INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69040, 2017, 'bib', 'Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation', 'Nakato, Ryuichiro|Shirahige, Katsuhiko', 'Research Center for Epigenetic Disease, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo, Japan.|Research Center for Epigenetic Disease, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo, Japan.', 'Japan|Japan', 23, 'Binding Sites|Chromatin Immunoprecipitation|DNA|Genome|Humans|Protein Binding|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbw023', '26979602', 'Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. Recent advances in sequencing technologies and analyses enable us to compare hundreds of samples simultaneously, such large-scale analysis has potential to reveal the high-dimensional interrelationship level for regulatory elements and annotate novel functional genomic regions de novo. Because many experimental considerations are relevant to the choice of a method in a ChIP-seq analysis, the overall design and quality management of the experiment are of critical importance. This review offers guiding principles of computation and sample preparation for ChIP-seq analyses, highlighting the validity and limitations of the state-of-the-art procedures at each step. We also discuss the latest challenges of single-cell analysis that will encourage a new era in this field.'),
(69041, 2017, 'bib', 'The BRaliBase dent-a tale of benchmark design and interpretation', 'Löwes, Benedikt|Chauve, Cedric|Ponty, Yann|Giegerich, Robert', 'Division of Cardiology, University of Nebraska Medical Center, USA.|Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.|LIX, CNRS/Inria AMIB, Ecole Polytechnique, Palaiseau, France.|Institute for Bioinformatics, Bielefeld University, Bielefeld, Germany.', 'United States|Canada|France|Germany', 6, 'Algorithms|Benchmarking|Computational Biology|Nucleic Acid Conformation|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbw022', '26984616', 'BRaliBase is a widely used benchmark for assessing the accuracy of RNA secondary structure alignment methods. In most case studies based on the BRaliBase benchmark, one can observe a puzzling drop in accuracy in the 40-60% sequence identity range, the so-called BRaliBase Dent. In this article, we show this dent is owing to a bias in the composition of the BRaliBase benchmark, namely the inclusion of a disproportionate number of transfer RNAs, which exhibit a conserved secondary structure. Our analysis, aside of its interest regarding the specific case of the BRaliBase benchmark, also raises important questions regarding the design and use of benchmarks in computational biology.'),
(69042, 2017, 'bib', 'A comprehensive study of RNA secondary structure alignment algorithms', 'Chiu, Jimmy Ka Ho|Chen, Yi-Ping Phoebe', 'Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria, Australia.|Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria, Australia.', 'Australia|Australia', 6, 'Algorithms|Base Sequence|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbw009', '26984617', 'RNA secondary structure alignment has received more attention since the discovery of the structure-function relationships in some non-protein-encoding RNAs. However, unlike the pure sequence alignment problem, which has been solved in polynomial time, secondary structure alignment incorporates the base pairings as another information dimension in addition to the base sequence. This problem therefore becomes more challenging. In this study, we classify the selected approaches, and algorithmically illustrate how these methods address the alignment problems with different structure types. Other features such as the types of base pair edit operations supported and the time complexity are also compared.'),
(69043, 2017, 'bib', 'Towards an open, collaborative, reusable framework for sharing hands-on bioinformatics training workshops', 'Watson-Haigh, Nathan S|Revote, Jerico|Suchecki, Radoslaw|Tyagi, Sonika|Corley, Susan M|Shang, Catherine A|McGrath, Annette', 'Australian Centre for Plant Functional Genomics (ACPFG), University of Adelaide, PMB 1, Glen Osmond, Australia.|Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia.|Australian Centre for Plant Functional Genomics (ACPFG), University of Adelaide, PMB 1, Glen Osmond, Australia.|Oncogenomics Laboratory, Queensland Institute of Medical Research, Herston, Brisbane, QLD, Australia.|Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, UNSW Australia, Sydney, New South Wales, Australia.|School of Biomedical Sciences and the Institute for Molecular Bioscience, The University of Queensland, Queensland, Brisbane, Australia.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 3, 'Computational Biology|Cooperative Behavior|Humans', 'Journal Article', '10.1093/bib/bbw013', '26984618', 'There is a clear demand for hands-on bioinformatics training. The development of bioinformatics workshop content is both time-consuming and expensive. Therefore, enabling trainers to develop bioinformatics workshops in a way that facilitates reuse is becoming increasingly important. The most widespread practice for sharing workshop content is through making PDF, PowerPoint and Word documents available online. While this effort is to be commended, such content is usually not so easy to reuse or repurpose and does not capture all the information required for a third party to rerun a workshop. We present an open, collaborative framework for developing and maintaining, reusable and shareable hands-on training workshop content.'),
