INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68835, 2014, 'bib', 'Applications of alignment-free methods in epigenomics', 'Pinello, Luca|Lo Bosco, Giosuè|Yuan, Guo-Cheng', 'Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.', 'Italy|Italy|Italy', 4, 'Artificial Intelligence|Chromosome Mapping|Computational Biology|Epigenesis, Genetic|Epigenomics|Humans|Models, Genetic|Sequence Alignment|Sequence Analysis, DNA|Support Vector Machine', 'Journal Article', '10.1093/bib/bbt078', '24197932', 'Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms. '),
(68836, 2015, 'bib', 'The semantic web in translational medicine: current applications and future directions', 'Machado, Catia M|Rebholz-Schuhmann, Dietrich|Freitas, Ana T|Couto, Francisco M', 'Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.', 'Italy|Italy|Italy|Italy', 36, 'Algorithms|Computational Biology|Humans|Information Dissemination|Internet|Translational Medical Research', 'Journal Article', '10.1093/bib/bbt079', '24197933', 'Semantic web technologies offer an approach to data integration and sharing, even for resources developed independently or broadly distributed across the web. This approach is particularly suitable for scientific domains that profit from large amounts of data that reside in the public domain and that have to be exploited in combination. Translational medicine is such a domain, which in addition has to integrate private data from the clinical domain with proprietary data from the pharmaceutical domain. In this survey, we present the results of our analysis of translational medicine solutions that follow a semantic web approach. We assessed these solutions in terms of their target medical use case, the resources covered to achieve their objectives, and their use of existing semantic web resources for the purposes of data sharing, data interoperability and knowledge discovery. The semantic web technologies seem to fulfill their role in facilitating the integration and exploration of data from disparate sources, but it is also clear that simply using them is not enough. It is fundamental to reuse resources, to define mappings between resources, to share data and knowledge. All these aspects allow the instantiation of translational medicine at the semantic web-scale, thus resulting in a network of solutions that can share resources for a faster transfer of new scientific results into the clinical practice. The envisioned network of translational medicine solutions is on its way, but it still requires resolving the challenges of sharing protected data and of integrating semantic-driven technologies into the clinical practice. '),
(68837, 2015, 'bib', 'Investigating biocomplexity through the agent-based paradigm', 'Kaul, Himanshu|Ventikos, Yiannis', 'Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.', 'Italy|Italy', 22, 'Computational Biology|Models, Biological', 'Journal Article', '10.1093/bib/bbt077', '24227161', 'Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex.'),
(68838, 2014, 'bib', 'A model-free approach for detecting interactions in genetic association studies', 'Li, Jiahan|Dan, Jun|Li, Chunlei|Wu, Rongling', 'Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.', 'Italy|Italy|Italy|Italy', 3, 'Body Mass Index|Computational Biology|Computer Simulation|Epistasis, Genetic|Female|Gene Regulatory Networks|Genetic Association Studies|Genome-Wide Association Study|Humans|Male|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Statistics, Nonparametric', 'Journal Article', '10.1093/bib/bbt082', '24273216', 'Over the past few decades, genome-wide association studies analyzed by efficient statistical procedures have successfully identified single-nucleotide polymorphisms (SNPs) that are associated with complex traits or human diseases. However, due to the overwhelming number of SNPs, most approaches have focused on additive genetic model without genome-wide SNP-SNP interactions. In this study, we propose an efficient statistical procedure in a genetic model-free framework for detecting SNPs exhibiting main genetic effects as well as epistatic interactions. Specifically, the association between phenotype and genotype is characterized by an unknown function to be estimated using nonparametric techniques, and a two-stage non-parametric independence screening procedure is proposed to sequentially identify potentially important main genetic effects and interactions. Finally, the subset of genetic predictors implied by two-stage non-parametric independence screening is analyzed by penalized regressions such as LASSO, and a final model is identified. In this framework, specific genetic model is not assumed and interactions are not only among marginally important SNPs. Therefore, SNPs that are involved in genetic regulatory networks but missed by previous studies are expected to be recognized. In simulation studies, we show that the procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as SNP-SNP interactions. A real data analysis further indicates the importance of epistatic interactions in explaining body mass index. '),
(68839, 2015, 'bib', 'LC-MS alignment in theory and practice: a comprehensive algorithmic review', 'Smith, Rob|Ventura, Dan|Prince, John T', 'Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.|Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it, Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.', 'Italy|Italy|Italy', 51, 'Algorithms|Chromatography, Liquid|Mass Spectrometry', 'Journal Article', '10.1093/bib/bbt080', '24273217', 'Liquid chromatography-mass spectrometry is widely used for comparative replicate sample analysis in proteomics, lipidomics and metabolomics. Before statistical comparison, registration must be established to match corresponding analytes from run to run. Alignment, the most popular correspondence approach, consists of constructing a function that warps the content of runs to most closely match a given reference sample. To date, dozens of correspondence algorithms have been proposed, creating a daunting challenge for practitioners in algorithm selection. Yet, existing reviews have highlighted only a few approaches. In this review, we describe 50 correspondence algorithms to facilitate practical algorithm selection. We elucidate the motivation for correspondence and analyze the limitations of current approaches, which include prohibitive runtimes, numerous user parameters, model limitations and the need for reference samples. We suggest and describe a paradigm shift for overcoming current correspondence limitations by building on known liquid chromatography-mass spectrometry behavior. '),
(68840, 2014, 'bib', 'Alignment-free phylogenetics and population genetics', 'Haubold, Bernhard', 'Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.', '-', 49, 'Animals|Computational Biology|Evolution, Molecular|Genetics, Population|Genome, Mitochondrial|Humans|Models, Genetic|Mutation|Phylogeny|Recombination, Genetic|Selection, Genetic|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbt083', '24291823', 'Phylogenetics and population genetics are central disciplines in evolutionary biology. Both are based on comparative data, today usually DNA sequences. These have become so plentiful that alignment-free sequence comparison is of growing importance in the race between scientists and sequencing machines. In phylogenetics, efficient distance computation is the major contribution of alignment-free methods. A distance measure should reflect the number of substitutions per site, which underlies classical alignment-based phylogeny reconstruction. Alignment-free distance measures are either based on word counts or on match lengths, and I apply examples of both approaches to simulated and real data to assess their accuracy and efficiency. While phylogeny reconstruction is based on the number of substitutions, in population genetics, the distribution of mutations along a sequence is also considered. This distribution can be explored by match lengths, thus opening the prospect of alignment-free population genomics. '),
(68841, 2015, 'bib', 'Comparison of software packages for detecting differential expression in RNA-seq studies', 'Seyednasrollah, Fatemeh|Laiho, Asta|Elo, Laura L', 'Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.', '-|-|-', 107, 'Animals|Data Interpretation, Statistical|Humans|Mice|Sequence Analysis, RNA|Software|Transcriptome', 'Comparative Study', '10.1093/bib/bbt086', '24300110', 'RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice. '),
(68842, 2015, 'bib', 'High-throughput DNA sequence data compression', 'Zhu, Zexuan|Zhang, Yongpeng|Ji, Zhen|He, Shan|Yang, Xiao', 'Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.', '-|-|-|-|-', 45, 'Data Compression|High-Throughput Nucleotide Sequencing|Molecular Sequence Data', 'Journal Article', '10.1093/bib/bbt087', '24300111', 'The exponential growth of high-throughput DNA sequence data has posed great challenges to genomic data storage, retrieval and transmission. Compression is a critical tool to address these challenges, where many methods have been developed to reduce the storage size of the genomes and sequencing data (reads, quality scores and metadata). However, genomic data are being generated faster than they could be meaningfully analyzed, leaving a large scope for developing novel compression algorithms that could directly facilitate data analysis beyond data transfer and storage. In this article, we categorize and provide a comprehensive review of the existing compression methods specialized for genomic data and present experimental results on compression ratio, memory usage, time for compression and decompression. We further present the remaining challenges and potential directions for future research. '),
(68843, 2015, 'bib', 'Searching for repetitions in biological networks: methods, resources and tools', 'Panni, Simona|Rombo, Simona E', 'Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.', '-|-', 20, 'Computational Biology|Models, Theoretical|Software', 'Journal Article', '10.1093/bib/bbt084', '24300112', 'We present here a compact overview of the data, models and methods proposed for the analysis of biological networks based on the search for significant repetitions. In particular, we concentrate on three problems widely studied in the literature: network alignment, network querying and network motif extraction. We provide (i) details of the experimental techniques used to obtain the main types of interaction data, (ii) descriptions of the models and approaches introduced to solve such problems and (iii) pointers to both the available databases and software tools. The intent is to lay out a useful roadmap for identifying suitable strategies to analyse cellular data, possibly based on the joint use of different interaction data types or analysis techniques. '),
(68844, 2015, 'bib', 'Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases', 'Zhang, Hong-Mei|Kuang, Shuzhen|Xiong, Xushen|Gao, Tianliuyun|Liu, Chenglin|Guo, An-Yuan', 'Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.', '-|-|-|-|-|-', 76, 'Animals|Cell Differentiation|Cell Proliferation|Feedback, Physiological|Gene Expression Regulation|Gene Regulatory Networks|Humans|MicroRNAs|Transcription Factors', 'Journal Article', '10.1093/bib/bbt085', '24307685', 'Transcription factors (TFs) and microRNAs (miRNAs) can jointly regulate target gene expression in the forms of feed-forward loops (FFLs) or feedback loops (FBLs). These regulatory loops serve as important motifs in gene regulatory networks and play critical roles in multiple biological processes and different diseases. Major progress has been made in bioinformatics and experimental study for the TF and miRNA co-regulation in recent years. To further speed up its identification and functional study, it is indispensable to make a comprehensive review. In this article, we summarize the types of FFLs and FBLs and their identified methods. Then, we review the behaviors and functions for the experimentally identified loops according to biological processes and diseases. Future improvements and challenges are also discussed, which includes more powerful bioinformatics approaches and high-throughput technologies in TF and miRNA target prediction, and the integration of networks of multiple levels. '),
(68845, 2015, 'bib', 'A QTL model to map the common genetic basis for correlative phenotypic plasticity', 'Zhou, Tao|Lyu, Yafei|Xu, Fang|Bo, Wenhao|Zhai, Yi|Zhang, Jian|Pang, Xiaoming|Zheng, Bingsong|Wu, Rongling', 'Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.|Corresponding author. Bernhard Haubold. haubold@evolbio.mpg.de.', '-|-|-|-|-|-|-|-|-', 6, 'Chromosome Mapping|Gene-Environment Interaction|Genes, Plant|Models, Statistical|Phenotype|Quantitative Trait Loci|Triticum', 'Journal Article', '10.1093/bib/bbt089', '24335788', 'As an important mechanism for adaptation to heterogeneous environment, plastic responses of correlated traits to environmental alteration may also be genetically correlated, but less is known about the underlying genetic basis. We describe a statistical model for mapping specific quantitative trait loci (QTLs) that control the interrelationship of phenotypic plasticity between different traits. The model is constructed by a bivariate mixture setting, implemented with the EM algorithm to estimate the genetic effects of QTLs on correlative plastic response. We provide a series of procedure that test (1) how a QTL controls the phenotypic plasticity of a single trait, and (2) how the QTL determines the correlation of environment-induced changes of different traits. The model is readily extended to test how epistatic interactions among QTLs play a part in the correlations of different plastic traits. The model was validated through computer simulation and used to analyse multi-environment data of genetic mapping in winter wheat, showing its utilization in practice. '),
(68846, 2014, 'bib', 'Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies', 'Giancarlo, Raffaele|Rombo, Simona E|Utro, Filippo', 'Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91067, Fax: (+39) 091 238 91024, raffaele@math.unipa.it, Simona E. Rombo, Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91028, Fax: (+39) 091 238 91024, E-mail: simona.rombo@math.unipa.it, Filippo Utro, Computational Genomics Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Tel.: (+1) 914 945 1549, Fax: (+1) 914 945 4217, E-mail: futro@us.ibm.com.|Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91067, Fax: (+39) 091 238 91024, raffaele@math.unipa.it, Simona E. Rombo, Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91028, Fax: (+39) 091 238 91024, E-mail: simona.rombo@math.unipa.it, Filippo Utro, Computational Genomics Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Tel.: (+1) 914 945 1549, Fax: (+1) 914 945 4217, E-mail: futro@us.ibm.com.|Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91067, Fax: (+39) 091 238 91024, raffaele@math.unipa.it, Simona E. Rombo, Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91028, Fax: (+39) 091 238 91024, E-mail: simona.rombo@math.unipa.it, Filippo Utro, Computational Genomics Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Tel.: (+1) 914 945 1549, Fax: (+1) 914 945 4217, E-mail: futro@us.ibm.com.', 'Italy|Italy|Italy', 39, 'Algorithms|Computational Biology|Data Compression|High-Throughput Nucleotide Sequencing|Metagenomics|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbt088', '24347576', 'High-throughput sequencing technologies produce large collections of data, mainly DNA sequences with additional information, requiring the design of efficient and effective methodologies for both their compression and storage. In this context, we first provide a classification of the main techniques that have been proposed, according to three specific research directions that have emerged from the literature and, for each, we provide an overview of the current techniques. Finally, to make this review useful to researchers and technicians applying the existing software and tools, we include a synopsis of the main characteristics of the described approaches, including details on their implementation and availability. Performance of the various methods is also highlighted, although the state of the art does not lend itself to a consistent and coherent comparison among all the methods presented here. '),
(68847, 2015, 'bib', 'A survey on prediction of specificity-determining sites in proteins', 'Chakraborty, Abhijit|Chakrabarti, Saikat', 'Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91067, Fax: (+39) 091 238 91024, raffaele@math.unipa.it, Simona E. Rombo, Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91028, Fax: (+39) 091 238 91024, E-mail: simona.rombo@math.unipa.it, Filippo Utro, Computational Genomics Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Tel.: (+1) 914 945 1549, Fax: (+1) 914 945 4217, E-mail: futro@us.ibm.com.|Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91067, Fax: (+39) 091 238 91024, raffaele@math.unipa.it, Simona E. Rombo, Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy. Tel.: (+39) 091 238 91028, Fax: (+39) 091 238 91024, E-mail: simona.rombo@math.unipa.it, Filippo Utro, Computational Genomics Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Tel.: (+1) 914 945 1549, Fax: (+1) 914 945 4217, E-mail: futro@us.ibm.com.', 'Italy|Italy', 29, 'Algorithms|Amino Acid Sequence|Evolution, Molecular|Humans|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bib/bbt092', '24413183', 'Specificity-determining sites (SDS) are the key positions of a protein family that show a specific conservation of amino acids, related to the subfamily members of that family. SDS play crucial role in developing functional variation within the protein family during the course of evolution. Thus, it is important to identify SDS to understand the evolutionary process of diversification of biological functions within a protein family. A wide range of computational tools have been designed to detect such SDS. In this review, we intend to examine the concept of SDS in more details along with the advancements and drawbacks of different computational approaches designed towards successful prediction of SDS. Further, we discussed the algorithms behind the computational approaches developed till date and provide an exhaustive comparison of performance of each method. We also introduce a new ensemble approach, SubSite as another tool to predict SDS through a user-friendly webserver available at www.hpppi.iicb.res.in/subsite. '),
(68848, 2014, 'bib', 'A bioinformaticians guide to the forefront of suffix array construction algorithms', 'Shrestha, Anish Man Singh|Frith, Martin C|Horton, Paul', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 17, 'Algorithms|Computational Biology|Databases, Nucleic Acid|Humans|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bib/bbt081', '24413184', 'The suffix array and its variants are text-indexing data structures that have become indispensable in the field of bioinformatics. With the uninitiated in mind, we provide an accessible exposition of the SA-IS algorithm, which is the state of the art in suffix array construction. We also describe DisLex, a technique that allows standard suffix array construction algorithms to create modified suffix arrays designed to enable a simple form of inexact matching needed to support spaced seeds and subset seeds used in many biological applications. '),
(68849, 2015, 'bib', 'The genomic and functional characteristics of disease genes', 'Collins, Andrew', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan', 4, 'Genetic Predisposition to Disease|Genomic Instability|High-Throughput Nucleotide Sequencing|Humans|Linkage Disequilibrium|Mutation|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbt091', '24425794', 'Increasing evidence indicates that genes containing disease causal variation have distinct functional and genomic properties. The importance of understanding these properties is highlighted by efforts to filter lists of variants from next-generation sequencing studies, where the number of potentially deleterious variants, which are in fact unrelated to disease, may be large. Available evidence indicates that the majority of disease genes are non-essential and their products occupy functionally peripheral positions in protein networks. They tend to be intermediate between genes that have core biological functions, particularly low mutation rates and low haplotype diversity, and genes for which high haplotype diversity and high mutation rates are advantageous (such as those involved in sensory perception and some immune system functions). Evidence presented here supports these conclusions through analysis of integrated data sets incorporating the latest mutational profiles, linkage disequilibrium structure and other genomic properties of individual genes. The analysis highlights the contrasting functions of genes predicted as least and most likely to contain disease variation and provides a basis for filtering gene variant lists to exclude the least plausible disease candidates. '),
(68850, 2015, 'bib', 'Sequencing technologies and tools for short tandem repeat variation detection', 'Cao, Minh Duc|Balasubramanian, Sureshkumar|Bodén, Mikael', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 11, 'Computational Biology|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Microsatellite Repeats|Sequence Alignment', 'Journal Article', '10.1093/bib/bbu001', '24504770', 'Short tandem repeats are highly polymorphic and associated with a wide range of phenotypic variation, some of which cause neurodegenerative disease in humans. With advances in high-throughput sequencing technologies, there are novel opportunities to study genetic variation. While available sequencing technologies and bioinformatics tools provide options for mining high-throughput sequencing data, their suitability for analysis of repeat variation is an open question, with tools for quantifying variability in repetitive sequence still in their infancy. We present here a comprehensive survey and empirical evaluation of current sequencing technologies and bioinformatics tools in all stages of an analysis pipeline. While there is not one optimal pipeline to suit all circumstances, we find that the choice of alignment and repeat genotyping tools greatly impacts the accuracy and efficiency by which short tandem repeat variation can be detected. We further note that to detect variation relevant to many repeat diseases, it is essential to choose technologies that offer either long read-lengths or paired-end sequencing, coupled with specific genotyping tools. '),
(68851, 2015, 'bib', 'An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples', 'Yadav, Vinod Kumar|De, Subhajyoti', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 34, 'Computational Biology|Gene Expression Profiling|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bib/bbu002', '24562872', 'Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation. We discuss several computational tools, which enable deconvolution of genomic and transcriptomic data from heterogeneous samples. We also performed a systematic comparative assessment of these tools. If properly used, these tools have potentials to complement single-cell genomics and immunoFISH analyses, and provide novel insights into tumor heterogeneity. '),
(68852, 2015, 'bib', 'Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data', 'Alkodsi, Amjad|Louhimo, Riku|Hautaniemi, Sampsa', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 32, 'Algorithms|Breast Neoplasms|Computational Biology|Computer Simulation|DNA Copy Number Variations|DNA, Neoplasm|Exome|Female|Gene Dosage|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bib/bbu004', '24599115', 'Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection. '),
(68853, 2015, 'bib', 'Translational research platforms integrating clinical and omics data: a review of publicly available solutions', 'Canuel, Vincent|Rance, Bastien|Avillach, Paul|Degoulet, Patrice|Burgun, Anita', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 55, 'Computational Biology|Databases, Genetic|Genomics|Humans|Precision Medicine|Software|Translational Medical Research', 'Journal Article', '10.1093/bib/bbu006', '24608524', 'The rise of personalized medicine and the availability of high-throughput molecular analyses in the context of clinical care have increased the need for adequate tools for translational researchers to manage and explore these data. We reviewed the biomedical literature for translational platforms allowing the management and exploration of clinical and omics data, and identified seven platforms: BRISK, caTRIP, cBio Cancer Portal, G-DOC, iCOD, iDASH and tranSMART. We analyzed these platforms along seven major axes. (1) The community axis regrouped information regarding initiators and funders of the project, as well as availability status and references. (2) We regrouped under the information content axis the nature of the clinical and omics data handled by each system. (3) The privacy management environment axis encompassed functionalities allowing control over data privacy. (4) In the analysis support axis, we detailed the analytical and statistical tools provided by the platforms. We also explored (5) interoperability support and (6) system requirements. The final axis (7) platform support listed the availability of documentation and installation procedures. A large heterogeneity was observed in regard to the capability to manage phenotype information in addition to omics data, their security and interoperability features. The analytical and visualization features strongly depend on the considered platform. Similarly, the availability of the systems is variable. This review aims at providing the reader with the background to choose the platform best suited to their needs. To conclude, we discuss the desiderata for optimal translational research platforms, in terms of privacy, interoperability and technical features. '),
(68854, 2015, 'bib', 'Towards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approaches', 'Chang, Catherine Ching Han|Tey, Beng Ti|Song, Jiangning|Ramanan, Ramakrishnan Nagasundara', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 5, 'Algorithms|Computational Biology|Computer Simulation|Databases, Protein|Internet|Kinetics|Machine Learning|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bib/bbu007', '24621527', 'The understanding of protein-folding mechanisms is often considered to be an important goal that will enable structural biologists to discover the mysterious relationship between the sequence, structure and function of proteins. The ability to predict protein-folding rates without the need for actual experimental work will assist the research work of structural biologists in many ways. Many bioinformatics tools have emerged in the past decade, and each has showcased different features. In this article, we review and compare eight web-based prediction tools that are currently available and that predominantly predict the protein-folding rate. The prediction performance, usability and utility, together with the prediction tool development and validation methodologies for these tools, are critically reviewed. This article is presented in a comprehensible manner to assist readers in the process of selecting the most appropriate bioinformatics tools to meet their needs. '),
(68855, 2015, 'bib', 'Advances in network-based metabolic pathway analysis and gene expression data integration', 'Rezola, Alberto|Pey, Jon|Tobalina, Luis|Rubio, Ángel|Beasley, John E|Planes, Francisco J', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan', 8, 'Algorithms|Computational Biology|Escherichia coli|Gene Expression|Gene Expression Profiling|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bib/bbu009', '24626528', 'With the emergence of metabolic networks, novel mathematical pathway concepts were introduced in the past decade, aiming to go beyond canonical maps. However, the use of network-based pathways to interpret omics data has been limited owing to the fact that their computation has, until very recently, been infeasible in large (genome-scale) metabolic networks. In this review article, we describe the progress made in the past few years in the field of network-based metabolic pathway analysis. In particular, we review in detail novel optimization techniques to compute elementary flux modes, an important pathway concept in this field. In addition, we summarize approaches for the integration of metabolic pathways with gene expression data, discussing recent advances using network-based pathway concepts. '),
(68856, 2015, 'bib', 'Rich annotation of DNA sequencing variants by leveraging the Ensembl Variant Effect Predictor with plugins', 'Yourshaw, Michael|Taylor, S Paige|Rao, Aliz R|Martín, Martín G|Nelson, Stanley F', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 22, 'Computational Biology|Databases, Nucleic Acid|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbu008', '24626529', 'High-throughput DNA sequencing has become a mainstay for the discovery of genomic variants that may cause disease or affect phenotype. A next-generation sequencing pipeline typically identifies thousands of variants in each sample. A particular challenge is the annotation of each variant in a way that is useful to downstream consumers of the data, such as clinical sequencing centers or researchers. These users may require that all data storage and analysis remain on secure local servers to protect patient confidentiality or intellectual property, may have unique and changing needs to draw on a variety of annotation data sets and may prefer not to rely on closed-source applications beyond their control. Here we describe scalable methods for using the plugin capability of the Ensembl Variant Effect Predictor to enrich its basic set of variant annotations with additional data on genes, function, conservation, expression, diseases, pathways and protein structure, and describe an extensible framework for easily adding additional custom data sets. '),
(68857, 2015, 'bib', 'Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA', 'Zhao, Qing|Shi, Xingjie|Xie, Yang|Huang, Jian|Shia, BenChang|Ma, Shuangge', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan', 71, 'Brain Neoplasms|Breast Neoplasms|Carcinoma, Squamous Cell|Computational Biology|Databases, Genetic|Female|Genomics|Glioblastoma|Humans|Least-Squares Analysis|Leukemia, Myeloid, Acute|Lung Neoplasms|Male|Neoplasms|Principal Component Analysis|Prognosis|Proportional Hazards Models', 'Journal Article', '10.1093/bib/bbu003', '24632304', 'With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply several existing methods and associate multidimensional genomic measurements with cancer outcomes in particular prognosis, with special focus on the predictive power of genomic signatures. We exploit clinical data and four types of genomic measurement including mRNA gene expression, DNA methylation, microRNA and copy number alterations for breast invasive carcinoma, glioblastoma multiforme, acute myeloid leukemia and lung squamous cell carcinoma collected by TCGA. To accommodate the high dimensionality, we extract important features using Principal Component Analysis, Partial Least Squares and Least Absolute Shrinkage and Selection Operator (Lasso), which are representative of dimension reduction and variable selection techniques and have been extensively adopted, and fit Cox survival models with combined important features. We calibrate the predictive power of each type of genomic measurement for the prognosis of four cancer types and find that the results vary across cancers. Our analysis also suggests that for most of the cancers in our study and the adopted methods, there is no substantial improvement in prediction when adding other genomic measurement after gene expression and clinical covariates have been included in the model. This is consistent with the findings that molecular features measured at the transcription level affect clinical outcomes more directly than those measured at the DNA/epigenetic level. '),
