of genes by examining gene expression levels of microbiota. Hence, combining metagenomic and metatranscriptomic analyses will help understand the activity or enrichment of a given gene set, such as drug-resistant genes among microbiome samples. Here, we summarize existing bioinformatics tools of metagenomic and metatranscriptomic data analysis, the purpose of which is to assist researchers in deciding the appropriate tools for their microbiome studies. Additionally, we propose an Integrated Meta-Function mapping pipeline to incorporate various reference databases and accelerate functional gene mapping procedures for both metagenomic and metatranscriptomic analyses.'),
(69214, 2018, 'bib', 'Deep learning for healthcare: review, opportunities and challenges', 'Miotto, Riccardo|Wang, Fei|Wang, Shuang|Jiang, Xiaoqian|Dudley, Joel T', 'Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, New York, NY.|Division of Health Informatics, Department of Healthcare Policy and Research at Weill Cornell Medicine at Cornell University, New York, NY.|Department of Biomedical Informatics at the University of California San Diego, La Jolla, CA.|Department of Biomedical Informatics at the University of California San Diego, La Jolla, CA.|the Institute for Next Generation Healthcare and associate professor in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, New York, NY.', '-|-|-|-|-', 348, 'Computational Biology|Data Mining|Deep Learning|Delivery of Health Care|Diagnostic Imaging|Electronic Health Records|Genomics|Humans|Telemedicine', 'Journal Article', '10.1093/bib/bbx044', '28481991', 'Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.'),
(69215, 2018, 'bib', 'Mutational signatures and mutable motifs in cancer genomes', 'Rogozin, Igor B|Pavlov, Youri I|Goncearenco, Alexander|De, Subhajyoti|Lada, Artem G|Poliakov, Eugenia|Panchenko, Anna R|Cooper, David N', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, USA.|Eppley Institute for Cancer Research, University of Nebraska Medical Center, USA.|National Center for Biotechnology Information, National Institutes of Health, USA.|Rutgers University Cancer Institute, USA.|Department Microbiology and Molecular Genetics, University of California, Davis, USA.|Laboratory of Retinal Cell and Molecular Biology, National Eye Institute, National Institutes of Health, USA.|National Center for Biotechnology Information, National Institutes of Health, USA.|Institute of Medical Genetics, Cardiff University, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 15, 'DNA|DNA Methylation|Evolution, Molecular|Gene Expression|Genetic Predisposition to Disease|Genomics|Humans|Models, Genetic|Mutagens|Mutation|Neoplasms|Oncogenes|Selection, Genetic', 'Journal Article', '10.1093/bib/bbx049', '28498882', 'Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the Emperor of Maladies. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into families based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.'),
(69216, 2018, 'bib', 'Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets', 'Mazandu, Gaston K|Chimusa, Emile R|Rutherford, Kayleigh|Zekeng, Elsa-Gayle|Gebremariam, Zoe Z|Onifade, Maryam Y|Mulder, Nicola J', 'Institute of Infectious Disease and Molecular Medicine at UCT and a Researcher at AIMS.|Division of Human Genetics, Department of Pathology, UCT.|Boston University in USA.|Institute of Biomedical Informatics, Taipei Medical University.|Institute of Infection and Global Health, University of Liverpool, UK.|African Institute for Mathematical Sciences jointly with University of Cape Coast, Ghana.|Department of Integrative Biomedical Sciences and the Head of the Computational Biology Division, UCT.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Ghana|United Kingdom', 4, 'Antitubercular Agents|Antiviral Agents|Datasets as Topic|Drug Development|Ebolavirus|Hemorrhagic Fever, Ebola|Host-Pathogen Interactions|Humans|Molecular Sequence Annotation|Mycobacterium tuberculosis|Reproducibility of Results|Tuberculosis', 'Journal Article', '10.1093/bib/bbx052', '28520909', 'Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.'),
(69217, 2018, 'bib', 'Genome-wide discovery of viral microRNAs based on phylogenetic analysis and structural evolution of various human papillomavirus subtypes', 'Weng, Shun-Long|Huang, Kai-Yao|Weng, Julia Tzu-Ya|Hung, Fang-Yu|Chang, Tzu-Hao|Lee, Tzong-Yi', 'Hsinchu MacKay Memorial Hospital.|Department of Computer Science and Engineering, Graduate Program in Biomedical Informatics, Yuan Ze University.|Graduate Program of Biomedical Informatics at Yuan Ze University in Taiwan.|Hsinchu MacKay Memorial Hospital.|Institute of Biomedical Informatics, Taipei Medical University.|Department of Computer Science and Engineering, Graduate Program in Biomedical Informatics, Yuan Ze University.', '-|-|-|-|-|-', 4, 'Carcinogenesis|Evolution, Molecular|Gene Expression Profiling|Gene Regulatory Networks|Genome, Viral|High-Throughput Nucleotide Sequencing|Host-Pathogen Interactions|Humans|MicroRNAs|Papillomaviridae|Phylogeny|RNA, Viral|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbx046', '28531277', 'In mammals, microRNAs (miRNAs) play key roles in controlling posttranscriptional regulation through binding to the mRNAs of target genes. Recently, it was discovered that viral miRNAs may be involved in human cancers and diseases. It is likely that viral miRNAs help viruses enter the latent phase of their life cycle and become undetected by the hosts immune system, while increasing the hosts risk for cancer development. Cervical cancer is typically related to the infection of human papillomavirus (HPV) through sexual transmission. To further understand the molecular mechanisms underlying the associations of HPV infection with genital diseases, we developed a systematic method for viral miRNA identification and viral miRNA-mediated regulatory network construction based on genome-wide sequence analysis. The complete genomes of certain high-risk HPV subtypes were used to predict putative viral pre-miRNAs by bioinformatics approaches. In addition, small RNA libraries in human cervical lesions from existing publications were collected to validate the predicted HPV pre-miRNAs. For the construction of virally encoded miRNA-mediated regulatory network of HPV infection, cervical squamous epithelial carcinoma gene expression data were extracted from the RNA sequencing platform in The Cancer Genome Atlas, the differentially expressed genes were used to identify the putative targets of viral miRNAs. Predicted cellular target genes of HPV-encoded miRNAs provide an overview of these viral miRNAs putative functions. Finally, a large-scale genome analysis was carried out to examine the phylogenetic relationship and structural evolution among genital HPV types that have the potential to cause genital cancer. In this study, we discovered putative HPV-encoded miRNAs, which were validated against the small RNA libraries in human cervical lesions. Furthermore, as indicated by their biological functions, host genes targeted by HPV-encoded miRNAs may play significant roles in virus infection and carcinogenesis. These viral miRNAs pose as promising candidates for the development of antiviral drugs. More importantly, the identified subtype-specific miRNAs have the potential to be used as biomarkers for HPV subtype determination.'),
(69218, 2018, 'bib', 'Bioinformatic analysis of bacteria and host cell dual RNA-sequencing experiments', 'Marsh, James W|Hayward, Regan J|Shetty, Amol C|Mahurkar, Anup|Humphrys, Michael S|Myers, Garry S A', 'The ithree institute, University of Technology Sydney.|The ithree institute, University of Technology Sydney.|Institute for Genome Sciences at the University of Maryland, Baltimore.|Institute for Genome Sciences at the University of Maryland, Baltimore.|Institute for Genome Sciences at the University of Maryland, Baltimore.|The ithree institute, University of Technology Sydney.', '-|-|-|-|-|-', 4, 'Chlamydia trachomatis|Computational Biology|Epithelial Cells|Gene Expression Regulation, Bacterial|Host-Pathogen Interactions|RNA, Bacterial|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbx043', '28535295', 'Bacterial pathogens subvert host cells by manipulating cellular pathways for survival and replication, in turn, host cells respond to the invading pathogen through cascading changes in gene expression. Deciphering these complex temporal and spatial dynamics to identify novel bacterial virulence factors or host response pathways is crucial for improved diagnostics and therapeutics. Dual RNA sequencing (dRNA-Seq) has recently been developed to simultaneously capture host and bacterial transcriptomes from an infected cell. This approach builds on the high sensitivity and resolution of RNA sequencing technology and is applicable to any bacteria that interact with eukaryotic cells, encompassing parasitic, commensal or mutualistic lifestyles. Several laboratory protocols have been presented that outline the collection, extraction and sequencing of total RNA for dRNA-Seq experiments, but there is relatively little guidance available for the detailed bioinformatic analyses required. This protocol outlines a typical dRNA-Seq experiment, based on a Chlamydia trachomatis-infected host cell, with a detailed description of the necessary bioinformatic analyses with currently available software tools.'),
(69219, 2018, 'bib', 'Optimizing drug development in oncology by clinical trial simulation: Why and how?', 'Gal, Jocelyn|Milano, Gérard|Ferrero, Jean-Marc|Saâda-Bouzid, Esma|Viotti, Julien|Chabaud, Sylvie|Gougis, Paul|Le Tourneau, Christophe|Schiappa, Renaud|Paquet, Agnes|Chamorey, Emmanuel', 'Epidemiology and Biostatistics Unit at the Antoine Lacassagne Center, Nice, France.|Antoine Lacassagne Center, Nice, France.|Antoine Lacassagne Center, Nice, France.|University of Nice, France.|University of Nice, France.|Centre Le´on Be´ rard, Lyons, France.|Pitie´-Salp^etrie`re Hospital in Paris, France.|Institute Curie of Paris, France.|Antoine Lacassagne Center, Nice, France.|Molecular and Cellular Pharmacology Institute of Sophia Antipolis, Valbonne, France.|Antoine Lacassagne Center, Nice, France.', 'France|France|France|France|France|France|France|France|France|France|France', 6, 'Biomedical Research|Clinical Trials as Topic|Computational Biology|Computer Simulation|Drug Approval|Drug Development|Europe|Health Policy|Humans|Medical Oncology|Neoplasms|Product Surveillance, Postmarketing|United States', 'Journal Article', '10.1093/bib/bbx055', '28575140', 'In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace real-life trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.'),
(69220, 2018, 'bib', 'Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling', 'Vijayakumar, Supreeta|Conway, Max|Lió, Pietro|Angione, Claudio', 'Department of Computer Science and Information Systems, Teesside University, UK.|Computer Laboratory, University of Cambridge, UK.|Computer Laboratory, University of Cambridge, UK.|Department of Computer Science and Information Systems, Teesside University, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Machine Learning|Metabolism|Models, Theoretical|Systems Biology', 'Journal Article', '10.1093/bib/bbx053', '28575143', 'Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a forest of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.'),
(69221, 2018, 'bib', 'ceRNAs in plants: computational approaches and associated challenges for target mimic research', 'Paschoal, Alexandre Rossi|Lozada-Chávez, Irma|Domingues, Douglas Silva|Stadler, Peter F', 'Federal University of Technology, Paraná (UTFPR), Brazil.|Interdisciplinary Center for Bioinformatics, University of Leipzig, Germany.|Department of Botany, Institute of Biosciences, S~ao Paulo State University (UNESP) in Rio Claro, Brazil.|University of Vienna.', 'Brazil|Germany|Brazil|-', 5, 'Computational Biology|MicroRNAs|Molecular Mimicry|Plants|RNA, Plant', 'Journal Article', '10.1093/bib/bbx058', '28575144', 'The competing endogenous RNA hypothesis has gained increasing attention as a potential global regulatory mechanism of microRNAs (miRNAs), and as a powerful tool to predict the function of many noncoding RNAs, including miRNAs themselves. Most studies have been focused on animals, although target mimic (TMs) discovery as well as important computational and experimental advances has been developed in plants over the past decade. Thus, our contribution summarizes recent progresses in computational approaches for research of miRNA:TM interactions. We divided this article in three main contributions. First, a general overview of research on TMs in plants is presented with practical descriptions of the available literature, tools, data, databases and computational reports. Second, we describe a common protocol for the computational and experimental analyses of TM. Third, we provide a bioinformatics approach for the prediction of TM motifs potentially cross-targeting both members within the same or from different miRNA families, based on the identification of consensus miRNA-binding sites from known TMs across sequenced genomes, transcriptomes and known miRNAs. This computational approach is promising because, in contrast to animals, miRNA families in plants are large with identical or similar members, several of which are also highly conserved. From the three consensus TM motifs found with our approach: MIM166, MIM171 and MIM159/319, the last one has found strong support on the recent experimental work by Reichel and Millar [Specificity of plant microRNA TMs: cross-targeting of mir159 and mir319. J Plant Physiol 2015,180:45-8]. Finally, we stress the discussion on the major computational and associated experimental challenges that have to be faced in future ceRNA studies.'),
(69222, 2018, 'bib', 'A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation', 'Välikangas, Tommi|Suomi, Tomi|Elo, Laura L', 'Computational Biomedicine Group, Turku Centre for Biotechnology Finland.|Computational Biomedicine research group at the Turku Centre for Biotechnology Finland.|Biomathematics, Research Director in Bioinformatics and Group Leader in Computational Biomedicine at Turku Centre for Biotechnology, University of Turku, Finland.', 'Finland|Finland|Finland', 22, 'Algorithms|Chromatography, Liquid|Mass Spectrometry|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bib/bbx054', '28575146', 'Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69223, 2018, 'bib', 'Dynamic-BM: multispecies Dynamic BodyMap database from temporal RNA-seq data', 'Cui, Ya|Chen, Xiaowei|Niu, Yiwei|Wang, Dongpeng|Luo, Huaxia|Fan, Zhen|Wang, Dan|Wu, Wei|Teng, Xueyi|He, Shunmin|Luo, Jianjun|Chen, Runsheng', 'Key Laboratory of RNA Biology, Institute of Biophysics and University of the Chinese Academy of Sciences, Beijing, China.|Core Facility for Protein Research and the Key Laboratory of RNA Biology, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics and University of the Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics and University of the Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|Core Facility for Protein Research and the Key Laboratory of RNA Biology, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics and University of the Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics and University of the Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Databases, Factual|Gene Expression Profiling|Internet|Sequence Analysis, RNA|User-Computer Interface', 'Journal Article', '10.1093/bib/bbx059', '28575155', 'Biological processes, especially developmental processes, are often dynamic. Previous BodyMap projects for human and mouse have provided researchers with portals to tissue-specific gene expression, but these efforts have not included dynamic gene expression patterns. Over the past few years, substantial progress in our understanding of the molecular mechanisms of protein-coding and long noncoding RNA (lncRNA) genes in development processes has been achieved through numerous time series RNA sequencing (RNA-seq) studies. However, none of the existing databases focuses on these time series data, thus rendering the exploration of dynamic gene expression patterns inconvenient. Here, we present Dynamic BodyMap (Dynamic-BM), a database for temporal gene expression profiles, obtained from 2203 time series of RNA-seq samples, covering >25 tissues from five species. Dynamic-BM has a user-friendly Web interface designed for browsing and searching the dynamic expression pattern of genes from different sources. It is an open resource for efficient data exploration, providing dynamic expression profiles of both protein-coding genes and lncRNAs to facilitate the generation of new hypotheses in developmental biology research. Additionally, Dynamic-BM includes a literature-based knowledgebase for lncRNAs associated with tissue development and a list of manually selected lncRNA candidates that may be involved in tissue development. Dynamic-BM is available at http://bioinfo.ibp.ac.cn/Dynamic-BM.'),
(69224, 2018, 'bib', 'The genetic architecture of heterochrony as a quantitative trait: lessons from a computational model', 'Sun, Lidan|Sang, Mengmeng|Zheng, Chenfei|Wang, Dongyang|Shi, Hexin|Liu, Kaiyue|Guo, Yanfang|Cheng, Tangren|Zhang, Qixiang|Wu, Rongling', 'Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture at Beijing Forestry University.|Computational Genetics in the Center for Computational Biology at Beijing Forestry University.|Computational Genetics in the Center for Computational Biology at Beijing Forestry University.|Computational Biology Center for Computational Biology at Beijing Forestry University.|Computational Biology Center for Computational Biology at Beijing Forestry University.|Computational Biology Center for Computational Biology at Beijing Forestry University.|Computational Biology Center for Computational Biology at Beijing Forestry University.|National Engineering Research Center for Floriculture at Beijing Forestry University.|National Engineering Research Center for Floriculture at Beijing Forestry University.|Center for Computational Biology at Beijing Forestry University.', '-|-|-|-|-|-|-|-|-|-', 1, 'Animals|Computer Simulation|Phenotype|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbx056', '28575183', 'Heterochrony is known as a developmental change in the timing or rate of ontogenetic events across phylogenetic lineages. It is a key concept synthesizing development into ecology and evolution to explore the mechanisms of how developmental processes impact on phenotypic novelties. A number of molecular experiments using contrasting organisms in developmental timing have identified specific genes involved in heterochronic variation. Beyond these classic approaches that can only identify single genes or pathways, quantitative models derived from current next-generation sequencing data serve as a more powerful tool to precisely capture heterochronic variation and systematically map a complete set of genes that contribute to heterochronic processes. In this opinion note, we discuss a computational framework of genetic mapping that can characterize heterochronic quantitative trait loci that determine the pattern and process of development. We propose a unifying model that charts the genetic architecture of heterochrony that perceives and responds to environmental perturbations and evolves over geologic time. The new model may potentially enhance our understanding of the adaptive value of heterochrony and its evolutionary origins, providing a useful context for designing new organisms that can best use future resources.'),
(69225, 2018, 'bib', 'Power and sample size calculations for high-throughput sequencing-based experiments', 'Li, Chung-I|Samuels, David C|Zhao, Ying-Yong|Shyr, Yu|Guo, Yan', 'Department of Statistics, National Cheng Kung University in Taiwan.|Department of Molecular Physiology and Biophysics, Vanderbilt University, USA.|School of Life Sciences, Northwest University, China.|Department of Biostatistics, Vanderbilt University, USA.|Department of Cancer Biology, Vanderbilt University.', 'United States|United States|China|United States|United States', 12, 'Chromatin Immunoprecipitation|Genome-Wide Association Study|Heterozygote|High-Throughput Nucleotide Sequencing|Humans|Microbiota|Mutation|Poisson Distribution|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbx061', '28605403', 'Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.'),
(69226, 2018, 'bib', 'Recent computational developments on CLIP-seq data analysis and microRNA targeting implications', 'Bottini, Silvia|Pratella, David|Grandjean, Valerie|Repetto, Emanuela|Trabucchi, Michele', 'Université Côte dAzur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France.|Université Côte dAzur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France.|Université Côte dAzur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France.|Université Côte dAzur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France.|Université Côte dAzur, Inserm, C3M, 151 route de St-Antoine-de-Ginestière, B.P. 2 3194, 06204 Nice, France.', 'France|France|France|France|France', 11, 'Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Immunoprecipitation|MicroRNAs|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbx063', '28605404', 'Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA-protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.'),
(69227, 2018, 'bib', 'Investigating microRNA-mediated regulation of the nascent nuclear transcripts in plants: a bioinformatics workflow', 'Yu, Dongliang|Tang, Zhonghai|Shao, Chaogang|Ma, Xiaoxia|Xiang, Taihe|Fan, Zhihong|Wang, Huizhong|Meng, Yijun', 'College of Life and Environmental Sciences, Hangzhou Normal University.|College of Bioscience and Biotechnology, Hunan Agricultural University.|College of Life Sciences, Huzhou University.|College of Life and Environmental Sciences, Hangzhou Normal University.|College of Life and Environmental Sciences, Hangzhou Normal University.|College of Life and Environmental Sciences, Hangzhou Normal University.|Zhejiang Provincial Key Laboratory for Genetic Improvement and Quality Control of Medicinal Plants, College of Life and Environmental Sciences, Hangzhou Normal University.|College of Life and Environmental Sciences, Hangzhou Normal University.', '-|-|-|-|-|-|-|-', 1, 'Binding Sites|Cell Nucleus|Computational Biology|Exons|Introns|MicroRNAs|RNA Splicing|RNA, Messenger|RNA, Plant|Workflow', 'Journal Article', '10.1093/bib/bbx069', '28633390', 'Most of the microRNAs (miRNAs) play their regulatory roles through posttranscriptional target decay or translational inhibition. For both plants and animals, these regulatory events were previously considered to take place in cytoplasm, as mature miRNAs were observed to be exported to the cytoplasm for Argonaute (AGO) loading and subsequent target binding. Recently, this notion was challenged by increasing pieces of evidence in the animal cells that uncovered the nuclear importation and action of the AGO-associated miRNAs. The nuclear-localized regulatory mode was also reported for the plant miRNAs. However, evidence is still lacking to show the universality and conservation of the miRNA-mediated regulation in the plant nuclei. Here, we introduced a bioinformatics workflow for genome-wide investigation of miRNA-guided, cleavage-based regulation of the nascent nuclear transcripts. Facilitated by the tool package PmiRNTSA (Plant microRNA-mediated nascent transcript slicing analyzer), plant biologists could perform a comprehensive search for the miRNA slicing sites located within the introns or the exon-intron/intron-exon junctions of the target transcripts, which are supported by degradome sequencing data. The results enable the researchers to examine the co-transcriptional regulatory model of the miRNAs for a specific plant species. Moreover, a case study was performed to search for the slicing sites located within the exon-intron/intron-exon junctions in two model plants. A case study was performed to show the feasibility and reliability of our workflow. Together, we hope that this work could inspire much more innovative research efforts to expand the current understanding of the miRNA action modes in plants.'),
(69228, 2018, 'bib', 'Biomedical text mining for research rigor and integrity: tasks, challenges, directions', 'Kilicoglu, Halil', 'Lister Hill National Center for Biomedical Communications, US National Library of Medicine.', '-', 18, 'Biomedical Research|Data Mining|Natural Language Processing|Publications|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbx057', '28633401', 'An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise.'),