(69044, 2017, 'bib', 'Zisland Explorer: detect genomic islands by combining homogeneity and heterogeneity properties', 'Wei, Wen|Gao, Feng|Du, Meng-Ze|Hua, Hong-Li|Wang, Ju|Guo, Feng-Biao', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia', 16, 'Genome, Archaeal|Genome, Bacterial|Genomic Islands|Genomics|Software', 'Journal Article', '10.1093/bib/bbw019', '26992782', 'Genomic islands are genomic fragments of alien origin in bacterial and archaeal genomes, usually involved in symbiosis or pathogenesis. In this work, we described Zisland Explorer, a novel tool to predict genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy. We have provided a web service as well as a graphical user interface and open-source code across multiple platforms for Zisland Explorer, which is available at http://cefg.uestc.edu.cn/Zisland_Explorer/ or http://tubic.tju.edu.cn/Zisland_Explorer/.'),
(69045, 2017, 'bib', 'Tensor factorization toward precision medicine', 'Luo, Yuan|Wang, Fei|Szolovits, Peter', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia', 18, 'Algorithms|Genotype|Humans|Precision Medicine', 'Journal Article', '10.1093/bib/bbw026', '26994614', 'Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest that tensor applications could serve as an effective tool to enable frequent updating of medical knowledge based on the continually growing scientific and clinical evidence. We encourage extensive experimental studies to tackle challenges including design choice of factorizations, integrating temporality and algorithm scalability.'),
(69046, 2017, 'bib', 'Template-based protein-protein docking exploiting pairwise interfacial residue restraints', 'Xue, Li C|Rodrigues, João P G L M|Dobbs, Drena|Honavar, Vasant|Bonvin, Alexandre M J J', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia', 10, 'Models, Molecular|Protein Binding|Proteins', 'Journal Article', '10.1093/bib/bbw027', '27013645', 'Although many advanced and sophisticated ab initio approaches for modeling protein-protein complexes have been proposed in past decades, template-based modeling (TBM) remains the most accurate and widely used approach, given a reliable template is available. However, there are many different ways to exploit template information in the modeling process. Here, we systematically evaluate and benchmark a TBM method that uses conserved interfacial residue pairs as docking distance restraints [referred to as alpha carbon-alpha carbon (CA-CA)-guided docking]. We compare it with two other template-based protein-protein modeling approaches, including a conserved non-pairwise interfacial residue restrained docking approach [referred to as the ambiguous interaction restraint (AIR)-guided docking] and a simple superposition-based modeling approach. Our results show that, for most cases, the CA-CA-guided docking method outperforms both superposition with refinement and the AIR-guided docking method. We emphasize the superiority of the CA-CA-guided docking on cases with medium to large conformational changes, and interactions mediated through loops, tails or disordered regions. Our results also underscore the importance of a proper refinement of superimposition models to reduce steric clashes. In summary, we provide a benchmarked TBM protocol that uses conserved pairwise interface distance as restraints in generating realistic 3D protein-protein interaction models, when reliable templates are available. The described CA-CA-guided docking protocol is based on the HADDOCK platform, which allows users to incorporate additional prior knowledge of the target system to further improve the quality of the resulting models.'),
(69047, 2017, 'bib', 'A review of bioinformatic pipeline frameworks', 'Leipzig, Jeremy', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia', 100, 'Computational Biology|Humans|Software', 'Journal Article', '10.1093/bib/bbw020', '27013646', 'High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface. Here I survey and compare the design philosophies of several current pipeline frameworks. I provide practical recommendations based on analysis requirements and the user base.'),