(68858, 2014, 'bib', 'Next-generation bioinformatics: connecting bases to genes, networks and disease', 'Horton, Paul', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan', 1, 'Computational Biology|Disease|Gene Regulatory Networks|Genetic Markers|High-Throughput Nucleotide Sequencing|Humans|Protein Interaction Maps', 'Editorial', '10.1093/bib/bbt090', '24637213', '-'),
(68859, 2015, 'bib', 'Genome-wide identification and predictive modeling of polyadenylation sites in eukaryotes', 'Ji, Guoli|Guan, Jinting|Zeng, Yong|Li, Qingshun Q|Wu, Xiaohui', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 16, 'Algorithms|Animals|Computational Biology|Eukaryota|Genome-Wide Association Study|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Polyadenylation|RNA, Messenger', 'Journal Article', '10.1093/bib/bbu011', '24695098', 'Polyadenylation [poly(A)] is a vital step in post-transcriptional processing of pre-mRNA. Alternative polyadenylation is a widespread mechanism of regulating gene expression in eukaryotes. Defining poly(A) sites contributes to the annotation of transcripts ends and the study of gene regulatory mechanisms. Here, we survey methods for collecting poly(A) sites using high-throughput sequencing technologies and summarize the general processes for genome-wide poly(A) site identifications. We also compare the performances of various poly(A) site prediction models and discuss the relationship between poly(A) site identification from sequencing projects and predictive modeling. Moreover, we attempt to address some potential problems in current researches and propose future directions related to polyadenylation research. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68860, 2015, 'bib', 'Letter to the Editor: On the term interaction and related phrases in the literature on Random Forests', 'Boulesteix, Anne-Laure|Janitza, Silke|Hapfelmeier, Alexander|Van Steen, Kristel|Strobl, Carolin', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 19, 'Algorithms|Biological Science Disciplines|Data Mining|Humans', 'Letter', '10.1093/bib/bbu012', '24723569', 'In an interesting and quite exhaustive review on Random Forests (RF) methodology in bioinformatics Touw et al. address--among other topics--the problem of the detection of interactions between variables based on RF methodology. We feel that some important statistical concepts, such as interaction, conditional dependence or correlation, are sometimes employed inconsistently in the bioinformatics literature in general and in the literature on RF in particular. In this letter to the Editor, we aim to clarify some of the central statistical concepts and point out some confusing interpretations concerning RF given by Touw et al. and other authors.'),
(68861, 2015, 'bib', 'Toward more realistic drug-target interaction predictions', 'Pahikkala, Tapio|Airola, Antti|Pietilä, Sami|Shakyawar, Sushil|Szwajda, Agnieszka|Tang, Jing|Aittokallio, Tero', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 29, 'Computational Biology|Databases, Pharmaceutical|Drug Discovery|Humans|Models, Biological|Models, Statistical|Quantitative Structure-Activity Relationship|Supervised Machine Learning', 'Journal Article', '10.1093/bib/bbu010', '24723570', 'A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors. '),
(68862, 2015, 'bib', 'How to establish a bioinformatics postgraduate degree programme--a case study from South Africa', 'Machanick, Philip|Tastan Bishop, Özlem', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 8, 'Computational Biology|Curriculum|Education, Graduate|Humans|South Africa|Students|Universities', 'Journal Article', '10.1093/bib/bbu014', '24794523', 'The Research Unit in Bioinformatics at Rhodes University (RUBi), South Africa, offers a Masters of Science in Bioinformatics. Growing demand for bioinformatics qualifications results in applications from across Africa. Courses aim to bridge gaps in the diverse backgrounds of students who range from biologists with no prior computing exposure to computer scientists with no biology background. The programme is evenly split between coursework and research, with diverse modules from a range of departments covering mathematics, statistics, computer science and biology, with emphasis on application to bioinformatics research. The early focus on research helps bring students up to speed with working as a researcher. We measure success of the programme by the high rate of subsequent entry to PhD study: 10 of 14 students who completed in the years 2011-2013. '),
(68863, 2015, 'bib', 'A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq', 'Ye, Meixia|Wang, Zhong|Wang, Yaqun|Wu, Rongling', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 2, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Developmental|Gene Expression Regulation, Plant|Genes, Plant|High-Throughput Nucleotide Sequencing|Models, Genetic|Models, Statistical|Multigene Family|Poisson Distribution|Populus|RNA, Plant|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbu013', '24817567', 'Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. '),
(68864, 2014, 'bib', 'Editorial: Alignment-free methods in computational biology', 'Vinga, Susana', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan', 36, 'Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Information Theory|Sequence Alignment|Sequence Analysis', 'Editorial', '10.1093/bib/bbu005', '24819825', '-'),
(68865, 2015, 'bib', 'Methodological aspects of whole-genome bisulfite sequencing analysis', 'Adusumalli, Swarnaseetha|Mohd Omar, Mohd Feroz|Soong, Richie|Benoukraf, Touati', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 39, 'Algorithms|Base Sequence|Chromosome Mapping|CpG Islands|DNA|DNA Methylation|High-Throughput Nucleotide Sequencing|Molecular Sequence Data|Sequence Analysis, DNA|Sulfites', 'Journal Article', '10.1093/bib/bbu016', '24867940', 'The combination of DNA bisulfite treatment with high-throughput sequencing technologies has enabled investigation of genome-wide DNA methylation beyond CpG sites and CpG islands. These technologies have opened new avenues to understand the interplay between epigenetic events, chromatin plasticity and gene regulation. However, the processing, managing and mining of this huge volume of data require specialized computational tools and statistical methods that are yet to be standardized. Here, we describe a complete bisulfite sequencing analysis workflow, including recently developed programs, highlighting each of the crucial analysis steps required, i.e. sequencing quality control, reads alignment, methylation scoring, methylation heterogeneity assessment, genomic features annotation, data visualization and determination of differentially methylated cytosines. Moreover, we discuss the limitations of these technologies and considerations to perform suitable analyses. '),
(68866, 2015, 'bib', 'Causes, consequences and solutions of phylogenetic incongruence', 'Som, Anup', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan', 49, 'Animals|Biological Evolution|Chromosome Mapping|Evolution, Molecular|Genetic Speciation|Humans|Models, Genetic|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbu015', '24872401', 'Phylogenetic analysis is used to recover the evolutionary history of species, genes or proteins. Understanding phylogenetic relationships between organisms is a prerequisite of almost any evolutionary study, as contemporary species all share a common history through their ancestry. Moreover, it is important because of its wide applications that include understanding genome organization, epidemiological investigations, predicting protein functions, and deciding the genes to be analyzed in comparative studies. Despite immense progress in recent years, phylogenetic reconstruction involves many challenges that create uncertainty with respect to the true evolutionary relationships of the species or genes analyzed. One of the most notable difficulties is the widespread occurrence of incongruence among methods and also among individual genes or different genomic regions. Presence of widespread incongruence inhibits successful revealing of evolutionary relationships and applications of phylogenetic analysis. In this article, I concisely review the effect of various factors that cause incongruence in molecular phylogenies, the advances in the field that resolved some factors, and explore unresolved factors that cause incongruence along with possible ways for tackling them. '),
(68867, 2015, 'bib', 'Review of statistical methodologies for the detection of parent-of-origin effects in family trio genome-wide association data with binary disease traits', 'Connolly, Siobhan|Heron, Elizabeth A', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 9, 'Chromosome Mapping|Data Interpretation, Statistical|Genetic Loci|Genetic Predisposition to Disease|Genetic Testing|Genome, Human|Genome-Wide Association Study|Genomic Imprinting|Humans|Quantitative Trait, Heritable|Risk Assessment', 'Journal Article', '10.1093/bib/bbu017', '24903222', 'The detection of parent-of-origin effects aims to identify whether the functionality of alleles, and in turn associated phenotypic traits, depends on the parental origin of the alleles. Different parent-of-origin effects have been identified through a variety of mechanisms and a number of statistical methodologies for their detection have been proposed, in particular for genome-wide association studies (GWAS). GWAS have had limited success in explaining the heritability of many complex disorders and traits, but successful identification of parent-of-origin effects using trio (mother, father and offspring) GWAS may help shed light on this missing heritability. However, it is important to choose the most appropriate parent-of-origin test or methodology, given knowledge of the phenotype, amount of available data and the type of parent-of-origin effect(s) being considered. This review brings together the parent-of-origin detection methodologies available, comparing them in terms of power and type I error for a number of different simulated data scenarios, and finally offering guidance as to the most appropriate choice for the different scenarios. '),
(68868, 2015, 'bib', 'Exploring the function of genetic variants in the non-coding genomic regions: approaches for identifying human regulatory variants affecting gene expression', 'Li, Mulin Jun|Yan, Bin|Sham, Pak Chung|Wang, Junwen', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 11, 'Algorithms|Base Sequence|Chromosome Mapping|Gene Expression Regulation|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Data|Open Reading Frames|Polymorphism, Single Nucleotide|Regulatory Sequences, Nucleic Acid|Sequence Alignment', 'Journal Article', '10.1093/bib/bbu018', '24916300', 'Understanding the genetic basis of human traits/diseases and the underlying mechanisms of how these traits/diseases are affected by genetic variations is critical for public health. Current genome-wide functional genomics data uncovered a large number of functional elements in the noncoding regions of human genome, providing new opportunities to study regulatory variants (RVs). RVs play important roles in transcription factor bindings, chromatin states and epigenetic modifications. Here, we systematically review an array of methods currently used to map RVs as well as the computational approaches in annotating and interpreting their regulatory effects, with emphasis on regulatory single-nucleotide polymorphism. We also briefly introduce experimental methods to validate these functional RVs. '),
(68869, 2015, 'bib', 'An open-pollinated design for mapping imprinting genes in natural populations', 'Sun, Lidan|Zhu, Xuli|Bo, Wenhao|Xu, Fang|Cheng, Tangren|Zhang, Qixiang|Wu, Rongling', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 10, 'Base Sequence|Chromosome Mapping|DNA, Plant|Genetics, Population|Genome-Wide Association Study|Genomic Imprinting|Haplotypes|Molecular Sequence Data|Pollination|Quantitative Trait, Heritable|Seeds|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbu019', '24927940', 'With the increasing recognition of its role in trait and disease development, it is crucial to account for genetic imprinting to illustrate the genetic architecture of complex traits. Genetic mapping can be innovated to test and estimate effects of genetic imprinting in a segregating population derived from experimental crosses. Here, we describe and assess a design for imprinting detection in natural plant populations. This design is to sample maternal plants at random from a natural population and collect open-pollinated (OP) seeds randomly from each maternal plant and germinate them into seedlings. A two-stage hierarchical platform is constructed to jointly analyze maternal and OP progeny markers. Through tracing the segregation and transmission of alleles from the parental to progeny generation, this platform allows parent-of-origin-dependent gene expression to be discerned, providing an avenue to estimate the effect of imprinting genes on a quantitative trait. The design is derived to estimate imprinting effects expressed at the haplotype level. Its usefulness and utilization were validated through computer simulation. This OP-based design provides a tool to detect the genomic distribution and pattern of imprinting genes as an important component of heritable variation that is neglected in traditional genetic studies of complex traits. '),
(68870, 2015, 'bib', 'Breast cancer classification: linking molecular mechanisms to disease prognosis', 'Taherian-Fard, Atefeh|Srihari, Sriganesh|Ragan, Mark A', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 13, 'Biomarkers, Tumor|Breast Neoplasms|Female|Gene Expression Profiling|Humans|Molecular Diagnostic Techniques|Neoplasm Proteins|Prognosis|Protein Interaction Mapping|Reproducibility of Results|Risk Assessment|Sensitivity and Specificity', 'Journal Article', '10.1093/bib/bbu020', '24950687', 'Breast cancer was traditionally perceived as a single disease, however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of xe2x88xbc550 breast tumours from The Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios covering molecular characteristics, biomarker status, tumour stages and survival patterns. We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer. '),
(68871, 2015, 'bib', 'Current innovations and future challenges of network motif detection', 'Tran, Ngoc Tam L|Mohan, Sominder|Xu, Zhuoqing|Huang, Chun-Hsi', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 21, 'Algorithms|Animals|Computer Simulation|Feedback, Physiological|Forecasting|Gene Expression Regulation|Humans|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Comparative Study', '10.1093/bib/bbu021', '24966356', 'Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs. This work analyzes 11 network motif detection tools and algorithms. Detailed comparisons and insightful directions for using these tools and algorithms are discussed. Key aspects of network motif detection are investigated. Network motif types and common network motifs as well as their biological functions are discussed. Applications of network motifs are also presented. Finally, the challenges, future improvements and future research directions for network motif detection are also discussed. '),
(68872, 2015, 'bib', 'Bioinformatics education--perspectives and challenges out of Africa', 'Tastan Bishop, Özlem|Adebiyi, Ezekiel F|Alzohairy, Ahmed M|Everett, Dean|Ghedira, Kais|Ghouila, Amel|Kumuthini, Judit|Mulder, Nicola J|Panji, Sumir|Patterton, Hugh-G|H3ABioNet Consortium|H3Africa Consortium', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 23, 'Africa|Computational Biology|Education|Genomics|History, 20th Century|History, 21st Century|Humans|Internet|Universities', 'Historical Article', '10.1093/bib/bbu022', '24990350', 'The discipline of bioinformatics has developed rapidly since the complete sequencing of the first genomes in the 1990s. The development of many high-throughput techniques during the last decades has ensured that bioinformatics has grown into a discipline that overlaps with, and is required for, the modern practice of virtually every field in the life sciences. This has placed a scientific premium on the availability of skilled bioinformaticians, a qualification that is extremely scarce on the African continent. The reasons for this are numerous, although the absence of a skilled bioinformatician at academic institutions to initiate a training process and build sustained capacity seems to be a common African shortcoming. This dearth of bioinformatics expertise has had a knock-on effect on the establishment of many modern high-throughput projects at African institutes, including the comprehensive and systematic analysis of genomes from African populations, which are among the most genetically diverse anywhere on the planet. Recent funding initiatives from the National Institutes of Health and the Wellcome Trust are aimed at ameliorating this shortcoming. In this paper, we discuss the problems that have limited the establishment of the bioinformatics field in Africa, as well as propose specific actions that will help with the education and training of bioinformaticians on the continent. This is an absolute requirement in anticipation of a boom in high-throughput approaches to human health issues unique to data from African populations. '),
(68873, 2015, 'bib', 'From miRNA regulation to miRNA-TF co-regulation: computational approaches and challenges', 'Le, Thuc Duy|Liu, Lin|Zhang, Junpeng|Liu, Bing|Li, Jiuyong', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 7, 'Animals|Base Sequence|Gene Expression Regulation|Humans|MicroRNAs|Molecular Sequence Data|RNA Processing, Post-Transcriptional|Regulatory Sequences, Ribonucleic Acid|Sequence Analysis, RNA|Transcription Factors', 'Journal Article', '10.1093/bib/bbu023', '25016381', 'microRNAs (miRNAs) are important gene regulators. They control a wide range of biological processes and are involved in several types of cancers. Thus, exploring miRNA functions is important for diagnostics and therapeutics. To date, there are few feasible experimental techniques for discovering miRNA regulatory mechanisms. Alternatively, predictions of miRNA-mRNA regulatory relationships by computational methods have increasingly achieved promising results. Computational approaches are proving their ability as effective tools in reducing the number of biological experiments that must be conducted and to assist with the design of the experiments. In this review, we categorize and review different computational approaches to identify miRNA activities and functions, including the co-regulation of miRNAs and transcription factors. Our main focuses are on the recent approaches that use multiple data types for exploring miRNA functions. We discuss the remaining challenges in the evaluation and selection of models based on the results from a case study. Finally, we analyse the remaining challenges of each computational approach and suggest some future research directions. '),
(68874, 2015, 'bib', 'Kernel methods for large-scale genomic data analysis', 'Wang, Xuefeng|Xing, Eric P|Schaid, Daniel J', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 16, 'Computational Biology|Data Interpretation, Statistical|Genome-Wide Association Study|Genomics|Humans|Logistic Models|Machine Learning|Models, Statistical|Polymorphism, Single Nucleotide|Support Vector Machine', 'Journal Article', '10.1093/bib/bbu024', '25053743', 'Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by todays explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. '),
(68875, 2015, 'bib', 'Functional mapping of seasonal transition in perennial plants', 'Ye, Meixia|Jiang, Libo|Mao, Ke|Wang, Yaqun|Wang, Zhong|Wu, Rongling', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan', 2, 'Acclimatization|Chromosome Mapping|Computer Simulation|Genetic Association Studies|Models, Genetic|Plant Development|Quantitative Trait Loci|Seasons', 'Journal Article', '10.1093/bib/bbu025', '25078026', 'Unlike annuals, all perennial plants undergo seasonal transitions during ontogeny. As an adaptive response to seasonal changes in climate, the seasonal pattern of growth is likely to be under genetic control, although its underlying genetic basis remains unknown. Here, we develop a computational model that can map specific quantitative trait loci (QTLs) responsible for seasonal transitions of growth in perennials. The model is founded on functional mapping, a statistical framework to map developmental dynamics, which is reformed to integrate a seasonally adjusted growth function. The new model is equipped with a capacity to characterize the genetic effects of QTLs on seasonal alternation at different ages and then to better elucidate the genetic architecture of development. The model is implemented with a series of testing procedures, including (i) how a QTL controls an overall ontogenetic growth curve, (ii) how the QTL determines seasonal trajectories of growth within years and (iii) how it determines the dynamic nature of age-specific season response. The model was validated through computer simulation. The extension of season adjustment to other types of biological curves is statistically straightforward, facilitating a wider variety of genetic studies into ontogenetic growth and development in perennial plants. '),
(68876, 2015, 'bib', 'Bio/chemoinformatics in India: an outlook', 'Gupta, Shipra|Chavan, Sonali|Deobagkar, Dileep N|Deobagkar, Deepti D', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 2, 'Career Choice|Chemistry|Computational Biology|India', 'Journal Article', '10.1093/bib/bbu028', '25159593', 'With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development. '),
(68877, 2015, 'bib', 'Exome sequence read depth methods for identifying copy number changes', 'Kadalayil, Latha|Rafiq, Sajjad|Rose-Zerilli, Matthew J J|Pengelly, Reuben J|Parker, Helen|Oscier, David|Strefford, Jonathan C|Tapper, William J|Gibson, Jane|Ennis, Sarah|Collins, Andrew', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 45, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Copy Number Variations|DNA, Neoplasm|Data Interpretation, Statistical|Exome|Humans|Leukemia, Lymphocytic, Chronic, B-Cell|Molecular Sequence Data|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbu027', '25169955', 'Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results. '),
(68878, 2015, 'bib', 'Buying in to bioinformatics: an introduction to commercial sequence analysis software', 'Smith, David Roy', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan', 16, 'Computational Biology|Phylogeny|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbu030', '25183247', 'Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bioinformatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right software for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, features and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software bundles. They have the potential to streamline your research, increase your productivity, energize your classroom and, if anything, add a bit of zest to the often dry detached world of bioinformatics. '),
(68879, 2015, 'bib', 'Correcting Illumina data', 'Molnar, Michael|Ilie, Lucian', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 9, 'Data Interpretation, Statistical|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbu029', '25183248', 'Next-generation sequencing technologies revolutionized the ways in which genetic information is obtained and have opened the door for many essential applications in biomedical sciences. Hundreds of gigabytes of data are being produced, and all applications are affected by the errors in the data. Many programs have been designed to correct these errors, most of them targeting the data produced by the dominant technology of Illumina. We present a thorough comparison of these programs. Both HiSeq and MiSeq types of Illumina data are analyzed, and correcting performance is evaluated as the gain in depth and breadth of coverage, as given by correct reads and k-mers. Time and memory requirements, scalability and parallelism are considered as well. Practical guidelines are provided for the effective use of these tools. We also evaluate the efficiency of the current state-of-the-art programs for correcting Illumina data and provide research directions for further improvement. '),
(68880, 2015, 'bib', 'Performance evaluation of DNA copy number segmentation methods', 'Pierre-Jean, Morgane|Rigaill, Guillem|Neuvial, Pierre', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 11, 'DNA Copy Number Variations|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbu026', '25202135', 'A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. To make an objective and reproducible performance assessment, we have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling publicly available SNP microarray data from genomic regions with known copy-number state. The original data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. This article describes this framework and its application to a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons of the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. This comparison study may be reproduced using the open source and cross-platform R package jointseg, which implements the proposed data generation and evaluation framework: http://r-forge.r-project.org/R/?group_id=1562. '),
(68881, 2015, 'bib', 'Towards more accurate prediction of ubiquitination sites: a comprehensive review of current methods, tools and features', 'Chen, Zhen|Zhou, Yuan|Zhang, Ziding|Song, Jiangning', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 49, 'Algorithms|Proteome|Ubiquitination', 'Journal Article', '10.1093/bib/bbu031', '25212598', 'Protein ubiquitination is one of the most important reversible post-translational modifications (PTMs). In many biochemical, pathological and pharmaceutical studies on understanding the function of proteins in biological processes, identification of ubiquitination sites is an important first step. However, experimental approaches for identifying ubiquitination sites are often expensive, labor-intensive and time-consuming, partly due to the dynamics and reversibility of ubiquitination. In silico prediction of ubiquitination sites is potentially a useful strategy for whole proteome annotation. A number of bioinformatics approaches and tools have recently been developed for predicting protein ubiquitination sites. However, these tools have different methodologies, prediction algorithms, functionality and features, which complicate their utility and application. The purpose of this review is to aid users in selecting appropriate tools for specific analyses and circumstances. We first compared five popular webservers and standalone software options, assessing their performance on four up-to-date ubiquitination benchmark datasets from Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Arabidopsis thaliana. We then discussed and summarized these tools to guide users in choosing among the tools efficiently and rapidly. Finally, we assessed the importance of features of existing tools for ubiquitination site prediction, ranking them by performance. We also discussed the features that make noticeable contributions to species-specific ubiquitination site prediction. '),
(68882, 2015, 'bib', 'Predictive modelling of gene expression from transcriptional regulatory elements', 'Budden, David M|Hurley, Daniel G|Crampin, Edmund J', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 17, 'Algorithms|Animals|Chromatin Immunoprecipitation|Gene Expression Regulation|Humans|Mice|Models, Genetic|Regulatory Sequences, Nucleic Acid|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbu034', '25231769', 'Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and xcfxb5-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment. '),
(68883, 2015, 'bib', 'Alternative applications for distinct RNA sequencing strategies', 'Han, Leng|Vickers, Kasey C|Samuels, David C|Guo, Yan', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 23, 'High-Throughput Nucleotide Sequencing|Mutation|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|RNA, Long Noncoding|Transcriptional Activation|Transcriptome', 'Journal Article', '10.1093/bib/bbu032', '25246237', 'Recent advances in RNA library preparation methods, platform accessibility and cost efficiency have allowed high-throughput RNA sequencing (RNAseq) to replace conventional hybridization microarray platforms as the method of choice for mRNA profiling and transcriptome analyses. RNAseq is a powerful technique to profile both long and short RNA expression, and the depth of information gained from distinct RNAseq methods is striking and facilitates discovery. In addition to expression analysis, distinct RNAseq approaches also allow investigators the ability to assess transcriptional elongation, DNA variance and exogenous RNA content. Here we review the current state of the art in transcriptome sequencing and address epigenetic regulation, quantification of transcription activation, RNAseq output and a diverse set of applications for RNAseq data. We detail how RNAseq can be used to identify allele-specific expression, single-nucleotide polymorphisms and somatic mutations and discuss the benefits and limitations of using RNAseq to monitor DNA characteristics. Moreover, we highlight the power of combining RNA- and DNAseq methods for genomic analysis. In summary, RNAseq provides the opportunity to gain greater insight into transcriptional regulation and output than simply miRNA and mRNA profiling. '),