(69229, 2018, 'bib', 'Nanopore sequencing data analysis: state of the art, applications and challenges', 'Magi, Alberto|Semeraro, Roberto|Mingrino, Alessandra|Giusti, Betti|DAurizio, Romina', 'Department of Statistics, National Cheng Kung University in Taiwan.|Department of Molecular Physiology and Biophysics, Vanderbilt University, USA.|School of Life Sciences, Northwest University, China.|Department of Biostatistics, Vanderbilt University, USA.|Department of Cancer Biology, Vanderbilt University.', 'United States|United States|China|United States|United States', 33, 'Algorithms|Computational Biology|Nanopores|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbx062', '28637243', 'The nanopore sequencing process is based on the transit of a DNA molecule through a nanoscopic pore, and since the 90s is considered as one of the most promising approaches to detect polymeric molecules. In 2014, Oxford Nanopore Technologies (ONT) launched a beta-testing program that supplied the scientific community with the first prototype of a nanopore sequencer: the MinION. Thanks to this program, several research groups had the opportunity to evaluate the performance of this novel instrument and develop novel computational approaches for analyzing this new generation of data.xe2x80x83Despite the short period of time from the release of the MinION, a large number of algorithms and tools have been developed for base calling, data handling, read mapping, de novo assembly and variant discovery. Here, we face the main computational challenges related to the analysis of nanopore data, and we carry out a comprehensive and up-to-date survey of the algorithmic solutions adopted by the bioinformatic community comparing performance and reporting limits and advantages of using this new generation of sequences for genomic analyses.xe2x80x83Our analyses demonstrate that the use of nanopore data dramatically improves the de novo assembly of genomes and allows for the exploration of structural variants with an unprecedented accuracy and resolution. However, despite the impressive improvements reached by ONT in the past 2xe2x80x89years, the use of these data for small-variant calling is still challenging, and at present, it needs to be coupled with complementary short sequences for mitigating the intrinsic biases of nanopore sequencing technology.'),
(69230, 2017, 'bib', 'The genetic architecture of heterochrony as a quantitative trait: lessons from a computational model', 'Sun, Lidan|Sang, Mengmeng|Zheng, Chenfei|Wang, Dongyang|Shi, Hexin|Liu, Kaiyue|Guo, Yanfang|Cheng, Tangren|Zhang, Qixiang|Wu, Rongling', 'Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.', '-|-|-|-|-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bbx075', '28645151', '-'),
(69231, 2017, 'bib', 'Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity', 'Wang, Huilin|Feng, Liubin|Webb, Geoffrey I|Kurgan, Lukasz|Song, Jiangning|Lin, Donghai', 'Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.|Department of Cancer Biology, Vanderbilt University.', '-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bbx076', '28651335', '-'),
(69232, 2019, 'bib', 'Alignment-free inference of hierarchical and reticulate phylogenomic relationships', 'Bernard, Guillaume|Chan, Cheong Xin|Chan, Yao-Ban|Chua, Xin-Yi|Cong, Yingnan|Hogan, James M|Maetschke, Stefan R|Ragan, Mark A', 'Institute for Molecular Bioscience.|University of Queensland.|University of Melbourne.|QFAB Bioinformatics.|University of Queensland.|Queensland University of Technology.|IBM Research Australia.|Institute for Molecular Bioscience.', '-|-|-|-|-|-|Australia|-', 18, 'Algorithms|Animals|Evolution, Molecular|Genome|Humans|Microbiota|Models, Genetic|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Viruses', 'Journal Article', '10.1093/bib/bbx067', '28673025', 'We are amidst an ongoing flood of sequence data arising from the application of high-throughput technologies, and a concomitant fundamental revision in our understanding of how genomes evolve individually and within the biosphere. Workflows for phylogenomic inference must accommodate data that are not only much larger than before, but often more error prone and perhaps misassembled, or not assembled in the first place. Moreover, genomes of microbes, viruses and plasmids evolve not only by tree-like descent with modification but also by incorporating stretches of exogenous DNA. Thus, next-generation phylogenomics must address computational scalability while rethinking the nature of orthogroups, the alignment of multiple sequences and the inference and comparison of trees. New phylogenomic workflows have begun to take shape based on so-called alignment-free (AF) approaches. Here, we review the conceptual foundations of AF phylogenetics for the hierarchical (vertical) and reticulate (lateral) components of genome evolution, focusing on methods based on k-mers. We reflect on what seems to be successful, and on where further development is needed.'),
(69233, 2018, 'bib', 'Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data', 'Yan, Jingwen|Risacher, Shannon L|Shen, Li|Saykin, Andrew J', 'Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA.', 'India|India|India|India', 50, 'Data Mining|Epistasis, Genetic|Genomics|Machine Learning|Proteomics|Systems Biology|Transcriptome', 'Journal Article', '10.1093/bib/bbx066', '28679163', 'In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.'),
(69234, 2019, 'bib', 'Association score testing for rare variants and binary traits in family data with shared controls', 'Saad, Mohamad|Wijsman, Ellen M', 'Department of Biostatistics, University of Washington, Seattle, USA.|Department of Biostatistics, University of Washington, Seattle, USA.', 'United States|United States', 0, 'Case-Control Studies|Computational Biology|Computer Simulation|Feasibility Studies|Female|Gene Frequency|Genetic Variation|Genome-Wide Association Study|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Linkage Disequilibrium|Male|Models, Genetic|Pedigree|Quantitative Trait Loci|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbx107', '28968627', 'Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (xcfx87corrected2,xc2xa0WQLS,xc2xa0andxc2xa0SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.'),
(69235, 2017, 'bib', 'Bioinformatics in Latin America and SoIBio impact, a tale of spin-off and expansion around genomes and protein structures', 'De Las Rivas, Javier|Bonavides-Martínez, Cesar|Campos-Laborie, Francisco Jose', 'Department of Biostatistics, University of Washington, Seattle, USA.|Department of Biostatistics, University of Washington, Seattle, USA.|Department of Biostatistics, University of Washington, Seattle, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bbx114', '28968628', '-'),
(69236, 2019, 'bib', 'Systematic review regulatory principles of non-coding RNAs in cardiovascular diseases', 'Li, Yongsheng|Huo, Caiqin|Pan, Tao|Li, Lili|Jin, Xiyun|Lin, Xiaoyu|Chen, Juan|Zhang, Jinwen|Guo, Zheng|Xu, Juan|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.', 'China|China|China|China|China|China|China|China|China|China|China', 5, 'Animals|Big Data|Cardiovascular Diseases|Computational Biology|Databases, Nucleic Acid|Gene Expression Profiling|Genetic Association Studies|Genetic Markers|Humans|Mice|MicroRNAs|RNA, Untranslated|Tissue Distribution', 'Journal Article', '10.1093/bib/bbx095', '28968629', 'Cardiovascular diseases (CVDs) continue to be a major cause of morbidity and mortality, and non-coding RNAs (ncRNAs) play critical roles in CVDs. With the recent emergence of high-throughput technologies, including small RNA sequencing, investigations of CVDs have been transformed from candidate-based studies into genome-wide undertakings, and a number of ncRNAs in CVDs were discovered in various studies. A comprehensive review of these ncRNAs would be highly valuable for researchers to get a complete picture of the ncRNAs in CVD. To address these knowledge gaps and clinical needs, in this review, we first discussed dysregulated ncRNAs and their critical roles in cardiovascular development and related diseases. Moreover, we reviewed >28xe2x80x89561 published papers and documented the ncRNA-CVD association benchmarking data sets to summarize the principles of ncRNA regulation in CVDs. This data set included 13xe2x80x89249 curated relationships between 9503 ncRNAs and 139 CVDs in 12 species. Based on this comprehensive resource, we summarized the regulatory principles of dysregulated ncRNAs in CVDs, including the complex associations between ncRNA and CVDs, tissue specificity and ncRNA synergistic regulation. The highlighted principles are that CVD microRNAs (miRNAs) are highly expressed in heart tissue and that they play central roles in miRNA-miRNA functional synergistic network. In addition, CVD-related miRNAs are close to one another in the functional network, indicating the modular characteristic features of CVD miRNAs. We believe that the regulatory principles summarized here will further contribute to our understanding of ncRNA function and dysregulation mechanisms in CVDs.'),
(69237, 2019, 'bib', 'Pathway enrichment analysis approach based on topological structure and updated annotation of pathway', 'Yang, Qian|Wang, Shuyuan|Dai, Enyu|Zhou, Shunheng|Liu, Dianming|Liu, Haizhou|Meng, Qianqian|Jiang, Bin|Jiang, Wei', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.|College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Peoples Republic of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Peoples Republic of China.', 'China|China|China|China|China|China|China|China|China', 7, 'Carcinogenesis|Computational Biology|Databases, Genetic|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Male|Molecular Sequence Annotation|Neoplasms|Signal Transduction|Software', 'Journal Article', '10.1093/bib/bbx091', '28968630', 'Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/.'),
(69238, 2019, 'bib', 'Computational approaches for discovery of mutational signatures in cancer', 'Baez-Ortega, Adrian|Gori, Kevin', 'Transmissible Cancer Group, University of Cambridge.|Transmissible Cancer Group, University of Cambridge.', '-|-', 8, 'Bayes Theorem|Computational Biology|DNA, Neoplasm|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Models, Statistical|Mutation|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbx082', '28968631', 'The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.'),
(69239, 2019, 'bib', 'Microbial genome analysis: the COG approach', 'Galperin, Michael Y|Kristensen, David M|Makarova, Kira S|Wolf, Yuri I|Koonin, Eugene V', 'Transmissible Cancer Group, University of Cambridge.|Transmissible Cancer Group, University of Cambridge.|Transmissible Cancer Group, University of Cambridge.|Transmissible Cancer Group, University of Cambridge.|Transmissible Cancer Group, University of Cambridge.', '-|-|-|-|-', 10, '-', 'Journal Article', '10.1093/bib/bbx117', '28968633', 'For the past 20xe2x80x89years, the Clusters of Orthologous Genes (COG) database had been a popular tool for microbial genome annotation and comparative genomics. Initially created for the purpose of evolutionary classification of protein families, the COG have been used, apart from straightforward functional annotation of sequenced genomes, for such tasks as (i) unification of genome annotation in groups of related organisms, (ii) identification of missing and/or undetected genes in complete microbial genomes, (iii) analysis of genomic neighborhoods, in many cases allowing prediction of novel functional systems, (iv) analysis of metabolic pathways and prediction of alternative forms of enzymes, (v) comparison of organisms by COG functional categories, and (vi) prioritization of targets for structural and functional characterization. Here we review the principles of the COG approach and discuss its key advantages and drawbacks in microbial genome analysis.'),
(69240, 2019, 'bib', 'Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models', 'Muñoz, Emir|Novácek, Vít|Vandenbussche, Pierre-Yves', 'Fujitsu Ireland Ltd., Co. Dublin, Ireland.|Insight Centre for Data Analytics, NUI Galway, Co. Galway, Ireland.|Fujitsu Ireland Ltd., Co. Dublin, Ireland.', 'Ireland|Ireland|Ireland', 7, 'Adverse Drug Reaction Reporting Systems|Computational Biology|Computer Simulation|Databases, Pharmaceutical|Drug-Related Side Effects and Adverse Reactions|Humans|Knowledge Bases|Machine Learning|Models, Statistical', 'Comparative Study', '10.1093/bib/bbx099', '28968655', 'Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it has been intensely studied. Recent works achieved promising results using machine learning. The presented work focuses on machine learning methods that use drug profiles for making predictions and use features from multiple data sources. We argue that despite promising results, existing works have limitations, especially regarding flexibility in experimenting with different data sets and/or predictive models. We suggest to address these limitations by generalization of the key principles used by the state of the art. Namely, we explore effects of: (1) using knowledge graphs-machine-readable interlinked representations of biomedical knowledge-as a convenient uniform representation of heterogeneous data, and (2) casting ADR prediction as a multi-label ranking problem. We present a specific way of using knowledge graphs to generate different feature sets and demonstrate favourable performance of selected off-the-shelf multi-label learning models in comparison with existing works. Our experiments suggest better suitability of certain multi-label learning methods for applications where ranking is preferred. The presented approach can be easily extended to other feature sources or machine learning methods, making it flexible for experiments tuned toward specific requirements of end users. Our work also provides a clearly defined and reproducible baseline for any future related experiments.'),
(69241, 2019, 'bib', 'Large-scale prediction of ADAR-mediated effective human A-to-I RNA editing', 'Yao, Li|Wang, Heming|Song, Yuanyuan|Dai, Zhen|Yu, Hao|Yin, Ming|Wang, Dongxu|Yang, Xin|Wang, Jinlin|Wang, Tiedong|Cao, Nan|Zhu, Jimin|Shen, Xizhong|Song, Guangqi|Zhao, Yicheng', 'Department of Gastroenterology, Zhongshan Hospital of Fudan University, Shanghai, China.|College of Life Science, Northeast Forestry University, Harbin, China.|Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China.|School of Life Science and Technology, ShanghaiTech University, Shanghai, China.|Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.|Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|College of Animal Sciences, Jilin University, Changchun, China.|Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.|Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|Department of Life Sciences, Southeast University, Nanjing, China.|Cluster of Excellence Rebirth, Hannover Medical School, Hannover, Germany.|Cluster of Excellence Rebirth, Hannover Medical School, Hannover, Germany.|Cluster of Excellence Rebirth, Hannover Medical School, Hannover, Germany.|Changchun Sub-branch, the Peoples Bank of China, Changchun, China.', 'China|China|China|China|China|China|China|China|China|China|China|Germany|Germany|Germany|China', 2, '3 Untranslated Regions|Adenosine|Adenosine Deaminase|Alternative Splicing|Base Sequence|Computational Biology|Databases, Nucleic Acid|Female|Gene Ontology|Humans|Inosine|Male|MicroRNAs|Mutation, Missense|RNA Editing|RNA Precursors|RNA-Binding Proteins|Sequence Analysis, RNA|Software|Support Vector Machine|Tissue Distribution', 'Journal Article', '10.1093/bib/bbx092', '28968662', 'Adenosine-to-inosine (A-to-I) editing by adenosine deaminase acting on the RNA (ADAR) proteins is one of the most frequent modifications during post- and co-transcription. To facilitate the assignment of biological functions to specific editing sites, we designed an automatic online platform to annotate A-to-I RNA editing sites in pre-mRNA splicing signals, microRNAs (miRNAs) and miRNA target untranslated regions (3 UTRs) from human (Homo sapiens) high-throughput sequencing data and predict their effects based on large-scale bioinformatic analysis. After analysing plenty of previously reported RNA editing events and human normal tissues RNA high-seq data, >60xe2x80x89000 potentially effective RNA editing events on functional genes were found. The RNA Editing Plus platform is available for free at https://www.rnaeditplus.org/, and we believe our platform governing multiple optimized methods will improve further studies of A-to-I-induced editing post-transcriptional regulation.'),
(69242, 2019, 'bib', 'Big data management challenges in health research-a literature review', 'Wang, Xiaoming|Williams, Carolyn|Liu, Zhen Hua|Croghan, Joe', 'National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.|National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.|Oracle Corporate, Redwood, CA.|National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.', 'United States|United States|United States|United States', 10, 'Big Data|Computational Biology|Database Management Systems|Humans|Knowledge Bases|Machine Learning|Research', 'Journal Article', '10.1093/bib/bbx086', '28968677', 'Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.'),
(69243, 2019, 'bib', 'DOOR: a prokaryotic operon database for genome analyses and functional inference', 'Cao, Huansheng|Ma, Qin|Chen, Xin|Xu, Ying', 'National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.|National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.|National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.|National Institute of Infectious and Allergy Diseases, NIH, Rockville, Maryland, USA.', 'United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bib/bbx088', '28968679', 'The rapid accumulation of fully sequenced prokaryotic genomes provides unprecedented information for biological studies of bacterial and archaeal organisms in a systematic manner. Operons are the basic functional units for conducting such studies. Here, we review an operon database DOOR (the Database of prOkaryotic OpeRons) that we have previously developed and continue to update. Currently, the database contains 6xe2x80x89975xe2x80x89454 computationally predicted operons in 2072 complete genomes. In addition, the database also contains the following information: (i) transcriptional units for 24 genomes derived using publicly available transcriptomic data, (ii) orthologous gene mapping across genomes, (iii) 6408 cis-regulatory motifs for transcriptional factors of some operons for 203 genomes, (iv) 3xe2x80x89456xe2x80x89718 Rho-independent terminators for 2072 genomes, as well as (v) a suite of tools in support of applications of the predicted operons. In this review, we will explain how such data are computationally derived and demonstrate how they can be used to derive a wide range of higher-level information needed for systems biology studies to tackle complex and fundamental biology questions.'),
(69244, 2019, 'bib', 'Bioinformatics in the Netherlands: the value of a nationwide community', 'van Gelder, Celia W G|Hooft, Rob W W|van Rijswijk, Merlijn N|van den Berg, Linda|Kok, Ruben G|Reinders, Marcel|Mons, Barend|Heringa, Jaap', 'Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.|Dutch Techcentre for Life Sciences (DTL), RM Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 1, 'Community Networks|Computational Biology|Humans|Netherlands|Sequence Analysis, DNA|Translational Medical Research', 'Journal Article', '10.1093/bib/bbx087', '28968694', 'This review provides a historical overview of the inception and development of bioinformatics research in the Netherlands. Rooted in theoretical biology by foundational figures such as Paulien Hogeweg (at Utrecht University since the 1970s), the developments leading to organizational structures supporting a relatively large Dutch bioinformatics community will be reviewed. We will show that the most valuable resource that we have built over these years is the close-knit national expert community that is well engaged in basic and translational life science research programmes. The Dutch bioinformatics community is accustomed to facing the ever-changing landscape of data challenges and working towards solutions together. In addition, this community is the stable factor on the road towards sustainability, especially in times where existing funding models are challenged and change rapidly.'),
(69245, 2019, 'bib', 'A broken promise: microbiome differential abundance methods do not control the false discovery rate', 'Hawinkel, Stijn|Mattiello, Federico|Bijnens, Luc|Thas, Olivier', 'Department of Mathematical Modelling, Statistics and Bioinformatics at Ghent University, Belgium.|Department of Mathematical Modelling, Statistics and Bioinformatics at Ghent University, Belgium.|Center for Statistics at Hasselt University, Belgium.|Department of Mathematical Modelling, Statistics and Bioinformatics at Ghent University, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 21, 'Algorithms|Biodiversity|Computational Biology|Computer Simulation|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Microbiota|Statistics, Nonparametric', 'Journal Article', '10.1093/bib/bbx104', '28968702', 'High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution, the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69246, 2019, 'bib', 'A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications', 'Li, Xue|Wang, Xinlei|Xiao, Guanghua', 'Department of Statistical Science at Southern Methodist University, Dallas, TX.|Department of Statistical Science at Southern Methodist University, Dallas, TX.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX.', '-|-|-', 1, 'Bayes Theorem|Carcinoma, Non-Small-Cell Lung|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Genomics|Humans|Lung Neoplasms|Markov Chains|Models, Statistical|Software', 'Comparative Study', '10.1093/bib/bbx101', '28968705', 'Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.'),
(69247, 2019, 'bib', 'Integrating regulatory features data for prediction of functional disease-associated SNPs', 'Dong, Shan-Shan|Guo, Yan|Yao, Shi|Chen, Yi-Xiao|He, Mo-Nan|Zhang, Yu-Jie|Chen, Xiao-Feng|Chen, Jia-Bin|Yang, Tie-Lin', 'Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xian Jiaotong University.', '-|-|-|-|-|-|-|-|-', 5, 'Algorithms|Computational Biology|Diabetes Mellitus, Type 2|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Hypertension|Machine Learning|Models, Genetic|Models, Statistical|Multifactorial Inheritance|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bib/bbx094', '28968709', 'Genome-wide association studies (GWASs) are an effective strategy to identify susceptibility loci for human complex diseases. However, missing heritability is still a big problem. Most GWASs single-nucleotide polymorphisms (SNPs) are located in noncoding regions, which has been considered to be the unexplored territory of the genome. Recently, data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects have shown that many GWASs SNPs in the noncoding regions fall within regulatory elements. In this study, we developed a pipeline named functional disease-associated SNPs prediction (FDSP), to identify novel susceptibility loci for complex diseases based on the interpretation of the functional features for known disease-associated variants with machine learning. We applied our pipeline to predict novel susceptibility SNPs for type 2 diabetes (T2D) and hypertension. The predicted SNPs could explain heritability beyond that explained by GWAS-associated SNPs. Functional annotation by expression quantitative trait loci analyses showed that the target genes of the predicted SNPs were significantly enriched in T2D or hypertension-related pathways in multiple tissues. Our results suggest that combining GWASs and regulatory features data could identify additional functional susceptibility SNPs for complex diseases. We hope FDSP could help to identify novel susceptibility loci for complex diseases and solve the missing heritability problem.'),
(69248, 2019, 'bib', 'Discovering cooperative biomarkers for heterogeneous complex disease diagnoses', 'Sun, Duanchen|Ren, Xianwen|Ari, Eszter|Korcsmaros, Tamas|Csermely, Peter|Wu, Ling-Yun', 'Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.|Biodynamic Optical Imaging Center, Peking University, Beijing, China.|Department of Genetics, Eötvös Loránd University, Budapest.|Institute of Food Research and the Earlham Institute, Norwich, UK.|Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.', 'China|China|United Kingdom|United Kingdom|Hungary|China', 2, 'Algorithms|Biomarkers, Tumor|Computational Biology|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genetic Markers|Humans|Models, Genetic|Models, Statistical|Multifactorial Inheritance|Neoplasms|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbx090', '28968712', 'Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.'),
(69249, 2019, 'bib', 'Understanding the disease genome: gene essentiality and the interplay of selection, recombination and mutation', 'Pengelly, Reuben J|Vergara-Lope, Alejandra|Alyousfi, Dareen|Jabalameli, M Reza|Collins, Andrew', 'Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK.|Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK.|Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK.|Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK.|Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Computational Biology|Disease|Genes, Essential|Genetic Predisposition to Disease|Genetic Variation|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Linkage Disequilibrium|Models, Genetic|Multifactorial Inheritance|Mutation|Recombination, Genetic|Selection, Genetic', 'Journal Article', '10.1093/bib/bbx110', '28968721', 'Despite the identification of many genetic variants contributing to human disease (the disease genome), establishing reliable molecular diagnoses remain challenging in many cases. The ability to sequence the genomes of patients has been transformative, but difficulty in interpretation of voluminous genetic variation often confounds recognition of underlying causal variants. There are numerous predictors of pathogenicity for individual DNA variants, but their utility is reduced because many plausibly pathogenic variants are probably neutral. The rapidly increasing quantity and quality of information on the properties of genes suggests that gene-specific information might be useful for prediction of causal variation when used alongside variant-specific predictors of pathogenicity. The key to understanding the role of genes in disease relates in part to gene essentiality, which has recently been approximated, for example, by quantifying the degree of intolerance of individual genes to loss-of-function variation. Increasing understanding of the interplay between genetic recombination, selection and mutation and their relationship to gene essentiality suggests that gene-specific information may be useful for the interpretation of sequenced genomes. Considered alongside additional distinctive properties of the disease genome, such as the timing of the evolutionary emergence of genes and the roles of their products in protein networks, the case for using gene-specific measures to guide filtering of sequenced genomes seems strong.'),