(69048, 2017, 'bib', 'MuSERA: Multiple Sample Enriched Region Assessment', 'Jalili, Vahid|Matteucci, Matteo|Morelli, Marco J|Masseroli, Marco', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia', 4, 'Chromatin Immunoprecipitation|Genome|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bib/bbw029', '27013647', 'Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context, they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser. We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www.bioinformatics.deib.polimi.it/MuSERA/.'),
(69049, 2017, 'bib', 'Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature', 'Wen, Yanhua|Wei, Yanjun|Zhang, Shumei|Li, Song|Liu, Hongbo|Wang, Fang|Zhao, Yue|Zhang, Dongwei|Zhang, Yan', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 11, 'Breast Neoplasms|Cell Line, Tumor|DNA Methylation|Epigenesis, Genetic|Gene Expression Regulation, Neoplastic|Humans|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/bbw028', '27016391', 'Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlass identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69050, 2017, 'bib', 'Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases', 'Bauer, Christian R|Knecht, Carolin|Fretter, Christoph|Baum, Benjamin|Jendrossek, Sandra|Rühlemann, Malte|Heinsen, Femke-Anouska|Umbach, Nadine|Grimbacher, Bodo|Franke, Andre|Lieb, Wolfgang|Krawczak, Michael|Hütt, Marc-Thorsten|Sax, Ulrich', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 9, 'Biomedical Research|Humans|Inflammation|Systems Analysis', 'Journal Article', '10.1093/bib/bbw024', '27016392', 'Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by informed non-experts but at the same time attuned to existing, more sophisticated analysis tools.'),
(69051, 2017, 'bib', 'Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics', 'Dhanda, Sandeep Kumar|Usmani, Salman Sadullah|Agrawal, Piyush|Nagpal, Gandharva|Gautam, Ankur|Raghava, Gajendra P S', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia', 22, 'Computational Biology|Epitopes, B-Lymphocyte|Epitopes, T-Lymphocyte|Humans|Peptides|Vaccines, Subunit', 'Journal Article', '10.1093/bib/bbw025', '27016393', 'The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.'),
(69052, 2017, 'bib', 'Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration', 'Cambiaghi, Alice|Ferrario, Manuela|Masseroli, Marco', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia', 15, 'Metabolomics|Proteomics', 'Journal Article', '10.1093/bib/bbw031', '27075479', 'Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different omics data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCoreTM, MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other omics data, highlighting their strong and weak aspects, a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.'),
(69053, 2017, 'bib', 'Development of a cloud-based Bioinformatics Training Platform', 'Revote, Jerico|Watson-Haigh, Nathan S|Quenette, Steve|Bethwaite, Blair|McGrath, Annette|Shang, Catherine A', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Australia|Computational Biology|High-Throughput Nucleotide Sequencing|Universities', 'Journal Article', '10.1093/bib/bbw032', '27084333', 'The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP.'),
(69054, 2017, 'bib', 'Heterogeneous molecular processes among the causes of how sequence similarity scores can fail to recapitulate phylogeny', 'Smith, Stephen A|Pease, James B', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia', 13, 'Computational Biology|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbw034', '27103098', 'Sequence similarity tools like Basic Local Alignment Search Tool (BLAST) are essential components of many functional genetic, genomic, phylogenetic and bioinformatic studies. Many modern analysis pipelines use significant sequence similarity scores (p- or E-values) and the ranked order of BLAST matches to test a wide range of hypotheses concerning homology, orthology, the timing of de novo gene birth/death and gene family expansion/contraction. Despite significant contrary findings, many of these tests still implicitly assume that stronger or higher-ranked E-value scores imply closer phylogenetic relationships between sequences. Here, we demonstrate that even though a general relationship does exist between the phylogenetic distance of two sequences and their E-value, significant and misleading errors occur in both the completeness and the order of results under realistic evolutionary scenarios. These results provide additional details to past evidence showing that studies should avoid drawing direct inferences of evolutionary relatedness from measures of sequence similarity alone, and should instead, where possible, use more rigorous phylogeny-based methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69055, 2017, 'bib', 'A high-dimensional linkage analysis model for characterizing crossover interference', 'Wang, Jing|Sun, Lidan|Jiang, Libo|Sang, Mengmeng|Ye, Meixia|Cheng, Tangran|Zhang, Qixiang|Wu, Rongling', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 1, 'Chromosome Mapping|Crossing Over, Genetic|Genetic Linkage|Genetic Markers|Genome', 'Journal Article', '10.1093/bib/bbw033', '27113727', 'Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.'),