(68884, 2015, 'bib', 'Systematic assessment of imputation performance using the 1000 Genomes reference panels', 'Liu, Qian|Cirulli, Elizabeth T|Han, Yujun|Yao, Song|Liu, Song|Zhu, Qianqian', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan', 7, 'Databases, Genetic|Genome, Human|Genome-Wide Association Study|Humans|Software', 'Journal Article', '10.1093/bib/bbu035', '25246238', 'Genotype imputation has been widely adopted in the postgenome-wide association studies (GWAS) era. Owing to its ability to accurately predict the genotypes of untyped variants, imputation greatly boosts variant density, allowing fine-mapping studies of GWAS loci and large-scale meta-analysis across different genotyping arrays. By leveraging genotype data from 90 whole-genome deeply sequenced individuals as the evaluation benchmark and the 1000 Genomes Project data as reference panels, we systematically examined four important issues related to genotype imputation practice. First, in a study of imputation accuracy, we found that IMPUTE2 and minimac have the best imputation performance among the three popular imputing software evaluated and that using a multi-population reference panel is beneficial. Second, the optimal imputation quality cutoff for removing poorly imputed variants varies according to the software used. Third, the major contributing factors to consistently poor imputation are low variant heterozygosity, high sequence similarity to other genomic regions, high GC content, segmental duplication and being far from genotyping markers. Lastly, in an evaluation of the imputability of all known GWAS regions, we found that GWAS loci associated with hematological measurements and immune system diseases are harder to impute, as compared with other human traits. Recommendations made based on the above findings may provide practical guidance for imputation exercise in future genetic studies. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68885, 2015, 'bib', 'Apoptosis regulatory protein-protein interaction demonstrates hierarchical scale-free fractal network', 'Nafis, Shazia|Kalaiarasan, Ponnusamy|Brojen Singh, R K|Husain, Mohammad|Bamezai, Rameshwar N K', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 2, 'Apoptosis|Apoptosis Regulatory Proteins|Fractals|Protein Binding', 'Journal Article', '10.1093/bib/bbu036', '25256288', 'Dysregulation or inhibition of apoptosis favors cancer and many other diseases. Understanding of the network interaction of the genes involved in apoptotic pathway, therefore, is essential, to look for targets of therapeutic intervention. Here we used the network theory methods, using experimentally validated 25 apoptosis regulatory proteins and identified important genes for apoptosis regulation, which demonstrated a hierarchical scale-free fractal protein-protein interaction network. TP53, BRCA1, UBIQ and CASP3 were recognized as a four key regulators. BRCA1 and UBIQ were also individually found to control highly clustered modules and play an important role in the stability of the overall network. The connection among the BRCA1, UBIQ and TP53 proteins was found to be important for regulation, which controlled their own respective communities and the overall network topology. The feedback loop regulation motif was identified among NPM1, BRCA1 and TP53, and these crucial motif topologies were also reflected in high frequency. The propagation of the perturbed signal from hubs was found to be active upto some distance, after which propagation started decreasing and TP53 was the most efficient signal propagator. From the functional enrichment analysis, most of the apoptosis regulatory genes associated with cardiovascular diseases and highly expressed in brain tissues were identified. Apart from TP53, BRCA1 was observed to regulate apoptosis by influencing motif, propagation of signals and module regulation, reflecting their biological significance. In future, biochemical investigation of the observed hub-interacting partners could provide further understanding about their role in the pathophysiology of cancer. '),
(68886, 2015, 'bib', 'Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies', 'Ganna, Andrea|Lee, Donghwan|Ingelsson, Erik|Pawitan, Yudi', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 19, 'Genes|High-Throughput Screening Assays|Sample Size|Software', 'Journal Article', '10.1093/bib/bbu033', '25256289', 'It is common and advised practice in biomedical research to validate experimental or observational findings in a population different from the one where the findings were initially assessed. This practice increases the generalizability of the results and decreases the likelihood of reporting false-positive findings. Validation becomes critical when dealing with high-throughput experiments, where the large number of tests increases the chance to observe false-positive results. In this article, we review common approaches to determine statistical thresholds for validation and describe the factors influencing the proportion of significant findings from a training sample that are replicated in a validation sample. We refer to this proportion as rediscovery rate (RDR). In high-throughput studies, the RDR is a function of false-positive rate and power in both the training and validation samples. We illustrate the application of the RDR using simulated data and real data examples from metabolomics experiments. We further describe an online tool to calculate the RDR using t-statistics. We foresee two main applications. First, if the validation study has not yet been collected, the RDR can be used to decide the optimal combination between the proportion of findings taken to validation and the size of the validation study. Secondly, if a validation study has already been done, the RDR estimated using the training data can be compared with the observed RDR from the validation data, hence, the success of the validation study can be assessed. '),
(68887, 2015, 'bib', 'Comparative analysis of methods for genome-wide nucleosome cartography', 'Quintales, Luis|Vázquez, Enrique|Antequera, Francisco', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 8, 'Chromatin Immunoprecipitation|Genome-Wide Association Study|Humans|Nucleosomes|Sequence Alignment', 'Comparative Study', '10.1093/bib/bbu037', '25296770', 'Nucleosomes contribute to compacting the genome into the nucleus and regulate the physical access of regulatory proteins to DNA either directly or through the epigenetic modifications of the histone tails. Precise mapping of nucleosome positioning across the genome is, therefore, essential to understanding the genome regulation. In recent years, several experimental protocols have been developed for this purpose that include the enzymatic digestion, chemical cleavage or immunoprecipitation of chromatin followed by next-generation sequencing of the resulting DNA fragments. Here, we compare the performance and resolution of these methods from the initial biochemical steps through the alignment of the millions of short-sequence reads to a reference genome to the final computational analysis to generate genome-wide maps of nucleosome occupancy. Because of the lack of a unified protocol to process data sets obtained through the different approaches, we have developed a new computational tool (NUCwave), which facilitates their analysis, comparison and assessment and will enable researchers to choose the most suitable method for any particular purpose. NUCwave is freely available at http://nucleosome.usal.es/nucwave along with a step-by-step protocol for its use. '),
(68888, 2015, 'bib', 'Deciphering oncogenic drivers: from single genes to integrated pathways', 'Chen, Jiajia|Sun, Maomin|Shen, Bairong', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 14, 'Algorithms|Animals|Base Sequence|Carcinogenesis|DNA Mutational Analysis|DNA, Neoplasm|Humans|Machine Learning|Molecular Sequence Data|Mutation|Oncogenes|Pattern Recognition, Automated|Sequence Analysis, DNA|Signal Transduction', 'Journal Article', '10.1093/bib/bbu039', '25378434', 'Technological advances in next-generation sequencing have uncovered a wide spectrum of aberrations in cancer genomes. The extreme diversity in cancer mutations necessitates computational approaches to differentiate between the drivers with vital function in cancer progression and those nonfunctional passengers. Although individual driver mutations are routinely identified, mutational profiles of different tumors are highly heterogeneous. There is growing consensus that pathways rather than single genes are the primary target of mutations. Here we review extant bioinformatics approaches to identifying oncogenic drivers at different mutational levels, highlighting the strategies for discovering driver pathways and networks from cancer mutation data. These approaches will help reduce the mutation complexity, thus providing a simplified picture of cancer. '),
(68889, 2015, 'bib', 'Network inference from AP-MS data: computational challenges and solutions', 'Teng, Ben|Zhao, Can|Liu, Xiaoqing|He, Zengyou', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 10, 'Chromatography, Affinity|Mass Spectrometry|Protein Binding|Proteins', 'Journal Article', '10.1093/bib/bbu038', '25378435', 'Protein-protein interaction is of primary importance to understand protein functions. In recent years, the high-throughput AP-MS experiments have generated a large amount of bait-prey data, posing great challenges on the computational analysis of such data for inferring true interactions and protein complexes. To date, many research efforts have been devoted to developing novel computational methods to analyze these AP-MS data sets. In this article, we review and classify the key computational methods developed for the inference of protein-protein interactions and the detection of protein complexes from the AP-MS experiments. We hope that our review as well as the challenges highlighted in the article will provide valuable insights into driving future research for further advancing the state-of-the-art technologies in computational prediction, characterization and analysis of protein-protein interactions and protein complexes from the AP-MS data. '),
(68890, 2015, 'bib', 'Case study: using sequence homology to identify putative phosphorylation sites in an evolutionarily distant species (honeybee)', 'Trost, Brett|Napper, Scott|Kusalik, Anthony', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 5, 'Animals|Bees|Biological Evolution|Humans|Insect Proteins|Likelihood Functions|Phosphorylation|Sequence Homology, Amino Acid|Species Specificity', 'Journal Article', '10.1093/bib/bbu040', '25380664', 'The majority of scientific resources are devoted to studying a relatively small number of model species, meaning that the ability to translate knowledge across species is of considerable importance. Obtaining species-specific knowledge enables targeted investigations of the biology and pathobiology of a particular species, and facilitates comparative analyses. Phosphorylation is the most widespread posttranslational modification in eukaryotes, and although many phosphorylation sites have been experimentally identified for some species, little or no data are available for others. Using the honeybee as a test organism, this case study illustrates the process of using protein sequence homology to identify putative phosphorylation sites in a species of interest using experimentally determined sites from other species. A number of issues associated with this process are examined and discussed. Several databases of experimentally determined phosphorylation sites exist, however, it can be difficult for the nonspecialist to ascertain how their contents compare. Thus, this case study assesses the content and comparability of several phosphorylation site databases. Additional issues examined include the efficacy of homology-based phosphorylation site prediction, the impact of the level of evolutionary relatedness between species in making these predictions, the ability to translate knowledge of phosphorylation sites across large evolutionary distances and the criteria that should be used in selecting probable phosphorylation sites in the species of interest. Although focusing on phosphorylation, the issues discussed here also apply to the homology-based cross-species prediction of other posttranslational modifications, as well as to sequence motifs in general. '),
(68891, 2015, 'bib', 'Discriminative pattern mining and its applications in bioinformatics', 'Liu, Xiaoqing|Wu, Jun|Gu, Feiyang|Wang, Jie|He, Zengyou', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 18, 'Algorithms|Computational Biology|Data Mining|Models, Theoretical|Software', 'Journal Article', '10.1093/bib/bbu042', '25433466', 'Discriminative pattern mining is one of the most important techniques in data mining. This challenging task is concerned with finding a set of patterns that occur with disproportionate frequency in data sets with various class labels. Such patterns are of great value for group difference detection and classifier construction. Research on finding interesting discriminative patterns in class-labeled data evolves rapidly and lots of algorithms have been proposed to specifically address this problem. Discriminative pattern mining techniques have proven their considerable value in biological data analysis. The archetypical applications in bioinformatics include phosphorylation motif discovery, differentially expressed gene identification, discriminative genotype pattern detection, etc. In this article, we present an overview of discriminative pattern mining and the corresponding effective methods, and subsequently we illustrate their applications to tackling the bioinformatics problems. In the end, we give a general discussion of potential challenges and future work for this task. '),
(68892, 2015, 'bib', 'Virtual Reference Environments: a simple way to make research reproducible', 'Hurley, Daniel G|Budden, David M|Crampin, Edmund J', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 14, 'Reproducibility of Results|Research', 'Journal Article', '10.1093/bib/bbu043', '25433467', 'Reproducible research has received increasing attention over the past few years as bioinformatics and computational biology methodologies become more complex. Although reproducible research is progressing in several valuable ways, we suggest that recent increases in internet bandwidth and disk space, along with the availability of open-source and free-software licences for tools, enable another simple step to make research reproducible. In this article, we urge the creation of minimal virtual reference environments implementing all the tools necessary to reproduce a result, as a standard part of publication. We address potential problems with this approach, and show an example environment from our own work. '),
(68893, 2015, 'bib', 'Comprehensive overview and assessment of computational prediction of microRNA targets in animals', 'Fan, Xiao|Kurgan, Lukasz', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 42, 'Animals|MicroRNAs', 'Journal Article', '10.1093/bib/bbu044', '25471818', 'MicroRNAs (miRNAs) are short endogenous noncoding RNAs that bind to target mRNAs, usually resulting in degradation and translational repression. Identification of miRNA targets is crucial for deciphering functional roles of the numerous miRNAs that are rapidly generated by sequencing efforts. Computational prediction methods are widely used for high-throughput generation of putative miRNA targets. We review a comprehensive collection of 38 miRNA sequence-based computational target predictors in animals that were developed over the past decade. Our in-depth analysis considers all significant perspectives including the underlying predictive methodologies with focus on how they draw from the mechanistic basis of the miRNA-mRNA interaction. We also discuss ease of use, availability, impact of the considered predictors and the evaluation protocols that were used to assess them. We are the first to comparatively and comprehensively evaluate seven representative methods when predicting miRNA targets at the duplex and gene levels. The gene-level evaluation is based on three benchmark data sets that rely on different ways to annotate targets including biochemical assays, microarrays and pSILAC. We offer practical advice on selection of appropriate predictors according to certain properties of miRNA sequences, characteristics of a specific application and desired levels of predictive quality. We also discuss future work related to the design of new models, data quality, improved usability, need for standardized evaluation and ability to predict mRNA expression changes. '),
(68894, 2015, 'bib', 'Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks', 'Thébault, Patricia|Bourqui, Romain|Benchimol, William|Gaspin, Christine|Sirand-Pugnet, Pascal|Uricaru, Raluca|Dutour, Isabelle', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 7, 'Computational Biology|Gene Regulatory Networks|RNA, Bacterial', 'Journal Article', '10.1093/bib/bbu045', '25477348', 'The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA-mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA-mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks. '),
(68895, 2015, 'bib', 'A selective review of robust variable selection with applications in bioinformatics', 'Wu, Cen|Ma, Shuangge', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 34, 'Computational Biology|Models, Theoretical', 'Journal Article', '10.1093/bib/bbu046', '25479793', 'A drastic amount of data have been and are being generated in bioinformatics studies. In the analysis of such data, the standard modeling approaches can be challenged by the heavy-tailed errors and outliers in response variables, the contamination in predictors (which may be caused by, for instance, technical problems in microarray gene expression studies), model mis-specification and others. Robust methods are needed to tackle these challenges. When there are a large number of predictors, variable selection can be as important as estimation. As a generic variable selection and regularization tool, penalization has been extensively adopted. In this article, we provide a selective review of robust penalized variable selection approaches especially designed for high-dimensional data from bioinformatics and biomedical studies. We discuss the robust loss functions, penalty functions and computational algorithms. The theoretical properties and implementation are also briefly examined. Application examples of the robust penalization approaches in representative bioinformatics and biomedical studies are also illustrated. '),
(68896, 2015, 'bib', 'A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions', 'Bandyopadhyay, Sanghamitra|Ray, Sumanta|Mukhopadhyay, Anirban|Maulik, Ujjwal', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 24, 'Computer Simulation|HIV-1|Humans|Protein Binding|Proteins', 'Journal Article', '10.1093/bib/bbu041', '25479794', 'The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community. '),
(68897, 2015, 'bib', 'Making the difference: integrating structural variation detection tools', 'Lin, Ke|Smit, Sandra|Bonnema, Guusje|Sanchez-Perez, Gabino|de Ridder, Dick', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan', 23, 'Data Interpretation, Statistical|Genetics, Population|Genome|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1093/bib/bbu047', '25504367', 'From prokaryotes to eukaryotes, phenotypic variation, adaptation and speciation has been associated with structural variation between genomes of individuals within the same species. Many computer algorithms detecting such variations (callers) have recently been developed, spurred by the advent of the next-generation sequencing technology. Such callers mainly exploit split-read mapping or paired-end read mapping. However, as different callers are geared towards different types of structural variation, there is still no single caller that can be considered a community standard, instead, increasingly the various callers are combined in integrated pipelines. In this article, we review a wide range of callers, discuss challenges in the integration step and present a survey of pipelines used in population genomics studies. Based on our findings, we provide general recommendations on how to set-up such pipelines. Finally, we present an outlook on future challenges in structural variation detection. '),
(68898, 2015, 'bib', 'LncTar: a tool for predicting the RNA targets of long noncoding RNAs', 'Li, Jianwei|Ma, Wei|Zeng, Pan|Wang, Junyi|Geng, Bin|Yang, Jichun|Cui, Qinghua', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 111, 'Algorithms|RNA, Long Noncoding', 'Journal Article', '10.1093/bib/bbu048', '25524864', 'Long noncoding RNAs (lncRNAs) represent a big category of noncoding RNA molecules, and increasing studies have shown that they play important roles in various critical biological processes. They show a diversity of functions through diverse mechanisms, among which regulating RNA molecules is one of the most popular ones. Given the big number of lncRNAs, it becomes urgent and important to predict the RNA targets of lncRNAs in a large scale for the comprehensive understanding of lncRNA functions and action mechanisms. Although several methods have been developed to predict RNA-RNA interactions, none of them can be used to predict the RNA targets of lncRNAs in a large scale. Here we presented a tool, LncTar, which shows the ability to efficiently predict the RNA targets of lncRNAs in a large scale. To test the accuracy of LncTar, we applied it to 10 experimentally supported lncRNA-mRNA interactions. As a result, LncTar successfully predicted 8 (80%) of the 10 lncRNA-mRNA pairs, suggesting that LncTar has a reliable accuracy. Finally, we believe that LncTar could be an efficient tool for the fast identification of the RNA targets of lncRNAs. LncTar is freely available at http://www.cuilab.cn/lnctar. '),
(68899, 2015, 'bib', 'Measures for the degree of overlap of gene signatures and applications to TCGA', 'Shi, Xingjie|Yi, Huangdi|Ma, Shuangge', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 4, 'Gene Expression Profiling|Humans|Neoplasms|Prognosis|Proportional Hazards Models', 'Journal Article', '10.1093/bib/bbu049', '25552438', 'For cancer and many other complex diseases, a large number of gene signatures have been generated. In this study, we use cancer as an example and note that other diseases can be analyzed in a similar manner. For signatures generated in multiple independent studies on the same cancer type and outcome, and for signatures on different cancer types, it is of interest to evaluate their degree of overlap. Many of the existing studies simply count the number (or percentage) of overlapped genes shared by two signatures. Such an approach has serious limitations. In this study, as a demonstrating example, we consider cancer prognosis data under the Cox model. Lasso, which is representative of a large number of regularization methods, is adopted for generating gene signatures. We examine two families of measures for quantifying the degree of overlap. The first family is based on the Cox-Lasso estimates at the optimal tunings, and the second family is based on estimates across the whole solution paths. Within each family, multiple measures, which describe the overlap from different perspectives, are introduced. The analysis of TCGA (The Cancer Genome Atlas) data on five cancer types shows that the degree of overlap varies across measures, cancer types and types of (epi)genetic measurements. More investigations are needed to better describe and understand the overlaps among gene signatures. '),
(68900, 2015, 'bib', 'Detecting associations of rare variants with common diseases: collapsing or haplotyping?', 'Wang, Meng|Lin, Shili', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 9, 'Bayes Theorem|Case-Control Studies|Genetic Predisposition to Disease|Haplotypes|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbu050', '25596401', 'In recent years, a myriad of new statistical methods have been proposed for detecting associations of rare single-nucleotide variants (SNVs) with common diseases. These methods can be generally classified as collapsing or haplotyping based. The former is the predominant class, composed of most of the rare variant association methods proposed to date. However, recent works have suggested that haplotyping-based methods may offer advantages and can even be more powerful than collapsing methods in certain situations. In this article, we review and compare collapsing- versus haplotyping-based methods/software in terms of both power and type I error. For collapsing methods, we consider three approaches: Combined Multivariate and Collapsing, Sequence Kernel Association Test and Family-Based Association Test (FBAT): the first two are population based and are among the most popular, the last test is family based, a modification from the popular FBAT to accommodate rare SNVs. For haplotyping-based methods, we include Logistic Bayesian Lasso (LBL) for population data and family-based LBL (famLBL) for family (trio) data. These two methods are selected, as they can be used to test association for specific rare and common haplotypes. Our results show that haplotype methods can be more powerful than collapsing methods if there are interacting SNVs leading to larger haplotype effects. Even if only common SNVs are genotyped, haplotype methods can still detect specific rare haplotypes that tag rare causal SNVs. As expected, family-based methods are robust, whereas population-based methods are susceptible, to population substructure. However, the population-based haplotype approach appears to have smaller inflation of type I error than its collapsing counterparts. '),
(68901, 2015, 'bib', 'Sample size calculation in metabolic phenotyping studies', 'Billoir, Elise|Navratil, Vincent|Blaise, Benjamin J', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 12, 'Metabolism|Phenotype|Sample Size', 'Journal Article', '10.1093/bib/bbu052', '25600654', 'The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki. '),
(68902, 2015, 'bib', 'The complexity, challenges and benefits of comparing two transporter classification systems in TCDB and Pfam', 'Chiang, Zachary|Vastermark, Ake|Punta, Marco|Coggill, Penelope C|Mistry, Jaina|Finn, Robert D|Saier, Milton H', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 4, 'Databases, Protein|Proteins', 'Comparative Study', '10.1093/bib/bbu053', '25614388', 'Transport systems comprise roughly 10% of all proteins in a cell, playing critical roles in many processes. Improving and expanding their classification is an important goal that can affect studies ranging from comparative genomics to potential drug target searches. It is not surprising that different classification systems for transport proteins have arisen, be it within a specialized database, focused on this functional class of proteins, or as part of a broader classification system for all proteins. Two such databases are the Transporter Classification Database (TCDB) and the Protein family (Pfam) database. As part of a long-term endeavor to improve consistency between the two classification systems, we have compared transporter annotations in the two databases to understand the rationale for differences and to improve both systems. Differences sometimes reflect the fact that one database has a particular transporter family while the other does not. Differing family definitions and hierarchical organizations were reconciled, resulting in recognition of 69 Pfam Domains of Unknown Function, which proved to be transport protein families to be renamed using TCDB annotations. Of over 400 potential new Pfam families identified from TCDB, 10% have already been added to Pfam, and TCDB has created 60 new entries based on Pfam data. This work, for the first time, reveals the benefits of comprehensive database comparisons and explains the differences between Pfam and TCDB. '),
(68903, 2015, 'bib', 'Pathogenicity prediction of non-synonymous single nucleotide variants in dilated cardiomyopathy', 'Mueller, Sabine C|Backes, Christina|Haas, Jan|INHERITANCE Study Group|Katus, Hugo A|Meder, Benjamin|Meese, Eckart|Keller, Andreas', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 8, 'Cardiomyopathy, Dilated|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbu054', '25638801', 'Non-synonymous single nucleotide variants (nsSNVs) in coding DNA regions can result in phenotypic differences between individuals, however, only some nsSNVs are causative for a certain disease. As just a fraction of respective nsSNVs is annotated in databases, computational biology tools are applied to predict the pathogenicity in silico. In addition to applications in oncology, novel molecular diagnostic tests have been developed for cardiovascular disorders as a leading cause of morbidity and mortality in industrialized nations. We explored the concordance and performance of 13 nsSNV pathogenicity prediction tools on panel sequencing results of dilated cardiomyopathy. The analyzed data set from the INHERITANCE study contained 842 nsSNVs discovered in 639 patients, screened for the full sequence of 76 genes related to cardiomyopathies. The single tools prediction revealed a surprisingly high heterogeneity and discordance based on the implemented prediction method. Known disease associations were not reported by the tools, limiting usability in clinics. Because different tools have different advantages, we combined their results. By clustering of correlated methods using similar prediction strategies and calculating a majority vote-based consensus, we found that the prediction accuracy and sensitivity can be further improved. Although challenges remain, different in silico tools bear the potential to predict the malignancy of nsSNVs, especially if different algorithms are combined. Most tools rely mainly on sequence features, beyond these, structural information is important to analyze the relationship of nsSNVs with disease phenotypes. Likewise, current tools consider single nsSNVs, which may, however, show a cumulative effect and turn neutral mutations in an ensemble into pathogenic variants. '),
(68904, 2015, 'bib', 'Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses', 'Zepeda Mendoza, Marie Lisandra|Sicheritz-Pontén, Thomas|Gilbert, M Thomas P', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan', 29, 'Gene-Environment Interaction|Genome, Human|Humans|Software', 'Journal Article', '10.1093/bib/bbv001', '25673291', 'DNA-based taxonomic and functional profiling is widely used for the characterization of organismal communities across a rapidly increasing array of research areas that include the role of microbiomes in health and disease, biomonitoring, and estimation of both microbial and metazoan species richness. Two principal approaches are currently used to assign taxonomy to DNA sequences: DNA metabarcoding and metagenomics. When initially developed, each of these approaches mandated their own particular methods for data analysis, however, with the development of high-throughput sequencing (HTS) techniques they have begun to share many aspects in data set generation and processing. In this review we aim to define the current characteristics, goals and boundaries of each field, and describe the different software used for their analysis. We argue that an appreciation of the potential and limitations of each method can help underscore the improvements required by each field so as to better exploit the richness of current HTS-based data sets. '),
(68905, 2015, 'bib', '2HiGWAS: a unifying high-dimensional platform to infer the global genetic architecture of trait development', 'Jiang, Libo|Liu, Jingyuan|Zhu, Xuli|Ye, Meixia|Sun, Lidan|Lacaze, Xavier|Wu, Rongling', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 11, 'Genome-Wide Association Study|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbv002', '25697399', 'Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits. '),
(68906, 2015, 'bib', 'Critical assessment of genome-scale metabolic networks: the need for a unified standard', 'Ravikrishnan, Aarthi|Raman, Karthik', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 37, 'Genome|Metabolism|Models, Theoretical', 'Journal Article', '10.1093/bib/bbv003', '25725218', 'Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using constraint-based modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations, this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models. '),