(69250, 2019, 'bib', 'Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability', 'Deng, Yulan|Luo, Shangyi|Deng, Chunyu|Luo, Tao|Yin, Wenkang|Zhang, Hongyi|Zhang, Yong|Zhang, Xinxin|Lan, Yujia|Ping, Yanyan|Xiao, Yun|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 6, 'Algorithms|Breast Neoplasms|Computational Biology|Computer Simulation|Databases, Genetic|Epistasis, Genetic|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genomics|Humans|Knowledge Bases|Models, Genetic|Neoplasms|Phenotype', 'Journal Article', '10.1093/bib/bbx109', '28968730', 'Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.'),
(69251, 2019, 'bib', 'MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization', 'Katoh, Kazutaka|Rozewicki, John|Yamada, Kazunori D', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.', 'China|China|China', 206, '-', 'Journal Article', '10.1093/bib/bbx108', '28968734', 'This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.'),
(69252, 2019, 'bib', 'Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes', 'Olson, Nathan D|Treangen, Todd J|Hill, Christopher M|Cepeda-Espinoza, Victoria|Ghurye, Jay|Koren, Sergey|Pop, Mihai', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.', 'China|China|China|China|China|China|China', 24, '-', 'Journal Article', '10.1093/bib/bbx098', '28968737', 'Metagenomic samples are snapshots of complex ecosystems at work. They comprise hundreds of known and unknown species, contain multiple strain variants and vary greatly within and across environments. Many microbes found in microbial communities are not easily grown in culture making their DNA sequence our only clue into their evolutionary history and biological function. Metagenomic assembly is a computational process aimed at reconstructing genes and genomes from metagenomic mixtures. Current methods have made significant strides in reconstructing DNA segments comprising operons, tandem gene arrays and syntenic blocks. Shorter, higher-throughput sequencing technologies have become the de facto standard in the field. Sequencers are now able to generate billions of short reads in only a few days. Multiple metagenomic assembly strategies, pipelines and assemblers have appeared in recent years. Owing to the inherent complexity of metagenome assembly, regardless of the assembly algorithm and sequencing method, metagenome assemblies contain errors. Recent developments in assembly validation tools have played a pivotal role in improving metagenomics assemblers. Here, we survey recent progress in the field of metagenomic assembly, provide an overview of key approaches for genomic and metagenomic assembly validation and demonstrate the insights that can be derived from assemblies through the use of assembly validation strategies. We also discuss the potential for impact of long-read technologies in metagenomics. We conclude with a discussion of future challenges and opportunities in the field of metagenomic assembly and validation.'),
(69253, 2019, 'bib', 'A global perspective on evolving bioinformatics and data science training needs', 'Attwood, Teresa K|Blackford, Sarah|Brazas, Michelle D|Davies, Angela|Schneider, Maria Victoria', 'School of Computer Science, The University of Manchester, Manchester.|School of Computer Science, The University of Manchester, Manchester.|Ontario Institute for Cancer Research.|Ontario Institute for Cancer Research.|University of Melbourne.', '-|-|-|-|-', 7, 'Biological Science Disciplines|Biomedical Research|Computational Biology|Data Curation|Data Science|Humans|Surveys and Questionnaires', 'Journal Article', '10.1093/bib/bbx100', '28968751', 'Bioinformatics is now intrinsic to life science research, but the past decade has witnessed a continuing deficiency in this essential expertise. Basic data stewardship is still taught relatively rarely in life science education programmes, creating a chasm between theory and practice, and fuelling demand for bioinformatics training across all educational levels and career roles. Concerned by this, surveys have been conducted in recent years to monitor bioinformatics and computational training needs worldwide. This article briefly reviews the principal findings of a number of these studies. We see that there is still a strong appetite for short courses to improve expertise and confidence in data analysis and interpretation, strikingly, however, the most urgent appeal is for bioinformatics to be woven into the fabric of life science degree programmes. Satisfying the relentless training needs of current and future generations of life scientists will require a concerted response from stakeholders across the globe, who need to deliver sustainable solutions capable of both transforming education curricula and cultivating a new cadre of trainer scientists.'),
(69254, 2019, 'bib', 'PATRIC as a unique resource for studying antimicrobial resistance', 'Antonopoulos, Dionysios A|Assaf, Rida|Aziz, Ramy Karam|Brettin, Thomas|Bun, Christopher|Conrad, Neal|Davis, James J|Dietrich, Emily M|Disz, Terry|Gerdes, Svetlana|Kenyon, Ronald W|Machi, Dustin|Mao, Chunhong|Murphy-Olson, Daniel E|Nordberg, Eric K|Olsen, Gary J|Olson, Robert|Overbeek, Ross|Parrello, Bruce|Pusch, Gordon D|Santerre, John|Shukla, Maulik|Stevens, Rick L|VanOeffelen, Margo|Vonstein, Veronika|Warren, Andrew S|Wattam, Alice R|Xia, Fangfang|Yoo, Hyunseung', 'University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.|University of Melbourne.', '-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-', 11, '-', 'Journal Article', '10.1093/bib/bbx083', '28968762', 'The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other omic data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15xe2x80x89000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1xe2x80x89610xe2x80x89744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.'),
(69255, 2019, 'bib', 'Computational resources for ribosome profiling: from database to Web server and software', 'Wang, Hongwei|Wang, Yan|Xie, Zhi', 'Zhongshan Ophthalmic Center, Sun Yat-sen University.|Zhongshan Ophthalmic Center, Sun Yat-sen University.|Zhongshan Ophthalmic Center, Sun Yat-sen University.', '-|-|-', 13, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Databases, Nucleic Acid|Gene Expression Profiling|Humans|Internet|Protein Biosynthesis|Ribosomes|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbx093', '28968766', 'Ribosome profiling is emerging as a powerful technique that enables genome-wide investigation of in vivo translation at sub-codon resolution. The increasing application of ribosome profiling in recent years has achieved remarkable progress toward understanding the composition, regulation and mechanism of translation. This benefits from not only the awesome power of ribosome profiling but also an extensive range of computational resources available for ribosome profiling. At present, however, a comprehensive review on these resources is still lacking. Here, we survey the recent computational advances guided by ribosome profiling, with a focus on databases, Web servers and software tools for storing, visualizing and analyzing ribosome profiling data. This review is intended to provide experimental and computational biologists with a reference to make appropriate choices among existing resources for the question at hand.'),
(69256, 2019, 'bib', 'VirGenA: a reference-based assembler for variable viral genomes', 'Fedonin, Gennady G|Fantin, Yury S|Favorov, Alexnader V|Shipulin, German A|Neverov, Alexey D', 'Department of Molecular Diagnostics, Central Research Institute for Epidemiology.|Department of Molecular Diagnostics, Central Research Institute for Epidemiology.|Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University.|Department of Molecular Diagnostics, Central Research Institute for Epidemiology.|Department of Molecular Diagnostics, Central Research Institute for Epidemiology.', '-|-|-|-|-', 6, 'Algorithms|Computational Biology|Computer Simulation|Databases, Genetic|Genetic Variation|Genome, Viral|Genotype|HIV-1|Hepacivirus|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bib/bbx079', '28968771', 'Characterization of the within-host genetic diversity of viral pathogens is required for selection of effective treatment of some important viral infections, e.g. HIV, HBV and HCV. Despite the technical ability of detection, there are conflicting data regarding the clinical significance of low-frequency variants, partially because of the difficulty of their distinguishing from experimental artifacts. The issue of cross-contamination is relevant for all highly sensitive techniques, including deep sequencing: even trace contamination leads to a significant increase of false positives in identified SNVs. Determination of infections by multiple genotypes of some viruses, the incidence of which can be considerable, especially in risk groups, is also clinically significant in some cases. We developed a new viral reference-guided assembler, VirGenA, that can separate mixtures of strains of different intraspecies genetic groups (genotypes, subtypes, clades, etc.) and assemble a separate consensus sequence for each group in a mixture. It produced long assemblies for mixture components of extremely low frequencies (<1%) allowing detection of cross-contamination of samples by divergent genotypes. We tested VirGenA on both clinical and simulated data. On both types of data, VirGenA shows better or similar results than the existing de novo assemblers. Cross-platform implementation (including source code) is freely available at https://github.com/gFedonin/VirGenA/releases.'),
(69257, 2019, 'bib', 'Federation in genomics pipelines: techniques and challenges', 'Chaterji, Somali|Koo, Jinkyu|Li, Ninghui|Meyer, Folker|Grama, Ananth|Bagchi, Saurabh', 'Computer Science, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Computer Science, Purdue University, Indiana, USA.|Argonne National Laboratory, Mathematics and Computer Science Division, Illinois, USA.|Computer Science, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.', 'India|India|India|United States|India|India', 7, 'Big Data|Computational Biology|Confidentiality|Databases, Genetic|Genetic Privacy|Genomics|Humans|Information Dissemination|Metagenomics|Software|United States', 'Journal Article', '10.1093/bib/bbx102', '28968781', 'Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.'),
(69258, 2019, 'bib', 'MicroScope-an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data', 'Médigue, Claudine|Calteau, Alexandra|Cruveiller, Stéphane|Gachet, Mathieu|Gautreau, Guillaume|Josso, Adrien|Lajus, Aurélie|Langlois, Jordan|Pereira, Hugo|Planel, Rémi|Roche, David|Rollin, Johan|Rouy, Zoe|Vallenet, David', 'Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.|Electrical and Computer Engineering, Purdue University, Indiana, USA.', 'India|India|India|India|India|India|India|India|India|India|India|India|India|India', 13, '-', 'Journal Article', '10.1093/bib/bbx113', '28968784', 'The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.'),
(69259, 2019, 'bib', 'MetSigDis: a manually curated resource for the metabolic signatures of diseases', 'Cheng, Liang|Yang, Haixiu|Zhao, Hengqiang|Pei, Xiaoya|Shi, Hongbo|Sun, Jie|Zhang, Yunpeng|Wang, Zhenzhen|Zhou, Meng', 'College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University.', '-|-|-|-|-|-|-|-|-', 53, 'Animals|Computational Biology|Databases, Factual|Disease|Humans|Metabolome|Metabolomics|Search Engine', 'Journal Article', '10.1093/bib/bbx103', '28968812', 'Complex diseases cannot be understood only on the basis of single gene, single mRNA transcript or single protein but the effect of their collaborations. The combination consequence in molecular level can be captured by the alterations of metabolites. With the rapidly developing of biomedical instruments and analytical platforms, a large number of metabolite signatures of complex diseases were identified and documented in the literature. Biologists hardship in the face of this large amount of papers recorded metabolic signatures of experiments results calls for an automated data repository. Therefore, we developed MetSigDis aiming to provide a comprehensive resource of metabolite alterations in various diseases. MetSigDis is freely available at http://www.bio-annotation.cn/MetSigDis/. By reviewing hundreds of publications, we collected 6849 curated relationships between 2420 metabolites and 129 diseases across eight species involving Homo sapiens and model organisms. All of these relationships were used in constructing a metabolite disease network (MDN). This network displayed scale-free characteristics according to the degree distribution (power-law distribution with R2xe2x80x89=xe2x80x890.909), and the subnetwork of MDN for interesting diseases and their related metabolites can be visualized in the Web. The common alterations of metabolites reflect the metabolic similarity of diseases, which is measured using Jaccard index. We observed that metabolite-based similar diseases are inclined to share semantic associations of Disease Ontology. A human disease network was then built, where a node represents a disease, and an edge indicates similarity of pair-wise diseases. The network validated the observation that linked diseases based on metabolites should have more overlapped genes.'),
(69260, 2019, 'bib', 'AtCircDB: a tissue-specific database for Arabidopsis circular RNAs', 'Ye, Jiazhen|Wang, Lin|Li, Shuzhang|Zhang, Qinran|Zhang, Qinglei|Tang, Wenhao|Wang, Kai|Song, Kun|Sablok, Gaurav|Sun, Xiaoyong|Zhao, Hongwei', 'Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Climate Change Cluster (C3), University of Technology Sydney, NSW, Australia.|Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China.|Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China.', 'China|China|China|China|China|China|China|China|Australia|China|China', 1, 'Algorithms|Arabidopsis|Big Data|Computational Biology|Databases, Nucleic Acid|Internet|MicroRNAs|RNA|RNA, Plant|Sequence Analysis, RNA|Tissue Distribution|User-Computer Interface', 'Journal Article', '10.1093/bib/bbx089', '28968841', 'Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84xe2x80x89685 circular RNAs, 30xe2x80x89648 tissue-specific circular RNAs and 3486 microRNA-circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA.'),
(69261, 2019, 'bib', 'Visualizing and comparing circular genomes using the CGView family of tools', 'Stothard, Paul|Grant, Jason R|Van Domselaar, Gary', 'Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China.|Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China.|Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China.', 'China|China|China', 5, '-', 'Journal Article', '10.1093/bib/bbx081', '28968859', 'Graphical genome maps are widely used to assess genome features and sequence characteristics. The CGView (Circular Genome Viewer) software family is a popular collection of tools for generating genome maps for bacteria, organelles and viruses. In this review, we describe the capabilities of the original CGView program along with those of subsequent companion applications, including the CGView Server and the CGView Comparison Tool. We also discuss GView, a graphical user interface-enabled rewrite of CGView, and the GView Server, which offers several integrated analyses for identifying shared or unique genome regions relative to a collection of comparison genomes. We conclude with some remarks about our current development efforts related to CGView aimed at adding new functionality while increasing ease of use.'),
(69262, 2019, 'bib', 'Bioinformatics in Latin America and SoIBio impact, a tale of spin-off and expansion around genomes and protein structures', 'De Las Rivas, Javier|Bonavides-Martxc3xadnez, Cesar|Campos-Laborie, Francisco Jose', 'University of Salamanca and CSIC, Spain.|Mexico.|Mexico.', 'Spain|Mexico|Mexico', 1, 'Biomedical Research|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Latin America|Protein Conformation|Proteins|Proteomics', 'Journal Article', '10.1093/bib/bbx064', '28981567', 'Owing to the emerging impact of bioinformatics and computational biology, in this article, we present an overview of the history and current state of the research on this field in Latin America (LA). It will be difficult to cover without inequality all the efforts, initiatives and works that have happened for the past two decades in this vast region (that includes >19 million km2 and >600 million people). Despite the difficulty, we have done an analytical search looking for publications in the field made by researchers from 19 LA countries in the past 25xe2x80x89years. In this way, we find that research in bioinformatics in this region should develop twice to approach the average world scientific production in the field. We also found some of the pioneering scientists who initiated and led bioinformatics in the region and were promoters of this new scientific field. Our analysis also reveals that spin-off began around some specific areas within the biomolecular sciences: studies on genomes (anchored in the new generation of deep sequencing technologies, followed by developments in proteomics) and studies on protein structures (supported by three-dimensional structural determination technologies and their computational advancement). Finally, we show that the contribution to this endeavour of the Iberoamerican Society for Bioinformatics, founded in Mexico in 2009, has been significant, as it is a leading forum to join efforts of many scientists from LA interested in promoting research, training and education in bioinformatics.'),
(69263, 2019, 'bib', 'Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species', 'Huh, Iksoo|Wu, Xin|Park, Taesung|Yi, Soojin V', 'School of Biological Sciences, Georgia Institute of Technology.|School of Biological Sciences, Georgia Institute of Technology.|Department of Statistics, Seoul National University.|School of Biological Sciences, Georgia Institute of Technology.', 'Georgia|Georgia|-|Georgia', 1, 'Animals|Bees|Computational Biology|Computer Simulation|CpG Islands|DNA Methylation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Models, Statistical|Sequence Analysis, DNA|Species Specificity|Sulfites', 'Journal Article', '10.1093/bib/bbx077', '28981571', 'DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.'),
(69264, 2019, 'bib', 'OPATs: Omnibus P-value association tests', 'Chen, Chia-Wei|Yang, Hsin-Chou', 'Institute of Statistical Science, Academia Sinica.|Institute of Statistical Science, Academia Sinica.', '-|-', 1, 'Arthritis, Rheumatoid|Case-Control Studies|Computational Biology|Computer Simulation|Genetic Markers|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Humans|Models, Genetic|Models, Statistical|Multifactorial Inheritance|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bib/bbx068', '28981573', 'Combining statistical significances (P-values) from a set of single-locus association tests in genome-wide association studies is a proof-of-principle method for identifying disease-associated genomic segments, functional genes and biological pathways. We review P-value combinations for genome-wide association studies and introduce an integrated analysis tool, Omnibus P-value Association Tests (OPATs), which provides popular analysis methods of P-value combinations. The software OPATs programmed in R and R graphical user interface features a user-friendly interface. In addition to analysis modules for data quality control and single-locus association tests, OPATs provides three types of set-based association test: window-, gene- and biopathway-based association tests. P-value combinations with or without threshold and rank truncation are provided. The significance of a set-based association test is evaluated by using resampling procedures. Performance of the set-based association tests in OPATs has been evaluated by simulation studies and real data analyses. These set-based association tests help boost the statistical power, alleviate the multiple-testing problem, reduce the impact of genetic heterogeneity, increase the replication efficiency of association tests and facilitate the interpretation of association signals by streamlining the testing procedures and integrating the genetic effects of multiple variants in genomic regions of biological relevance. In summary, P-value combinations facilitate the identification of marker sets associated with disease susceptibility and uncover missing heritability in association studies, thereby establishing a foundation for the genetic dissection of complex diseases and traits. OPATs provides an easy-to-use and statistically powerful analysis tool for P-value combinations. OPATs, examples, and user guide can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/genetics/association/OPATs.htm.'),
(69265, 2019, 'bib', 'An account of in silico identification tools of secreted effector proteins in bacteria and future challenges', 'Zeng, Cong|Zou, Lingyun', 'Bioinformatics Center, Third Military Medical University (TMMU), China.|Bioinformatics Center of TMMU.', 'China|-', 7, 'Algorithms|Bacterial Proteins|Bacterial Secretion Systems|Bayes Theorem|Computational Biology|Computer Simulation|Databases, Factual|Host Microbial Interactions|Humans|Machine Learning|Markov Chains|Models, Biological|Neural Networks (Computer)|Support Vector Machine|Virulence', 'Journal Article', '10.1093/bib/bbx078', '28981574', 'Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used in the identification of effector proteins before the subsequent experimental verification because they tolerate laborious biological procedures and are genome scale, automated and highly efficient. Prevalent examples include machine learning methods and statistical techniques. In this article, we summarize the computational progress toward predicting secreted effector proteins in bacteria, with an opening of an introduction of features that are used to discriminate effectors from non-effectors. The mechanism, contribution and deficiency of previous developed detection tools are presented, which are further benchmarked based on a curated testing data set. According to the results of benchmarking, potential improvements of the prediction performance are discussed, which include (1) more informative features for discriminating the effectors from non-effectors, (2) the construction of comprehensive training data set of the machine learning algorithms, (3) the advancement of reliable prediction methods and (4) a better interpretation of the mechanisms behind the molecular processes. The future of in silico identification of bacterial secreted effectors includes both opportunities and challenges.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69266, 2018, 'bib', 'Proper joint analysis of summary association statistics requires the adjustment of heterogeneity in SNP coverage pattern', 'Zhang, Han|Wheeler, William|Song, Lei|Yu, Kai', 'Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA.|Information Management Services Inc., USA.|Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA.', 'United States|United States|United States|United States', 0, 'Genetic Heterogeneity|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbx072', '28981575', 'As meta-analysis results published by consortia of genome-wide association studies (GWASs) become increasingly available, many association summary statistics-based multi-locus tests have been developed to jointly evaluate multiple single-nucleotide polymorphisms (SNPs) to reveal novel genetic architectures of various complex traits. The validity of these approaches relies on the accurate estimate of z-score correlations at considered SNPs, which in turn requires knowledge on the set of SNPs assessed by each study participating in the meta-analysis. However, this exact SNP coverage information is usually unavailable from the meta-analysis results published by GWAS consortia. In the absence of the coverage information, researchers typically estimate the z-score correlations by making oversimplified coverage assumptions. We show through real studies that such a practice can generate highly inflated type I errors, and we demonstrate the proper way to incorporate correct coverage information into multi-locus analyses. We advocate that consortia should make SNP coverage information available when posting their meta-analysis results, and that investigators who develop analytic tools for joint analyses based on summary data should pay attention to the variation in SNP coverage and adjust for it appropriately.'),