(69056, 2017, 'bib', 'Literature-based discovery of new candidates for drug repurposing', 'Yang, Hsih-Te|Ju, Jiun-Huang|Wong, Yue-Ting|Shmulevich, Ilya|Chiang, Jung-Hsien', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia', 5, 'Data Mining|Drug Repositioning|Humans', 'Journal Article', '10.1093/bib/bbw030', '27113728', 'Drug development is an expensive and time-consuming process, these could be reduced if the existing resources could be used to identify candidates for drug repurposing. This study sought to do this by text mining a large-scale literature repository to curate repurposed drug lists for different cancers. We devised a pattern-based relationship extraction method to extract disease-gene and gene-drug direct relationships from the literature. These direct relationships are used to infer indirect relationships using the ABC model. A gene-shared ranking method based on drug target similarity was then proposed to prioritize the indirect relationships. Our method of assessing drug target similarity correlated to existing anatomical therapeutic chemical code-based methods with a Pearson correlation coefficient of 0.9311. The indirect relationships ranking method achieved a significant mean average precision score of top 100 most common diseases. We also confirmed the suitability of candidates identified for repurposing as anticancer drugs by conducting a manual review of the literature and the clinical trials. Eventually, for visualization and enrichment of huge amount of repurposed drug information, a chord diagram was demonstrated to rapidly identify two novel indications for further biological evaluations.'),
(69057, 2017, 'bib', 'Impacts of somatic mutations on gene expression: an association perspective', 'Jia, Peilin|Zhao, Zhongming', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia', 8, 'Gene Expression|Humans|Mutation|Neoplasms|Oncogenes', 'Journal Article', '10.1093/bib/bbw037', '27127206', 'Assessing the functional impacts of somatic mutations in cancer genomes is critical for both identifying driver mutations and developing molecular targeted therapies. Currently, it remains a fundamental challenge to distinguish the patterns through which mutations execute their biological effects and to infer biological mechanisms underlying these patterns. To this end, we systematically studied the association between somatic mutations in protein-coding regions and expression profiles, which represents an indirect measurement of impacts. We defined mutation features (mutation type, cluster and status) and built linear regression models to assess mutation associations with mRNA expression and protein expression. Our results presented a comprehensive landscape of the associations between mutation features and expression profile in multiple cancer types, including 62 genes showing mutation type associated expression changes, 21 genes showing mutation cluster associations and 51 genes showing mutation status associations. We revealed four characteristics of the patterns that mutations impact on expression. First, we showed that mutation type (truncation versus amino acid-altering mutations) was the most important determinant of expression levels. Second, we detected mutation clusters in well-studied oncogenes that were associated with gene expression. Third, we found both similarities and differences in association patterns existed within and across cancer types. Fourth, although many of the observed associations stay stable at both mRNA and protein expression levels, there are also novel associations uniquely observed at the protein level, which warrant future investigation. Taken together, our findings provided implications for cancer driver gene prioritization and insights into the functional consequences of somatic mutations.'),
(69058, 2017, 'bib', 'Ontology-based annotations and semantic relations in large-scale (epi)genomics data', 'Galeota, Eugenia|Pelizzola, Mattia', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia', 8, 'Biological Ontologies|Chromatin Immunoprecipitation|Genomics|Humans|Semantics', 'Journal Article', '10.1093/bib/bbw036', '27142216', 'Public repositories of large-scale biological data currently contain hundreds of thousands of experiments, including high-throughput sequencing and microarray data. The potential of using these resources to assemble data sets combining samples previously not associated is vastly unexplored. This requires the ability to associate samples with clear annotations and to relate experiments matched with different annotation terms. In this study, we illustrate the semantic annotation of Gene Expression Omnibus samples metadata using concepts from biomedical ontologies, focusing on the association of thousands of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) samples with a given target, tissue and