(68907, 2015, 'bib', 'Current status and future challenges in T-cell receptor/peptide/MHC molecular dynamics simulations', 'Knapp, Bernhard|Demharter, Samuel|Esmaielbeiki, Reyhaneh|Deane, Charlotte M', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 26, 'Humans|Major Histocompatibility Complex|Molecular Dynamics Simulation|Peptides|Receptors, Antigen, T-Cell', 'Journal Article', '10.1093/bib/bbv005', '25725219', 'The interaction between T-cell receptors (TCRs) and major histocompatibility complex (MHC)-bound epitopes is one of the most important processes in the adaptive human immune response. Several hypotheses on TCR triggering have been proposed. Many of them involve structural and dynamical adjustments in the TCR/peptide/MHC interface. Molecular Dynamics (MD) simulations are a computational technique that is used to investigate structural dynamics at atomic resolution. Such simulations are used to improve understanding of signalling on a structural level. Here we review how MD simulations of the TCR/peptide/MHC complex have given insight into immune system reactions not achievable with current experimental methods. Firstly, we summarize methods of TCR/peptide/MHC complex modelling and TCR/peptide/MHC MD trajectory analysis methods. Then we classify recently published simulations into categories and give an overview of approaches and results. We show that current studies do not come to the same conclusions about TCR/peptide/MHC interactions. This discrepancy might be caused by too small sample sizes or intrinsic differences between each interaction process. As computational power increases future studies will be able to and should have larger sample sizes, longer runtimes and additional parts of the immunological synapse included. '),
(68908, 2015, 'bib', 'Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions', 'Caraus, Iurie|Alsuwailem, Abdulaziz A|Nadon, Robert|Makarenkov, Vladimir', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 16, 'DNA, Complementary|High-Throughput Nucleotide Sequencing|RNA Interference|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbv004', '25750417', 'Significant efforts have been made recently to improve data throughput and data quality in screening technologies related to drug design. The modern pharmaceutical industry relies heavily on high-throughput screening (HTS) and high-content screening (HCS) technologies, which include small molecule, complementary DNA (cDNA) and RNA interference (RNAi) types of screening. Data generated by these screening technologies are subject to several environmental and procedural systematic biases, which introduce errors into the hit identification process. We first review systematic biases typical of HTS and HCS screens. We highlight that study design issues and the way in which data are generated are crucial for providing unbiased screening results. Considering various data sets, including the publicly available ChemBank data, we assess the rates of systematic bias in experimental HTS by using plate-specific and assay-specific error detection tests. We describe main data normalization and correction techniques and introduce a general data preprocessing protocol. This protocol can be recommended for academic and industrial researchers involved in the analysis of current or next-generation HTS data. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68909, 2015, 'bib', 'Circadian systems biology in Metazoa', 'Lin, Li-Ling|Huang, Hsuan-Cheng|Juan, Hsueh-Fen|2014 Taida Cancer Systems Biology Study Group', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 1, 'Animals|Circadian Rhythm|Systems Biology', 'Journal Article', '10.1093/bib/bbv006', '25758249', 'Systems biology, which can be defined as integrative biology, comprises multistage processes that can be used to understand components of complex biological systems of living organisms and provides hierarchical information to decoding life. Using systems biology approaches such as genomics, transcriptomics and proteomics, it is now possible to delineate more complicated interactions between circadian control systems and diseases. The circadian rhythm is a multiscale phenomenon existing within the body that influences numerous physiological activities such as changes in gene expression, protein turnover, metabolism and human behavior. In this review, we describe the relationships between the circadian control system and its related genes or proteins, and circadian rhythm disorders in systems biology studies. To maintain and modulate circadian oscillation, cells possess elaborative feedback loops composed of circadian core proteins that regulate the expression of other genes through their transcriptional activities. The disruption of these rhythms has been reported to be associated with diseases such as arrhythmia, obesity, insulin resistance, carcinogenesis and disruptions in natural oscillations in the control of cell growth. This review demonstrates that lifestyle is considered as a fundamental factor that modifies circadian rhythm, and the development of dysfunctions and diseases could be regulated by an underlying expression network with multiple circadian-associated signals. '),
(68910, 2015, 'bib', 'Letter from the Editorial Board: Aims of Briefings in Bioinformatics further defined', '-', '-', '-', 0, 'Computational Biology|Editorial Policies|Humans|Publishing|Review Literature as Topic', 'Letter', '10.1093/bib/bbv014', '25762554', '-'),
(68911, 2015, 'bib', 'Letter to the editor: On reviews and papers on new methods', 'Boulesteix, Anne-Laure', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan', 0, 'Computational Biology|Humans|Publishing|Review Literature as Topic', 'Letter', '10.1093/bib/bbu051', '25762555', '-'),
(68912, 2015, 'bib', 'Evaluation of a two-stage framework for prediction using big genomic data', 'Jiang, Xia|Neapolitan, Richard E', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 1, 'Algorithms|Evaluation Studies as Topic|Genome-Wide Association Study|Genomics|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbv010', '25788325', 'We are in the era of abundant big or high-dimensional data. These data afford us the opportunity to discover predictors of an event of interest, and to estimate occurrence of the event based on values of these predictors. For example, genome-wide association studies examine millions of single-nucleotide polymorphisms (SNPs), along with disease status. We can learn SNPs that affect disease status from these data sets, and use the knowledge learned to predict disease likelihood. Owing to the large number of features, it is difficult for many prediction methods to use all the features directly. The ReliefF algorithm ranks a set of features in terms of how well they predict a target. It can be used to identify good predictors, which can then be provided to a prediction method. We compared the performance of eight prediction methods when predicting binary outcomes using high-dimensional discrete data sets. We performed two-stage prediction, where ReliefF is used in the first stage to identify good predictors. Bayesian network (BN)-based methods performed best overall. Furthermore, ReliefF did not improve their performance. The BN-based methods use the Bayesian Dirichlet Equivalent Uniform score to evaluate candidate models, and use BN inference algorithms to perform prediction. This score and these algorithms were developed for discrete variables. This perhaps explains why they perform better in this domain. Many prediction methods are available, and researchers have little reason for choosing one over the other in the domain of binary prediction using high-dimensional data sets. Our results indicate that the best choices overall are BN-based methods. '),
(68913, 2015, 'bib', 'A comparative study of RNA-seq analysis strategies', 'Jänes, Jürgen|Hu, Fengyuan|Lewin, Alexandra|Turro, Ernest', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan|Japan|Japan', 14, 'Databases, Genetic|RNA, Messenger|Sequence Analysis, RNA', 'Comparative Study', '10.1093/bib/bbv007', '25788326', 'Three principal approaches have been proposed for inferring the set of transcripts expressed in RNA samples using RNA-seq. The simplest approach uses curated annotations, which assumes the transcripts in a sample are a subset of the transcripts listed in a curated database. A more ambitious method involves aligning reads to a reference genome and using the alignments to infer the transcript structures, possibly with the aid of a curated transcript database. The most challenging approach is to assemble reads into putative transcripts de novo without the aid of reference data. We have systematically assessed the properties of these three approaches through a simulation study. We have found that the sensitivity of computational transcript set estimation is severely limited. Computational approaches (both genome-guided and de novo assembly) produce a large number of artefacts, which are assigned large expression estimates and absorb a substantial proportion of the signal when performing expression analysis. The approach using curated annotations shows good expression correlation even when the annotations are incomplete. Furthermore, any incorrect transcripts present in a curated set do not absorb much signal, so it is preferable to have a curation set with high sensitivity than high precision. Software to simulate transcript sets, expression values and sequence reads under a wider range of parameter values and to compare sensitivity, precision and signal-to-noise ratios of different methods is freely available online (https://github.com/boboppie/RSSS) and can be expanded by interested parties to include methods other than the exemplars presented in this article. '),
(68914, 2015, 'bib', 'Predicting protein interface residues using easily accessible on-line resources', 'Maheshwari, Surabhi|Brylinski, Michal', 'Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.|Computational Biology Research Center, AIST, Tokyo, Japan. computome@gmail.com.', 'Japan|Japan', 25, 'Databases, Protein|Internet|Protein Binding|Proteins', 'Journal Article', '10.1093/bib/bbv009', '25797794', 'It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we review 10 methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experimental structures and high-quality homology models, structure-based methods outperform those using only protein sequences, with global template-based approaches providing the best performance. For moderate-quality models, sequence-based methods often perform better than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in several methods quantitatively improve the results only for experimental structures, suggesting that these procedures should be tuned up for computer-generated models. Finally, we anticipate that advanced meta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites. '),
(68915, 2016, 'bib', 'Multiscale modelling in immunology: a review', 'Cappuccio, Antonio|Tieri, Paolo|Castiglione, Filippo', 'Laboratory of Integrative biology of human dendritic cells and T cells, U932 Immunity and cancer, Institut Curie, 26 Rue d`Ulm, 75005 Paris, France.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy p.tieri@iac.cnr.it.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'France|Italy|Italy', 22, 'Humans|Models, Immunological', 'Journal Article', '10.1093/bib/bbv012', '25810307', 'One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.'),
(68916, 2015, 'bib', 'Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study', 'Fan, Ming|Kuwahara, Hiroyuki|Wang, Xiaolei|Wang, Suojin|Gao, Xin', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 8, 'Escherichia coli|Gene Expression|Models, Genetic|Oligonucleotide Array Sequence Analysis|RNA, Messenger|SOS Response (Genetics)', 'Comparative Study', '10.1093/bib/bbv015', '25818863', 'Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large. '),
(68917, 2016, 'bib', 'A survey of tools for analysing DNA fingerprints', 'Heras, Jónathan|Domínguez, César|Mata, Eloy|Pascual, Vico|Lozano, Carmen|Torres, Carmen|Zarazaga, Myriam', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 3, 'DNA Fingerprinting|DNA, Bacterial|Electrophoresis, Gel, Pulsed-Field', 'Journal Article', '10.1093/bib/bbv016', '25825453', 'DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. This technique has multiple applications in different fields (medical diagnosis, forensic science, parentage testing, food industry, agriculture and many others). An important task in molecular epidemiology of infectious diseases is the analysis and comparison of pulsed-field gel electrophoresis (PFGE) patterns. This is applied to determine the clonal diversity of bacteria in the follow-up of outbreaks or for tracking specific clones of special relevance. The resulting images produced by DNA fingerprinting are sometimes difficult to interpret, and multiple tools have been developed to simplify this task. In this article, we present a survey of tools for analysing DNA fingerprints. In particular, we compare 33 tools using a set of predefined criteria. The comparison was carried out by hands-on experiences-whenever possible-and inspecting the documentation of the tools. As no system is preferred in all the possible scenarios, we have created a spreadsheet that can be customized by researchers to determine the best system for their needs.'),
(68918, 2015, 'bib', 'Assessing the consistency of public human tissue RNA-seq data sets', 'Danielsson, Frida|James, Tojo|Gomez-Cabrero, David|Huss, Mikael', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 9, 'Brain|Databases, Genetic|Gene Expression Profiling|Humans|Kidney|Myocardium|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbv017', '25829468', 'Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch effects, differences in RNA extraction and sequencing library preparation methods and bioinformatics processing pipelines. It would be unfortunate if different experiments were in fact incomparable, as there is great promise in data fusion and meta-analysis applied to sequencing data sets. We therefore compared reported gene expression measurements for ostensibly similar samples (specifically, human brain, heart and kidney samples) in several different RNA-seq studies to assess their overall consistency and to examine the factors contributing most to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias from bioinformatics pipelines. We conclude that published human tissue RNA-seq expression measurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The article is supplemented by a detailed walkthrough with embedded R code and figures. '),
(68919, 2016, 'bib', 'Adapting bioinformatics curricula for big data', 'Greene, Anna C|Giffin, Kristine A|Greene, Casey S|Moore, Jason H', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 23, 'Computational Biology|Curriculum|Humans', 'Journal Article', '10.1093/bib/bbv018', '25829469', 'Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. '),
(68920, 2016, 'bib', 'A survey of current trends in computational drug repositioning', 'Li, Jiao|Zheng, Si|Chen, Bin|Butte, Atul J|Swamidass, S Joshua|Lu, Zhiyong', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 191, 'Computational Biology|Data Mining|Drug Combinations|Drug Repositioning|Genomics|Humans|Machine Learning|Molecular Structure|Phenotype|Surveys and Questionnaires', 'Journal Article', '10.1093/bib/bbv020', '25832646', 'Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning. '),
(68921, 2015, 'bib', 'Molecular portraits: the evolution of the concept of transcriptome-based cancer signatures', 'Modelska, Angelika|Quattrone, Alessandro|Re, Angela', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 6, 'Evolution, Molecular|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Proteome|Transcriptome', 'Journal Article', '10.1093/bib/bbv013', '25832647', 'Cancer results from dysregulation of multiple steps of gene expression programs. We review how transcriptome profiling has been widely explored for cancer classification and biomarker discovery but resulted in limited clinical impact. Therefore, we discuss alternative and complementary omics approaches. '),
(68922, 2015, 'bib', 'The role of ontologies in biological and biomedical research: a functional perspective', 'Hoehndorf, Robert|Schofield, Paul N|Gkoutos, Georgios V', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 88, 'Biomedical Research|Data Mining|Internet|Machine Learning', 'Journal Article', '10.1093/bib/bbv011', '25863278', 'Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain, provision of a vocabulary for a domain, provision of metadata that describes the intended meaning of the classes and relations in ontologies, and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications. '),
(68923, 2016, 'bib', 'Crowdsourcing in biomedicine: challenges and opportunities', 'Khare, Ritu|Good, Benjamin M|Leaman, Robert|Su, Andrew I|Lu, Zhiyong', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 25, 'Computational Biology|Crowdsourcing|Data Mining|Humans|Internet|Search Engine|Smartphone|Social Media|Video Games', 'Journal Article', '10.1093/bib/bbv021', '25888696', 'The use of crowdsourcing to solve important but complex problems in biomedical and clinical sciences is growing and encompasses a wide variety of approaches. The crowd is diverse and includes online marketplace workers, health information seekers, science enthusiasts and domain experts. In this article, we review and highlight recent studies that use crowdsourcing to advance biomedicine. We classify these studies into two broad categories: (i) mining big data generated from a crowd (e.g. search logs) and (ii) active crowdsourcing via specific technical platforms, e.g. labor markets, wikis, scientific games and community challenges. Through describing each study in detail, we demonstrate the applicability of different methods in a variety of domains in biomedical research, including genomics, biocuration and clinical research. Furthermore, we discuss and highlight the strengths and limitations of different crowdsourcing platforms. Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine. '),
(68924, 2015, 'bib', 'A review of ensemble methods for de novo motif discovery in ChIP-Seq data', 'Lihu, Andrei|Holban, xc5x9etefan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 7, 'Algorithms|Chromatin Immunoprecipitation', 'Journal Article', '10.1093/bib/bbv022', '25888697', 'De novo motif discovery is a difficult computational task. Historically, dedicated algorithms always reported a high percentage of false positives. Their performance did not improve considerably even after they adapted to handle large amounts of chromatin immunoprecipitation sequencing (ChIP-Seq) data. Several studies have advocated aggregating complementary algorithms, combining their predictions to increase the accuracy of the results. This led to the development of ensemble methods. To form a better view on modern ensembles, we review all compound tools designed for ChIP-Seq. After a brief introduction to basic algorithms and early ensembles, we describe the most recent tools. We highlight their limitations and strengths by presenting their architecture, the input options and their output. To provide guidance for next-generation sequencing practitioners, we observe the differences and similarities between them. Last but not least, we identify and recommend several features to be implemented by any novel ensemble algorithm.'),
(68925, 2015, 'bib', 'Optimization of miRNA-seq data preprocessing', 'Tam, Shirley|Tsao, Ming-Sound|McPherson, John D', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 51, 'Gene Expression|High-Throughput Nucleotide Sequencing|MicroRNAs|Sequence Alignment', 'Journal Article', '10.1093/bib/bbv019', '25888698', 'The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments. '),
(68926, 2015, 'bib', 'Docking small peptides remains a great challenge: an assessment using AutoDock Vina', 'Rentzsch, Robert|Renard, Bernhard Y', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 18, 'Molecular Docking Simulation|Peptides|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbv008', '25900849', 'There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner. '),
(68927, 2015, 'bib', 'Comparing the evolutionary conservation between human essential genes, human orthologs of mouse essential genes and human housekeeping genes', 'Lv, Wenhua|Zheng, Jiajia|Luan, Meiwei|Shi, Miao|Zhu, Hongjie|Zhang, Mingming|Lv, Hongchao|Shang, Zhenwei|Duan, Lian|Zhang, Ruijie|Jiang, Yongshuai', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 5, 'Animals|Evolution, Molecular|Genes, Essential|Humans|Linkage Disequilibrium|Mice', 'Comparative Study', '10.1093/bib/bbv025', '25911641', 'Human housekeeping genes are often confused with essential human genes, and several studies regard both types of genes as having the same level of evolutionary conservation. However, this is not necessarily the case. To clarify this, we compared the differences between human housekeeping genes and essential human genes with respect to four aspects: the evolutionary rate (dN/dS), protein sequence identity, single-nucleotide polymorphism (SNP) density and level of linkage disequilibrium (LD). The results showed that housekeeping genes had lower evolutionary rates, higher sequence identities, lower SNP densities and higher levels of LD compared with essential genes. Together, these findings indicate that housekeeping and essential genes are two distinct types of genes, and that housekeeping genes have a higher level of evolutionary conservation. Therefore, we suggest that researchers should pay careful attention to the distinctions between housekeeping genes and essential genes. Moreover, it is still controversial whether we should substitute human orthologs of mouse essential genes for human essential genes. Therefore, we compared the evolutionary features between human orthologs of mouse essential genes and human housekeeping genes and we got inconsistent results in long-term and short-term evolutionary characteristics implying the irrationality of simply replacing human essential genes with human orthologs of mouse essential genes. '),
(68928, 2016, 'bib', 'Toward a next-generation atlas of RNA secondary structure', 'Bai, Youhuang|Dai, Xiaozhuan|Harrison, Andrew|Johnston, Caroline|Chen, Ming', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 6, 'Algorithms|Animals|Base Pairing|Base Sequence|Computational Biology|Computer Simulation|Databases, Nucleic Acid|Epigenesis, Genetic|High-Throughput Nucleotide Sequencing|Humans|Machine Learning|Models, Molecular|Nucleic Acid Conformation|Phylogeny|RNA|RNA Splicing|RNA, Messenger|Sequence Analysis, RNA|Stochastic Processes|Thermodynamics', 'Journal Article', '10.1093/bib/bbv026', '25922372', 'RNA structure plays a crucial role in gene maturation, regulation and function. Determining the form and frequency of RNA folds is essential for a better understanding of how RNA exerts its functions. Low-throughput studies have focused on RNA primary sequences and expression levels, but with an emphasis on relatively small numbers of transcripts. However, with the recent advent of high-throughput technologies, it is realistic to begin analyzing RNA secondary structures on a genome-wide scale. Here, we review genome-wide RNA secondary structure profiles as well as advances in computational structure predictions. We further discuss the novel characteristics of RNA secondary structure across messenger RNAs. Probing RNA secondary structure by high-throughput sequencing will enable us to build atlases of RNA secondary structures, an important step in helping us to understand the versatility of RNA functions in diverse cellular processes. '),
(68929, 2016, 'bib', 'A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues', 'Yan, Jing|Friedrich, Stefanie|Kurgan, Lukasz', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 40, 'Amino Acid Sequence|Binding Sites|Computational Biology|Consensus Sequence|DNA|DNA-Binding Proteins|Databases, Protein|Humans|Machine Learning|Molecular Sequence Data|Protein Binding|RNA|RNA-Binding Proteins|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bib/bbv023', '25935161', 'Motivated by the pressing need to characterize protein-DNA and protein-RNA interactions on large scale, we review a comprehensive set of 30 computational methods for high-throughput prediction of RNA- or DNA-binding residues from protein sequences. We summarize these predictors from several significant perspectives including their design, outputs and availability. We perform empirical assessment of methods that offer web servers using a new benchmark data set characterized by a more complete annotation that includes binding residues transferred from the same or similar proteins. We show that predictors of DNA-binding (RNA-binding) residues offer relatively strong predictive performance but they are unable to properly separate DNA- from RNA-binding residues. We design and empirically assess several types of consensuses and demonstrate that machine learning (ML)-based approaches provide improved predictive performance when compared with the individual predictors of DNA-binding residues or RNA-binding residues. We also formulate and execute first-of-its-kind study that targets combined prediction of DNA- and RNA-binding residues. We design and test three types of consensuses for this prediction and conclude that this novel approach that relies on ML design provides better predictive quality than individual predictors when tested on prediction of DNA- and RNA-binding residues individually. It also substantially improves discrimination between these two types of nucleic acids. Our results suggest that development of a new generation of predictors would benefit from using training data sets that combine both RNA- and DNA-binding proteins, designing new inputs that specifically target either DNA- or RNA-binding residues and pursuing combined prediction of DNA- and RNA-binding residues. '),
(68930, 2016, 'bib', 'Community challenges in biomedical text mining over 10 years: success, failure and the future', 'Huang, Chung-Chi|Lu, Zhiyong', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 84, 'Algorithms|Computational Biology|Data Mining|Databases, Genetic|Humans|Natural Language Processing', 'Journal Article', '10.1093/bib/bbv024', '25935162', 'One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. '),
(68931, 2016, 'bib', 'Progress and challenges in predicting protein interfaces', 'Esmaielbeiki, Reyhaneh|Krawczyk, Konrad|Knapp, Bernhard|Nebel, Jean-Christophe|Deane, Charlotte M', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 16, 'Amino Acid Sequence|Antigen-Antibody Complex|Binding Sites|Computational Biology|Databases, Protein|Epitopes|Humans|Imaging, Three-Dimensional|Machine Learning|Models, Molecular|Protein Binding|Protein Conformation|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bib/bbv027', '25971595', 'The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field. '),
(68932, 2016, 'bib', 'Robust and exact structural variation detection with paired-end and soft-clipped alignments: SoftSV compared with eight algorithms', 'Bartenhagen, Christoph|Dugas, Martin', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 19, 'Algorithms|Chromosome Breakpoints|Computational Biology|Databases, Nucleic Acid|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Mutation|Sequence Alignment|Sequence Analysis, DNA|Sequence Deletion|Software', 'Comparative Study', '10.1093/bib/bbv028', '25998133', 'Structural variation (SV) plays an important role in genetic diversity among the population in general and specifically in diseases such as cancer. Modern next-generation sequencing (NGS) technologies provide paired-end sequencing data at high depth with increasing read lengths. This development enabled the analysis of split-reads to detect SV breakpoints with single-nucleotide resolution. But ambiguous mappings and breakpoint sequences with further co-occurring mutations hamper split-read alignments against a reference sequence. The trade-off between high sensitivity and low false-positive rate is problematic and often requires a lot of fine-tuning of the analysis method based on knowledge about its algorithm and the characteristics of the data set. We present SoftSV, a method for exact breakpoint detection for small and large deletions, inversions, tandem duplications and inter-chromosomal translocations, which relies solely on the mutual alignment of soft-clipped reads within the neighborhood of discordantly mapped paired-end reads. Unlike other SV detection algorithms, our approach does not require thresholds regarding sequencing coverage or mapping quality. We evaluate SoftSV together with eight approaches (Breakdancer, Clever, CREST, Delly, GASVPro, Pindel, Socrates and SoftSearch) on simulated and real data sets. Our results show that sensitive and reliable SV detection is subject to many different factors like read length, sequence coverage and SV type. While most programs have their individual drawbacks, our greedy approach turns out to be the most robust and sensitive on many experimental setups. Sensitivities above 85% and positive predictive values between 80 and 100% could be achieved consistently for all SV types on simulated data sets starting at relatively short 75 bp reads and low 10-15× sequence coverage.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68933, 2016, 'bib', 'Individualized identification of disease-associated pathways with disrupted coordination of gene expression', 'Wang, Hongwei|Cai, Hao|Ao, Lu|Yan, Haidan|Zhao, Wenyuan|Qi, Lishuang|Gu, Yunyan|Guo, Zheng', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 11, 'Adenocarcinoma|Adenocarcinoma of Lung|Antineoplastic Agents, Hormonal|Breast Neoplasms|Computational Biology|Databases, Genetic|Disease-Free Survival|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Predisposition to Disease|Humans|Kaplan-Meier Estimate|Lung Neoplasms|Male|Middle Aged|Neoplasms, Hormone-Dependent|Oligonucleotide Array Sequence Analysis|Precision Medicine|Prognosis|Tamoxifen', 'Journal Article', '10.1093/bib/bbv030', '26023086', 'Current pathway analysis approaches are primarily dedicated to capturing deregulated pathways at the population level and cannot provide patient-specific pathway deregulation information. In this article, the authors present a simple approach, called individPath, to detect pathways with significantly disrupted intra-pathway relative expression orderings for each disease sample compared with the stable, normal intra-pathway relative expression orderings pre-determined in previously accumulated normal samples. Through the analysis of multiple microarray data sets for lung and breast cancer, the authors demonstrate individPaths effectiveness for detecting cancer-associated pathways with disrupted relative expression orderings at the individual level and dissecting the heterogeneity of pathway deregulation among different patients. The portable use of this simple approach in clinical contexts is exemplified by the identification of prognostic intra-pathway gene pair signatures to predict overall survival of resected early-stage lung adenocarcinoma patients and signatures to predict relapse-free survival of estrogen receptor-positive breast cancer patients after tamoxifen treatment. '),
(68934, 2016, 'bib', 'Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction', 'Laehnemann, David|Borkhardt, Arndt|McHardy, Alice Carolyn', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 58, 'Algorithms|Computational Biology|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbv029', '26026159', 'Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here. '),