(69267, 2018, 'bib', 'SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data', 'Zhang, Qiong|Liu, Wei|Liu, Chunjie|Lin, Sheng-Yan|Guo, An-Yuan', 'Huazhong University of Science and Technology, China.|Huazhong University of Science and Technology, China.|Huazhong University of Science and Technology, China.|Huazhong University of Science and Technology, China.|Huazhong University of Science and Technology, China.', 'China|China|China|China|China', 6, 'Algorithms|Cluster Analysis|Datasets as Topic|Fuzzy Logic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1093/bib/bbx074', '28981576', 'Different tissues and diseases have distinct transcriptional profilings with specifically expressed genes (SEGs). So, the identification of SEGs is an important issue in the studies of gene function, biological development, disease mechanism and biomarker discovery. However, few accurate and easy-to-use tools are available for RNA sequencing (RNA-seq) data to detect SEGs. Here, we presented SEGtool, a tool based on fuzzy c-means, Jaccard index and greedy annealing method for SEG detection automatically and self-adaptively ignoring data distribution. Testing result showed that our SEGtool outperforms the existing tools, which was mainly developed for microarray data. By applying SEGtool to Genotype-Tissue Expression (GTEx) human tissue data set, we detected 3181 SEGs with tissue-related functions. Regulatory networks reveal tissue-specific transcription factors regulating many SEGs, such as ETV2 in testis, HNF4A in liver and NEUROD1 in brain. Applied to a case study of single-cell sequencing (SCS) data from embryo cells, we identified many SEGs in specific stages of human embryogenesis. Notably, SEGtool is suitable for RNA-seq data and even SCS data with high specificity and accuracy. An implementation of SEGtool R package is freely available at http://bioinfo.life.hust.edu.cn/SEGtool/.'),
(69268, 2019, 'bib', 'Multi-omic data analysis using O-miner', 'Sangaralingam, Ajanthah|Dayem Ullah, Abu Z|Marzec, Jacek|Gadaleta, Emanuela|Nagano, Ai|Ross-Adams, Helen|Wang, Jun|Lemoine, Nicholas R|Chelala, Claude', 'Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, Queen Mary University of London.|Barts Cancer Institute, co-Lead of the Computational Biology Centre at the Life Science Initiative, Queen Mary University of London.', '-|-|-|-|-|-|-|-|-', 3, 'Computational Biology|DNA Methylation|Data Analysis|Databases, Genetic|Gene Dosage|Gene Expression Profiling|Genomics|Humans|Internet|Neoplasms|Sequence Analysis, RNA|Software|Software Design|Whole Genome Sequencing', 'Journal Article', '10.1093/bib/bbx080', '28981577', 'Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from -omics technologies. Created from a biologists perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.'),
(69269, 2019, 'bib', 'On the dynamical incompleteness of the Protein Data Bank', 'Marino-Buslje, Cristina|Monzon, Alexander Miguel|Zea, Diego Javier|Fornasari, Marxc3xada Silvina|Parisi, Gustavo', 'Leloir Institute Foundation.|Department of Science and Technology at Universidad Nacional de Quilmes.|Bioinformatics Unit at Leloir Institute Foundation.|Universidad Nacional de Quilmes.|Universidad Nacional de Quilmes.', '-|-|-|-|-', 2, 'Computational Biology|Crystallography, X-Ray|Databases, Protein|Genomics|Humans|Molecular Dynamics Simulation|Protein Conformation|Proteins|Proteomics|Sequence Homology, Amino Acid|Structural Homology, Protein', 'Journal Article', '10.1093/bib/bbx084', '28981583', 'Major scientific challenges that are beyond the capability of individuals need to be addressed by multi-disciplinary and multi-institutional consortia. Examples of these endeavours include the Human Genome Project, and more recently, the Structural Genomics (SG) initiative.xe2x80x83The SG initiative pursues the expansion of structural coverage to include at least one structural representative for each protein family to derive the remaining structures using homology modelling. However, biological function is inherently connected with protein dynamics that can be studied by knowing different structures of the same protein. This ensemble of structures provides snapshots of protein conformational diversity under native conditions. Thus, sequence redundancy in the Protein Data Bank (PDB) (i.e. crystallization of the same protein under different conditions) is therefore an essential input contributing to experimentally based studies of protein dynamics and providing insights into protein function.xe2x80x83In this work, we show that sequence redundancy, a key concept for exploring protein dynamics, is highly biased and fundamentally incomplete in the PDB. Additionally, our results show that dynamical behaviour of proteins cannot be inferred using homologous proteins. Minor to moderate changes in sequence can produce great differences in dynamical behaviour.xe2x80x83Nonetheless, the structural and dynamical incompleteness of the PDB is apparently unrelated concepts in SG. While the first could be reversed by promoting the extension of the structural coverage, we would like to emphasize that further focused efforts will be needed to amend the incompleteness of the PDB in terms of dynamical information content, essential to fully understand protein function.'),
(69270, 2018, 'bib', 'Mathematical modeling and computational prediction of cancer drug resistance', 'Sun, Xiaoqiang|Hu, Bin', 'Zhong-shan School of Medicine, Sun Yat-Sen University.|School of Information Science and Engineering, Lanzhou University.', '-|-', 29, 'Antineoplastic Agents|Drug Resistance, Neoplasm|Epigenesis, Genetic|Humans|Kinetics|Molecular Dynamics Simulation|Neoplasms|Protein Processing, Post-Translational|Signal Transduction|Tumor Microenvironment', 'Journal Article', '10.1093/bib/bbx065', '28981626', 'Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.'),
(69271, 2019, 'bib', 'MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis', 'Meyer, Folker|Bagchi, Saurabh|Chaterji, Somali|Gerlach, Wolfgang|Grama, Ananth|Harrison, Travis|Paczian, Tobias|Trimble, William L|Wilke, Andreas', 'School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.', '-|-|-|-|-|-|-|-|-', 15, '-', 'Journal Article', '10.1093/bib/bbx105', '29028869', 'As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014,8:1-3] is an example, we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the communitys data analysis tasks.'),
(69272, 2019, 'bib', 'A review of methods and databases for metagenomic classification and assembly', 'Breitwieser, Florian P|Lu, Jennifer|Salzberg, Steven L', 'School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.', '-|-|-', 61, '-', 'Journal Article', '10.1093/bib/bbx120', '29028872', 'Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.'),
(69273, 2017, 'bib', 'Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction', 'Zhou, Yao|Isabel Vales, M|Wang, Aoxue|Zhang, Zhiwu', 'School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.|School of Information Science and Engineering, Lanzhou University.', '-|-|-|-', 2, '-', 'Journal Article', '10.1093/bib/bbx133', '29028875', '-'),
(69274, 2019, 'bib', 'Systematic comparative study of computational methods for T-cell receptor sequencing data analysis', 'Afzal, Saira|Gil-Farina, Irene|Gabriel, Richard|Ahmad, Shahzad|von Kalle, Christof|Schmidt, Manfred|Fronza, Raffaele', 'National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.|National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.|National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.|National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.|National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.|National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.|National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 2, 'Amino Acid Sequence|Base Sequence|Computational Biology|Computer Simulation|Databases, Genetic|HeLa Cells|High-Throughput Nucleotide Sequencing|Humans|Jurkat Cells|Receptors, Antigen, T-Cell|Sequence Analysis|T-Lymphocytes', 'Comparative Study', '10.1093/bib/bbx111', '29028876', 'High-throughput sequencing technologies have exposed the possibilities for the in-depth evaluation of T-cell receptor (TCR) repertoires. These studies are highly relevant to gain insights into human adaptive immunity and to decipher the composition and diversity of antigen receptors in physiological and disease conditions. The major objective of TCR sequencing data analysis is the identification of V, D and J gene segments, complementarity-determining region 3 (CDR3) sequence extraction and clonality analysis. With the advancement in sequencing technologies, new TCR analysis approaches and programs have been developed. However, there is still a deficit of systematic comparative studies to assist in the selection of an optimal analysis approach. Here, we present a detailed comparison of 10 state-of-the-art TCR analysis tools on samples with different complexities by taking into account many aspects such as clonotype detection [unique V(D)J combination], CDR3 identification or accuracy in error correction. We used our in silico and experimental data sets with known clonalities enabling the identification of potential tool biases. We also established a new strategy, named clonal plane, which allows quantifying and comparing the clonality of multiple samples. Our results provide new insights into the effect of method selection on analysis results, and it will assist users in the selection of an appropriate analysis method.'),
(69275, 2019, 'bib', 'Web-based drug repurposing tools: a survey', 'Sam, Elizabeth|Athri, Prashanth', 'Department of Computer Science & Engineering Amrita, University Bengaluru, India.|Department of Computer Science & Engineering Amrita, University Bengaluru, India.', 'India|India', 9, 'Algorithms|Binding Sites|Computational Biology|Databases, Pharmaceutical|Drug Discovery|Drug Repositioning|High-Throughput Screening Assays|Humans|Internet|Ligands|Search Engine|Software', 'Journal Article', '10.1093/bib/bbx125', '29028878', 'Drug repurposing (a.k.a. drug repositioning) is the search for new indications or molecular targets distinct from a drugs putative activity, pharmacological effect or binding specificities. With the ever-increasing rates of termination of drugs in clinical trials, drug repositioning has risen as one of the effective solutions against the risk of drug failures. Repositioning finds a way to reverse the grim but real trend that Erooms law portends for the pharmaceutical and biotech industry, and drug discovery in general. Further, the advent of high-throughput technologies to explore biological systems has enabled the generation of zeta bytes of data and a massive collection of databases that store them. Computational analytics and mining are frequently used as effective tools to explore this byzantine series of biological and biomedical data. However, advanced computational tools are often difficult to understand or use, thereby limiting their accessibility to scientists without a strong computational background. Hence it is of great importance to build user-friendly interfaces to extend the user-base beyond computational scientists, to include life scientists who may have deeper chemical and biological insights. This survey is focused on systematically presenting the available Web-based tools that aid in repositioning drugs.'),
(69276, 2019, 'bib', 'A new pan-European Train-the-Trainer programme for bioinformatics: pilot results on feasibility, utility and sustainability of learning', 'Via, Allegra|Attwood, Teresa K|Fernandes, Pedro L|Morgan, Sarah L|Schneider, Maria Victoria|Palagi, Patricia M|Rustici, Gabriella|Tractenberg, Rochelle E', 'tional Research Council of Italy (CNR), Institute of Molecular Biology and Pathology (IBPM), c/o Department of Biochemical Sciences A. Rossi Fanelli, Sapienza University, P.le Aldo Moro 5, Rome, Italy.|University of Manchester.|Instituto Gulbenkian de Ciência.|EMBL-EBI.|University of Melbourne.|SIB Swiss Institute of Bioinformatics and ELIXIR-CH training coordinator.|University of Cambridge, UK.|American Statistical Association.', 'Italy|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Biological Science Disciplines|Biomedical Research|Computational Biology|Curriculum|Data Curation|Education, Continuing|Feasibility Studies|Humans|Pilot Projects', 'Journal Article', '10.1093/bib/bbx112', '29028883', 'Demand for training life scientists in bioinformatics methods, tools and resources and computational approaches is urgent and growing. To meet this demand, new trainers must be prepared with effective teaching practices for delivering short hands-on training sessions-a specific type of education that is not typically part of professional preparation of life scientists in many countries. A new Train-the-Trainer (TtT) programme was created by adapting existing models, using input from experienced trainers and experts in bioinformatics, and from educational and cognitive sciences. This programme was piloted across Europe from May 2016 to January 2017. Preparation included drafting the training materials, organizing sessions to pilot them and studying this paradigm for its potential to support the development and delivery of future bioinformatics training by participants. Seven pilot TtT sessions were carried out, and this manuscript describes the results of the pilot year. Lessons learned include (i) support is required for logistics, so that new instructors can focus on their teaching, (ii) institutions must provide incentives to include training opportunities for those who want/need to become new or better instructors, (iii) formal evaluation of the TtT materials is now a priority, (iv) a strategy is needed to recruit, train and certify new instructor trainers (faculty), and (v) future evaluations must assess utility. Additionally, defining a flexible but rigorous and reliable process of TtT certification may incentivize participants and will be considered in future.'),
(69277, 2019, 'bib', 'Comparative analysis of differential gene expression tools for RNA sequencing time course data', 'Spies, Daniel|Renz, Peter F|Beyer, Tobias A|Ciaudo, Constance', 'Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.|Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.|Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.|Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 16, 'Bayes Theorem|Computational Biology|Computer Simulation|Databases, Nucleic Acid|Gene Expression Profiling|Humans|Markov Chains|Models, Statistical|Molecular Sequence Annotation|Sequence Analysis, RNA|Signal-To-Noise Ratio|Software|Time Factors', 'Comparative Study', '10.1093/bib/bbx115', '29028903', 'RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.'),
(69278, 2019, 'bib', 'Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods', 'Yang, Shiping|Li, Hong|He, Huaqin|Zhou, Yuan|Zhang, Ziding', 'State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.|College of Life Sciences, Fujian Agriculture and Forestry University.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.', 'China|China|-|China|China', 7, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Computational Biology|Databases, Protein|Gene Expression Profiling|Gene Ontology|Host-Pathogen Interactions|Machine Learning|Models, Biological|Plant Diseases|Plant Immunity|Plant Proteins|Plants|Protein Interaction Mapping', 'Journal Article', '10.1093/bib/bbx123', '29028906', 'The identification of plant-pathogen protein-protein interactions (PPIs) is an attractive and challenging research topic for deciphering the complex molecular mechanism of plant immunity and pathogen infection. Considering that the experimental identification of plant-pathogen PPIs is time-consuming and labor-intensive, computational methods are emerging as an important strategy to complement the experimental methods. In this work, we first evaluated the performance of traditional computational methods such as interolog, domain-domain interaction and domain-motif interaction in predicting known plant-pathogen PPIs. Owing to the low sensitivity of the traditional methods, we utilized Random Forest to build an inter-species PPI prediction model based on multiple sequence encodings and novel network attributes in the established plant PPI network. Critical assessment of the features demonstrated that the integration of sequence information and network attributes resulted in significant and robust performance improvement. Additionally, we also discussed the influence of Gene Ontology and gene expression information on the prediction performance. The Web server implementing the integrated prediction method, named InterSPPI, has been made freely available at http://systbio.cau.edu.cn/intersppi/index.php. InterSPPI could achieve a reasonably high accuracy with a precision of 73.8% and a recall of 76.6% in the independent test. To examine the applicability of InterSPPI, we also conducted cross-species and proteome-wide plant-pathogen PPI prediction tests. Taken together, we hope this work can provide a comprehensive understanding of the current status of plant-pathogen PPI predictions, and the proposed InterSPPI can become a useful tool to accelerate the exploration of plant-pathogen interactions.'),
(69279, 2019, 'bib', 'PHAST, PHASTER and PHASTEST: Tools for finding prophage in bacterial genomes', 'Arndt, David|Marcu, Ana|Liang, Yongjie|Wishart, David S', 'State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University.', 'China|China|China|China', 20, '-', 'Journal Article', '10.1093/bib/bbx121', '29028989', 'PHAST (PHAge Search Tool) and its successor PHASTER (PHAge Search Tool - Enhanced Release) have become two of the most widely used web servers for identifying putative prophages in bacterial genomes. Here we review the main capabilities of these web resources, provide some practical guidance regarding their use and discuss possible future improvements. PHAST, which was first described in 2011, made its debut just as whole bacterial genome sequencing and was becoming inexpensive and relatively routine. PHAST quickly gained popularity among bacterial genome researchers because of its web accessibility, its ease of use along with its enhanced accuracy and rapid processing times. PHASTER, which appeared in 2016, provided a number of much-needed enhancements to the PHAST server, including greater processing speed (to cope with very large submission volumes), increased database sizes, a more modern user interface, improved graphical displays and support for metagenomic submissions. Continuing developments in the field, along with increased interest in automated phage and prophage finding, have already led to several improvements to the PHASTER server and will soon lead to the development of a successor to PHASTER (to be called PHASTEST).'),
(69280, 2019, 'bib', 'A rank-based algorithm of differential expression analysis for small cell line data with statistical control', 'Li, Xiangyu|Cai, Hao|Wang, Xianlong|Ao, Lu|Guo, You|He, Jun|Gu, Yunyan|Qi, Lishuang|Guan, Qingzhou|Lin, Xu|Guo, Zheng', 'Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University, China.|Fujian Medical University and Harbin Medical University.', 'China|China|China|China|China|China|China|China|China|China|-', 3, 'Algorithms|Cell Line, Tumor|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbx135', '29040359', 'To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.'),
(69281, 2019, 'bib', 'Elucidating the editome: bioinformatics approaches for RNA editing detection', 'Diroma, Maria Angela|Ciaccia, Loredana|Pesole, Graziano|Picardi, Ernesto', 'University of Bari, Italy.|University of Bari, Italy.|University of Bari, Italy.|University of Bari, Italy.', 'Italy|Italy|Italy|Italy', 3, 'Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|RNA Editing|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbx129', '29040360', 'RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or the processing of noncoding RNAs. In recent years, the detection of editing sites has been improved through the availability of high-throughput RNA sequencing (RNA-Seq) technologies. Accurate bioinformatics pipelines are essential for the analysis of next-generation sequencing (NGS) data to ensure the correct identification of edited sites. Several pipelines, using various read mappers and variant callers with a wide range of adjustable parameters, are available for the detection of RNA editing events. In this review, we discuss some of the most recent and popular tools and provide guidelines for RNA-Seq data generation and analysis for the detection of RNA editing in massive transcriptome data. Using simulated and real data sets, we provide an overview of their behavior, emphasizing the fact that the RNA editing detection in NGS data sets remains a challenging task.'),
(69282, 2019, 'bib', 'Prediction of protein-protein interactions between fungus (Magnaporthe grisea) and rice (Oryza sativa L)', 'Ma, Shiwei|Song, Qi|Tao, Huan|Harrison, Andrew|Wang, Shaobo|Liu, Wei|Lin, Shoukai|Zhang, Ziding|Ai, Yufang|He, Huaqin', 'College of Life Sciences, Fujian Agriculture and Forestry University, China.|College of Life Sciences, Fujian Agriculture and Forestry University, China.|College of Life Sciences, Fujian Agriculture and Forestry University, China.|Department of Mathematical Sciences, University of Essex, UK.|College of Life Sciences, Fujian Agriculture and Forestry University, China.|College of Life Sciences, Fujian Agriculture and Forestry University, China.|Fujian Provincial Key Laboratory of Ecology-toxicological Effects and Control for Emerging Contaminants, Putian University.|College of Biological Sciences, China Agriculture University, China.|College of Life Sciences, Fujian Agriculture and Forestry University, China.|College of Life Sciences, Fujian Agriculture and Forestry University, China.', 'China|China|China|United Kingdom|China|China|United Kingdom|China|China|China', 1, 'Computational Biology|Fungal Proteins|Gene Regulatory Networks|Magnaporthe|Oryza|Plant Proteins|Protein Interaction Maps', 'Journal Article', '10.1093/bib/bbx132', '29040362', 'Rice blast disease caused by the fungus Magnaporthe grisea (M. grisea) is one of the most serious diseases for the cultivated rice Oryza sativa (O. sativa). A key factor causing rice blast disease and defense might be protein-protein interactions (PPIs) between rice and fungus. In this research, we have developed a computational pipeline to predict PPIs between blast fungus and rice. After cross-prediction by interolog-based and domain-based method, we achieved 532 potential PPIs between 27 fungus proteins and 236 rice proteins. Accuracy of jackknife test, 10-fold cross-validation test and independent test for these PPIs were 90.43, 93.85 and 84.67%, respectively, by using support vector machine classification method. Meanwhile, the pathogenic genes of blast fungus were enriched in the predicted PPIs network when compared with 1000 random interaction networks. The rice regulatory network was downloaded and divided into 228 subnetworks with over six nodes, and the top seven subnetworks affected by blast fungus through PPIs were investigated. The results indicated that 34 upregulated and 12 downregulated master regulators in rice interacting with the fungus proteins in response to the infection of blast fungus. The common master regulators in rice in response to the infection of M. grisea, Xanthomonas oryzae pv.oryzae and rice stripe virus were analyzed. The ubiquitin proteasome pathway was the common pathway in rice regulated by these three pathogens, while apoptosis signaling pathway was induced by fungus and bacteria. In summary, the results in this article provide insight into the process of blast fungus infection.'),
(69283, 2019, 'bib', 'A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies', 'Merino, Gabriela A|Conesa, Ana|Fernández, Elmer A', 'National University of Còrdoba, Argentina.|Microbiology and Cell Sciences Department of the University of Florida at Gainesville, FL, USA.|National Council of Science and Technology of Argentina.', 'Argentina|United States|Argentina', 5, 'Alternative Splicing|Benchmarking|Case-Control Studies|Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Male|Neoplasm Proteins|Prostate|Prostatic Neoplasms|Protein Isoforms|Sequence Analysis, RNA|Workflow', 'Journal Article', '10.1093/bib/bbx122', '29040385', 'Over the last few years, RNA-seq has been used to study alterations in alternative splicing related to several diseases. Bioinformatics workflows used to perform these studies can be divided into two groups, those finding changes in the absolute isoform expression and those studying differential splicing. Many computational methods for transcriptomics analysis have been developed, evaluated and compared, however, there are not enough reports of systematic and objective assessment of processing pipelines as a whole. Moreover, comparative studies have been performed considering separately the changes in absolute or relative isoform expression levels. Consequently, no consensus exists about the best practices and appropriate workflows to analyse alternative and differential splicing. To assist the adequate pipeline choice, we present here a benchmarking of nine commonly used workflows to detect differential isoform expression and splicing. We evaluated the workflows performance over different experimental scenarios where changes in absolute and relative isoform expression occurred simultaneously. In addition, the effect of the number of isoforms per gene, and the magnitude of the expression change over pipeline performances were also evaluated. Our results suggest that workflow performance is influenced by the number of replicates per condition and the conditions heterogeneity. In general, workflows based on DESeq2, DEXSeq, Limma and NOISeq performed well over a wide range of transcriptomics experiments. In particular, we suggest the use of workflows based on Limma when high precision is required, and DESeq2 and DEXseq pipelines to prioritize sensitivity. When several replicates per condition are available, NOISeq and Limma pipelines are indicated.'),
(69284, 2019, 'bib', 'Toward completion of the Earths proteome: an update a decade later', 'Mier, Pablo|Andrade-Navarro, Miguel A', 'Faculty of Biology, Johannes Gutenberg University Mainz, Gresemundweg, Mainz, Germany.|Faculty of Biology, Johannes Gutenberg University Mainz, Gresemundweg, Mainz, Germany.', 'Germany|Germany', 1, 'Animals|Computational Biology|Databases, Protein|Humans|Knowledge Bases|Proteins|Proteome|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bib/bbx127', '29040399', 'Protein databases are steadily growing driven by the spread of new more efficient sequencing techniques. This growth is dominated by an increase in redundancy (homologous proteins with various degrees of sequence similarity) and by the incapability to process and curate sequence entries as fast as they are created. To understand these trends and aid bioinformatic resources that might be compromised by the increasing size of the protein sequence databases, we have created a less-redundant protein data set. In parallel, we analyzed the evolution of protein sequence databases in terms of size and redundancy. While the SwissProt database has decelerated its growth mostly because of a focus on increasing the level of annotation of its sequences, its counterpart TrEMBL, much less limited by curation steps, is still in a phase of accelerated growth. However, we predict that before 2020, almost all entries deposited in UniProtKB will be homologous to known proteins. We propose that new sequencing projects can be made more useful if they are driven to sequencing voids, parts of the tree of life far from already sequenced species or model organisms. We show these voids are present in the Archaea and Eukarya domains of life. The approach to the certainty of the redundancy of new protein sequence entries leads to the consideration that most of the protein diversity on Earth has already been described, which we estimate to be of around 3.75 million proteins, revising down the prediction we did a decade ago.'),
(69285, 2019, 'bib', 'Towards dynamic genome-scale models', 'Gilbert, David|Heiner, Monika|Jayaweera, Yasoda|Rohr, Christian', 'Faculty of Biology, Johannes Gutenberg University Mainz, Gresemundweg, Mainz, Germany.|Faculty of Biology, Johannes Gutenberg University Mainz, Gresemundweg, Mainz, Germany.|Faculty of Biology, Johannes Gutenberg University Mainz, Gresemundweg, Mainz, Germany.|Faculty of Biology, Johannes Gutenberg University Mainz, Gresemundweg, Mainz, Germany.', 'Germany|Germany|Germany|Germany', 2, '-', 'Journal Article', '10.1093/bib/bbx096', '29040409', 'The analysis of the dynamic behaviour of genome-scale models of metabolism (GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions, such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large-up to whole genome in size-and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.'),
(69286, 2019, 'bib', 'Translational medicine in the Age of Big Data', 'Tatonetti, Nicholas P', 'Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, NY, USA.', 'United States', 1, 'Computational Biology|Data Mining|Databases, Factual|Humans|Information Storage and Retrieval|Medical Informatics|Precision Medicine|Translational Medical Research', 'Journal Article', '10.1093/bib/bbx116', '29040418', 'The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Collecting observations has always been a prerequisite for discovery, and great leaps in scientific understanding are accompanied by an expansion of this ability. Particle physics, astronomy and climate science, for example, have all greatly benefited from the development of new technologies enabling the collection of larger and more diverse data. Unlike medicine, however, each of these fields also has a mature theoretical framework on which new data can be evaluated and incorporated-to say it another way, there are no first principals from which a healthy human could be analytically derived. The worry, and it is a valid concern, is that, without a strong theoretical underpinning, the inundation of data will cause medical research to devolve into a haphazard enterprise without discipline or rigor. The Age of Big Data harbors tremendous opportunity for biomedical advances, but will also be treacherous and demanding on future scientists.'),
(69287, 2019, 'bib', 'Evaluation of variable selection methods for random forests and omics data sets', 'Degenhardt, Frauke|Seifert, Stephan|Szymczak, Silke', 'Institute of Clinical Molecular Biology, Kiel University, Germany.|Institute of Medical Informatics and Statistics, Kiel University, Germany.|Institute of Medical Informatics and Statistics, Kiel University, Germany.', 'Germany|Germany|Germany', 14, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Computer Simulation|DNA Methylation|Female|Gene Expression Profiling|Humans|Machine Learning', 'Evaluation Studies', '10.1093/bib/bbx124', '29045534', 'Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). xe2x80x83In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69288, 2019, 'bib', 'MicroRNAs and complex diseases: from experimental results to computational models', 'Chen, Xing|Xie, Di|Zhao, Qi|You, Zhu-Hong', 'School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Mathematics, Liaoning University.|School of Mathematics, Liaoning University.|Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science.', 'China|-|-|-', 62, 'Biomarkers, Tumor|Computational Biology|Genetic Association Studies|Genetic Predisposition to Disease|Humans|MicroRNAs|Neoplasms|RNA, Messenger', 'Journal Article', '10.1093/bib/bbx130', '29045685', 'Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.'),
(69289, 2019, 'bib', 'Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer', 'Adabor, Emmanuel S|Acquaah-Mensah, George K', 'Stellenbosch University.|armaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences, USA.', 'United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Humans|Machine Learning|Phenotype|Prognosis|Receptor, ErbB-2|Receptors, Estrogen|Receptors, Progesterone', 'Journal Article', '10.1093/bib/bbx138', '29045694', 'Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen receptors, progesterone receptors and HER2 receptors are difficult to treat. Regarding large data repositories such as The Cancer Genome Atlas, available wet-lab methods for establishing the presence of these receptors do not always conclusively cover all available samples. To this end, we introduce median-supplement methods to identify hormonal and HER2 receptor status phenotypes of breast cancer patients using gene expression profiles. In these approaches, supplementary instances based on median patient gene expression are introduced to balance a training set from which we build simple models to identify the receptor expression status of patients. In addition, for the purpose of benchmarking, we examine major machine learning approaches that are also applicable to the problem of finding receptor status in breast cancer. We show that our methods are robust and have high sensitivity with extremely low false-positive rates compared with the well-established methods. A successful application of these methods will permit the simultaneous study of large collections of samples of breast cancer patients as well as save time and cost while standardizing interpretation of outcomes of such studies.'),
(69290, 2019, 'bib', 'Computational tools for plant small RNA detection and categorization', 'Morgado, Lionel|Johannes, Frank', 'armaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences, USA.|armaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences, USA.', 'United States|United States', 5, '-', 'Journal Article', '10.1093/bib/bbx136', '29059285', 'Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.'),
(69291, 2019, 'bib', 'Precision medicine needs pioneering clinical bioinformaticians', 'Gómez-López, Gonzalo|Dopazo, Joaquín|Cigudosa, Juan C|Valencia, Alfonso|Al-Shahrour, Fátima', 'Spanish National Cancer Research Centre.|Clinical Bioinformatics Area of the Fundacio´n Progreso y Salud (Seville).|NIMGenetics, Madrid, Spain.|Barcelona Supercomputing Centre.|Spanish National Cancer Research Centre.', '-|-|Spain|-|-', 4, '-', 'Journal Article', '10.1093/bib/bbx144', '29077790', 'Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of precision bioinformatics, and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.'),
(69292, 2019, 'bib', 'Proteomics and phosphoproteomics in precision medicine: applications and challenges', 'Giudice, Girolamo|Petsalaki, Evangelia', 'European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.', '-|-', 2, '-', 'Journal Article', '10.1093/bib/bbx141', '29077858', 'Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.'),
(69293, 2019, 'bib', 'Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk', 'Li, Yongsheng|Jin, Xiyun|Wang, Zishan|Li, Lili|Chen, Hong|Lin, Xiaoyu|Yi, Song|Zhang, Yunpeng|Xu, Juan', 'European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.', '-|-|-|-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bbx137', '29077860', 'Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA-target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA-target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies.'),
(69294, 2017, 'bib', 'Editorial', 'Bishop, Martin', 'European Molecular Biology Laboratory European Bioinformatics Institute.', '-', 0, 'Computational Biology|Periodicals as Topic|Publishing', 'Editorial', '10.1093/bib/bbx145', '29077865', '-'),
(69295, 2019, 'bib', 'Evaluation and comparison of methods for recapitulation of 3D spatial chromatin structures', 'Park, Jincheol|Lin, Shili', 'European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.', '-|-', 0, '-', 'Journal Article', '10.1093/bib/bbx134', '29091999', 'How chromosomes fold and how distal genomic elements interact with one another at a genomic scale have been actively pursued in the past decade following the seminal work describing the Chromosome Conformation Capture (3C) assay. Essentially, 3C-based technologies produce two-dimensional (2D) contact maps that capture interactions between genomic fragments. Accordingly, a plethora of analytical methods have been proposed to take a 2D contact map as input to recapitulate the underlying whole genome three-dimensional (3D) structure of the chromatin. However, their performance in terms of several factors, including data resolution and ability to handle contact map features, have not been sufficiently evaluated. This task is taken up in this article, in which we consider several recent and/or well-regarded methods, both optimization-based and model-based, for their aptness of producing 3D structures using contact maps generated based on a population of cells. These methods are evaluated and compared using both simulated and real data. Several criteria have been used. For simulated data sets, the focus is on accurate recapitulation of the entire structure given the existence of the gold standard. For real data sets, comparison with distances measured by Florescence in situ Hybridization and consistency with several genomic features of known biological functions are examined.'),
(69296, 2019, 'bib', 'A generally applicable lightweight method for calculating a value structure for tools and services in bioinformatics infrastructure projects', 'Mayer, Gerhard|Quast, Christian|Felden, Janine|Lange, Matthias|Prinz, Manuel|Pühler, Alfred|Lawerenz, Chris|Scholz, Uwe|Glöckner, Frank Oliver|Müller, Wolfgang|Marcus, Katrin|Eisenacher, Martin', 'European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.|European Molecular Biology Laboratory European Bioinformatics Institute.', '-|-|-|-|-|-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bib/bbx140', '29092005', 'Sustainable noncommercial bioinformatics infrastructures are a prerequisite to use and take advantage of the potential of big data analysis for research and economy. Consequently, funders, universities and institutes as well as users ask for a transparent value model for the tools and services offered. In this article, a generally applicable lightweight method is described by which bioinformatics infrastructure projects can estimate the value of tools and services offered without determining exactly the total costs of ownership. Five representative scenarios for value estimation from a rough estimation to a detailed breakdown of costs are presented. To account for the diversity in bioinformatics applications and services, the notion of service-specific service provision units is introduced together with the factors influencing them and the main underlying assumptions for these value influencing factors. Special attention is given on how to handle personnel costs and indirect costs such as electricity. Four examples are presented for the calculation of the value of tools and services provided by the German Network for Bioinformatics Infrastructure (de.NBI): one for tool usage, one for (Web-based) database analyses, one for consulting services and one for bioinformatics training events. Finally, from the discussed values, the costs of direct funding and the costs of payment of services by funded projects are calculated and compared.'),
(69297, 2019, 'bib', 'Bioinformatics on a national scale: an example from Switzerland', 'Baillie Gerritsen, Vivienne|Palagi, Patricia M|Durinx, Christine', 'University of Geneva.|University of Brasilia.|University of Antwerp, Belgium.', '-|-|Belgium', 0, 'Algorithms|Computational Biology|Data Curation|Humans|Molecular Biology|Switzerland', 'Journal Article', '10.1093/bib/bbx073', '29106442', 'Switzerland has been a pioneer in the field of bioinformatics since the early 1980s. As time passed, the need for one entity to gather and represent bioinformatics on a national scale was felt and, in 1998, the SIB Swiss Institute of Bioinformatics was created. Hence, 2018 marks the Institutes 20th anniversary. Today, the Institute federates 65 research and service groups across the country-whose activity domains range from genomics, proteomics, medicine and health to structural biology, systems biology, phylogeny and evolution-and a group whose sole task is dedicated to training. The Institute hosts 12 competence centres that provide bioinformatics and biocuration expertise to life scientists across the country. SIB sensed early on that the wealth of data produced by modern technologies in medicine and the growing self-awareness of patients was about to revolutionize the way medical data are considered. In 2012, it created a Clinical Bioinformatics group to address the issue of personalized health, thus working towards a more global approach to patient management, and more targeted and effective therapies. In this respect, SIB has a major role in the Swiss Personalized Health Network to make patient-related data available to research throughout the country. The uniqueness of the Institutes governance structure has also inspired the structure of other European life science organizations, notably ELIXIR.'),
(69298, 2019, 'bib', 'An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies', 'Li, Boyang|Lu, Qiongshi|Zhao, Hongyu', 'Department of Biostatistics, Yale School of Public Health.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bbx131', '29106447', 'Functionally annotating genetic variations is an essential yet challenging topic in human genetics research. As large consortia including ENCODE and Roadmap Epigenomics Project continue to generate high-throughput transcriptomic and epigenomic data, many computational frameworks have been developed to integrate these experimental data to predict functionality of genetic variations in both protein-coding and noncoding regions. Here, we compare a number of recently developed annotation frameworks for noncoding regions through enrichment analysis on genome-wide association studies (GWASs). We also compare several different strategies to quantify enrichment using GWAS summary statistics. Our analyses highlight the importance of jointly modeling context-specific annotations with genome-wide data in providing statistically powerful and biologically interpretable enrichment for complex disease associations. Our findings provide insights into when and how computational genome annotations may benefit future complex disease studies on the genome-wide scale.'),
(69299, 2018, 'bib', 'CircView: a visualization and exploration tool for circular RNAs', 'Feng, Jing|Xiang, Yu|Xia, Siyu|Liu, Huan|Wang, Jun|Ozguc, Fatma Muge|Lei, Lijun|Kong, Ruoshan|Diao, Lixia|He, Chunjiang|Han, Leng', 'International School of Software, Wuhan University.|Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School.|School of Basic Medical Sciences, Wuhan University.|Wuhan Institute of Virology, Chinese Academy of Sciences.|School of Basic Medical Sciences, Wuhan University.|Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School.|School of Basic Medical Sciences, Wuhan University.|International School of Software, Wuhan University.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center.|School of Basic Medical Sciences and Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University.|Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School.', '-|-|-|-|-|-|-|-|-|-|-', 5, 'Evolution, Molecular|Nucleic Acid Conformation|RNA|Species Specificity', 'Journal Article', '10.1093/bib/bbx070', '29106456', 'Circular RNAs (circRNAs) are novel rising stars of noncoding RNAs, which are highly abundant and evolutionarily conserved across species. Number of publications related to circRNAs increased sharply in recent years, representing emerging focuses in the field. Therefore, tools, pipelines and databases have been developed to identify and store circRNAs. However, there is no existing tool to visualize and explore circRNAs. Therefore, we introduce CircView, a user-friendly visualization tool for circRNAs detected from existing tools. CircView enables users to visualize circRNAs and to quantify number of samples with detected circRNAs. CircView allows users to explore circRNAs detected by unique or multiple tools. Furthermore, CircView allows users to view the regulatory elements, such as microRNA response elements and RNA-binding protein binding sites. CircView is a unique tool to visualize and explore circRNAs, which helps users to better understand potential functions of circRNAs and design the functional experiments.'),
(69300, 2018, 'bib', 'A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia', 'Garali, Imene|Adanyeguh, Isaac M|Ichou, Farid|Perlbarg, Vincent|Seyer, Alexandre|Colsch, Benoit|Moszer, Ivan|Guillemot, Vincent|Durr, Alexandra|Mochel, Fanny|Tenenhaus, Arthur', 'Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Pierre and Marie Curie University.|ICANalytics department, institute of cardiometabolism and nutrition, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|SpectMet platform of the MedDay Pharmaceuticals company, Paris, France.|LEMM Laboratory at CEA-Saclay, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Institut Pasteur, Statistical Genetics group, Bioinformatics/Biostatistics Core Facility.|Pitié-Salpêtriére University Hospital in Paris.|University Pierre and Marie Curie (UPMC) and the Pitié-Salpêtriére University Hospital.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.', 'France|-|France|France|France|France|France|-|-|-|France', 13, 'Algorithms|Biomarkers|Brain|Case-Control Studies|Guidelines as Topic|Humans|Reproducibility of Results|Spinocerebellar Ataxias', 'Journal Article', '10.1093/bib/bbx060', '29106465', 'The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views. Such data sets are indeed intrinsically structured in blocks, where each block represents a set of variables observed on a group of individuals. Therefore, classical statistical tools cannot be applied without altering their organization, with the risk of information loss. Regularized generalized canonical correlation analysis (RGCCA) and its sparse generalized canonical correlation analysis (SGCCA) counterpart are component-based methods for exploratory analyses of data sets structured in blocks of variables. Rather than operating sequentially on parts of the measurements, the RGCCA/SGCCA-based integrative analysis method aims at summarizing the relevant information between and within the blocks. It processes a priori information defining which blocks are supposed to be linked to one another, thus reflecting hypotheses about the biology underlying the data blocks. It also requires the setting of extra parameters that need to be carefully adjusted.Here, we provide practical guidelines for the use of RGCCA/SGCCA. We also illustrate the flexibility and usefulness of RGCCA/SGCCA on a unique cohort of patients with four genetic subtypes of spinocerebellar ataxia, in which we obtained multiple data sets from brain volumetry and magnetic resonance spectroscopy, and metabolomic and lipidomic analyses. As a first step toward the extraction of multimodal biomarkers, and through the reduction to a few meaningful components and the visualization of relevant variables, we identified possible markers of disease progression.'),
(69301, 2019, 'bib', 'Establishing a distributed national research infrastructure providing bioinformatics support to life science researchers in Australia', 'Schneider, Maria Victoria|Griffin, Philippa C|Tyagi, Sonika|Flannery, Madison|Dayalan, Saravanan|Gladman, Simon|Watson-Haigh, Nathan|Bayer, Philipp E|Charleston, Michael|Cooke, Ira|Cook, Rob|Edwards, Richard J|Edwards, David|Gorse, Dominique|McConville, Malcolm|Powell, David|Wilkins, Marc R|Lonie, Andrew', 'Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France|Fr', 1, 'Australia|Biological Science Disciplines|Biomedical Research|Computational Biology|Data Curation|Humans', 'Journal Article', '10.1093/bib/bbx071', '29106479', 'EMBL Australia Bioinformatics Resource (EMBL-ABR) is a developing national research infrastructure, providing bioinformatics resources and support to life science and biomedical researchers in Australia. EMBL-ABR comprises 10 geographically distributed national nodes with one coordinating hub, with current funding provided through Bioplatforms Australia and the University of Melbourne for its initial 2-year development phase. The EMBL-ABR mission is to: (1) increase Australias capacity in bioinformatics and data sciences, (2) contribute to the development of training in bioinformatics skills, (3) showcase Australian data sets at an international level and (4) enable engagement in international programs. The activities of EMBL-ABR are focussed in six key areas, aligning with comparable international initiatives such as ELIXIR, CyVerse and NIH Commons. These key areas-Tools, Data, Standards, Platforms, Compute and Training-are described in this article.'),
(69302, 2019, 'bib', 'Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters', 'Blin, Kai|Kim, Hyun Uk|Medema, Marnix H|Weber, Tilmann', 'Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.|Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France.', 'France|France|France|France', 7, '-', 'Journal Article', '10.1093/bib/bbx146', '29112695', 'Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.'),
(69303, 2019, 'bib', 'Comprehensive evaluation of non-hybrid genome assembly tools for third-generation PacBio long-read sequence data', 'Jayakumar, Vasanthan|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, Japan.|Department of Biosciences and Informatics, Keio University, Japan.', 'Japan|Japan', 22, '-', 'Journal Article', '10.1093/bib/bbx147', '29112696', 'Long reads obtained from third-generation sequencing platforms can help overcome the long-standing challenge of the de novo assembly of sequences for the genomic analysis of non-model eukaryotic organisms. Numerous long-read-aided de novo assemblies have been published recently, which exhibited superior quality of the assembled genomes in comparison with those achieved using earlier second-generation sequencing technologies. Evaluating assemblies is important in guiding the appropriate choice for specific research needs. In this study, we evaluated 10 long-read assemblers using a variety of metrics on Pacific Biosciences (PacBio) data sets from different taxonomic categories with considerable differences in genome size. The results allowed us to narrow down the list to a few assemblers that can be effectively applied to eukaryotic assembly projects. Moreover, we highlight how best to use limited genomic resources for effectively evaluating the genome assemblies of non-model organisms.'),
(69304, 2019, 'bib', 'Coloured Petri nets for multilevel, multiscale and multidimensional modelling of biological systems', 'Liu, Fei|Heiner, Monika|Gilbert, David', 'School of Software Engineering, South China University of Technology, Guangzhou, P.R. China.|Department of Computer Science, Brandenburg University of Technology Cottbus-Senftenberg.|Department of Computer Science, Brunel University London.', 'China|-|-', 9, '-', 'Journal Article', '10.1093/bib/bbx150', '29112705', 'Owing to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional modelling of some biological systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional modelling of biological systems. This understanding of how to use ColPNs for modelling biological systems will assist readers in selecting appropriate ColPN classes for specific modelling circumstances.'),
(69305, 2019, 'bib', 'Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma', 'Shahriyari, Leili', 'Mathematical Biosciences Institute, Ohio State University, Columbus, Ohio, USA.', 'United States', 12, '-', 'Journal Article', '10.1093/bib/bbx153', '29112707', 'One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes).'),