(68935, 2016, 'bib', 'Revealing protein-lncRNA interaction', 'Ferrè, Fabrizio|Colantoni, Alessio|Helmer-Citterich, Manuela', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 36, 'Computational Biology|Computer Simulation|High-Throughput Nucleotide Sequencing|Humans|Models, Molecular|Nucleic Acid Conformation|Protein Conformation|Protein Interaction Maps|RNA, Long Noncoding|RNA-Binding Proteins|SELEX Aptamer Technique', 'Journal Article', '10.1093/bib/bbv031', '26041786', 'Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs), similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations. '),
(68936, 2016, 'bib', 'A primer on precision medicine informatics', 'Sboner, Andrea|Elemento, Olivier', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 4, 'Computational Biology|DNA Mutational Analysis|Genetic Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Precision Medicine|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbv032', '26048401', 'In this review, we describe key components of a computational infrastructure for a precision medicine program that is based on clinical-grade genomic sequencing. Specific aspects covered in this review include software components and hardware infrastructure, reporting, integration into Electronic Health Records for routine clinical use and regulatory aspects. We emphasize informatics components related to reproducibility and reliability in genomic testing, regulatory compliance, traceability and documentation of processes, integration into clinical workflows, privacy requirements, prioritization and interpretation of results to report based on clinical needs, rapidly evolving knowledge base of genomic alterations and clinical treatments and return of results in a timely and predictable fashion. We also seek to differentiate between the use of precision medicine in germline and cancer. '),
(68937, 2016, 'bib', 'Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks', 'Zeng, Xiangxiang|Zhang, Xuan|Zou, Quan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 215, 'Computer Simulation|Data Mining|Databases, Genetic|Gene Expression Regulation|Genetic Predisposition to Disease|Humans|MicroRNAs|Models, Genetic|Protein Interaction Maps|Proteome|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbv033', '26059461', 'MicroRNAs (miRNA) play critical roles in regulating gene expressions at the posttranscriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNAs involvement in the pathogenesis of disease. In previous years, biological experimentation is the main method used to identify whether miRNA was associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time-consumption and high cost). Because of the limitations of experimental methods in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this review, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter. '),
(68938, 2016, 'bib', 'Microbial bioinformatics for food safety and production', 'Alkema, Wynand|Boekhorst, Jos|Wels, Michiel|van Hijum, Sacha A F T', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 7, 'Computational Biology|Fermentation|Food Contamination|Food Microbiology|Food Safety|Food Technology|Genetics, Microbial|Hazard Analysis and Critical Control Points|Microbiota', 'Journal Article', '10.1093/bib/bbv034', '26082168', 'In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput omics technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety. '),
(68939, 2016, 'bib', 'Building a virtual ligand screening pipeline using free software: a survey', 'Glaab, Enrico', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 39, 'Algorithms|Drug Discovery|High-Throughput Screening Assays|Ligands|Molecular Docking Simulation|Protein Interaction Mapping|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/bbv037', '26094053', 'Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems. '),
(68940, 2016, 'bib', 'Systems biological approaches to the cardiac signaling network', 'Kang, Jun Hyuk|Lee, Ho-Sung|Kang, Yun-Won|Cho, Kwang-Hyun', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 3, 'Genomics|Humans|Signal Transduction', 'Journal Article', '10.1093/bib/bbv039', '26108228', 'Recent systems biological studies of cardiac systems have greatly advanced our understanding of cardiac physiology with a particular focus on the excitation-contraction coupling. With these advancements, there is a growing interest in systems analysis of the cardiac signaling network because its dynamical property is closely associated with cardiac diseases. In this article, we review recent attempts at computational modeling of the cardiac signaling network and provide a system-level perspective on the analysis of the large-scale cardiac signaling network. We discuss why the systems biological approach is useful and what novel insights it can provide for the development of personalized therapeutic strategies for cardiac diseases in the post-genomic era.'),
(68941, 2016, 'bib', 'Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective', 'Poplawski, Alicia|Marini, Federico|Hess, Moritz|Zeller, Tanja|Mazur, Johanna|Binder, Harald', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 6, 'Algorithms|Biological Science Disciplines|Data Mining|Databases, Genetic|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Software|User-Computer Interface', 'Comparative Study', '10.1093/bib/bbv036', '26108229', 'RNA-sequencing (RNA-seq) has become an established way for measuring gene expression in model organisms and humans. While methods development for refining the corresponding data processing and analysis pipeline is ongoing, protocols for typical steps have been proposed and are widely used. Several user interfaces have been developed for making such analysis steps accessible to life scientists without extensive knowledge of command line tools. We performed a systematic search and evaluation of such interfaces to investigate to what extent these can indeed facilitate RNA-seq data analysis. We found a total of 29 open source interfaces, and six of the more widely used interfaces were evaluated in detail. Central criteria for evaluation were ease of configuration, documentation, usability, computational demand and reporting. No interface scored best in all of these criteria, indicating that the final choice will depend on the specific perspective of users and the corresponding weighting of criteria. Considerable technical hurdles had to be overcome in our evaluation. For many users, this will diminish potential benefits compared with command line tools, leaving room for future improvement of interfaces. '),
(68942, 2016, 'bib', 'Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates', 'Gu, Xun', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 5, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Phylogeny|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbv035', '26108230', 'RNA-seq has been an increasingly popular high-throughput platform to identify differentially expressed (DE) genes, which is much more reproducible and accurate than the previous microarray technology. Yet, a number of statistical issues remain to be resolved in data analysis, largely due to the high-throughput data volume and over-dispersion of read counts. These problems become more challenging for those biologists who use RNA-seq to measure genome-wide expression profiles in different combinations of sampling resources (species or genotypes) or treatments. In this paper, the author first reviews the statistical methods available for detecting DE genes, which have implemented negative binomial (NB) models and/or quasi-likelihood (QL) approaches to account for the over-dispersion problem in RNA-seq samples. The author then studies how to carry out the DE test in the context of phylogeny, i.e., RNA-seq samples are from a range of species as phylogenetic replicates. The author proposes a computational framework to solve this phylo-DE problem: While an NB model is used to account for data over-dispersion within biological replicates, over-dispersion among phylogenetic replicates is taken into account by QL, plus some special treatments for phylogenetic bias. This work helps to design cost-effective RNA-seq experiments in the field of biodiversity or phenotype plasticity that may involve hundreds of species under a phylogenetic framework. '),
(68943, 2016, 'bib', 'A roadmap to multifactor dimensionality reduction methods', 'Gola, Damian|Mahachie John, Jestinah M|van Steen, Kristel|König, Inke R', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 9, 'Algorithms|Computer Simulation|Models, Statistical|Multifactor Dimensionality Reduction|Pattern Recognition, Automated|Protein Interaction Mapping', 'Journal Article', '10.1093/bib/bbv038', '26108231', 'Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity. '),
(68944, 2016, 'bib', 'Untangling the complexity of blood coagulation network: use of computational modelling in pharmacology and diagnostics', 'Shibeko, Alexey M|Panteleev, Mikhail A', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 13, 'Blood Coagulation|Down-Regulation|Humans|Thrombosis', 'Journal Article', '10.1093/bib/bbv040', '26116831', 'Blood coagulation is a complex biochemical network that plays critical roles in haemostasis (a physiological process that stops bleeding on injury) and thrombosis (pathological vessel occlusion). Both up- and down-regulation of coagulation remain a major challenge for modern medicine, with the ultimate goal to correct haemostasis without causing thrombosis and vice versa. Mathematical/computational modelling is potentially an important tool for understanding blood coagulation disorders and their treatment. It can save a huge amount of time and resources, and provide a valuable alternative or supplement when clinical studies are limited, or not ethical, or technically impossible. This article reviews contemporary state of the art in the modelling of blood coagulation for practical purposes: to reveal the molecular basis of a disease, to understand mechanisms of drug action, to predict pharmacodynamics and drug-drug interactions, to suggest potential drug targets or to improve quality of diagnostics. Different model types and designs used for this are discussed. Functional mechanisms of procoagulant bypassing agents and investigations of coagulation inhibitors were the two particularly popular applications of computational modelling that gave non-trivial results. Yet, like any other tool, modelling has its limitations, mainly determined by insufficient knowledge of the system, uncertainty and unreliability of complex models. We show how to some extent this can be overcome and discuss what can be expected from the mathematical modelling of coagulation in not-so-far future.'),
(68945, 2016, 'bib', 'Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms', 'Sikdar, Sinjini|Gill, Ryan|Datta, Susmita', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Data Mining|Databases, Protein|Molecular Sequence Data|Peptide Mapping|Proteins|Reproducibility of Results|Sensitivity and Specificity|Systems Integration|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bib/bbv043', '26141827', 'Many approaches have been proposed for the protein identification problem based on tandem mass spectrometry (MS/MS) data. In these experiments, proteins are digested into peptides and the resulting peptide mixture is subjected to mass spectrometry. Some interesting putative peptide features (peaks) are selected from the mass spectra. Following that, the precursor ions undergo fragmentation and are analyzed by MS/MS. The process of identification of peptides from the mass spectra and the constituent proteins in the sample is called protein identification from MS/MS data. There are many two-step protein identification procedures, reviewed in the literature, which first attempt to identify the peptides in a separate process and then use these results to infer the proteins. However, in recent years, there have been attempts to provide a one-step solution to protein identification, which simultaneously identifies the proteins and the peptides in the sample.'),
(68946, 2016, 'bib', 'Understanding tissue expression evolution: from expression phylogeny to phylogenetic network', 'Gu, Xun', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 2, 'Animals|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Phylogeny|Transcriptome|Viscera', 'Journal Article', '10.1093/bib/bbv041', '26141828', 'Our understanding of tissue expression evolution in multi-cellular model organisms has been considerably advanced with the help of high-throughput technologies from EST, microarray to RNA-seq. Yet, many controversies remained unsolved, ranging from the evolutionary patterns of tissue expressions to expression phylogenetic analysis. Moreover, despite numerous reports published, it is desirable to have a general framework for study of tissue expression evolution. In this article, we first provide an up-to-date and concise review for the study of tissue expression evolution in multi-cellular organisms. While the expression phylogeny of the same tissues sampled from closely or intermediately related species largely reflects the species phylogeny, we demonstrate that phylogenetic network approach may shed some lights for our understanding of the developmental similarity and evolutionary relatedness during the multi-tissue evolution. '),
(68947, 2016, 'bib', 'Practical analysis of specificity-determining residues in protein families', 'Chagoyen, Mónica|García-Martín, Juan A|Pazos, Florencio', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 9, 'Amino Acids|Conserved Sequence|Multigene Family|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bib/bbv045', '26141829', 'Determining the residues that are important for the molecular activity of a protein is a topic of broad interest in biomedicine and biotechnology. This knowledge can help understanding the proteins molecular mechanism as well as to fine-tune its natural function eventually with biotechnological or therapeutic implications. Some of the protein residues are essential for the function common to all members of a family of proteins, while others explain the particular specificities of certain subfamilies (like binding on different substrates or cofactors and distinct binding affinities). Owing to the difficulty in experimentally determining them, a number of computational methods were developed to detect these functional residues, generally known as specificity-determining positions (or SDPs), from a collection of homologous protein sequences. These methods are mature enough for being routinely used by molecular biologists in directing experiments aimed at getting insight into the functional specificity of a family of proteins and eventually modifying it. In this review, we summarize some of the recent discoveries achieved through SDP computational identification in a number of relevant protein families, as well as the main approaches and software tools available to perform this type of analysis. '),
(68948, 2016, 'bib', 'Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification', 'Glaab, Enrico', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 12, 'Biomarkers|Disease Progression|Humans|Neoplasms', 'Journal Article', '10.1093/bib/bbv044', '26141830', 'For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.'),
(68949, 2016, 'bib', 'Current status and prospects of computational resources for natural product dereplication: a review', 'Mohamed, Ahmed|Nguyen, Canh Hao|Mamitsuka, Hiroshi', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 7, 'Algorithms|Biological Products|Chromatography|Data Mining|Database Management Systems|Databases, Pharmaceutical|Magnetic Resonance Spectroscopy|Mass Spectrometry|Pattern Recognition, Automated|Small Molecule Libraries', 'Journal Article', '10.1093/bib/bbv042', '26153512', 'Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions. '),
(68950, 2016, 'bib', 'Personalized cancer immunotherapy using Systems Medicine approaches', 'Gupta, Shailendra K|Jaitly, Tanushree|Schmitz, Ulf|Schuler, Gerold|Wolkenhauer, Olaf|Vera, Julio', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 8, 'Algorithms|Computational Biology|Humans|Immunotherapy|Neoplasms|Systems Analysis', 'Journal Article', '10.1093/bib/bbv046', '26174229', 'The immune system is by definition multi-scale because it involves biochemical networks that regulate cell fates across cell boundaries, but also because immune cells communicate with each other by direct contact or through the secretion of local or systemic signals. Furthermore, tumor and immune cells communicate, and this interaction is affected by the tumor microenvironment. Altogether, the tumor-immunity interaction is a complex multi-scale biological system whose analysis requires a systemic view to succeed in developing efficient immunotherapies for cancer and immune-related diseases. In this review we discuss the necessity and the structure of a systems medicine approach for the design of anticancer immunotherapies. We support the idea that the approach must be a combination of algorithms and methods from bioinformatics and patient-data-driven mathematical models conceived to investigate the role of clinical interventions in the tumor-immunity interaction. For each step of the integrative approach proposed, we review the advancement with respect to the computational tools and methods available, but also successful case studies. We particularized our idea for the case of identifying novel tumor-associated antigens and therapeutic targets by integration of patients immune and tumor profiling in case of aggressive melanoma.'),
(68951, 2016, 'bib', 'Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins', 'Li, Chen|Ching Han Chang, Catherine|Nagel, Jeremy|Porebski, Benjamin T|Hayashida, Morihiro|Akutsu, Tatsuya|Song, Jiangning|Buckle, Ashley M', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 10, 'Algorithms|Computer Simulation|Dimerization|Models, Chemical|Models, Molecular|Protein Conformation|Protein Domains|Proteins|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bib/bbv047', '26177815', 'Coiled-coils refer to a bundle of helices coiled together like strands of a rope. It has been estimated that nearly 3% of protein-encoding regions of genes harbour coiled-coil domains (CCDs). Experimental studies have confirmed that CCDs play a fundamental role in subcellular infrastructure and controlling trafficking of eukaryotic cells. Given the importance of coiled-coils, multiple bioinformatics tools have been developed to facilitate the systematic and high-throughput prediction of CCDs in proteins. In this article, we review and compare 12 sequence-based bioinformatics approaches and tools for coiled-coil prediction. These approaches can be categorized into two classes: coiled-coil detection and coiled-coil oligomeric state prediction. We evaluated and compared these methods in terms of their input/output, algorithm, prediction performance, validation methods and software utility. All the independent testing data sets are available at http://lightning.med.monash.edu/coiledcoil/. In addition, we conducted a case study of nine human polyglutamine (PolyQ) disease-related proteins and predicted CCDs and oligomeric states using various predictors. Prediction results for CCDs were highly variable among different predictors. Only two peptides from two proteins were confirmed to be CCDs by majority voting. Both domains were predicted to form dimeric coiled-coils using oligomeric state prediction. We anticipate that this comprehensive analysis will be an insightful resource for structural biologists with limited prior experience in bioinformatics tools, and for bioinformaticians who are interested in designing novel approaches for coiled-coil and its oligomeric state prediction. '),
(68952, 2016, 'bib', 'Bioinformatics methods in drug repurposing for Alzheimers disease', 'Siavelis, John C|Bourdakou, Marilena M|Athanasiadis, Emmanouil I|Spyrou, George M|Nikita, Konstantina S', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 36, 'Alzheimer Disease|Computational Biology|Drug Repositioning|Hippocampus|Humans|Molecular Targeted Therapy|Nerve Tissue Proteins|Neuroprotective Agents|Protein Interaction Mapping', 'Journal Article', '10.1093/bib/bbv048', '26197808', 'Alarming epidemiological features of Alzheimers disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substances indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cells gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimers disease. '),
(68953, 2016, 'bib', 'Empirical comparison of structure-based pathway methods', 'Jaakkola, Maria K|Elo, Laura L', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 4, 'Algorithms|Gene Expression Profiling|Humans|Metabolic Networks and Pathways|Protein Interaction Mapping|Proteome|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction|Software', 'Comparative Study', '10.1093/bib/bbv049', '26197809', 'Multiple methods have been proposed to estimate pathway activities from expression profiles, and yet, there is not enough information available about the performance of those methods. This makes selection of a suitable tool for pathway analysis difficult. Although methods based on simple gene lists have remained the most common approach, various methods that also consider pathway structure have emerged. To provide practical insight about the performance of both list-based and structure-based methods, we tested six different approaches to estimate pathway activities in two different case study settings of different characteristics. The first case study setting involved six renal cell cancer data sets, and the differences between expression profiles of case and control samples were relatively big. The second case study setting involved four type 1 diabetes data sets, and the profiles of case and control samples were more similar to each other. In general, there were marked differences in the outcomes of the different pathway tools even with the same input data. In the cancer studies, the results of a tested method were typically consistent across the different data sets, yet different between the methods. In the more challenging diabetes studies, almost all the tested methods detected as significant only few pathways if any. '),
(68954, 2016, 'bib', 'Dynamics of ubiquitin-mediated signalling: insights from mathematical modelling and experimental studies', 'Nguyen, Lan K', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 7, 'Humans|Protein Processing, Post-Translational|Proteins|Systems Biology|Ubiquitin|Ubiquitination', 'Journal Article', '10.1093/bib/bbv052', '26210356', 'Post-translational modification of cellular proteins by ubiquitin is a pivotal regulatory event that controls not only protein degradation, but also a variety of non-proteolytic functions. Ubiquitination is involved in a broad array of physiological processes, and its dysregulation has been associated with many human diseases, including neuronal disorders and cancers. Ubiquitin-mediated signalling has thus come to the forefront of biomedical research. It is increasingly apparent that ubiquitination is a highly complex and dynamic process, evidenced by a myriad of ways of ubiquitin chain formation, tightly regulatory mechanisms involving E3 ligases and deubiquitinating enzymes and extensive crosstalk with other post-translational modifications. To unravel the complexity of ubiquitination and understand the dynamic properties of ubiquitin-mediated signalling are challenging, but critical topics in ubiquitin research, which will undoubtedly benefit our effort in developing strategies that could target ubiquitin signalling for therapeutics. Computational modelling and model-based approaches are emerging as promising tools that help tackle the complexity and provide useful frameworks for quantitative and dynamical analysis of ubiquitin signalling. In this article, I will discuss recent advances in our understanding of the dynamic behaviour of ubiquitination from both theoretical and experimental studies, and aspects of ubiquitin signalling that may have major dynamical consequences. It is expected the discussed issues will be of relevant interest to both the ubiquitin and systems biology fields.'),
(68955, 2016, 'bib', 'Evaluation of somatic copy number estimation tools for whole-exome sequencing data', 'Nam, Jae-Yong|Kim, Nayoung K D|Kim, Sang Cheol|Joung, Je-Gun|Xi, Ruibin|Lee, Semin|Park, Peter J|Park, Woong-Yang', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 24, 'Algorithms|Chromosome Mapping|DNA Copy Number Variations|Exome|High-Throughput Nucleotide Sequencing|Humans|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbv055', '26210357', 'Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68956, 2016, 'bib', 'VCF-Miner: GUI-based application for mining variants and annotations stored in VCF files', 'Hart, Steven N|Duffy, Patrick|Quest, Daniel J|Hossain, Asif|Meiners, Mike A|Kocher, Jean-Pierre', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 26, 'Algorithms|Database Management Systems|Databases, Genetic|Genetic Predisposition to Disease|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbv051', '26210358', 'Next-generation sequencing platforms are widely used to discover variants associated with disease. The processing of sequencing data involves read alignment, variant calling, variant annotation and variant filtering. The standard file format to hold variant calls is the variant call format (VCF) file. According to the format specifications, any arbitrary annotation can be added to the VCF file for downstream processing. However, most downstream analysis programs disregard annotations already present in the VCF and re-annotate variants using the annotation provided by that particular program. This precludes investigators who have collected information on variants from literature or other sources from including these annotations in the filtering and mining of variants. We have developed VCF-Miner, a graphical user interface-based stand-alone tool, to mine variants and annotation stored in the VCF. Powered by a MongoDB database engine, VCF-Miner enables the stepwise trimming of non-relevant variants. The grouping feature implemented in VCF-Miner can be used to identify somatic variants by contrasting variants in tumor and in normal samples or to identify recessive/dominant variants in family studies. It is not limited to human data, but can also be extended to include non-diploid organisms. It also supports copy number or any other variant type supported by the VCF specification. VCF-Miner can be used on a personal computer or large institutional servers and is freely available for download from http://bioinformaticstools.mayo.edu/research/vcf-miner/. '),
(68957, 2016, 'bib', 'A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer', 'Oh, Jung Hun|Deasy, Joseph O', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 6, 'Data Mining|Drug Resistance, Neoplasm|Humans|Neoplasms|Systems Biology', 'Journal Article', '10.1093/bib/bbv053', '26220932', 'Chemoresistance is a major obstacle to the successful treatment of many human cancer types. Increasing evidence has revealed that chemoresistance involves many genes and multiple complex biological mechanisms including cancer stem cells, drug efflux mechanism, autophagy and epithelial-mesenchymal transition. Many studies have been conducted to investigate the possible molecular mechanisms of chemoresistance. However, understanding of the biological mechanisms in chemoresistance still remains limited. We surveyed the literature on chemoresistance-related genes and pathways of multiple cancer types. We then used a curated pathway database to investigate significant chemoresistance-related biological pathways. In addition, to investigate the importance of chemoresistance-related markers in protein-protein interaction networks identified using the curated database, we used a gene-ranking algorithm designed based on a graph-based scoring function in our previous study. Our comprehensive survey and analysis provide a systems biology-based overview of the underlying mechanisms of chemoresistance.'),
(68958, 2016, 'bib', 'The detection and characterization of pleiotropy: discovery, progress, and promise', 'Tyler, Anna L|Crawford, Dana C|Pendergrass, Sarah A', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 14, 'Animals|Caenorhabditis elegans|Computational Biology|Drosophila|Epistasis, Genetic|Genetic Pleiotropy|Genome-Wide Association Study|Humans|Mice|Models, Genetic|Phenotype|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbv050', '26223525', 'The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture. '),
(68959, 2016, 'bib', 'A very simple and fast way to access and validate algorithms in reproducible research', 'Stegmayer, Georgina|Pividori, Milton|Milone, Diego H', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 10, 'Algorithms|Computational Biology|Humans|Internet|Reproducibility of Results|Software', 'Journal Article', '10.1093/bib/bbv054', '26223526', 'The reproducibility of research in bioinformatics refers to the notion that new methodologies/algorithms and scientific claims have to be published together with their data and source code, in a way that other researchers may verify the findings to further build more knowledge on them. The replication and corroboration of research results are key to the scientific process, and many journals are discussing the matter nowadays, taking concrete steps in this direction. In this journal itself, a recent opinion note has appeared highlighting the increasing importance of this topic in bioinformatics and computational biology, inviting the community to further discuss the matter. In agreement with that article, we would like to propose here another step into that direction with a tool that allows the automatic generation of a web interface, named web-demo, directly from source code in a simple and straightforward way. We believe this contribution can help make research not only reproducible but also more easily accessible. A web-demo associated to a published paper can accelerate an algorithm validation with real data, wide-spreading its use with just a few clicks. '),
(68960, 2016, 'bib', 'Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR', 'Marabita, Francesco|de Candia, Paola|Torri, Anna|Tegnér, Jesper|Abrignani, Sergio|Rossi, Riccardo L', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 123, 'Algorithms|Biomarkers|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Real-Time Polymerase Chain Reaction|Reference Values|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bib/bbv056', '26238539', 'The high-throughput analysis of microRNAs (miRNAs) circulating within the blood of healthy and diseased individuals is an active area of biomarker research. Whereas quantitative real-time reverse transcription polymerase chain reaction (qPCR)-based methods are widely used, it is yet unresolved how the data should be normalized. Here, we show that a combination of different algorithms results in the identification of candidate reference miRNAs that can be exploited as normalizers, in both discovery and validation phases. Using the methodology considered here, we identify normalizers that are able to reduce nonbiological variation in the data and we present several case studies, to illustrate the relevance in the context of physiological or pathological scenarios. In conclusion, the discovery of stable reference miRNAs from high-throughput studies allows appropriate normalization of focused qPCR assays. '),