(69306, 2019, 'bib', 'Privacy-preserving techniques of genomic data-a survey', 'Aziz, Md Momin Al|Sadat, Md Nazmus|Alhadidi, Dima|Wang, Shuang|Jiang, Xiaoqian|Brown, Cheryl L|Mohammed, Noman', 'Department of Computer Science at the University of Manitoba, Winnipeg, Canada.|Department of Computer Science at the University of Manitoba, Winnipeg, Canada.|Faculty of Computer Science at the University of New Brunswick, Frederiction, Canada.|Department of Biomedical Informatics at the University of California in San Diego, La Jolla, CA, USA.|Department of Biomedical Informatics at the University of California in San Diego, La Jolla, CA, USA.|Department of Political Science and Public Administration at the University of North Carolina at Charlotte, NC, USA.|Department of Computer Science at the University of Manitoba, Winnipeg, Canada.', 'Canada|Canada|Canada|United States|United States|United States|Canada', 2, '-', 'Journal Article', '10.1093/bib/bbx139', '29121240', 'Genomic data hold salient information about the characteristics of a living organism. Throughout the past decade, pinnacle developments have given us more accurate and inexpensive methods to retrieve genome sequences of humans. However, with the advancement of genomic research, there is a growing privacy concern regarding the collection, storage and analysis of such sensitive human data. Recent results show that given some background information, it is possible for an adversary to reidentify an individual from a specific genomic data set. This can reveal the current association or future susceptibility of some diseases for that individual (and sometimes the kinship between individuals) resulting in a privacy violation. Regardless of these risks, our genomic data hold much importance in analyzing the well-being of us and the future generation. Thus, in this article, we discuss the different privacy and security-related problems revolving around human genomic data. In addition, we will explore some of the cardinal cryptographic concepts, which can bring efficacy in secure and private genomic data computation. This article will relate the gaps between these two research areas-Cryptography and Genomics.'),
(69307, 2019, 'bib', 'A survey of software tools for microRNA discovery and characterization using RNA-seq', 'Bortolomeazzi, Michele|Gaffo, Enrico|Bortoluzzi, Stefania', 'Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.', 'Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1093/bib/bbx148', '29126230', 'Since the small RNA-sequencing (sRNA-seq) technology became available, it allowed the discovery of thousands new microRNAs (miRNAs) in humans and many other species, providing new data on these small RNAs (sRNAs) of high biological and translational relevance. MiRNA discovery has not yet reached saturation, even in the most studied model organisms, and many researchers are using sRNA-seq in studies with different aims in biomedicine, fundamental research and in applied animal sciences. We review several miRNA discovery and characterization software tools that implement different strategies, providing a useful guide for researchers to select the programs best suiting their study objectives and data. After a brief introduction on miRNA biogenesis, function and characteristics, useful to understand the biological background considered by the algorithms, we survey the current state of miRNA discovery bioinformatics discussing 26 different sRNA-seq-based miRNA prediction software and toolkits released in the past 6 years, including 15 methods specific for miRNA prediction and 11 more general-purpose software suites for sRNA-seq data analysis. We highlight the main features of mature miRNAs and miRNA precursors considered by the methods categorizing them according to prediction strategy and implementation. In addition, we describe a typical miRNA prediction and analysis workflow by delineating the objectives, potentialities and main steps of sRNA-seq data analysis projects, from preparatory data processing to miRNA prediction, quantification and diverse downstream analyses. Finally, we outline the caveats affecting sRNA-seq-based prediction tools, and we indicate the possibilities offered by data set pooling and by integration with other types of high-throughput sequencing data.'),
(69308, 2019, 'bib', 'RNA methylation and diseases: experimental results, databases, Web servers and computational models', 'Chen, Xing|Sun, Ya-Zhou|Liu, Hui|Zhang, Lin|Li, Jian-Qiang|Meng, Jia', 'School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|Department of Biological Sciences, Xian Jiaotong-Liverpool University.', 'China|China|China|China|China|-', 11, '-', 'Journal Article', '10.1093/bib/bbx142', '29165544', 'Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research.'),
(69309, 2019, 'bib', 'Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches', 'Wang, Jiawei|Yang, Bingjiao|An, Yi|Marquez-Lago, Tatiana|Leier, André|Wilksch, Jonathan|Hong, Qingyang|Zhang, Yang|Hayashida, Morihiro|Akutsu, Tatsuya|Webb, Geoffrey I|Strugnell, Richard A|Song, Jiangning|Lithgow, Trevor', 'Biomedicine Discovery Institute and the Department of Microbiology at Monash University, Australia.|National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, College of Mechanical Engineering from Yanshan University, China.|College of Information Engineering, Northwest A&F University, China.|Department of Genetics, University of Alabama at Birmingham (UAB) School of Medicine, USA.|Department of Genetics and the Informatics Institute, University of Alabama at Birmingham (UAB) School of Medicine, USA.|Department of Microbiology and Immunology at the University of Melbourne, Australia.|Central South University, China.|Computer Science and Engineering in 2015 fromNorthwestern Polytechnical University, China.|Informatics in 2005 from Kyoto University, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan.|Faculty of Information Technology, Monash Centre for Data Science, Monash University.|Department of Microbiology and Immunology, Faculty of Medicine Dentistry and Health Sciences, University of Melbourne.|Department of Biochemistry and Molecular Biology, Monash University, Australia.|Department of Microbiology at Monash University, Australia.', 'Australia|China|China|United States|United States|Australia|China|China|Japan|Japan|United States|United States|Australia|Australia', 14, '-', 'Journal Article', '10.1093/bib/bbx164', '29186295', 'In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naïve Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs, (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the majority voting strategy led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections.'),
(69310, 2019, 'bib', 'Translational bioinformatics in mental health: open access data sources and computational biomarker discovery', 'Tenenbaum, Jessica D|Bhuvaneshwar, Krithika|Gagliardi, Jane P|Fultz Hollis, Kate|Jia, Peilin|Ma, Liang|Nagarajan, Radhakrishnan|Rakesh, Gopalkumar|Subbian, Vignesh|Visweswaran, Shyam|Zhao, Zhongming|Rozenblit, Leon', 'Department of Biostatistics and Bioinformatics at the Duke University School of Medicine.|Innovation Center for Biomedical Informatics (ICBI) at Georgetown University.|Duke University School of Medicine.|Department of Biomedical Informatics and Clinical Epidemiology at Oregon Health and Science University.|University of Texas Health Science Center at Houston.|Bioinformatics and Systems Medicine Laboratory (BSML), Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston.|College of Medicine, University of Kentucky.|Duke University Medical Center.|Department of Biomedical Engineering and the Department of Systems and Industrial Engineering at the University of Arizona.|University of Pittsburgh.|University of Texas Health Science Center at Houston.|Prometheus Research.', '-|-|-|-|-|-|-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bbx157', '29186302', 'Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.'),
(69311, 2019, 'bib', 'Construction of a hierarchical gene regulatory network centered around a transcription factor', 'Wei, Hairong', 'State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang, China.', 'China', 1, '-', 'Journal Article', '10.1093/bib/bbx152', '29186304', 'We have modified a multitude of transcription factors (TFs) in numerous plant species and some animal species, and obtained transgenic lines that exhibit phenotypic alterations. Whenever we observe phenotypic changes in a TFs transgenic lines, we are always eager to identify its target genes, collaborative regulators and even upstream high hierarchical regulators. This issue can be addressed by establishing a multilayered hierarchical gene regulatory network (ML-hGRN) centered around a given TF. In this article, a practical approach for constructing an ML-hGRN centered on a TF using a combined approach of top-down and bottom-up network construction methods is described. Strategies for constructing ML-hGRNs are vitally important, as these networks provide key information to advance our understanding of how biological processes are regulated.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69312, 2019, 'bib', 'Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches', 'Oulas, Anastasis|Minadakis, George|Zachariou, Margarita|Sokratous, Kleitos|Bourdakou, Marilena M|Spyrou, George M', 'Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.|Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.|Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.|Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.|Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.|Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.', 'Cyprus|Cyprus|Cyprus|Cyprus|Cyprus|Cyprus', 17, '-', 'Journal Article', '10.1093/bib/bbx151', '29186305', 'Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the systems individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.'),
(69313, 2019, 'bib', 'Resources and tools for the high-throughput, multi-omic study of intestinal microbiota', 'Blanco-Míguez, Aitor|Fdez-Riverola, Florentino|Sánchez, Borja|Lourenço, Anália', 'Computer Science Doctoral programme, University of Vigo.|Biomedical Research Centre (CINBIO), University of Vigo.|IPLA-CSIC.|Dpto. de Informática - Universidade de Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.', '-|-|-|Spain', 4, '-', 'Journal Article', '10.1093/bib/bbx156', '29186315', 'The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.'),
(69314, 2019, 'bib', 'A review of databases predicting the effects of SNPs in miRNA genes or miRNA-binding sites', 'Fehlmann, Tobias|Sahay, Shashwat|Keller, Andreas|Backes, Christina', 'Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bib/bbx155', '29186316', 'Modern precision medicine comprises the knowledge and understanding of individual differences in the genomic sequence of patients to provide tailor-made treatments. Regularly, such variants are considered in coding regions only, and their effects are predicted based on their impact on the amino acid sequence of expressed proteins. However, assessing the effects of variants in noncoding elements, in particular microRNAs (miRNAs) and their binding sites, is important as well, as a single miRNA can influence the expression patterns of many genes at the same time. To analyze the effects of variants in miRNAs and their target sites, several databases storing variant impact predictions have been published. In this review, we will compare the core functionalities and features of these databases and discuss the importance of up-to-date data resources in the context of web applications. Finally, we will outline some recommendations for future developments in the field.'),
(69315, 2019, 'bib', 'Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders', 'Dovrolis, Nikolas|Kolios, George|Spyrou, George M|Maroulakou, Ioanna', 'Democritus University of Thrace, Greece.|Democritus University of Thrace, Greece.|Cyprus Institute of Neurology and Genetics.|University of Thrace.', 'Greece|Greece|Cyprus|-', 11, '-', 'Journal Article', '10.1093/bib/bbx154', '29186317', 'Almost 2500 years after Hippocrates observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microfloras compositional diversity regulates via its genes (the microbiome) the hosts health and provides preliminary insights into disease progression and regulation. The microbiomes involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the hosts phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.'),
(69316, 2019, 'bib', 'Computer-aided biomarker discovery for precision medicine: data resources, models and applications', 'Lin, Yuxin|Qian, Fuliang|Shen, Li|Chen, Feifei|Chen, Jiajia|Shen, Bairong', 'Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.|Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.|Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.|Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.|School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, China.|Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.', 'China|China|China|China|China|China', 7, '-', 'Journal Article', '10.1093/bib/bbx158', '29194464', 'Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the big data term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.'),
(69317, 2019, 'bib', 'Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis', 'Chen, Yun-Ru|Huang, Hsuan-Cheng|Lin, Chen-Ching', 'Institute of Biomedical Informatics, National Yang-Ming University, Taipei.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bib/bbx166', '29194477', 'The development of disease involves a systematic disturbance inside cells and is associated with changes in the interactions or regulations among genes forming biological networks. The bridges inside a network are critical in shortening the distances between nodes. We observed that, inside the human gene regulatory network, one strongly connected core bridged the whole network. Other regulations outside the core formed a weakly connected component surrounding the core like a peripheral structure. Furthermore, the regulatory feedback loops (FBLs) inside the core compose an interface-like structure between the core and periphery. We then denoted the regulatory FBLs as the interface core. Notably, both the cancer-associated and essential biomolecules and regulations were significantly overrepresented in the interface core. These results implied that the interface core is not only critical for the network structure but central in cellular systems. Furthermore, the enrichment of the cancer-associated and essential regulations in the interface core might be attributed to its bridgeness in the network. More importantly, we identified one regulatory FBL between HNF4A and NR2F2 that possesses the highest bridgeness in the interface core. Further investigation suggested that the disturbance of the HNF4A-NR2F2 FBL might protect tumor cells from apoptotic processes. Our results emphasize the relevance of the regulatory network properties to cellular systems and might reveal a critical role of the interface core in cancer.'),
(69318, 2019, 'bib', 'Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates', 'Laczny, Cedric C|Galata, Valentina|Plum, Achim|Posch, Andreas E|Keller, Andreas', 'Chair for Clinical Bioinformatics at Saarland University.|Chair for Clinical Bioinformatics at Saarland University.|Ares Genetics GmbH and Curetis GmbH.|Ares Genetics GmbH.|Chair for Clinical Bioinformatics at Saarland University.', '-|-|-|-|-', 6, '-', 'Journal Article', '10.1093/bib/bbx162', '29220507', 'High-throughput next-generation shotgun sequencing of pathogenic bacteria is growing in clinical relevance, especially for chromosomal DNA-based taxonomic identification and for antibiotic resistance prediction. Genetic exchange is facilitated for extrachromosomal DNA, e.g. plasmid-borne antibiotic resistance genes. Consequently, accurate identification of plasmids from whole-genome sequencing (WGS) data remains one of the major challenges for sequencing-based precision medicine in infectious diseases. Here, we assess the heterogeneity of four state-of-the-art tools (cBar, PlasmidFinder, plasmidSPAdes and Recycler) for the in silico prediction of plasmid-derived sequences from WGS data. Heterogeneity, sensitivity and precision were evaluated by reference-independent and reference-dependent benchmarking using 846 Gram-negative clinical isolates. Interestingly, the majority of predicted sequences were tool-specific, resulting in a pronounced heterogeneity across tools for the reference-independent assessment. In the reference-dependent assessment, sensitivity and precision values were found to substantially vary between tools and across taxa, with cBar exhibiting the highest median sensitivity (87.45%) but a low median precision (27.05%). Furthermore, integrating the individual tools into an ensemble approach showed increased sensitivity (95.55%) while reducing the precision (25.62%). CBar and plasmidSPAdes exhibited the strongest concordance with respect to identified antibiotic resistance factors. Moreover, false-positive plasmid predictions typically contained only few antibiotic resistance factors. In conclusion, while high degrees of heterogeneity and variation in sensitivity and precision were observed across the different tools and taxa, existing tools are valuable for investigating the plasmid-borne resistome. Nevertheless, additional studies on representative clinical data sets will be necessary to translate in silico plasmid prediction approaches from research to clinical application.'),
(69319, 2019, 'bib', 'Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine', 'Zanin, Massimiliano|Chorbev, Ivan|Stres, Blaz|Stalidzans, Egils|Vera, Julio|Tieri, Paolo|Castiglione, Filippo|Groen, Derek|Zheng, Huiru|Baumbach, Jan|Schmid, Johannes A|Basilio, José|Klimek, Peter|Debeljak, Nataša|Rozman, Damjana|Schmidt, Harald H H W', 'Universidade Nove de Lisboa.|Faculty for Computer Science and Engineering, University Ss. Cyril and Methodius in Skopje.|Microbiology at University of Ljubljana, Slovenia.|Virtual Physiological Human Institute.|Systems Tumor Immunology at the FAU Erlangen-Nuremberg, Germany.|Network biology, systems medicine and theoretical immunology.|Complex systems andmathematical biology.|Simulation and Modelling at Brunel University London.|Computer Science at Ulster University, United Kingdom.|Computational Biomedicine, University of Southern Denmark.|Inflammation, cardiovascular diseases and cancer, at molecular, cellular and clinical levels.|Medical University of Vienna.|Section for Science of Complex Systems at the Medical University of Vienna.|University of Ljubljana, Faculty of Medicine.|University of Ljubljana, Faculty of Medicine.|Target discovery and drug development.', '-|-|Slovenia|-|Germany|-|-|-|United Kingdom|Denmark|-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bbx160', '29220509', 'Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.'),
(69320, 2019, 'bib', 'A survey and evaluations of histogram-based statistics in alignment-free sequence comparison', 'Luczak, Brian B|James, Benjamin T|Girgis, Hani Z', 'Target discovery and drug development.|Target discovery and drug development.|Target discovery and drug development.', '-|-|-', 3, '-', 'Journal Article', '10.1093/bib/bbx161', '29220512', 'Since the dawn of the bioinformatics field, sequence alignment scores have been the main method for comparing sequences. However, alignment algorithms are quadratic, requiring long execution time. As alternatives, scientists have developed tens of alignment-free statistics for measuring the similarity between two sequences.'),
(69321, 2019, 'bib', 'A snapshot of 3649 Web-based services published between 1994 and 2017 shows a decrease in availability after 2 years', 'xc5x90sz, xc3x81gnes|Pongor, Lxc5x91rinc Sxc3xa1ndor|Szirmai, Danuta|Gyxc5x91rffy, Balxc3xa1zs', 'MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary', 2, '-', 'Journal Article', '10.1093/bib/bbx159', '29228189', 'The long-term availability of online Web services is of utmost importance to ensure reproducibility of analytical results. However, because of lack of maintenance following acceptance, many servers become unavailable after a short period of time. Our aim was to monitor the accessibility and the decay rate of published Web services as well as to determine the factors underlying trends changes.'),
(69322, 2019, 'bib', 'Conceptual and computational framework for logical modelling of biological networks deregulated in diseases', 'Montagud, Arnau|Traynard, Pauline|Martignetti, Loredana|Bonnet, Eric|Barillot, Emmanuel|Zinovyev, Andrei|Calzone, Laurence', 'MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 3, '-', 'Journal Article', '10.1093/bib/bbx163', '29237040', 'Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical models properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline.'),
(69323, 2019, 'bib', 'Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains', 'Zhang, Jian|Ma, Zhiqiang|Kurgan, Lukasz', 'MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.', 'Hungary|Hungary|Hungary', 19, '-', 'Journal Article', '10.1093/bib/bbx168', '29253082', 'Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein-DNA or protein-RNA binding, only a few have a wider scope that covers both protein-protein and protein-nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.'),
(69324, 2018, 'bib', 'Bioinformatics in the Netherlands: the value of a nationwide community', 'van Gelder, Celia W G|Hooft, Rob W W|van Rijswijk, Merlijn N|van den Berg, Linda|Kok, Ruben G|Reinders, Marcel|Mons, Barend|Heringa, Jaap', 'MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.|MTA TTK Lendxc3xbclet Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudxc3xb3sok kxc3xb6rxc3xbatja 2, H-1117, Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 0, '-', 'Journal Article', '10.1093/bib/bbx171', '29267862', '-'),
(69325, 2019, 'bib', 'Bioinformatics for precision oncology', 'Singer, Jochen|Irmisch, Anja|Ruscheweyh, Hans-Joachim|Singer, Franziska|Toussaint, Nora C|Levesque, Mitchell P|Stekhoven, Daniel J|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland.|Department of Dermatology at the University of Zurich Hospital in Zurich, Switzerland.|Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland.|ETH Zurich in Basel, Switzerland.|ETH Zurich in Zurich, Switzerland.|University of Zurich Hospital in Zurich, Switzerland.|ETH Zurich in Zurich, Switzerland.|Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 9, '-', 'Journal Article', '10.1093/bib/bbx143', '29272324', 'Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations.'),
(69326, 2019, 'bib', 'Developing a personalome for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes', 'Vitali, Francesca|Li, Qike|Schissler, A Grant|Berghout, Joanne|Kenost, Colleen|Lussier, Yves A', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Nevada, Reno.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States|United States|United States', 5, '-', 'Journal Article', '10.1093/bib/bbx149', '29272327', 'The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individuals -omics profile (personalome), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about average disease processes, thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subjects personalome for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive personalomes through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.'),
(69327, 2019, 'bib', 'Multi-omics integration-a comparison of unsupervised clustering methodologies', 'Tini, Giulia|Marchetti, Luca|Priami, Corrado|Scott-Boyer, Marie-Pier', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States', 19, '-', 'Journal Article', '10.1093/bib/bbx167', '29272335', 'With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.'),
(69328, 2019, 'bib', 'BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches', 'Liu, Bin', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States', 93, '-', 'Journal Article', '10.1093/bib/bbx165', '29272359', 'With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize users convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.'),
(69329, 2019, 'bib', 'Link synthetic lethality to drug sensitivity of cancer cells', 'Wang, Ruiping|Han, Yue|Zhao, Zhangxiang|Yang, Fan|Chen, Tingting|Zhou, Wenbin|Wang, Xianlong|Qi, Lishuang|Zhao, Wenyuan|Guo, Zheng|Gu, Yunyan', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bib/bbx172', '29300844', 'Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%xe2x88xbc34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.'),
(69330, 2019, 'bib', 'Drug knowledge bases and their applications in biomedical informatics research', 'Zhu, Yongjun|Elemento, Olivier|Pathak, Jyotishman|Wang, Fei', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bib/bbx169', '29304188', 'Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.'),
(69331, 2018, 'bib', 'Metabolomics technology and bioinformatics for precision medicine', 'Azad, Rajeev K|Shulaev, Vladimir', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States', 6, '-', 'Journal Article', '10.1093/bib/bbx170', '29304189', 'Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.'),