(68961, 2016, 'bib', 'Mitochondria sequence mapping strategies and practicability of mitochondria variant detection from exome and RNA sequencing data', 'Zhang, Pan|Samuels, David C|Lehmann, Brian|Stricker, Thomas|Pietenpol, Jennifer|Shyr, Yu|Guo, Yan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 10, 'Algorithms|Base Sequence|Breast Neoplasms|Cell Line, Tumor|DNA, Mitochondrial|Exome|Genetic Variation|Humans|Molecular Sequence Data|Neoplastic Cells, Circulating|Polymorphism, Single Nucleotide|RNA, Neoplasm|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbv057', '26249222', 'The rapid progress in high-throughput sequencing has significantly enriched our capacity for studying the mitochondrial DNA (mtDNA). In addition to performing specific mitochondrial targeted sequencing, an increasingly popular alternative approach is using the off-target reads from exome sequencing to infer mtDNA variants, including single nucleotide polymorphisms (SNPs) and heteroplasmy. However, the effectiveness and practicality of this approach have not been tested. Recently, RNAseq data have also been suggested as a good source for alternative data mining, but whether mitochondrial variants can be detected from RNAseq data has not been validated. We designed a study to evaluate the practicability of mtDNA variant detection using exome and RNA sequencing data. Five breast cancer cell lines were sequenced through mitochondrial targeted, exome, and RNA sequencing. Mitochondrial targeted sequencing was used as the gold standard to compute the validation and false discovery rates of SNP and heteroplasmy detection in exome and RNAseq data. We found that exome and RNA sequencing can accurately detect mitochondrial SNPs. However, the lower false discovery rate makes exome sequencing a better choice for heteroplasmy detection than RNAseq. Furthermore, we examined three alignment strategies and found that aligning reads directly to the mitochondrial reference genome or aligning reads to the nuclear and mitochondrial references genomes simultaneously produced the best results, and that aligning to the nuclear genome first and afterwards to the mitochondrial genome performed poorly. In conclusion, our study provides important guidelines for future studies that intend to use either exome sequencing or RNAseq data to infer mitochondrial SNPs and heteroplasmy. '),
(68962, 2016, 'bib', 'Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases', 'Naz, Mufassra|Kodamullil, Alpha Tom|Hofmann-Apitius, Martin', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 4, 'Gene Expression|Genetic Variation|Humans|Neurodegenerative Diseases', 'Journal Article', '10.1093/bib/bbv063', '26249223', 'The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimers disease and Parkinsons disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein-protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimers disease can be identified based on an integrative mining approach that identifies chains of causation that include single nucleotide polymorphism information in BEL models.'),
(68963, 2016, 'bib', 'Problems, challenges and promises: perspectives on precision medicine', 'Duffy, David J', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 19, 'Humans|Precision Medicine', 'Journal Article', '10.1093/bib/bbv060', '26249224', 'The precision medicine (systems medicine) concept promises to achieve a shift to future healthcare systems with a more proactive and predictive approach to medicine, where the emphasis is on disease prevention rather than the treatment of symptoms. The individualization of treatment for each patient will be at the centre of this approach, with all of a patients medical data being computationally integrated and accessible. Precision medicine is being rapidly embraced by biomedical researchers, pioneering clinicians and scientific funding programmes in both the European Union (EU) and USA. Precision medicine is a key component of both Horizon 2020 (the EU Framework Programme for Research and Innovation) and the White Houses Precision Medicine Initiative. Precision medicine promises to revolutionize patient care and treatment decisions. However, the participants in precision medicine are faced with a considerable central challenge. Greater volumes of data from a wider variety of sources are being generated and analysed than ever before, yet, this heterogeneous information must be integrated and incorporated into personalized predictive models, the output of which must be intelligible to non-computationally trained clinicians. Drawing primarily from the field of oncology, this article will introduce key concepts and challenges of precision medicine and some of the approaches currently being implemented to overcome these challenges. Finally, this article also covers the criticisms of precision medicine overpromising on its potential to transform patient care.'),
(68964, 2016, 'bib', 'Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer', 'Qi, Lishuang|Chen, Libin|Li, Yang|Qin, Yuan|Pan, Rufei|Zhao, Wenyuan|Gu, Yunyan|Wang, Hongwei|Wang, Ruiping|Chen, Xiangqi|Guo, Zheng', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 27, 'Algorithms|Biomarkers, Tumor|Carcinoma, Non-Small-Cell Lung|Gene Expression Profiling|Genetic Predisposition to Disease|Humans|Lung Neoplasms|Neoplasm Staging|Prevalence|Prognosis|Reproducibility of Results|Risk Assessment|Sensitivity and Specificity|Survival Analysis|Treatment Outcome', 'Journal Article', '10.1093/bib/bbv064', '26254430', 'Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories. '),
(68965, 2016, 'bib', 'Algorithms for modeling global and context-specific functional relationship networks', 'Zhu, Fan|Panwar, Bharat|Guan, Yuanfang', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 2, 'Algorithms|Gene Regulatory Networks|Genome|Genomics|Humans', 'Journal Article', '10.1093/bib/bbv065', '26254431', 'Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.'),
(68966, 2016, 'bib', 'Computational analysis of protein interaction networks for infectious diseases', 'Pan, Archana|Lahiri, Chandrajit|Rajendiran, Anjana|Shanmugham, Buvaneswari', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 12, 'Bacteria|Humans|Protein Interaction Maps', 'Journal Article', '10.1093/bib/bbv059', '26261187', 'Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.'),
(68967, 2016, 'bib', 'Review: High-performance computing to detect epistasis in genome scale data sets', 'Upton, Alex|Trelles, Oswaldo|Cornejo-García, José Antonio|Perkins, James Richard', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 19, 'Algorithms|Epistasis, Genetic|Genome|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbv058', '26272945', 'It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations, the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.'),
(68968, 2016, 'bib', 'Drug-target interaction prediction: databases, web servers and computational models', 'Chen, Xing|Yan, Chenggang Clarence|Zhang, Xiaotian|Zhang, Xu|Dai, Feng|Yin, Jian|Zhang, Yongdong', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 78, 'Databases, Factual|Drug Delivery Systems|Drug Discovery|Humans', 'Journal Article', '10.1093/bib/bbv066', '26283676', 'Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.'),
(68969, 2016, 'bib', 'The identification and characterization of novel transcripts from RNA-seq data', 'Weirick, Tyler|Militello, Giuseppe|Müller, Raphael|John, David|Dimmeler, Stefanie|Uchida, Shizuka', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 17, 'Base Sequence|High-Throughput Nucleotide Sequencing|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bib/bbv067', '26283677', 'Owing greatly to the advancement of next-generation sequencing (NGS), the amount of NGS data is increasing rapidly. Although there are many NGS applications, one of the most commonly used techniques RNA sequencing (RNA-seq) is rapidly replacing microarray-based techniques in laboratories around the world. As more and more of such techniques are standardized, allowing technicians to perform these experiments with minimal hands-on time and reduced experimental/operator-dependent biases, the bottleneck of such techniques is clearly visible, that is, data analysis. Further complicating the matter, increasing evidence suggests most of the genome is transcribed into RNA, however, the majority of these RNAs are not translated into proteins. These RNAs that do not become proteins are called noncoding RNAs (ncRNAs). Although some time has passed since the discovery of ncRNAs, their annotations remain poor, making analysis of RNA-seq data challenging. Here, we examine the current limitations of RNA-seq analysis using case studies focused on the detection of novel transcripts and examination of their characteristics. Finally, we validate the presence of novel transcripts using biological experiments, showing novel transcripts can be accurately identified when a series of filters is applied. In conclusion, novel transcripts that are identified from RNA-seq must be examined carefully before proceeding to biological experiments.'),
(68970, 2016, 'bib', 'Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes', 'Cheng, Feixiong|Zhao, Junfei|Zhao, Zhongming', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 54, 'Exome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms', 'Journal Article', '10.1093/bib/bbv068', '26307061', 'Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported, however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.'),
(68971, 2016, 'bib', 'Systems medicine of inflammaging', 'Castellani, Gastone C|Menichetti, Giulia|Garagnani, Paolo|Giulia Bacalini, Maria|Pirazzini, Chiara|Franceschi, Claudio|Collino, Sebastiano|Sala, Claudia|Remondini, Daniel|Giampieri, Enrico|Mosca, Ettore|Bersanelli, Matteo|Vitali, Silvia|Valle, Italo Faria do|Liò, Pietro|Milanesi, Luciano', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 20, 'Biomarkers|Cross-Sectional Studies|Humans|Inflammation|Neoplasms|Systems Analysis|Systems Biology', 'Journal Article', '10.1093/bib/bbv062', '26307062', 'Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.'),
(68972, 2016, 'bib', 'The RNA world in the 21st century-a systems approach to finding non-coding keys to clinical questions', 'Schmitz, Ulf|Naderi-Meshkin, Hojjat|Gupta, Shailendra K|Wolkenhauer, Olaf|Vera, Julio', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 12, 'High-Throughput Nucleotide Sequencing|Humans|RNA|Sequence Analysis, RNA|Systems Analysis', 'Journal Article', '10.1093/bib/bbv061', '26330575', 'There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.'),
(68973, 2016, 'bib', 'BATMat: Bioinformatics Autodiscovery of Training Materials', 'Lapatas, Vasileios|Stefanidakis, Michalis', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 0, 'Computational Biology|Internet|Search Engine|Software', 'Journal Article', '10.1093/bib/bbv071', '26330576', 'We present Bioinformatics Autodiscovery of Training Materials (BATMat), an open-source, Google-based, targeted, automatic search tool for training materials related to bioinformatics. BATMat helps gain access with one click to filtered and portable information containing links to existing materials (when present). It also offers functionality to sort results according to source site or title.'),
(68974, 2016, 'bib', 'Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis and beyond', 'Lee, Sungmin|Min, Hyeyoung|Yoon, Sungroh', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 2, 'Algorithms|Computational Biology|Gene Expression Profiling', 'Journal Article', '10.1093/bib/bbv073', '26330577', 'A wide variety of large-scale data have been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large-scale practical uses because of factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash-based solid-state drives (SSDs), and with the renewed interest in near-data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop-in replacement of hard disk drives by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD-based profiling and performance exploration of important but time-consuming bioinformatics programs. For an informative review, we perform in-depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit and transcriptome analysis using RNA sequencing. We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices.'),
(68975, 2016, 'bib', 'Comparison of haplotype-based statistical tests for disease association with rare and common variants', 'Datta, Ananda S|Biswas, Swati', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 8, 'Bayes Theorem|Genetic Association Studies|Genetic Variation|Haplotypes|Humans|Models, Genetic', 'Comparative Study', '10.1093/bib/bbv072', '26338417', 'Recent literature has highlighted the advantages of haplotype association methods for detecting rare variants associated with common diseases. As several new haplotype association methods have been proposed in the past few years, a comparison of new and standard methods is important and timely for guidance to the practitioners. We consider nine methods-Haplo.score, Haplo.glm, Hapassoc, Bayesian hierarchical Generalized Linear Model (BhGLM), Logistic Bayesian LASSO (LBL), regularized GLM (rGLM), Haplotype Kernel Association Test, wei-SIMc-matching and Weighted Haplotype and Imputation-based Tests. These can be divided into two types-individual haplotype-specific tests and global tests depending on whether there is just one overall test for a haplotype region (global) or there is an individual test for each haplotype in the region. Haplo.score is the only method that tests for both, Haplo.glm, Hapassoc, BhGLM and LBL are individual haplotype-specific, while the rest are global tests. For comparison, we also apply a popular collapsing method-Sequence Kernel Association Test (SKAT) and its two variants-SKAT-O (Optimal) and SKAT-C (Combined). We carry out an extensive comparison on our simulated data sets as well as on the Genetic Analysis Workshop (GAW) 18 simulated data. Further, we apply the methods to GAW18 real hypertension data and Dallas Heart Study sequence data. We find that LBL, Haplo.score (global test) and rGLM perform well over the scenarios considered here. Also, haplotype methods are more powerful (albeit more computationally intensive) than SKAT and its variants in scenarios where multiple causal variants act interactively to produce haplotype effects.'),
(68976, 2016, 'bib', 'Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers', 'Puchades-Carrasco, Leonor|Palomino-Schätzlein, Martina|Pérez-Rambla, Clara|Pineda-Lucena, Antonio', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 10, 'Biomarkers|Computational Biology|Humans|Magnetic Resonance Spectroscopy|Metabolome|Metabolomics', 'Journal Article', '10.1093/bib/bbv077', '26342127', 'Metabolomics, a systems biology approach focused on the global study of the metabolome, offers a tremendous potential in the analysis of clinical samples. Among other applications, metabolomics enables mapping of biochemical alterations involved in the pathogenesis of diseases, and offers the opportunity to noninvasively identify diagnostic, prognostic and predictive biomarkers that could translate into early therapeutic interventions. Particularly, metabolomics by Nuclear Magnetic Resonance (NMR) has the ability to simultaneously detect and structurally characterize an abundance of metabolic components, even when their identities are unknown. Analysis of the data generated using this experimental approach requires the application of statistical and bioinformatics tools for the correct interpretation of the results. This review focuses on the different steps involved in the metabolomics characterization of biofluids for clinical applications, ranging from the design of the study to the biological interpretation of the results. Particular emphasis is devoted to the specific procedures required for the processing and interpretation of NMR data with a focus on the identification of clinically relevant biomarkers.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68977, 2016, 'bib', 'Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline', 'Rahmatallah, Yasir|Emmert-Streib, Frank|Glazko, Galina', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 26, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|RNA|Reproducibility of Results|Sample Size|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbv069', '26342128', 'Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis. Here we provide a brief review of several statistically different GSA approaches (competitive and self-contained) that can be adapted from microarrays practice as well as those specifically designed for RNA-seq. We evaluate their performance (in terms of Type I error rate, power, robustness to the sample size and heterogeneity, as well as the sensitivity to different types of selection biases) on simulated and real RNA-seq data. Not surprisingly, the performance of various GSA approaches depends only on the statistical hypothesis they test and does not depend on whether the test was developed for microarrays or RNA-seq data. Interestingly, we found that competitive methods have lower power as well as robustness to the samples heterogeneity than self-contained methods, leading to poor results reproducibility. We also found that the power of unsupervised competitive methods depends on the balance between up- and down-regulated genes in tested gene sets. These properties of competitive methods have been overlooked before. Our evaluation provides a concise guideline for selecting GSA approaches, best performing under particular experimental settings in the context of RNA-seq.'),
(68978, 2016, 'bib', 'Methodologies and experimental platforms for generating and analysing microarray and mass spectrometry-based omics data to support P4 medicine', 'Guzzi, Pietro H|Agapito, Giuseppe|Milano, Marianna|Cannataro, Mario', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 14, 'Humans|Mass Spectrometry|Microarray Analysis|Precision Medicine', 'Journal Article', '10.1093/bib/bbv076', '26351205', 'Predictive, preventive, personalized and participatory (P4) medicine is an emerging medical model that is based on the customization of all medical aspects (i.e. practices, drugs, decisions) of the individual patient. P4 medicine presupposes the elucidation of the so-called omic world, under the assumption that this knowledge may explain differences of patients with respect to disease prevention, diagnosis and therapies. Here, we elucidate the role of some selected omics sciences for different aspects of disease management, such as early diagnosis of diseases, prevention of diseases, selection of personalized appropriate and optimal therapies based on molecular profiling of patients. After introducing basic concepts of P4 medicine and omics sciences, we review some computational tools and approaches for analysing selected omics data, with a special focus on microarray and mass spectrometry data, which may be used to support P4 medicine. Some applications of biomarker discovery and pharmacogenomics and some experiences on the study of drug reactions are also described.'),
(68979, 2016, 'bib', 'Collaborative science in the next-generation sequencing era: a viewpoint on how to combine exome sequencing data across sites to identify novel disease susceptibility genes', 'Hart, Steven N|Maxwell, Kara N|Thomas, Tinu|Ravichandran, Vignesh|Wubberhorst, Bradley|Klein, Robert J|Schrader, Kasmintan|Szabo, Csilla|Weitzel, Jeffrey N|Neuhausen, Susan L|Nathanson, Katherine|Offit, Kenneth|Couch, Fergus J|Vijai, Joseph', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 2, 'Exome|Genetic Predisposition to Disease|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1093/bib/bbv075', '26358132', 'The purpose of this article is to inform readers about technical challenges that we encountered when assembling exome sequencing data from the Simplifying Complex Exomes (SIMPLEXO) consortium-whose mandate is the discovery of novel genes predisposing to breast and ovarian cancers. Our motivation is to share these obstacles-and our solutions to them-as a means of communicating important technical details that should be discussed early in projects involving massively parallel sequencing.'),
(68980, 2016, 'bib', 'Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals', 'Zeng, Tao|Zhang, Wanwei|Yu, Xiangtian|Liu, Xiaoping|Li, Meiyi|Chen, Luonan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 27, 'Algorithms|Biomarkers|Databases, Factual|Disease Progression|Humans', 'Journal Article', '10.1093/bib/bbv078', '26411472', 'Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically small-sample size in high-dimension space, i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as large-sample size in low-dimension space, i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression data from a malaria vaccine trial by big-data-based edge biomarkers from module network rewiring-analysis. The illustrative results show that the identified module biomarkers can accurately distinguish vaccines with or without protection and outperformed previous reported gene signatures in terms of effectiveness and efficiency.'),
(68981, 2016, 'bib', 'Correct machine learning on protein sequences: a peer-reviewing perspective', 'Walsh, Ian|Pollastri, Gianluca|Tosatto, Silvio C E', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 20, 'Algorithms|Amino Acid Sequence|Computational Biology|Humans|Machine Learning|Proteins', 'Journal Article', '10.1093/bib/bbv082', '26411473', 'Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning.'),
(68982, 2016, 'bib', 'Nucleosome positioning: resources and tools online', 'Teif, Vladimir B', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 26, 'Base Sequence|Computational Biology|Genome|Humans|Nucleosomes|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/bbv086', '26411474', 'Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this review is to cover a practical aspect of this field, namely, to provide a guide to the multitude of nucleosome positioning resources available online. These include almost 300 experimental datasets of genome-wide nucleosome occupancy profiles determined in different cell types and more than 40 computational tools for the analysis of experimental nucleosome positioning data and prediction of intrinsic nucleosome formation probabilities from the DNA sequence. A manually curated, up to date list of these resources will be maintained at http://generegulation.info.'),
(68983, 2016, 'bib', 'The digital revolution in phenotyping', 'Oellrich, Anika|Collier, Nigel|Groza, Tudor|Rebholz-Schuhmann, Dietrich|Shah, Nigam|Bodenreider, Olivier|Boland, Mary Regina|Georgiev, Ivo|Liu, Hongfang|Livingston, Kevin|Luna, Augustin|Mallon, Ann-Marie|Manda, Prashanti|Robinson, Peter N|Rustici, Gabriella|Simon, Michelle|Wang, Liqin|Winnenburg, Rainer|Dumontier, Michel', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Ital', 20, 'Humans|Information Storage and Retrieval|Phenotype|Research Design|Translational Medical Research', 'Journal Article', '10.1093/bib/bbv083', '26420780', 'Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support bench to bedside efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.'),
(68984, 2016, 'bib', 'Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery', 'Gonzalez, Graciela H|Tahsin, Tasnia|Goodale, Britton C|Greene, Anna C|Greene, Casey S', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 69, 'Computational Biology|Data Interpretation, Statistical|Data Mining|Drug Repositioning|Genomics|Humans|Pharmacogenetics|Precision Medicine|Signal Transduction|Toxicology', 'Journal Article', '10.1093/bib/bbv087', '26420781', 'Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. '),
(68985, 2016, 'bib', 'How computer science can help in understanding the 3D genome architecture', 'Shavit, Yoli|Merelli, Ivan|Milanesi, Luciano|Lio, Pietro', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 8, 'Epigenomics|Genome', 'Journal Article', '10.1093/bib/bbv085', '26433013', 'Chromosome conformation capture techniques are producing a huge amount of data about the architecture of our genome. These data can provide us with a better understanding of the events that induce critical regulations of the cellular function from small changes in the three-dimensional genome architecture. Generating a unified view of spatial, temporal, genetic and epigenetic properties poses various challenges of data analysis, visualization, integration and mining, as well as of high performance computing and big data management. Here, we describe the critical issues of this new branch of bioinformatics, oriented at the comprehension of the three-dimensional genome architecture, which we call Nucleome Bioinformatics, looking beyond the currently available tools and methods, and highlight yet unaddressed challenges and the potential approaches that could be applied for tackling them. Our review provides a map for researchers interested in using computer science for studying Nucleome Bioinformatics, to achieve a better understanding of the biological processes that occur inside the nucleus.'),
(68986, 2016, 'bib', 'Discretization of gene expression data revised', 'Gallo, Cristian A|Cecchini, Rocio L|Carballido, Jessica A|Micheletto, Sandra|Ponzoni, Ignacio', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 31, 'Algorithms|Data Mining|Gene Expression|Gene Expression Profiling', 'Journal Article', '10.1093/bib/bbv074', '26438418', 'Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference. Selection of an appropriate discretization process has a major impact on the design and outcome of the inference algorithms, as there are a number of relevant issues that need to be considered. This study presents a revision of the current state-of-the-art discretization techniques, together with the key subjects that need to be considered when designing or selecting a discretization approach for gene expression data.'),
(68987, 2016, 'bib', 'Computational modelling of atherosclerosis', 'Parton, Andrew|McGilligan, Victoria|OKane, Maurice|Baldrick, Francina R|Watterson, Steven', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 20, 'Atherosclerosis|Computer Simulation|Humans', 'Journal Article', '10.1093/bib/bbv081', '26438419', 'Atherosclerosis is one of the principle pathologies of cardiovascular disease with blood cholesterol a significant risk factor. The World Health Organization estimates that approximately 2.5 million deaths occur annually because of the risk from elevated cholesterol, with 39% of adults worldwide at future risk. Atherosclerosis emerges from the combination of many dynamical factors, including haemodynamics, endothelial damage, innate immunity and sterol biochemistry. Despite its significance to public health, the dynamics that drive atherosclerosis remain poorly understood. As a disease that depends on multiple factors operating on different length scales, the natural framework to apply to atherosclerosis is mathematical and computational modelling. A computational model provides an integrated description of the disease and serves as an in silico experimental system from which we can learn about the disease and develop therapeutic hypotheses. Although the work completed in this area to date has been limited, there are clear signs that interest is growing and that a nascent field is establishing itself. This article discusses the current state of modelling in this area, bringing together many recent results for the first time. We review the work that has been done, discuss its scope and highlight the gaps in our understanding that could yield future opportunities.'),
(68988, 2016, 'bib', 'A Comparison of Base-calling Algorithms for Illumina Sequencing Technology', 'Cacho, Ashley|Smirnova, Ekaterina|Huzurbazar, Snehalata|Cui, Xinping', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 4, 'Algorithms|Base Sequence|High-Throughput Nucleotide Sequencing|Models, Statistical|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bib/bbv088', '26443614', 'Recent advances in next-generation sequencing technology have yielded increasing cost-effectiveness and higher throughput produced per run, in turn, greatly influencing the analysis of DNA sequences. Among the various sequencing technologies, Illumina is by far the most widely used platform. However, the Illumina sequencing platform suffers from several imperfections that can be attributed to the chemical processes inherent to the sequencing-by-synthesis technology. With the enormous amounts of reads produced, statistical methodologies and computationally efficient algorithms are required to improve the accuracy and speed of base-calling. Over the past few years, several papers have proposed methods to model the various imperfections, giving rise to accurate and/or efficient base-calling algorithms. In this article, we provide a comprehensive comparison of the performance of recently developed base-callers and we present a general statistical model that unifies a large majority of these base-callers.'),
(68989, 2016, 'bib', 'MST2-RASSF protein-protein interactions through SARAH domains', 'Sánchez-Sanz, Goar|Matallanas, David|Nguyen, Lan K|Kholodenko, Boris N|Rosta, Edina|Kolch, Walter|Buchete, Nicolae-Viorel', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'Animals|Apoptosis|Models, Molecular|Protein Binding|Protein-Serine-Threonine Kinases|Signal Transduction', 'Journal Article', '10.1093/bib/bbv070', '26443615', 'The detailed, atomistic-level understanding of molecular signaling along the tumor-suppressive Hippo signaling pathway that controls tissue homeostasis by balancing cell proliferation and death through apoptosis is a promising avenue for the discovery of novel anticancer drug targets. The activation of kinases such as Mammalian STE20-Like Protein Kinases 1 and 2 (MST1 and MST2)-modulated through both homo- and heterodimerization (e.g. interactions with Ras association domain family, RASSF, enzymes)-is a key upstream event in this pathway and remains poorly understood. On the other hand, RASSFs (such as RASSF1A or RASSF5) act as important apoptosis activators and tumor suppressors, although their exact regulatory roles are also unclear. We present recent molecular studies of signaling along the Ras-RASSF-MST pathway, which controls growth and apoptosis in eukaryotic cells, including a variety of modern molecular modeling and simulation techniques. Using recently available structural information, we discuss the complex regulatory scenario according to which RASSFs perform dual signaling functions, either preventing or promoting MST2 activation, and thus control cell apoptosis. Here, we focus on recent studies highlighting the special role being played by the specific interactions between the helical Salvador/RASSF/Hippo (SARAH) domains of MST2 and RASSF1a or RASSF5 enzymes. These studies are crucial for integrating atomistic-level mechanistic information about the structures and conformational dynamics of interacting proteins, with information available on their system-level functions in cellular signaling.'),