(69332, 2019, 'bib', 'SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue', 'Tang, Qin|Zhang, Qiong|Lv, Yao|Miao, Ya-Ru|Guo, An-Yuan', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bib/bbx173', '29309507', 'Human specifically expressed genes (SEGs) usually serve as potential biomarkers for disease diagnosis and treatment. However, the regulation underlying their specific expression remains to be revealed. In this study, we constructed SEG regulation database (SEGreg, available at http://bioinfo.life.hust.edu.cn/SEGreg) for showing SEGs and their transcription factors (TFs) and microRNA (miRNA) regulations under different physiological conditions, which include normal tissue, cancer tissue and cell line. In total, SEGreg collected 6387, 1451, 4506 and 5320 SEGs from expression profiles of 34 cancer types and 55 tissues of The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Human Body Map and Genotype-Tissue Expression databases/projects, respectively. The cancer or tissue corresponding expressed miRNAs and TFs were identified from miRNA and gene expression profiles, and their targets were collected from several public resources. Then the regulatory networks of all SEGs were constructed and integrated into SEGreg. Through a user-friendly interface, users can browse and search SEGreg by gene name, data source, tissue, cancer type and regulators. In summary, SEGreg is a specialized resource to explore SEGs and their regulations, which provides clues to reveal the mechanisms of carcinogenesis and biological processes.'),
(69333, 2019, 'bib', 'Recent development of Ori-Finder system and DoriC database for microbial replication origins', 'Luo, Hao|Quan, Chun-Lan|Peng, Chong|Gao, Feng', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States', 11, '-', 'Journal Article', '10.1093/bib/bbx174', '29329409', 'DNA replication begins at replication origins in all three domains of life. Identification and characterization of replication origins are important not only in providing insights into the structure and function of the replication origins but also in understanding the regulatory mechanisms of the initiation step in DNA replication. The Z-curve method has been used in the identification of replication origins in archaeal genomes successfully since 2002. Furthermore, the Web servers of Ori-Finder and Ori-Finder 2 have been developed to predict replication origins in both bacterial and archaeal genomes based on the Z-curve method, and the replication origins with manual curation have been collected into an online database, DoriC. Ori-Finder system and DoriC database are currently used in the research field of DNA replication origins in prokaryotes, including: (i) identification of oriC regions in bacterial and archaeal genomes, (ii) discovery and analysis of the conserved sequences within oriC regions, and (iii) strand-biased analysis of bacterial genomes. Up to now, more and more predicted results by Ori-Finder system were supported by subsequent experiments, and Ori-Finder system has been used to identify the replication origins inxe2x80x89>xe2x80x89100 newly sequenced prokaryotes in their genome reports. In addition, the data in DoriC database have been widely used in the large-scale analyses of replication origins and strand bias in prokaryotic genomes. Here, we review the development of Ori-Finder system and DoriC database as well as their applications. Some future directions and aspects for extending the application of Ori-Finder and DoriC are also presented.'),
(69334, 2019, 'bib', 'Characteristics and evolution of the ecosystem of software tools supporting research in molecular biology', 'Pazos, Florencio|Chagoyen, Monica', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bby001', '29351590', 'Daily work in molecular biology presently depends on a large number of computational tools. An in-depth, large-scale study of that ecosystem of Web tools, its characteristics, interconnectivity, patterns of usage/citation, temporal evolution and rate of decay is crucial for understanding the forces that shape it and for informing initiatives aimed at its funding, long-term maintenance and improvement. In particular, the long-term maintenance of these tools is compromised because of their specific development model. Hundreds of published studies become irreproducible de facto, as the software tools used to conduct them become unavailable. In this study, we present a large-scale survey of >5400 publications describing Web servers within the two main bibliographic resources for disseminating new software developments in molecular biology. For all these servers, we studied their citation patterns, the subjects they address, their citation networks and the temporal evolution of these factors. We also analysed how these factors affect the availability of these servers (whether they are alive). Our results show that this ecosystem of tools is highly interconnected and adapts to the trendy subjects in every moment. The servers present characteristic temporal patterns of citation/usage, and there is a worrying rate of server death, which is influenced by factors such as the server popularity and the institutions that hosts it. These results can inform initiatives aimed at the long-term maintenance of these resources.'),
(69335, 2019, 'bib', 'Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey', 'Ezzat, Ali|Wu, Min|Li, Xiao-Li|Kwoh, Chee-Keong', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bib/bby002', '29377981', 'Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69336, 2019, 'bib', 'Upstream analysis of alternative splicing: a review of computational approaches to predict context-dependent splicing factors', 'Carazo, Fernando|Romero, Juan P|Rubio, Angel', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States', 8, '-', 'Journal Article', '10.1093/bib/bby005', '29390045', 'Alternative splicing (AS) has shown to play a pivotal role in the development of diseases, including cancer. Specifically, all the hallmarks of cancer (angiogenesis, cell immortality, avoiding immune system response, etc.) are found to have a counterpart in aberrant splicing of key genes. Identifying the context-specific regulators of splicing provides valuable information to find new biomarkers, as well as to define alternative therapeutic strategies. The computational models to identify these regulators are not trivial and require three conceptual steps: the detection of AS events, the identification of splicing factors that potentially regulate these events and the contextualization of these pieces of information for a specific experiment. In this work, we review the different algorithmic methodologies developed for each of these tasks. Main weaknesses and strengths of the different steps of the pipeline are discussed. Finally, a case study is detailed to help the reader be aware of the potential and limitations of this computational approach.'),
(69337, 2019, 'bib', 'How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives', 'Dal Molin, Alessandra|Di Camillo, Barbara', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States', 5, '-', 'Journal Article', '10.1093/bib/bby007', '29394315', 'The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow. In this review, we aim to provide an overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, we suggest possible analysis frameworks depending on specific experimental designs. Together, we propose an evaluation of challenges and open questions and future perspectives in the field. In particular, we go through the different steps of scRNA-seq experimental protocols such as cell isolation, messenger RNA capture, reverse transcription, amplification and use of quantitative standards such as spike-ins and Unique Molecular Identifiers (UMIs). We then analyse the current methodological challenges related to preprocessing, alignment, quantification, normalization, batch effect correction and methods to control for confounding effects.'),
(69338, 2019, 'bib', 'Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems', 'Zalguizuri, Andrés|Caetano-Anollés, Gustavo|Lepek, Viviana Claudia', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bby009', '29394318', 'In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu.ar/orgsissec/.'),
(69339, 2019, 'bib', 'Modeling biological problems in computer science: a case study in genome assembly', 'Medvedev, Paul', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States', 2, '-', 'Journal Article', '10.1093/bib/bby003', '29394324', 'As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. In this tutorial, we use the example of the genome assembly problem to demonstrate how to go from a question in the biological realm to a solution in the computer science realm. We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded.'),
(69340, 2019, 'bib', 'Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer', 'Zhang, Junpeng|Le, Thuc Duy|Liu, Lin|Li, Jiuyong', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bby008', '29401217', 'It is known that noncoding RNAs (ncRNAs) cover xe2x88xbc98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA-mRNA interactions and the third-party databases of storing lncRNA-mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA-mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA-mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.'),
(69341, 2019, 'bib', 'Microarray is an efficient tool for circRNA profiling', 'Li, Shasha|Teng, Shuaishuai|Xu, Junquan|Su, Guannan|Zhang, Yu|Zhao, Jianqing|Zhang, Suwei|Wang, Haiyan|Qin, Wenyan|Lu, Zhi John|Guo, Yong|Zhu, Qianyong|Wang, Dong', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 27, '-', 'Journal Article', '10.1093/bib/bby006', '29415187', 'Circular RNAs (circRNAs) are emerging as a new class of endogenous and regulatory noncoding RNAs in latest years. With the widespread application of RNA sequencing (RNA-seq) technology and bioinformatics prediction, large numbers of circRNAs have been identified. However, at present, we lack a comprehensive characterization of all these circRNAs in interested samples. In this study, we integrated 87xc2xa0935 circRNAs sequences that cover most of circRNAs identified till now represented in circBase to design microarray probes targeting back-splice site of each circRNA to profile expression of those circRNAs. By comparing the circRNA detection efficiency of RNA-seq with this circRNA microarray, we revealed that microarray is more efficient than RNA-seq for circRNA profiling. Then, we found xe2x88xbc80xc2xa0000 circRNAs were expressed in cervical tumors and matched normal tissues, and xe2x88xbc25xc2xa0000 of them were differently expressed. Notably, many of these circRNAs detected by this microarray can be validated by quantitative reverse transcription polymerase chain reaction (RT-qPCR) or RNA-seq. Strikingly, as many as xe2x88xbc18xc2xa0000 circRNAs could be robustly detected in cell-free plasma samples, and the expression of xe2x88xbc2700 of them differed after surgery for tumor removal. Our findings provided a comprehensive and genome-wide characterization of circRNAs in paired normal tissues and tumors and plasma samples from multiple individuals. In addition, we also provide a rich resource with 41 microarray data sets and 10 RNA-seq data sets and strong evidences for circRNA expression in cervical cancer. In conclusion, circRNAs could be efficiently profiled by circRNA microarray to target their reported back-splice sites in interested samples.'),
(69342, 2019, 'bib', 'Open-source chemogenomic data-driven algorithms for predicting drug-target interactions', 'Hao, Ming|Bryant, Stephen H|Wang, Yanli', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States', 12, '-', 'Journal Article', '10.1093/bib/bby010', '29420684', 'While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.'),
(69343, 2019, 'bib', 'Artificial intelligence in drug combination therapy', 'Tsigelny, Igor F', 'BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States', 1, '-', 'Journal Article', '10.1093/bib/bby004', '29438494', 'Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.'),
(69344, 2019, 'bib', 'The BioCyc collection of microbial genomes and metabolic pathways', 'Karp, Peter D|Billington, Richard|Caspi, Ron|Fulcher, Carol A|Latendresse, Mario|Kothari, Anamika|Keseler, Ingrid M|Krummenacker, Markus|Midford, Peter E|Ong, Quang|Ong, Wai Kit|Paley, Suzanne M|Subhraveti, Pallavi', 'SRI Bioinformatics Research Group (BRG).|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.', '-|-|-|-|-|-|-|-|-|-|-|-|-', 20, '-', 'Journal Article', '10.1093/bib/bbx085', '29447345', 'BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of query tools, visualization services and analysis software. Recent advances in BioCyc include an expansion in the content of BioCyc in terms of both the number of genomes and the types of information available for each genome, an expansion in the amount of curated content within BioCyc, and new developments in the BioCyc software tools including redesigned gene/protein pages and metabolite pages, new search tools, a new sequence-alignment tool, a new tool for visualizing groups of related metabolic pathways, and a facility called SmartTables, which enables biologists to perform analyses that previously would have required a programmers assistance.'),
(69345, 2018, 'bib', 'Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes', 'Niu, Sheng-Yong|Yang, Jinyu|McDermaid, Adam|Zhao, Jing|Kang, Yu|Ma, Qin', 'SRI International.|SRI International.|SRI International.|SRI International.|SRI International.|SRI International.', '-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bby012', '29481588', '-'),
(69346, 2019, 'bib', 'Recent developments of software and database in microbial genomics and functional genomics', 'Gao, Feng', 'Department of Physics, School of Science, Tianjin University, Tianjin, China.', 'China', 0, '-', 'Research Support, Non-U.S. Govt', '10.1093/bib/bby013', '29481602', '-'),
(69347, 2019, 'bib', 'Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data', 'Yip, Shun H|Sham, Pak Chung|Wang, Junwen', 'Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.', 'China|China|China', 15, '-', 'Journal Article', '10.1093/bib/bby011', '29481632', 'Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.'),
(69348, 2019, 'bib', 'It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data', 'Xie, Juan|Ma, Anjun|Fennell, Anne|Ma, Qin|Zhao, Jing', 'Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.|Department of Physics, School of Science, Tianjin University, Tianjin, China.', 'China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bby014', '29490019', 'Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17xe2x80x89years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.'),
(69349, 2019, 'bib', 'Where to search top-K biomedical ontologies?', 'Oliveira, Daniela|Butt, Anila Sahar|Haller, Armin|Rebholz-Schuhmann, Dietrich|Sahay, Ratnesh', 'Insight Centre for Data Analytics, NUI Galway, Ireland.|CSIRO, Canberra, Australia.|Australian National University, Canberra, Australia.|Insight Centre for Data Analytics, NUI Galway, Ireland.|Insight Centre for Data Analytics, NUI Galway, Ireland.', 'Ireland|Australia|Australia|Ireland|Ireland', 0, '-', 'Journal Article', '10.1093/bib/bby015', '29579141', 'Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements.'),
(69350, 2019, 'bib', 'Comprehensive analysis of Helicobacter pylori infection-associated diseases based on miRNA-mRNA interaction network', 'Yang, Jue|Song, Hui|Cao, Kun|Song, Jialei|Zhou, Jianjiang', 'State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550014, China.|Key Laboratory of Endemic and Ethnic Diseases (Guizhou Medical University), Ministry of Education, Guiyang 550004, China.|Department of general surgery, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China.|The Laboratory of Cell Biochemistry and Topogenic Regulation, College of Bioengineering and Faculty of Sciences, Chongqing University, Chongqing 400044, China.|Key Laboratory of Endemic and Ethnic Diseases (Guizhou Medical University), Ministry of Education, Guiyang 550004, China.', 'China|China|China|China|China', 5, '-', 'Journal Article', '10.1093/bib/bby018', '29579224', 'Helicobacter pylori (H. pylori) infection remains a cause of significant morbidity and mortality worldwide. Comprehensive understanding of the pathogenic mechanism of H. pylori and its interaction with host will contribute to developing novel prophylactical and therapeutical strategies. Here, we first determined microRNA (miRNA) levels in H. pylori-infected patients with gastritis, duodenal ulcer, gastric cancer or mucosa-associated lymphoid tissue lymphoma using miRNA data sets. Thirty-four differentially expressed miRNAs were identified and functional enrichment analysis of those miRNA target genes revealed that H. pylori infection were strongly associated with pathway in cancer and regulation of mRNA synthesis. Using disease connectivity analysis of 28 hub genes, we found that H. pylori may increase the risk of many extragastric diseases (e.g. cardiovascular disease, hemic and lymphatic diseases and nervous system disease). Altogether, our integrated analysis provided a new method to predict pathogen-human disease connectivity based on miRNA-mRNA interaction network and indicated anti-H. pylori therapy as an effective means of human diseases prevention.'),
(69351, 2019, 'bib', 'Distinct telomere length and molecular signatures in seminoma and non-seminoma of testicular germ cell tumor', 'Sun, Hua|Kim, Pora|Jia, Peilin|Park, Ae Kyung|Liang, Han|Zhao, Zhongming', 'Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bib/bby020', '29579225', 'Testicular germ cell tumors (TGCTs) are classified into two main subtypes, seminoma (SE) and non-seminoma (NSE), but their molecular distinctions remain largely unexplored. Here, we used expression data for mRNAs and microRNAs (miRNAs) from The Cancer Genome Atlas (TCGA) to perform a systematic investigation to explain the different telomere length (TL) features between NSE (nxe2x80x89=xe2x80x8948) and SE (nxe2x80x89=xe2x80x8955). We found that TL elongation was dominant in NSE, whereas TL shortening prevailed in SE. We further showed that both mRNA and miRNA expression profiles could clearly distinguish these two subtypes. Notably, four telomere-related genes (TelGenes) showed significantly higher expression and positively correlated with telomere elongation in NSE than SE: three telomerase activity-related genes (TERT, WRAP53 and MYC) and an independent telomerase activity gene (ZSCAN4). We also found that the expression of genes encoding Yamanaka factors was positively correlated with telomere lengthening in NSE. Among them, SOX2 and MYC were highly expressed in NSE versus SE, while POU5F1 and KLF4 had the opposite patterns. These results suggested that enhanced expression of both TelGenes (TERT, WRAP53, MYC and ZSCAN4) and Yamanaka factors might induce telomere elongation in NSE. Conversely, the relative lack of telomerase activation and low expression of independent telomerase activity pathway during cell division may be contributed to telomere shortening in SE. Taken together, our results revealed the potential molecular profiles and regulatory roles involving the TL difference between NSE and SE, and provided a better molecular understanding of this complex disease.'),
(69352, 2019, 'bib', 'Interactive and coordinated visualization approaches for biological data analysis', 'Cruz, António|Arrais, Joel P|Machado, Penousal', 'Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informática.|Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informática.|Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informática.', '-|-|-', 3, '-', 'Journal Article', '10.1093/bib/bby019', '29590305', 'The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.'),
(69353, 2019, 'bib', 'Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes', 'Li, Gaoyang|Cao, Huansheng|Xu, Ying', 'College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.|Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA, USA.|College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.', 'China|Georgia|China', 1, '-', 'Journal Article', '10.1093/bib/bby022', '29596572', 'We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites, (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes, (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes, and (iv) the three subnetworks cover on average xe2x88xbc56, xe2x88xbc31 and xe2x88xbc13% of a networks nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses, and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.'),
(69354, 2019, 'bib', 'Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions', 'Senol Cali, Damla|Kim, Jeremie S|Ghose, Saugata|Alkan, Can|Mutlu, Onur', 'Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Computer Engineering, Bilkent University, Bilkent, Ankara, Turkey.|Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|Turkey|United States', 27, '-', 'Journal Article', '10.1093/bib/bby017', '29617724', 'Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.'),
(69355, 2019, 'bib', 'A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data', 'Zhang, Zhuo|Li, Hao|Jiang, Shuai|Li, Ruijiang|Li, Wanying|Chen, Hebing|Bo, Xiaochen', 'Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.', 'China|China|China|China|China|China|China', 4, '-', 'Journal Article', '10.1093/bib/bby023', '29617727', 'The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive atlas of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.'),
(69356, 2019, 'bib', 'Molecular subtyping of cancer: current status and moving toward clinical applications', 'Zhao, Lan|Lee, Victor H F|Ng, Michael K|Yan, Hong|Bijlsma, Maarten F', 'Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.|Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.|Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.|Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Academic Medical Center, Amsterdam, The Netherlands.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Netherlands', 6, '-', 'Journal Article', '10.1093/bib/bby026', '29659698', 'Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.'),
(69357, 2019, 'bib', 'Progress in single-access information systems for wheat and rice crop improvement', 'Scheben, Armin|Chan, Chon-Kit Kenneth|Mansueto, Locedie|Mauleon, Ramil|Larmande, Pierre|Alexandrov, Nickolai|Wing, Rod A|McNally, Kenneth L|Quesneville, Hadi|Edwards, David', 'School of Biological Sciences and Institute of Agriculture, University of Western Australia, 6009 Perth, WA, Australia.|School of Biological Sciences and Institute of Agriculture, University of Western Australia, 6009 Perth, WA, Australia.|International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, The Philippines.|International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, The Philippines.|IRD, UMR DIADE (Plant Diversity Adaptation and Development Research unit) , 911 Avenue Agropolis, 34394 Montpellier, France.|International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, The Philippines.|Arizona Genomics Institute, University of Arizona, Tucson, Arizona, USA.|International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, The Philippines.|URGI, INRA, Université Paris-Saclay, 78026, Versailles, France.|School of Biological Sciences and Institute of Agriculture, University of Western Australia, 6009 Perth, WA, Australia.', 'Australia|Australia|Philippines|Philippines|France|Philippines|United States|Philippines|France|Australia', 1, '-', 'Journal Article', '10.1093/bib/bby016', '29659709', 'Improving productivity of the staple crops wheat and rice is essential to feed the growing global population, particularly in the context of a changing climate. However, current rates of yield gain are insufficient to support the predicted population growth. New approaches are required to accelerate the breeding process, and many of these are driven by the application of large-scale crop data. To leverage the substantial volumes and types of data that can be applied for precision breeding, the wheat and rice research communities are working towards the development of integrated systems to access and standardize the dispersed, heterogeneous available data. Here, we outline the initiatives of the International Wheat Information System (WheatIS) and the International Rice Informatics Consortium (IRIC) to establish Web-based single-access systems and data mining tools to make the available resources more accessible, drive discovery and accelerate the production of new crop varieties. We discuss the progress of WheatIS and IRIC towards unifying specialized wheat and rice databases and building custom software platforms to manage and interrogate these data. Single-access crop information systems will strengthen scientific collaboration, optimize the use of public research funds and help achieve the required yield gains in the two most important global food crops.'),
(69358, 2019, 'bib', 'Analysis of long noncoding RNAs highlights region-specific altered expression patterns and diagnostic roles in Alzheimers disease', 'Zhou, Meng|Zhao, Hengqiang|Wang, Xinyu|Sun, Jie|Su, Jianzhong', 'School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China and College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China.|School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.|School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China and College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China.|School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.', 'China|China|China|China|China', 28, '-', 'Journal Article', '10.1093/bib/bby021', '29672663', 'Increasing evidence has revealed the multiple roles of long noncoding RNAs (lncRNAs) in neurodevelopment, brain function and aging, and their dysregulation was implicated in many types of neurological diseases. However, expression pattern and diagnostic role of lncRNAs in Alzheimers disease (AD) remain largely unknown and has gained significant attention. In this study, we performed a comparative analysis for lncRNA expression profiles in four brain regions in brain aging and AD. Our analysis revealed age- and disease-dependent region-specific lncRNA expression patterns in aging and AD. Moreover, we identified a panel of nine lncRNAs (termed LncSigAD9) in a discovery cohort of 114 samples using supervised machine learning and stepwise selection method. The LncSigAD9 was able to differentiate between AD and healthy controls with high diagnostic sensitivity and specificity both in the discovery cohort (86.3 and 89.5%) and the additional independent AD cohort (90.8 and 83.8%). The receiver operating characteristic curves for the LncSigAD9 were 0.863 and 0.939 for discovery and independent cohorts, respectively. Furthermore, the LncSigAD9 demonstrated higher diagnostic performance than nine-minus-one lncRNA signature and mRNA-based signature with a similar number of genes. In silico functional analysis indicated the involvement of lncRNA expression variation in brain development- and metabolism-related biological processes. Taken together, our study highlights the importance of lncRNAs in brain aging and AD, and demonstrated the utility of lncRNAs as a promising biomarker for early AD diagnosis and treatment.'),