(68990, 2016, 'bib', 'Multiple Testing of Gene Sets from Gene Ontology: Possibilities and Pitfalls', 'Meijer, Rosa J|Goeman, Jelle J', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 4, 'Gene Ontology|Genetic Testing|Humans', 'Journal Article', '10.1093/bib/bbv091', '26446060', 'The use of multiple testing procedures in the context of gene-set testing is an important but relatively underexposed topic. If a multiple testing method is used, this is usually a standard familywise error rate (FWER) or false discovery rate (FDR) controlling procedure in which the logical relationships that exist between the different (self-contained) hypotheses are not taken into account. Taking those relationships into account, however, can lead to more powerful variants of existing multiple testing procedures and can make summarizing and interpreting the final results easier. We will show that, from the perspective of interpretation as well as from the perspective of power improvement, FWER controlling methods are more suitable than FDR controlling methods. As an example of a possible power improvement, we suggest a modified version of the popular method by Holm, which we also implemented in the R package cherry.'),
(68991, 2016, 'bib', 'Pathway Tools version 190 update: software for pathway/genome informatics and systems biology', 'Karp, Peter D|Latendresse, Mario|Paley, Suzanne M|Krummenacker, Markus|Ong, Quang D|Billington, Richard|Kothari, Anamika|Weaver, Daniel|Lee, Thomas|Subhraveti, Pallavi|Spaulding, Aaron|Fulcher, Carol|Keseler, Ingrid M|Caspi, Ron', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 104, 'Computational Biology|Genome|Genomics|Internet|Metabolic Networks and Pathways|Software Design|Systems Biology', 'Journal Article', '10.1093/bib/bbv079', '26454094', 'Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation, addition of editors for signaling pathways, for genome sequences and for cellular architecture, storage of gene essentiality data and phenotype data, display of multiple alignments, and of signaling and electron-transport pathways, and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms.'),
(68992, 2016, 'bib', 'Statistical methods for detecting differentially methylated regions based on MethylCap-seq data', 'Ayyala, Deepak N|Frankhouser, David E|Ganbat, Javkhlan-Ochir|Marcucci, Guido|Bundschuh, Ralf|Yan, Pearlly|Lin, Shili', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'CpG Islands|DNA Methylation|Epigenomics|Humans|Leukemia, Myeloid, Acute|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/bbv089', '26454095', 'DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from curse of dimensionality and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation studies are conducted to highlight the performance of these tests under different settings. Based on our observation, we make recommendations on the optimal test to use. We illustrate the superiority of mean vector tests in detecting cancer-related canonical gene pathways, which are significantly enriched for acute myeloid leukemia and ovarian cancer.'),
(68993, 2016, 'bib', 'Public data and open source tools for multi-assay genomic investigation of disease', 'Kannan, Lavanya|Ramos, Marcel|Re, Angela|El-Hachem, Nehme|Safikhani, Zhaleh|Gendoo, Deena M A|Davis, Sean|Gomez-Cabrero, David|Castelo, Robert|Hansen, Kasper D|Carey, Vincent J|Morgan, Martin|Culhane, Aedín C|Haibe-Kains, Benjamin|Waldron, Levi', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 18, 'Computational Biology|Genomics|MicroRNAs|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbv080', '26463000', 'Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources, however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.'),
(68994, 2016, 'bib', 'Transcriptomic and metabolomic data integration', 'Cavill, Rachel|Jennen, Danyel|Kleinjans, Jos|Briedé, Jacob Jan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 19, 'Humans|Metabolomics|Transcriptome', 'Journal Article', '10.1093/bib/bbv090', '26467821', 'Many studies now produce parallel data sets from different omics technologies, however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.'),
(68995, 2016, 'bib', 'Updates on drug-target network, facilitating polypharmacology and data integration by growth of DrugBank database', 'Barneh, Farnaz|Jafari, Mohieddin|Mirzaie, Mehdi', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 8, 'Databases, Factual|Drug Delivery Systems|Drug Interactions|Drug-Related Side Effects and Adverse Reactions|Humans|Polypharmacology', 'Journal Article', '10.1093/bib/bbv094', '26490381', 'Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.'),
(68996, 2016, 'bib', 'Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment', 'Shameer, Khader|Tripathi, Lokesh P|Kalari, Krishna R|Dudley, Joel T|Sowdhamini, Ramanathan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 4, 'Genetic Variation|Genotype|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Proteome', 'Journal Article', '10.1093/bib/bbv084', '26494363', 'Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical, thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex, such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68997, 2016, 'bib', 'Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients', 'Yasrebi, Haleh', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy', 10, 'Breast Neoplasms|Gene Expression Profiling|Humans|Proportional Hazards Models|Risk Assessment|Survival Analysis', 'Comparative Study', '10.1093/bib/bbv092', '26504096', 'Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z-score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z-score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z-score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.'),
(68998, 2016, 'bib', 'Image-based modelling of organogenesis', 'Iber, Dagmar|Karimaddini, Zahra|Ünal, Erkan', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 2, 'Computer Simulation|Models, Biological|Organogenesis|Systems Biology', 'Journal Article', '10.1093/bib/bbv093', '26510443', 'One of the major challenges in biology concerns the integration of data across length and time scales into a consistent framework: how do macroscopic properties and functionalities arise from the molecular regulatory networks-and how can they change as a result of mutations? Morphogenesis provides an excellent model system to study how simple molecular networks robustly control complex processes on the macroscopic scale despite molecular noise, and how important functional variants can emerge from small genetic changes. Recent advancements in three-dimensional imaging technologies, computer algorithms and computer power now allow us to develop and analyse increasingly realistic models of biological control. Here, we present our pipeline for image-based modelling that includes the segmentation of images, the determination of displacement fields and the solution of systems of partial differential equations on the growing, embryonic domains. The development of suitable mathematical models, the data-based inference of parameter sets and the evaluation of competing models are still challenging, and current approaches are discussed.'),
(68999, 2016, 'bib', 'Computational resources and strategies to construct single-molecule metabolic models of microbial cells', 'Gameiro, Denise|Pérez-Pérez, Martín|Pérez-Rodríguez, Gael|Monteiro, Gonçalo|Azevedo, Nuno F|Lourenço, Anália', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 4, 'Databases, Factual|Escherichia coli|Kinetics|Models, Biological|Software', 'Journal Article', '10.1093/bib/bbv096', '26515531', 'Recent computational methodologies, such as individual-based modelling, pave the way to the search for explanatory insight into the collective behaviour of molecules. Many reviews offer an up-to-date perspective about such methodologies, but little is discussed about the practical information requirements involved. The biological information used as input should be easily and routinely determined in the laboratory, publicly available and, preferably, organized in programmatically accessible databases. This review is the first to provide a systematic and comprehensive overview of available resources for the modelling of metabolic events at the molecular scale. The glycolysis pathway of Escherichia coli, which is one of the most studied pathways in Microbiology, serves as case study. This curation addressed structural information about E. coli (i.e. defining the simulation environment), the reactions forming the glycolysis pathway including the enzymes and the metabolites (i.e. the molecules to be represented), the kinetics of each reaction (i.e. behavioural logic of the molecules) and diffusion parameters for all enzymes and metabolites (i.e. molecule movement in the environment). Furthermore, the interpretation of relevant biological features, such as molecular diffusion and enzyme kinetics, and the connection of experimental determination and simulation validation are detailed. Notably, the information from classical theories, such as enzymatic rates and diffusion coefficients, is translated to simulation parameters, such as collision efficiency and particle velocity.'),
(69000, 2016, 'bib', 'An evaluation of methods to test predefined genomic regions for differential methylation in bisulfite sequencing data', 'Klein, Hans-Ulrich|Hebestreit, Katja', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 17, 'DNA Methylation|Genome, Human|Genomics|Humans|Sequence Analysis, DNA|Sulfites', 'Journal Article', '10.1093/bib/bbv095', '26515532', 'In the biology of tissue development and diseases, DNA methylation plays an important role. For a deeper understanding, it is crucial to accurately compare DNA methylation patterns between groups of samples representing different conditions. A widely used method to investigate DNA methylation in the CpG context is bisulfite sequencing, which produces data on the single-nucleotide scale. While there are benefits to analyzing CpG sites on a basepair level, there are both biological and statistical reasons to test entire genomic regions for differential methylation. However, the analysis of DNA methylation is hampered by the lack of best practice standards. Here, we compared multiple approaches for testing predefined genomic regions for differential DNA methylation in bisulfite sequencing data. Nine methods were evaluated: BiSeq, COHCAP, Goemans Global Test, Limma, methylKit/eDMR, RADMeth and three log-linear regression approaches with different distribution assumptions. We applied these methods to simulated data and determined their sensitivity and specificity. This revealed performance differences, which were also seen when applied to real data. Methods that first test single CpG sites and then test regions based on transformed CpG-wise P-values performed better than methods that summarize methylation levels or raw reads. Interestingly, smoothing of methylation levels had a negligible impact. In particular, Global Test, BiSeq and RADMeth/z-test outperformed the other methods we evaluated, providing valuable guidance for more accurate analysis of DNA methylation.'),
(69001, 2016, 'bib', 'In the loop: promoter-enhancer interactions and bioinformatics', 'Mora, Antonio|Sandve, Geir Kjetil|Gabrielsen, Odd Stokke|Eskeland, Ragnhild', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 16, 'Chromatin|Computational Biology|Enhancer Elements, Genetic|Genome-Wide Association Study|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/bbv097', '26586731', 'Enhancer-promoter regulation is a fundamental mechanism underlying differential transcriptional regulation. Spatial chromatin organization brings remote enhancers in contact with target promoters in cis to regulate gene expression. There is considerable evidence for promoter-enhancer interactions (PEIs). In the recent years, genome-wide analyses have identified signatures and mapped novel enhancers, however, being able to precisely identify their target gene(s) requires massive biological and bioinformatics efforts. In this review, we give a short overview of the chromatin landscape and transcriptional regulation. We discuss some key concepts and problems related to chromatin interaction detection technologies, and emerging knowledge from genome-wide chromatin interaction data sets. Then, we critically review different types of bioinformatics analysis methods and tools related to representation and visualization of PEI data, raw data processing and PEI prediction. Lastly, we provide specific examples of how PEIs have been used to elucidate a functional role of non-coding single-nucleotide polymorphisms. The topic is at the forefront of epigenetic research, and by highlighting some future bioinformatics challenges in the field, this review provides a comprehensive background for future PEI studies.'),
(69002, 2016, 'bib', 'Multiple sequence alignment modeling: methods and applications', 'Chatzou, Maria|Magis, Cedrik|Chang, Jia-Ming|Kemena, Carsten|Bussotti, Giovanni|Erb, Ionas|Notredame, Cedric', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 34, 'Algorithms|DNA|Genomics|Proteins|Reproducibility of Results|Sequence Alignment', 'Journal Article', '10.1093/bib/bbv099', '26615024', 'This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.'),
(69003, 2016, 'bib', 'Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond', 'Pfau, Thomas|Pacheco, Maria Pires|Sauter, Thomas', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy|Italy', 11, 'Databases, Factual|Genome|Metabolic Networks and Pathways|Models, Biological|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbv100', '26615025', 'Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.'),
(69004, 2016, 'bib', 'Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data', 'Tsuji, Junko|Weng, Zhiping', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 1, 'Algorithms|CpG Islands|DNA Methylation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bib/bbv103', '26628557', 'Cytosine methylation regulates many biological processes such as gene expression, chromatin structure and chromosome stability. The whole genome bisulfite sequencing (WGBS) technique measures the methylation level at each cytosine throughout the genome. There are an increasing number of publicly available pipelines for analyzing WGBS data, reflecting many choices of read mapping algorithms as well as preprocessing and postprocessing methods. We simulated single-end and paired-end reads based on three experimental data sets, and comprehensively evaluated 192 combinations of three preprocessing, five postprocessing and five widely used read mapping algorithms. We also compared paired-end data with single-end data at the same sequencing depth for performance of read mapping and methylation level estimation. Bismark and LAST were the most robust mapping algorithms. We found that Mott trimming and quality filtering individually improved the performance of both read mapping and methylation level estimation, but combining them did not lead to further improvement. Furthermore, we confirmed that paired-end sequencing reduced error rate and enhanced sensitivity for both read mapping and methylation level estimation, especially for short reads and in repetitive regions of the human genome.'),
(69005, 2017, 'bib', 'Model parameters of molecular evolution explain genomic correlations', 'Gu, Xun|Tang, Wenda', 'Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.|Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy.', 'Italy|Italy', 0, 'Evolution, Molecular|Genome|Genomics|Mutation|Proteins', 'Journal Article', '10.1093/bib/bbv098', '26628558', 'One long-standing research focus in evolutionary genomics is trying to resolve how biological variables (expression, essentiality, protein-protein interaction, structural stability, etc.) determine the rate of protein evolution. While these studies have considerably deepened our understanding of molecular evolution, many issues remain unsolved. In this opinion article, after having a brief survey of literatures, we establish relationships between model parameters of molecular evolution and genomic variables, based on which, most-observed genomic correlations and confounds can be explained by model parameter combinations under different conditions, which include the strength of stabilizing selection, mutational variance, expression sufficiency, gene pleiotropy, as well as the effective population size. We suggest that the problem to discern biological variable(s) that may determine the rate of protein evolution can be tackled at two levels. The first level, as discussed here, is to demonstrate how the model of molecular evolution can predict potential genomic correlations under various conditions. And the second level is to estimate genome-wide variations of model parameters (or combinations) that help to identify canonical biological variables that may underlie the rate variation among genes that ranges up to at least three magnitudes.'),
(69006, 2016, 'bib', 'Current Progress in Bioinformatics 2016', 'Altman, Russ', 'Stanford.', '-', 2, 'Computational Biology|Crowdsourcing|Data Mining|Drug Repositioning|Genetic Pleiotropy|Humans', 'Editorial', '10.1093/bib/bbv105', '26628559', '-'),
(69007, 2016, 'bib', 'Surveying and benchmarking techniques to analyse DNA gel fingerprint images', 'Heras, Jónathan|Domínguez, César|Mata, Eloy|Pascual, Vico', 'Stanford.|Stanford.|Stanford.|Stanford.', '-|-|-|-', 0, 'Algorithms|Benchmarking|DNA|Image Processing, Computer-Assisted', 'Journal Article', '10.1093/bib/bbv102', '26634918', 'DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. The analysis of DNA gel fingerprint images usually consists of five consecutive steps: image pre-processing, lane segmentation, band detection, normalization and fingerprint comparison. In this article, we firstly survey the main methods that have been applied in the literature in each of these stages. Secondly, we focus on lane-segmentation and band-detection algorithms-as they are the steps that usually require user-intervention-and detect the seven core algorithms used for both tasks. Subsequently, we present a benchmark that includes a data set of images, the gold standards associated with those images and the tools to measure the performance of lane-segmentation and band-detection algorithms. Finally, we implement the core algorithms used both for lane segmentation and band detection, and evaluate their performance using our benchmark. As a conclusion of that study, we obtain that the average profile algorithm is the best starting point for lane segmentation and band detection.'),
(69008, 2016, 'bib', 'Progress and challenges in bioinformatics approaches for enhancer identification', 'Kleftogiannis, Dimitrios|Kalnis, Panos|Bajic, Vladimir B', 'Stanford.|Stanford.|Stanford.', '-|-|-', 33, 'Computational Biology|Enhancer Elements, Genetic|Histones', 'Journal Article', '10.1093/bib/bbv101', '26634919', 'Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration.'),
(69009, 2017, 'bib', 'NCBI-compliant genome submissions: tips and tricks to save time and money', 'Pirovano, Walter|Boetzer, Marten|Derks, Martijn F L|Smit, Sandra', 'BaseClear B,V,, Genome analysis and technology department, Einsteinweg 5, Leiden 2333 CC, The Netherlands.|BaseClear B.V., Einsteinweg 5, 2333 CC Leiden, Leiden,The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 4, 'Databases, Nucleic Acid|Genome|Humans|Internet|National Library of Medicine (U.S.)|United States', 'Journal Article', '10.1093/bib/bbv104', '26655251', 'Genome sequences nowadays play a central role in molecular biology and bioinformatics. These sequences are shared with the scientific community through sequence databases. The sequence repositories of the International Nucleotide Sequence Database Collaboration (INSDC, comprising GenBank, ENA and DDBJ) are the largest in the world. Preparing an annotated sequence in such a way that it will be accepted by the database is challenging because many validation criteria apply. In our opinion, it is an undesirable situation that researchers who want to submit their sequence need either a lot of experience or help from partners to get the job done. To save valuable time and money, we list a number of recommendations for people who want to submit an annotated genome to a sequence database, as well as for tool developers, who could help to ease the process.'),
(69010, 2016, 'bib', 'Systematic dissection of dysregulated transcription factor-miRNA feed-forward loops across tumor types', 'Jiang, Wei|Mitra, Ramkrishna|Lin, Chen-Ching|Wang, Quan|Cheng, Feixiong|Zhao, Zhongming', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Epithelial-Mesenchymal Transition|Gene Regulatory Networks|Humans|MicroRNAs|Neoplasms|Transcription Factors', 'Journal Article', '10.1093/bib/bbv107', '26655252', 'Transcription factor and microRNA (miRNA) can mutually regulate each other and jointly regulate their shared target genes to form feed-forward loops (FFLs). While there are many studies of dysregulated FFLs in a specific cancer, a systematic investigation of dysregulated FFLs across multiple tumor types (pan-cancer FFLs) has not been performed yet. In this study, using The Cancer Genome Atlas data, we identified 26 pan-cancer FFLs, which were dysregulated in at least five tumor types. These pan-cancer FFLs could communicate with each other and form functionally consistent subnetworks, such as epithelial to mesenchymal transition-related subnetwork. Many proteins and miRNAs in each subnetwork belong to the same protein and miRNA family, respectively. Importantly, cancer-associated genes and drug targets were enriched in these pan-cancer FFLs, in which the genes and miRNAs also tended to be hubs and bottlenecks. Finally, we identified potential anticancer indications for existing drugs with novel mechanism of action. Collectively, this study highlights the potential of pan-cancer FFLs as a novel paradigm in elucidating pathogenesis of cancer and developing anticancer drugs.'),
(69011, 2016, 'bib', 'Analysis of sequencing data for probing RNA secondary structures and protein-RNA binding in studying posttranscriptional regulations', 'Hu, Xihao|Wu, Yang|Lu, Zhi John|Yip, Kevin Y', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Nucleic Acid Conformation|Protein Binding|Protein Structure, Secondary|RNA|RNA-Binding Proteins|Transcriptome', 'Journal Article', '10.1093/bib/bbv106', '26655457', 'High-throughput sequencing has been used to study posttranscriptional regulations, where the identification of protein-RNA binding is a major and fast-developing sub-area, which is in turn benefited by the sequencing methods for whole-transcriptome probing of RNA secondary structures. In the study of RNA secondary structures using high-throughput sequencing, bases are modified or cleaved according to their structural features, which alter the resulting composition of sequencing reads. In the study of protein-RNA binding, methods have been proposed to immuno-precipitate (IP) protein-bound RNA transcripts in vitro or in vivo By sequencing these transcripts, the protein-RNA interactions and the binding locations can be identified. For both types of data, read counts are affected by a combination of confounding factors, including expression levels of transcripts, sequence biases, mapping errors and the probing or IP efficiency of the experimental protocols. Careful processing of the sequencing data and proper extraction of important features are fundamentally important to a successful analysis. Here we review and compare different experimental methods for probing RNA secondary structures and binding sites of RNA-binding proteins (RBPs), and the computational methods proposed for analyzing the corresponding sequencing data. We suggest how these two types of data should be integrated to study the structural properties of RBP binding sites as a systematic way to better understand posttranscriptional regulations.'),
(69012, 2016, 'bib', 'A proteogenomic approach to understand splice isoform functions through sequence and expression-based computational modeling', 'Li, Hong-Dong|Omenn, Gilbert S|Guan, Yuanfang', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 2, 'Alternative Splicing|Base Sequence|Computer Simulation|Gene Expression Profiling|Protein Isoforms|Proteogenomics|Proteomics|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbv109', '26740460', 'The products of multi-exon genes are a mixture of alternatively spliced isoforms, from which the translated proteins can have similar, different or even opposing functions. It is therefore essential to differentiate and annotate functions for individual isoforms. Computational approaches provide an efficient complement to expensive and time-consuming experimental studies. The input data of these methods range from DNA sequence, to RNA selection pressure, to expressed sequence tags, to full-length complementary DNA, to exon array, to RNA-seq expression, to proteomic data. Notably, RNA-seq technology generates quantitative profiling of transcript expression at the genome scale, with an unprecedented amount of expression data available for developing isoform function prediction methods. Integrative analysis of these data at different molecular levels enables a proteogenomic approach to systematically interrogate isoform functions. Here, we briefly review the state-of-the-art methods according to their input data sources, discuss their advantages and limitations and point out potential ways to improve prediction accuracies.'),
(69013, 2017, 'bib', 'Multi-hierarchical profiling: an emerging and quantitative approach to characterizing diverse biological networks', 'Zhang, Yingying|Wang, Zhong|Wang, Yongyan', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 3, 'Algorithms|Computational Biology', 'Journal Article', '10.1093/bib/bbv112', '26740461', 'Multi-hierarchical profiling may offer valuable insights into the structural stability and functional direction of biological networks in cellular development, pathological process and disease variation. Owing to the emergence of several new techniques, such as bioinformatics for omics data, structural biology and structural bioinformatics, the pace of network hierarchical research has accelerated a lot in recent years. Here, we discuss and compare the techniques available for quantifying multilevel hierarchies, with a focus on their features, capabilities and drawbacks when used for different applications. Then, we classify these methods into three types: topological spatial-scales, multilevel hierarchical control and feature ordering. We observe that challenges and limitations do exist in functional hierarchical identification. And, we also provide useful suggestions on how to analyze the dynamic data of complex network studies.'),
(69014, 2016, 'bib', 'A comprehensive comparison of tools for differential ChIP-seq analysis', 'Steinhauser, Sebastian|Kurzawa, Nils|Eils, Roland|Herrmann, Carl', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 18, 'Binding Sites|Chromatin Immunoprecipitation|Genome|Genomics|Sequence Analysis, DNA|Transcription Factors', 'Comparative Study', '10.1093/bib/bbv110', '26764273', 'ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this is a common analysis for gene expression, for which a large number of computational approaches have been validated, the same question for ChIP-seq is particularly challenging owing to the complexity of ChIP-seq data in terms of noisiness and variability. Many different tools have been developed and published in recent years. However, a comprehensive comparison and review of these tools is still missing. Here, we have reviewed 14 tools, which have been developed to determine differential enrichment between two conditions. They differ in their algorithmic setups, and also in the range of applicability. Hence, we have benchmarked these tools on real data sets for transcription factors and histone modifications, as well as on simulated data sets to quantitatively evaluate their performance. Overall, there is a great variety in the type of signal detected by these tools with a surprisingly low level of agreement. Depending on the type of analysis performed, the choice of method will crucially impact the outcome.'),
(69015, 2017, 'bib', 'A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays', 'Bonham-Carter, Oliver|Thapa, Ishwor|From, Steven|Bastola, Dhundy', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 1, 'Binding Sites|Protein Processing, Post-Translational|Proteome', 'Journal Article', '10.1093/bib/bbv111', '26764274', 'Post-translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism-specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non-Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM-associated residue reaction sites (RSs), where PTMs interact with protein. Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino-acid compositions of Mt and non-Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.'),
(69016, 2017, 'bib', 'Making authentic science accessible-the benefits and challenges of integrating bioinformatics into a high-school science curriculum', 'Machluf, Yossy|Gelbart, Hadas|Ben-Dor, Shifra|Yarden, Anat', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Computational Biology|Curriculum|Humans|Knowledge|Learning|Students', 'Journal Article', '10.1093/bib/bbv113', '26801769', 'Despite the central place held by bioinformatics in modern life sciences and related areas, it has only recently been integrated to a limited extent into high-school teaching and learning programs. Here we describe the assessment of a learning environment entitled Bioinformatics in the Service of Biotechnology. Students learning outcomes and attitudes toward the bioinformatics learning environment were measured by analyzing their answers to questions embedded within the activities, questionnaires, interviews and observations. Students difficulties and knowledge acquisition were characterized based on four categories: the required domain-specific knowledge (declarative, procedural, strategic or situational), the scientific field that each question stems from (biology, bioinformatics or their combination), the associated cognitive-process dimension (remember, understand, apply, analyze, evaluate, create) and the type of question (open-ended or multiple choice). Analysis of students cognitive outcomes revealed learning gains in bioinformatics and related scientific fields, as well as appropriation of the bioinformatics approach as part of the students scientific toolbox. For students, questions stemming from the old world biology field and requiring declarative or strategic knowledge were harder to deal with. This stands in contrast to their teachers prediction. Analysis of students affective outcomes revealed positive attitudes toward bioinformatics and the learning environment, as well as their perception of the teachers role. Insights from this analysis yielded implications and recommendations for curriculum design, classroom enactment, teacher education and research. For example, we recommend teaching bioinformatics in an integrative and comprehensive manner, through an inquiry process, and linking it to the wider science curriculum.'),
(69017, 2017, 'bib', 'A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology', 'Yan, Qin|Zhu, Xuli|Jiang, Libo|Ye, Meixia|Sun, Lidan|Terblanche, John S|Wu, Rongling', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Chromosome Mapping|Ecosystem|Genome-Wide Association Study|Genotype|Likelihood Functions|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbv116', '26801770', 'Whole-organism metabolic rate co-varies allometrically with body mass, and is also affected by temperature through different biochemical mechanisms. Here we implement a computational platform to map specific quantitative trait loci (QTLs) that govern the dependence of metabolic rate on size and temperature. The model was formulated within settings of genetic mapping or genome-wide association studies through a mapping population genotyped by a set of molecular markers throughout the genome and phenotyped for metabolic parameters over a range of temperature. The model, estimated by a maximum-likelihood approach, allows a genome-wide search for the underlying metabolic QTLs and the estimation of genotype-specific parameters that specify the metabolism of an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.'),