(69359, 2019, 'bib', 'Disease prediction by cell-free DNA methylation', 'Feng, Hao|Jin, Peng|Wu, Hao', 'Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.|Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA.', 'United States|United States|United States', 11, '-', 'Journal Article', '10.1093/bib/bby029', '29672679', 'Disease diagnosis using cell-free DNA (cfDNA) has been an active research field recently. Most existing approaches perform diagnosis based on the detection of sequence variants on cfDNA, thus, their applications are limited to diseases associated with high mutation rate such as cancer. Recent developments start to exploit the epigenetic information on cfDNA, which could have substantially wider applications. In this work, we provide thorough reviews and discussions on the statistical method developments and data analysis strategies for using cfDNA epigenetic profiles, in particular DNA methylation, to construct disease diagnostic models. We focus on two important aspects: marker selection and prediction model construction, under different scenarios. We perform simulations and real data analysis to compare different approaches, and provide recommendations for data analysis.'),
(69360, 2019, 'bib', 'Navigating the disease landscape: knowledge representations for contextualizing molecular signatures', 'Saqi, Mansoor|Lysenko, Artem|Guo, Yi-Ke|Tsunoda, Tatsuhiko|Auffray, Charles', 'Mansoor Saqi Data Science Institute, Imperial College London, UK.|Artem Lysenko Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.|Yi-Ke Guo Data Science Institute, Imperial College London, UK.|Tatsuhiko Tsunoda Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan CREST, JST, Tokyo, Japan Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.|Charles Auffray European Institute for Systems Biology and Medicine, Lyon, France.', 'United Kingdom|Japan|United Kingdom|Japan|France', 4, '-', 'Journal Article', '10.1093/bib/bby025', '29684165', 'Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69361, 2019, 'bib', 'Community-driven roadmap for integrated disease maps', 'Ostaszewski, Marek|Gebel, Stephan|Kuperstein, Inna|Mazein, Alexander|Zinovyev, Andrei|Dogrusoz, Ugur|Hasenauer, Jan|Fleming, Ronan M T|Le Novère, Nicolas|Gawron, Piotr|Ligon, Thomas|Niarakis, Anna|Nickerson, David|Weindl, Daniel|Balling, Rudi|Barillot, Emmanuel|Auffray, Charles|Schneider, Reinhard', 'Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.|Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France.|European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.|Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France.|Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey.|Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, Netherlands.|The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, United Kingdom.|Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.|Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany.|GenHotel EA3886, Univ Evry, Université Paris-Saclay, Evry 91025, France.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.|Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France.|European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.|Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Luxembourg|France|France|France|Turkey|Germany|Netherlands|United Kingdom|Luxembourg|Germ', 10, '-', 'Journal Article', '10.1093/bib/bby024', '29688273', 'The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps, sharing parts of maps that encode common human disease mechanisms, providing technical solutions for complexity management of maps, and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.'),
(69362, 2019, 'bib', 'Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data', 'Pucher, Bettina M|Zeleznik, Oana A|Thallinger, Gerhard G', 'Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010 Graz, Austria.|Channing Division of Network Medicine, Brigham and Womens Hospital and Harvard Medical School, 181 Longwood Ave, Boston MA 02115, USA.|Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010 Graz, Austria.', 'Austria|United States|Austria', 4, '-', 'Journal Article', '10.1093/bib/bby027', '29688321', 'Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.'),
(69363, 2019, 'bib', 'Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants', 'Negri, Tatianne da Costa|Alves, Wonder Alexandre Luz|Bugatti, Pedro Henrique|Saito, Priscila Tiemi Maeda|Domingues, Douglas Silva|Paschoal, Alexandre Rossi', 'Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.|Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.|Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil.|Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil.|Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Department of Botany, Institute of Biosciences, São Paulo State University, UNESP, Rio Claro, SP, Brazil.|Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 9, '-', 'Journal Article', '10.1093/bib/bby034', '29697740', 'Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/) to identify lncRNAs in plants.'),
(69364, 2019, 'bib', 'Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools', 'Antonov, Ivan V|Mazurov, Evgeny|Borodovsky, Mark|Medvedeva, Yulia A', 'Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Science, Moscow, Russian Federation.|Privat Treid LLC, Moscow 109029, Russian Federation.|Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation.|Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Science, Moscow, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation', 8, 'Benchmarking|Computational Biology|Humans|RNA, Long Noncoding|RNA, Messenger|Transcriptome', 'Journal Article', '10.1093/bib/bby032', '29697742', 'The genomes of mammalian species are pervasively transcribed producing as many noncoding as protein-coding RNAs. There is a growing body of evidence supporting their functional role. Long noncoding RNA (lncRNA) can bind both nucleic acids and proteins through several mechanisms. A reliable computational prediction of the most probable mechanism of lncRNA interaction can facilitate experimental validation of its function. In this study, we benchmarked computational tools capable to discriminate lncRNA from mRNA and predict lncRNA interactions with other nucleic acids. We assessed the performance of 9 tools for distinguishing protein-coding from noncoding RNAs, as well as 19 tools for prediction of RNA-RNA and RNA-DNA interactions. Our conclusions about the considered tools were based on their performances on the entire genome/transcriptome level, as it is the most common task nowadays. We found that FEELnc and CPAT distinguish between coding and noncoding mammalian transcripts in the most accurate manner. ASSA, RIBlast and LASTAL, as well as Triplexator, turned out to be the best predictors of RNA-RNA and RNA-DNA interactions, respectively. We showed that the normalization of the predicted interaction strength to the transcript length and GC content may improve the accuracy of inferring RNA interactions. Yet, all the current tools have difficulties to make accurate predictions of short-trans RNA-RNA interactions-stretches of sparse contacts. All over, there is still room for improvement in each category, especially for predictions of RNA interactions.'),
(69365, 2019, 'bib', 'Post genome-wide association analysis: dissecting computational pathway/network-based approaches', 'Chimusa, Emile R|Dalvie, Shareefa|Dandara, Collet|Wonkam, Ambroise|Mazandu, Gaston K', 'Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Level 3, Wernher and Beit North, Private Bag, Rondebosch, 7700, Anzio road, Observatory Cape Town, South Africa.|Department of Psychiatry and Mental Health, University of Cape Town, Observatory, 7925, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa, African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa and Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Anzio Road, Observatory, 7925, Cape Town, South Africa.', 'South Africa|South Africa|South Africa|South Africa|South Africa', 1, '-', 'Journal Article', '10.1093/bib/bby035', '29701762', 'Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.'),
(69366, 2019, 'bib', 'Application of Atlas of Cancer Signalling Network in preclinical studies', 'Monraz Gomez, L Cristobal|Kondratova, Maria|Ravel, Jean-Marie|Barillot, Emmanuel|Zinovyev, Andrei|Kuperstein, Inna', 'Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France.|Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France.|Genetic Laboratory, Nancys Regional University Hospital, Vandœuvre-lès-Nancy and INSERM UMR 954, Lorraine University, Vandœuvre-lès-Nancy.|Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France.|Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France.|Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France.', 'France|France|-|France|France|France', 4, '-', 'Journal Article', '10.1093/bib/bby031', '29726961', 'Cancer initiation and progression are associated with multiple molecular mechanisms. The knowledge of these mechanisms is expanding and should be converted into guidelines for tackling the disease. Here, we discuss the formalization of biological knowledge into a comprehensive resource: the Atlas of Cancer Signalling Network (ACSN) and the Google Maps-based tool NaviCell, which supports map navigation. The application of ACSN for omics data visualization, in the context of signalling maps, is possible via the NaviCell Web Service module and through the NaviCom tool. It allows generation of network-based molecular portraits of cancer using multilevel omics data. We review how these resources and tools are applied for cancer preclinical studies. Structural analysis of the maps together with omics data helps to rationalize the synergistic effects of drugs and allows design of complex disease stage-specific druggable interventions. The use of ACSN modules and maps as signatures of biological functions can help in cancer data analysis and interpretation. In addition, they empowered finding of associations between perturbations in particular molecular mechanisms and the risk to develop a specific type of cancer. These approaches are helpful, among others, to study the interplay between molecular mechanisms of cancer. It opens an opportunity to decipher how gene interactions govern the hallmarks of cancer in specific contexts. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other diseases.'),
(69367, 2019, 'bib', 'Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer', 'Barneh, Farnaz|Mirzaie, Mehdi|Nickchi, Payman|Tan, Tuan Zea|Thiery, Jean Paul|Piran, Mehran|Salimi, Mona|Goshadrou, Fatemeh|Aref, Amir R|Jafari, Mohieddin', 'Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.|Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.|Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.|Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore, Translational Centre for Development and Research, National University Health System, MD11, #03-10, 10 Medical Drive, Singapore 117597, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore.|Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.|Department of Physiology and Pharmacology, Pasteur Institute of Iran, Tehran, Iran.|Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.|Department of Medical Oncology, Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston 02215, USA.|Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.', 'United States|United States|United States|Singapore|Singapore|United States|United States|United States|United States|United States', 5, '-', 'Journal Article', '10.1093/bib/bby030', '29726962', 'With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.'),
(69368, 2018, 'bib', 'Proteomic analysis and prediction of amino acid variations that influence protein posttranslational modifications', 'Shi, Shaoping|Wang, Lina|Cao, Man|Chen, Guodong|Yu, Jialin', 'Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, Jiangxi 330031, China.|Department of Science, Nanchang Institute of Technology, Nanchang, Jiangxi 330031, China.|Department of Mathematics, School of Sciences, Nanchang University, Nanchang, Jiangxi 330031, China.|Department of Mathematics, School of Sciences, Nanchang University, Nanchang, Jiangxi 330031, China.|Department of Mathematics, School of Sciences, Nanchang University, Nanchang, Jiangxi 330031, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bby036', '29788276', 'Accumulative studies have indicated that amino acid variations through changing the type of residues of the target sites or key flanking residues could directly or indirectly influence protein posttranslational modifications (PTMs) and bring about a detrimental effect on protein function. Computational mutation analysis can greatly narrow down the efforts on experimental work. To increase the utilization of current computational resources, we xefxacx81rst provide an overview of computational prediction of amino acid variations that influence protein PTMs and their functional analysis. We also discuss the challenges that are faced while developing novel in silico approaches in the future. The development of better methods for mutation analysis-related protein PTMs will help to facilitate the development of personalized precision medicine.'),
(69369, 2018, 'bib', 'Survey of miRNA-miRNA cooperative regulation principles across cancer types', 'Shao, Tingting|Wang, Guangjuan|Chen, Hong|Xie, Yunjin|Jin, Xiyun|Bai, Jing|Xu, Juan|Li, Xia|Huang, Jian|Jin, Yan|Li, Yongsheng', 'College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.|Department of Medical Genetics, Harbin Medical University, Harbin 150081, China.|College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China.', 'China|China|China|China|China|China|China|China|China|China|China', 6, '-', 'Journal Article', '10.1093/bib/bby038', '29800060', 'Cooperative regulation among multiple microRNAs (miRNAs) is a complex type of posttranscriptional regulation in human, however, the global view of the system-level regulatory principles across cancers is still unclear. Here, we investigated miRNA-miRNA cooperative regulatory landscape across 18 cancer types and summarized the regulatory principles of miRNAs. The miRNA-miRNA cooperative pan-cancer network exhibited a scale-free and modular architecture. Cancer types with similar tissue origins had high similarity in cooperative network structure and expression of cooperative miRNA pairs. In addition, cooperative miRNAs showed divergent properties, including higher expression, greater expression variation and a stronger regulatory strength towards targets and were likely to regulate cancer hallmark-related functions. We found a marked rewiring of miRNA-miRNA cooperation between various cancers and revealed conserved and rewired network miRNA hubs. We further identified the common hubs, cancer-specific hubs and other hubs, which tend to target known anticancer drug targets. Finally, miRNA cooperative modules were found to be associated with patient survival in several cancer types. Our study highlights the potential of pan-cancer miRNA-miRNA cooperative regulation as a novel paradigm that may aid in the discovery of tumorigenesis mechanisms and development of anticancer drugs.'),
(69370, 2018, 'bib', 'Predicting novel microRNA: a comprehensive comparison of machine learning approaches', 'Stegmayer, Georgina|Di Persia, Leandro E|Rubiolo, Mariano|Gerard, Matias|Pividori, Milton|Yones, Cristian|Bugnon, Leandro A|Rodriguez, Tadeo|Raad, Jonathan|Milone, Diego H', 'sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 6, '-', 'Journal Article', '10.1093/bib/bby037', '29800232', 'The importance of microRNAs (miRNAs) is widely recognized in the community nowadays because these short segments of RNA can play several roles in almost all biological processes. The computational prediction of novel miRNAs involves training a classifier for identifying sequences having the highest chance of being precursors of miRNAs (pre-miRNAs). The big issue with this task is that well-known pre-miRNAs are usually few in comparison with the hundreds of thousands of candidate sequences in a genome, which results in high class imbalance. This imbalance has a strong influence on most standard classifiers, and if not properly addressed in the model and the experiments, not only performance reported can be completely unrealistic but also the classifier will not be able to work properly for pre-miRNA prediction. Besides, another important issue is that for most of the machine learning (ML) approaches already used (supervised methods), it is necessary to have both positive and negative examples. The selection of positive examples is straightforward (well-known pre-miRNAs). However, it is difficult to build a representative set of negative examples because they should be sequences with hairpin structure that do not contain a pre-miRNA.'),
(69371, 2018, 'bib', 'Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features', 'Bao, Yu|Marini, Simone|Tamura, Takeyuki|Kamada, Mayumi|Maegawa, Shingo|Hosokawa, Hiroshi|Song, Jiangning|Akutsu, Tatsuya', 'sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.|sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 1, '-', 'Journal Article', '10.1093/bib/bby041', '29860277', 'As one of the few irreversible protein posttranslational modifications, proteolytic cleavage is involved in nearly all aspects of cellular activities, ranging from gene regulation to cell life-cycle regulation. Among the various protease-specific types of proteolytic cleavage, cleavages by casapses/granzyme B are considered as essential in the initiation and execution of programmed cell death and inflammation processes. Although a number of substrates for both types of proteolytic cleavage have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remains to be fully characterized.To tackle this issue and complement experimental efforts for substrate identification, systematic bioinformatics studies of known cleavage sites provide important insights into caspase/granzyme B substrate specificity, and facilitate the discovery of novel substrates. In this article, we review and benchmark 12 state-of-the-art sequence-based bioinformatics approaches and tools for caspases/granzyme B cleavage prediction. We evaluate and compare these methods in terms of their input/output, algorithms used, prediction performance, validation methods and software availability and utility. In addition, we construct independent data sets consisting of caspases/granzyme B substrates from different species and accordingly assess the predictive power of these different predictors for the identification of cleavage sites. We find that the prediction results are highly variable among different predictors. Furthermore, we experimentally validate the predictions of a case study by performing caspase cleavage assay. We anticipate that this comprehensive review and survey analysis will provide an insightful resource for biologists and bioinformaticians who are interested in using and/or developing tools for caspase/granzyme B cleavage prediction.'),
(69372, 2018, 'bib', 'A comparison of mechanistic signaling pathway activity analysis methods', 'Amadoz, Alicia|Hidalgo, Marta R|Çubuk, Cankut|Carbonell-Caballero, José|Dopazo, Joaquín', 'Department of Bioinformatics, Igenomix S.L., 46980 Valencia, Spain.|Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain.|Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain.|Chromatin and Gene expression Lab, Gene Regulation, Stem Cells and Cancer Program, Centre de Regulació Genòmica (CRG), The Barcelona Institute of Science and Technology, PRBB, Barcelona 08003, Spain.|Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain.', 'Spain|Spain|Spain|Spain|Spain', 5, '-', 'Journal Article', '10.1093/bib/bby040', '29868818', 'Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.'),
(69373, 2018, 'bib', 'In silico clinical trials: concepts and early adoptions', 'Pappalardo, Francesco|Russo, Giulia|Tshinanu, Flora Musuamba|Viceconti, Marco', 'Department of Drug Sciences, University of Catania, Catania, Italy.|Department of Biomedical and Biotechnological Sciences, University of Catania, Catania 95123, Italy.|Federal Agency for Medicines and Health Products, Brussels, Belgium and INSERM U1248, Université de Limoges, Limoges, France.|Department of Mechanical Engineering, University of Sheffield, Sheffield, UK and INSIGNEO Institute for In Silico Medicine, University of Sheffield, Sheffield, UK.', 'Italy|Italy|Belgium|United Kingdom', 2, '-', 'Journal Article', '10.1093/bib/bby043', '29868882', 'Innovations in information and communication technology infuse all branches of science, including life sciences. Nevertheless, healthcare is historically slow in adopting technological innovation, compared with other industrial sectors. In recent years, new approaches in modelling and simulation have started to provide important insights in biomedicine, opening the way for their potential use in the reduction, refinement and partial substitution of both animal and human experimentation. In light of this evidence, the European Parliament and the United States Congress made similar recommendations to their respective regulators to allow wider use of modelling and simulation within the regulatory process. In the context of in silico medicine, the term in silico clinical trials refers to the development of patient-specific models to form virtual cohorts for testing the safety and/or efficacy of new drugs and of new medical devices. Moreover, it could be envisaged that a virtual set of patients could complement a clinical trial (reducing the number of enrolled patients and improving statistical significance), and/or advise clinical decisions. This article will review the current state of in silico clinical trials and outline directions for a full-scale adoption of patient-specific modelling and simulation in the regulatory evaluation of biomedical products. In particular, we will focus on the development of vaccine therapies, which represents, in our opinion, an ideal target for this innovative approach.'),
(69374, 2018, 'bib', 'Microbial genomic island discovery, visualization and analysis', 'Bertelli, Claire|Tilley, Keith E|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada', 8, '-', 'Journal Article', '10.1093/bib/bby042', '29868902', 'Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.'),
(69375, 2018, 'bib', 'Regulatory variants: from detection to predicting impact', 'Rojano, Elena|Seoane, Pedro|Ranea, Juan A G|Perkins, James R', 'Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain.|Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain.|CIBER de Enfermedades Raras, ISCIII, Madrid, Spain and Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain.|Research laboratory, IBIMA-Regional University Hospital of Malaga, UMA, Malaga 29009, Spain.', 'Spain|Spain|Spain|Spain', 13, '-', 'Journal Article', '10.1093/bib/bby039', '29893792', 'Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution.We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.'),
(69376, 2019, 'bib', 'iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites', 'Song, Jiangning|Wang, Yanan|Li, Fuyi|Akutsu, Tatsuya|Rawlings, Neil D|Webb, Geoffrey I|Chou, Kuo-Chen', 'Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|Gordon Life Science Institute, Boston, MA 02478, USA and Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.', 'Australia|China|Australia|Japan|United Kingdom|Australia|China', 90, '-', 'Journal Article', '10.1093/bib/bby028', '29897410', 'Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.'),
(69377, 2019, 'bib', 'Robust genetic interaction analysis', 'Wu, Mengyun|Ma, Shuangge', 'Mengyun Wu and Shuangge Ma, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China and Yale School of Public Health, New Haven, CT 06520, USA.|Mengyun Wu and Shuangge Ma, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China and Yale School of Public Health, New Haven, CT 06520, USA.', 'China|China', 5, '-', 'Journal Article', '10.1093/bib/bby033', '29897421', 'For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.'),
(69378, 2018, 'bib', 'Connectome verification: inter-rater and connection reliability of tract-tracing-based intrinsic hypothalamic connectivity', 'Schmitt, Oliver|Eipert, Peter|Schwanke, Sebastian|Lessmann, Felix|Meinhardt, Jennifer|Beier, Julia|Kadir, Kanar|Karnitzki, Adrian|Sellner, Linda|Klünker, Ann-Christin|Ruß, Frauke|Jenssen, Jörg', 'Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.|Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bib/bby048', '29897426', 'Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example.');