(69018, 2017, 'bib', 'MTD: a mammalian transcriptomic database to explore gene expression and regulation', 'Sheng, Xin|Wu, Jiayan|Sun, Qianqian|Li, Xue|Xian, Feng|Sun, Manman|Fang, Wan|Chen, Meili|Yu, Jun|Xiao, Jingfa', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 6, 'Animals|Databases, Genetic|Exons|Gene Expression Profiling|Gene Expression Regulation|Genome|Genomics|Humans|Mice|Rats|Swine|Transcriptome', 'Journal Article', '10.1093/bib/bbv117', '26822098', 'A systematic transcriptome survey is essential for the characterization and comprehension of the molecular basis underlying phenotypic variations. Recently developed RNA-seq methodology has facilitated efficient data acquisition and information mining of transcriptomes in multiple tissues/cell lines. Current mammalian transcriptomic databases are either tissue-specific or species-specific, and they lack in-depth comparative features across tissues and species. Here, we present a mammalian transcriptomic database (MTD) that is focused on mammalian transcriptomes, and the current version contains data from humans, mice, rats and pigs. Regarding the core features, the MTD browses genes based on their neighboring genomic coordinates or joint KEGG pathway and provides expression information on exons, transcripts and genes by integrating them into a genome browser. We developed a novel nomenclature for each transcript that considers its genomic position and transcriptional features. The MTD allows a flexible search of genes or isoforms with user-defined transcriptional characteristics and provides both table-based descriptions and associated visualizations. To elucidate the dynamics of gene expression regulation, the MTD also enables comparative transcriptomic analysis in both intraspecies and interspecies manner. The MTD thus constitutes a valuable resource for transcriptomic and evolutionary studies. The MTD is freely accessible at http://mtd.cbi.ac.cn.'),
(69019, 2017, 'bib', 'Computational methods to identify metabolic sub-networks based on metabolomic profiles', 'Frainay, Clément|Jourdan, Fabien', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands', 25, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Metabolomics', 'Journal Article', '10.1093/bib/bbv115', '26822099', 'Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.'),
(69020, 2017, 'bib', 'Computational recognition for long non-coding RNA (lncRNA): Software and databases', 'Yotsukura, Sohiya|duVerle, David|Hancock, Timothy|Natsume-Kitatani, Yayoi|Mamitsuka, Hiroshi', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 14, 'Computational Biology|Databases, Genetic|Databases, Nucleic Acid|Humans|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bib/bbv114', '26839320', 'Since the completion of the Human Genome Project, it has been widely established that most DNA is not transcribed into proteins. These non-protein-coding regions are believed to be moderators within transcriptional and post-transcriptional processes, which play key roles in the onset of diseases. Long non-coding RNAs (lncRNAs) are generally lacking in conserved motifs typically used for detection and thus hard to identify, but nonetheless present certain characteristic features that can be exploited by bioinformatics methods. By combining lncRNA detection with known miRNA, RNA-binding protein and chromatin interaction, current tools are able to recognize and functionally annotate large number of lncRNAs. This review discusses databases and platforms dedicated to cataloging and annotating lncRNAs, as well as tools geared at discovering novel sequences. We emphasize the issues posed by the diversity of lncRNAs and their complex interaction mechanisms, as well as technical issues such as lack of unified nomenclature. We hope that this wide overview of existing platforms and databases might help guide biologists toward the tools they need to analyze their experimental data, while our discussion of limitations and of current lncRNA-related methods may assist in the development of new computational tools.'),
(69021, 2017, 'bib', 'Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations', 'Luo, Yuan|Uzuner, Özlem|Szolovits, Peter', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 13, 'Algorithms|Computational Biology|Data Mining|Humans|Semantics', 'Journal Article', '10.1093/bib/bbw001', '26851224', 'Research on extracting biomedical relations has received growing attention recently, with numerous biological and clinical applications including those in pharmacogenomics, clinical trial screening and adverse drug reaction detection. The ability to accurately capture both semantic and syntactic structures in text expressing these relations becomes increasingly critical to enable deep understanding of scientific papers and clinical narratives. Shared task challenges have been organized by both bioinformatics and clinical informatics communities to assess and advance the state-of-the-art research. Significant progress has been made in algorithm development and resource construction. In particular, graph-based approaches bridge semantics and syntax, often achieving the best performance in shared tasks. However, a number of problems at the frontiers of biomedical relation extraction continue to pose interesting challenges and present opportunities for great improvement and fruitful research. In this article, we place biomedical relation extraction against the backdrop of its versatile applications, present a gentle introduction to its general pipeline and shared resources, review the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69022, 2017, 'bib', 'GED: a manually curated comprehensive resource for epigenetic modification of gametogenesis', 'Bai, Weiyang|Yang, Wen|Wang, Wenjing|Wang, Yang|Liu, Can|Jiang, Qinghua|Hua, Jinlian|Liao, Mingzhi', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 10, 'Animals|DNA Methylation|Databases, Factual|Epigenesis, Genetic|Gametogenesis|Humans|MicroRNAs', 'Journal Article', '10.1093/bib/bbw007', '26851225', 'Reproductive infertility affects seventh of couples, which is most attributed to the obstacle of gametogenesis. Characterizing the epigenetic modification factors involved in gametogenesis is fundamental to understand the molecular mechanisms and to develop treatments for human infertility. Although the genetic factors have been implicated in gametogenesis, no dedicated bioinformatics resource for gametogenesis is available. To elucidate the relationship of epigenetic modification and mammalian gametogenesis, we developed a new database, gametogenesis epigenetic modification database (GED), a manually curated database, which aims at providing a comprehensive resource of epigenetic modification of gametogenesis. The database integrates three kinds information of epigenetic modifications during gametogenesis (DNA methylation, histone modification and RNA regulation), and the gametogenesis has been detailed as 16 stages in seven mammal species (Homo sapiens, Mus musculus, Rattus norvegicus, Sus scrofa, Bos taurus, Capra hircus and Ovis aries). Besides, we have predicted the linear pathways of epigenetic modification which were composed of 211 genes/proteins and microRNAs that were involved in gametogenesis. GED is a user-friendly Web site, through which users can obtain the comprehensive epigenetic factor information and molecular pathways by visiting our database freely. GED is free available at http://gametsepi.nwsuaflmz.com.'),
(69023, 2017, 'bib', 'Iterative error correction of long sequencing reads maximizes accuracy and improves contig assembly', 'Sameith, Katrin|Roscito, Juliana G|Hiller, Michael', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 7, 'Algorithms|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbw003', '26868358', 'Next-generation sequencers such as Illumina can now produce reads up to 300xe2x80x89bp with high throughput, which is attractive for genome assembly. A first step in genome assembly is to computationally correct sequencing errors. However, correcting all errors in these longer reads is challenging. Here, we show that reads with remaining errors after correction often overlap repeats, where short erroneous k-mers occur in other copies of the repeat. We developed an iterative error correction pipeline that runs the previously published String Graph Assembler (SGA) in multiple rounds of k-mer-based correction with an increasing k-mer size, followed by a final round of overlap-based correction. By combining the advantages of small and large k-mers, this approach corrects more errors in repeats and minimizes the total amount of erroneous reads. We show that higher read accuracy increases contig lengths two to three times. We provide SGA-Iteratively Correcting Errors (https://github.com/hillerlab/IterativeErrorCorrection/) that implements iterative error correction by using modules from SGA.'),
(69024, 2017, 'bib', 'The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope', 'Qiu, Tianyi|Qiu, Jingxuan|Feng, Jun|Wu, Dingfeng|Yang, Yiyan|Tang, Kailin|Cao, Zhiwei|Zhu, Ruixin', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 18, 'Ligands|Models, Biological|Proteins|Quantitative Structure-Activity Relationship', 'Journal Article', '10.1093/bib/bbw004', '26873661', 'As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.'),
(69025, 2017, 'bib', 'Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams', 'Shameer, Khader|Badgeley, Marcus A|Miotto, Riccardo|Glicksberg, Benjamin S|Morgan, Joseph W|Dudley, Joel T', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 57, 'Computational Biology|Data Collection|Humans|Software', 'Journal Article', '10.1093/bib/bbv118', '26876889', 'Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.'),
(69026, 2017, 'bib', 'An analysis of human microbe-disease associations', 'Ma, Wei|Zhang, Lu|Zeng, Pan|Huang, Chuanbo|Li, Jianwei|Geng, Bin|Yang, Jichun|Kong, Wei|Zhou, Xuezhong|Cui, Qinghua', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 22, 'Bacterial Infections|Data Mining|Humans', 'Journal Article', '10.1093/bib/bbw005', '26883326', 'The microbiota living in the human body has critical impacts on our health and disease, but a systems understanding of its relationships with disease remains limited. Here, we use a large-scale text mining-based manually curated microbe-disease association data set to construct a microbe-based human disease network and investigate the relationships between microbes and disease genes, symptoms, chemical fragments and drugs. We reveal that microbe-based disease loops are significantly coherent. Microbe-based disease connections have strong overlaps with those constructed by disease genes, symptoms, chemical fragments and drugs. Moreover, we confirm that the microbe-based disease analysis is able to predict novel connections and mechanisms for disease, microbes, genes and drugs. The presented network, methods and findings can be a resource helpful for addressing some issues in medicine, for example, the discovery of bench knowledge and bedside clinical solutions for disease mechanism understanding, diagnosis and therapy.'),
(69027, 2017, 'bib', 'Gene co-expression analyses: an overview from microarray collections in Arabidopsis thaliana', 'Di Salle, Pasquale|Incerti, Guido|Colantuono, Chiara|Chiusano, Maria Luisa', 'Department of Agriculture, University of Naples Federico II, Portici, Italy.|Dipartimento di Agraria , University of Naples Federico II, via Università, Portici (NA), Italy.|Department of Agriculture, University of Naples Federico II, Portici, Italy.|Department of Agriculture, University of Naples Federico II, Portici, Italy.', 'Italy|Italy|Italy|Italy', 6, 'Arabidopsis|Gene Expression Profiling|Gene Expression Regulation, Plant|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbw002', '26891982', 'Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the guilty by association principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.'),
(69028, 2017, 'bib', 'A benchmark of gene expression tissue-specificity metrics', 'Kryuchkova-Mostacci, Nadezda|Robinson-Rechavi, Marc', 'Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, Swiss Institute of Bioinformatics, Lausanne, Switzerland.', 'Switzerland|Switzerland', 32, 'Benchmarking|Evolution, Molecular|Gene Expression|Gene Expression Profiling|Organ Specificity', 'Journal Article', '10.1093/bib/bbw008', '26891983', 'One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g. evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study, we compare nine measures of tissue specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyse their capacity to distinguish gene categories, their robustness to the choice and number of tissues used and their capture of evolutionary conservation signal.'),
(69029, 2017, 'bib', 'Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices', 'Taylor, Sandra L|Ruhaak, L Renee|Kelly, Karen|Weiss, Robert H|Kim, Kyoungmi', 'Division of Biostatistics, Department of Public Health Sciences, University of California School of Medicine, CA, USA.|Department of Chemistry, University of California, CA, USA.|Division of Hematology and Oncology, University of California Davis Comprehensive Cancer Center , Sacramento, California, USA.|Division of Nephrology, Department of Internal Medicine,  University of California,  CA, USA.|Division of Biostatistics, Department of Public Health Sciences, University of California ,  California, USA.', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Bayes Theorem|Cluster Analysis|Least-Squares Analysis|Mass Spectrometry', 'Journal Article', '10.1093/bib/bbw010', '26896791', 'With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly.'),
(69030, 2017, 'bib', 'Resolving the problem of multiple accessions of the same transcript deposited across various public databases', 'Weirick, Tyler|John, David|Uchida, Shizuka', 'Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.', 'Germany|Germany|Germany', 3, 'Algorithms|Databases, Genetic|Genome|Genomics|Molecular Sequence Annotation|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bib/bbw017', '26921280', 'Maintaining the consistency of genomic annotations is an increasingly complex task because of the iterative and dynamic nature of assembly and annotation, growing numbers of biological databases and insufficient integration of annotations across databases. As information exchange among databases is poor, a novel sequence from one reference annotation could be annotated in another. Furthermore, relationships to nearby or overlapping annotated transcripts are even more complicated when using different genome assemblies. To better understand these problems, we surveyed current and previous versions of genomic assemblies and annotations across a number of public databases containing long noncoding RNA. We identified numerous discrepancies of transcripts regarding their genomic locations, transcript lengths and identifiers. Further investigation showed that the positional differences between reference annotations of essentially the same transcript could lead to differences in its measured expression at the RNA level. To aid in resolving these problems, we present the algorithm Universal Genomic Accession Hash (UGAHash) and created an open source web tool to encourage the usage of the UGAHash algorithm. The UGAHash web tool (http://ugahash.uni-frankfurt.de) can be accessed freely without registration. The web tool allows researchers to generate Universal Genomic Accessions for genomic features or to explore annotations deposited in the public databases of the past and present versions. We anticipate that the UGAHash web tool will be a valuable tool to check for the existence of transcripts before judging the newly discovered transcripts as novel.'),
(69031, 2017, 'bib', 'SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning', 'Wu, Zengrui|Cheng, Feixiong|Li, Jie|Li, Weihua|Liu, Guixia|Tang, Yun', 'Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.', 'China|China|China|China|China|China', 55, 'Computer Simulation|Drug Delivery Systems|Drug Discovery|Drug Repositioning|ROC Curve', 'Journal Article', '10.1093/bib/bbw012', '26944082', 'Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.'),
(69032, 2017, 'bib', 'Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms', 'Dapas, Matthew|Kandpal, Manoj|Bi, Yingtao|Davuluri, Ramana V', 'Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Department of Veterinary Surgery & Radiology, College of Veterinary & Animal Sciences, GBPUAT, Pantnagar - 263 145, Uttarakhand, India.|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA.|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA.', 'China|India|United States|United States', 8, 'Algorithms|Bayes Theorem|Exons|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Protein Isoforms|RNA|Sequence Analysis, RNA', 'Comparative Study', '10.1093/bib/bbw016', '26944083', 'Given that the majority of multi-exon genes generate diverse functional products, it is important to evaluate expression at the isoform level. Previous studies have demonstrated strong gene-level correlations between RNA sequencing (RNA-seq) and microarray platforms, but have not studied their concordance at the isoform level. We performed transcript abundance estimation on raw RNA-seq and exon-array expression profiles available for common glioblastoma multiforme samples from The Cancer Genome Atlas using different analysis pipelines, and compared both the isoform- and gene-level expression estimates between programs and platforms. The results showed better concordance between RNA-seq/exon-array and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) platforms for fold change estimates than for raw abundance estimates, suggesting that fold change normalization against a control is an important step for integrating expression data across platforms. Based on RT-qPCR validations, eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs achieved the best performance for RNA-seq and exon-array platforms, respectively, for deriving the isoform-level fold change values. While eXpress achieved the highest correlation with the RT-qPCR and exon-array (MMBGX) results overall, RSEM was more highly correlated with MMBGX for the subset of transcripts that are highly variable across the samples. eXpress appears to be most successful in discriminating lowly expressed transcripts, but IsoformEx and RSEM correlate more strongly with MMBGX for highly expressed transcripts. The results also reinforce how potentially important isoform-level expression changes can be masked by gene-level estimates, and demonstrate that exon arrays yield comparable results to RNA-seq for evaluating isoform-level expression changes.'),
(69033, 2017, 'bib', 'iFORM/eQTL: an ultrahigh-dimensional platform for inferring the global genetic architecture of gene transcripts', 'Gosik, Kirk|Kong, Lan|Chinchilli, Vernon M|Wu, Rongling', 'Department of Statistics, The Pennsylvania State University, University Park, PA, USA.|Department of Public Health Sciences Pennsylvania State University College of Medicine, Hershey, PA, USA.|Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.|Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.', 'United States|United States|United States|United States', 4, 'Gene Expression|Gene Expression Profiling|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbw014', '26944084', 'Knowledge about how changes in gene expression are encoded by expression quantitative trait loci (eQTLs) is a key to construct the genotype-phenotype map for complex traits or diseases. Traditional eQTL mapping is to associate one transcript with a single marker at a time, thereby limiting our inference about a complete picture of the genetic architecture of gene expression. Here, we implemented an ultrahigh-dimensional variable selection model to build a computing platform that can systematically scan main effects and interaction effects among all possible loci and identify a set of significant eQTLs modulating differentiation and function of gene expression. This platform, named iFORM/eQTL, was assembled by forward-selection-based procedures to tackle complex covariance structures of gene-gene interactions. iFORM/eQTL can particularly discern the role of cis-QTLs, trans-QTLs and their epistatic interactions in gene expression. Results from the reanalysis of a published genetic and genomic data set through iFORM/eQTL gain new discoveries on the genetic origin of gene expression differentiation in Caenorhabditis elegans, which could not be detected by a traditional one-locus/one-transcript analysis approach.'),
(69034, 2017, 'bib', 'A comprehensive overview of lncRNA annotation resources', 'Xu, Jinyuan|Bai, Jing|Zhang, Xinxin|Lv, Yanling|Gong, Yonghui|Liu, Ling|Zhao, Hongying|Yu, Fulong|Ping, Yanyan|Zhang, Guanxiong|Lan, Yujia|Xiao, Yun|Li, Xia', '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, 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, 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|China', 53, 'Chromatin|Humans|Molecular Sequence Annotation|RNA, Long Noncoding', 'Journal Article', '10.1093/bib/bbw015', '26944085', 'Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring toxe2x80x89>xe2x80x89205xc2xa0000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed LNCat (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.'),
(69035, 2017, 'bib', 'Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs', 'Wang, Jinan|Guo, Zihu|Fu, Yingxue|Wu, Ziyin|Huang, Chao|Zheng, Chunli|Shar, Piar Ali|Wang, Zhenzhong|Xiao, Wei|Wang, Yonghua', 'Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|College of Life Science, Northwest A & F University, Yangling, Shaanxi, 712100, China, Center of Bioinformatics, Northwest A & F University, Yangling, Shaanxi, China.|Jiangsu Kanion Pharmaceutical Co. Ltd., Lianyungang, PR China.|State Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu, China.|Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong.', 'China|China|China|China|China|China|China|China|China|China', 6, 'Drug Delivery Systems|Drug Discovery|Drug Interactions|Humans|Ligands', 'Journal Article', '10.1093/bib/bbw018', '26962012', 'Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimers disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery.'),
(69036, 2017, 'bib', 'Multi-marker linkage disequilibrium mapping of quantitative trait loci', 'Lee, Soyoun|Yang, Jie|Huang, Jiayu|Chen, Hao|Hou, Wei|Wu, Song', 'Department of Pediatric Oncology and the Linde Program in Cancer Chemical Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.|Department of Public Health and General Medicine, School of Integrated Traditional and Western Medicine, Anhui University of Chinese Medicine , Hefei, China.|Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.|Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida, USA.|Department of Statistics, University of Florida, Gainesville, FL, USA.', 'United States|China|United States|United States|United States|United States', 0, 'Chromosome Mapping|Genetic Markers|Genome-Wide Association Study|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbw006', '26966282', 'Single nucleotide polymorphisms (SNPs), the most common genetic markers in genome-wide association studies, are usually in linkage disequilibrium (LD) with each other within a small genomic region. Both single- and two-marker-based LD mapping methods have been developed by taking advantage of the LD structures. In this study, a more general LD mapping framework with an arbitrary number of markers has been developed to further improve LD mapping and its detection power. This method is referred as multi-marker linkage disequilibrium mapping (mmLD). For the parameter estimation, we implemented a two-phase estimation procedure: first, haplotype frequencies were estimated for known markers, then, haplotype frequencies were updated to include the unknown quantitative trait loci based on estimates from the first step. For the hypothesis testing, we proposed a novel sequential likelihood ratio test procedure, which iteratively removed haplotypes with zero frequency and subsequently determined the proper degree of freedom. To compare the proposed mmLD method with other existing mapping methods, e.g. the adjusted single-marker LD mapping and the SKAT_C, we performed extensive simulations under various scenarios. The simulation results demonstrated that the mmLD has the same or higher power than the existing methods, while maintaining the correct type I errors. We further applied the mmLD to a public data set, GAW17, to investigate its applicability. The result showed the good performance of mmLD. We concluded that this improved mmLD method will be useful for future genome-wide association studies and genetic association analyses.'),
(69037, 2017, 'bib', 'Effect of lossy compression of quality scores on variant calling', 'Ochoa, Idoia|Hernaez, Mikel|Goldfeder, Rachel|Weissman, Tsachy|Ashley, Euan', 'Electrical Engineering department, 350 Serra Mall, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA, USA.|Department of Medicine, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States|United States', 23, 'Algorithms|Data Compression|Databases, Genetic|Genome|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbw011', '26966283', 'Recent advancements in sequencing technology have led to a drastic reduction in genome sequencing costs. This development has generated an unprecedented amount of data that must be stored, processed, and communicated. To facilitate this effort, compression of genomic files has been proposed. Specifically, lossy compression of quality scores is emerging as a natural candidate for reducing the growing costs of storage. A main goal of performing DNA sequencing in population studies and clinical settings is to identify genetic variation. Though the field agrees that smaller files are advantageous, the cost of lossy compression, in terms of variant discovery, is unclear.Bioinformatic algorithms to identify SNPs and INDELs use base quality score information, here, we evaluate the effect of lossy compression of quality scores on SNP and INDEL detection. Specifically, we investigate how the output of the variant caller when using the original data differs from that obtained when quality scores are replaced by those generated by a lossy compressor. Using gold standard genomic datasets and simulated data, we are able to analyze how accurate the output of the variant calling is, both for the original data and that previously lossily compressed. We show that lossy compression can significantly alleviate the storage while maintaining variant calling performance comparable to that with the original data. Further, in some cases lossy compression can lead to variant calling performance that is superior to that using the original file. We envisage our findings and framework serving as a benchmark in future development and analyses of lossy genomic data compressors.'),
(69038, 2016, 'bib', 'Dimension reduction techniques for the integrative analysis of multi-omics data', 'Meng, Chen|Zeleznik, Oana A|Thallinger, Gerhard G|Kuster, Bernhard|Gholami, Amin M|Culhane, Aedín C', 'Department of Medicine, Stanford University, Stanford, CA, USA.|Department of Medicine, Stanford University, Stanford, CA, USA.|Department of Medicine, Stanford University, Stanford, CA, USA.|Department of Medicine, Stanford University, Stanford, CA, USA.|Department of Medicine, Stanford University, Stanford, CA, USA.|Department of Medicine, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States|United States|United States', 36, 'Genomics|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1093/bib/bbv108', '26969681', 'State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput omics technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.'),
(69039, 2017, 'bib', 'Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativa L)', 'Li, Kuan|Xu, Chaoqun|Huang, Jian|Liu, Wei|Zhang, Lina|Wan, Weifeng|Tao, Huan|Li, Ling|Lin, Shoukai|Harrison, Andrew|He, Huaqin', 'State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, P. R. China.|College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.|College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.|State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, China, State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China, Huzhou Center of Bio-Synthetic Innovation, 1366 Hongfeng Road, Huzhou, China.|Department of Biology, University of California at San Diego, La Jolla, California, USA.|College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.|College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.|Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute of Sichuan Agricultural University, Chengdu, Sichuan Province, China.|College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, UK.|College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.', 'China|China|China|China|United Kingdom|China|China|China|China|United Kingdom|China', 3, 'Bayes Theorem|Heterotrimeric GTP-Binding Proteins|Oryza|Plant Proteins|Support Vector Machine', 'Journal Article', '10.1093/bib/bbw021', '26970777', 'Heterotrimeric G protein signaling cascades are one of the primary metazoan sensing mechanisms linking a cell to environment. However, the number of experimentally identified effectors of G protein in plant is limited. We have therefore studied which tools are best suited for predicting G protein effectors in rice. Here, we compared the predicting performance of four classifiers with eight different encoding schemes on the effectors of G proteins by using 10-fold cross-validation. Four methods were evaluated: random forest, naive Bayes, K-nearest neighbors and support vector machine. We applied these methods to experimentally identified effectors of G proteins and randomly selected non-effector proteins, and tested their sensitivity and specixefxacx81city. The result showed that random forest classifier with composition of K-spaced amino acid pairs and composition of motif or domain (CKSAAP_PROSITE_200) combination method yielded the best performance, with accuracy and the Mathews correlation coefficient reaching 74.62% and 0.49, respectively. We have developed G-Effector, an online predictor, which outperforms BLAST, PSI-BLAST and HMMER on predicting the effectors of G proteins. This provided valuable guidance for the researchers to select classifiers combined with different feature selection encoding schemes. We used G-Effector to screen the effectors of G protein in rice, and confirmed the candidate effectors by gene co-expression data. Interestingly, one of the top 15 candidates, which did not appear in the training data set, was validated in a previous research work. Therefore, the candidate effectors list in this article provides both a clue for researchers as to their function and a framework of validation for future experimental work. It is accessible at http://bioinformatics.fafu.edu.cn/geffector.');