disease state. Next, we demonstrate the feasibility of quantitatively measuring the semantic similarity between different samples, with the aim of combining experiments associated with the same or similar semantic annotations, thus allowing the generation of large data sets without the need of additional experiments. We compared tools based on Unified Medical Language System with tools that use topic-specific ontologies, showing that the second approach outperforms the first both in the annotation process and in the computation of semantic similarity measures. Finally, we demonstrated the potential of this approach by identifying semantically homogeneous groups of ChIP-seq samples targeting the Myc transcription factor, and expanding this data set with semantically coherent epigenetic samples. The semantic information of these data sets proved to be coherent with the ChIP-seq signal and with the current knowledge about this transcription factor.'),
(69059, 2017, 'bib', 'Features that define the best ChIP-seq peak calling algorithms', 'Thomas, Reuben|Thomas, Sean|Holloway, Alisha K|Pollard, Katherine S', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia', 32, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|High-Throughput Nucleotide Sequencing|Histones|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bib/bbw035', '27169896', 'Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an important tool for studying gene regulatory proteins, such as transcription factors and histones. Peak calling is one of the first steps in the analysis of these data. Peak calling consists of two sub-problems: identifying candidate peaks and testing candidate peaks for statistical significance. We surveyed 30 methods and identified 12 features of the two sub-problems that distinguish methods from each other. We picked six methods GEM, MACS2, MUSIC, BCP, Threshold-based method (TM) and ZINBA] that span this feature space and used a combination of 300 simulated ChIP-seq data sets, 3 real data sets and mathematical analyses to identify features of methods that allow some to perform better than the others. We prove that methods that explicitly combine the signals from ChIP and input samples are less powerful than methods that do not. Methods that use windows of different sizes are more powerful than the ones that do not. For statistical testing of candidate peaks, methods that use a Poisson test to rank their candidate peaks are more powerful than those that use a Binomial test. BCP and MACS2 have the best operating characteristics on simulated transcription factor binding data. GEM has the highest fraction of the top 500 peaks containing the binding motif of the immunoprecipitated factor, with 50% of its peaks within 10 base pairs of a motif. BCP and MUSIC perform best on histone data. These findings provide guidance and rationale for selecting the best peak caller for a given application.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69060, 2017, 'bib', 'Detecting multi-way epistasis in family-based association studies', 'Loucoubar, Cheikh|Grant, Audrey V|Bureau, Jean-François|Casademont, Isabelle|Bar, Ndjido Ardo|Bar-Hen, Avner|Diop, Mamadou|Faye, Joseph|Sarr, Fatoumata Diene|Badiane, Abdoulaye|Tall, Adama|Trape, Jean-François|Cliquet, Freddy|Schwikowski, Benno|Lathrop, Mark|Paul, Richard Edward|Sakuntabhai, Anavaj', 'The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.|The Genome Analysis Centre, Norwich, ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, The Swedish University for Agricultural Sciences, Uppsala, Sweden, European Molecular Biology Laboratory, Heidelberg, Germany, Ontario Institute for Cancer Research, Toronto, Canada, Instituto Gulbenkian de Ciência, Oeiras, Portugal, The University of New South Wales, Sydney, Australia, Netherlands Bioinformatics Centre, Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, CSC - IT Center for Science Ltd., Espoo, Finland, Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA, CSIRO, Bioinformatics Core, Canberra, Australia, The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland, Academis, Illstrasse 12, Berlin, Germany, The Nowgen Centre, 29 Grafton Street, Manchester, UK, Department of Physics, Sapienza University, Rome, Italy, The Roslin Institute, Edinburgh, UK and The University of Manchester, Manchester, UK.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 0, 'Epistasis, Genetic|Genome|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Models, Genetic|Pedigree', 'Journal Article', '10.1093/bib/bbw039', '27178992', 'The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.'),
(69061, 2016, 'bib', 'Computational Systems Biomedicine', 'Dubitzky, Werner', 'Belfast.', '-', 1, 'Computational Biology|Humans|Systems Analysis|Systems Biology', 'Editorial', '10.1093/bib/bbw038', '27193556', '-'),
(69062, 2017, 'bib', 'Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code', 'Yotsukura, Sohiya|Karasuyama, Masayuki|Takigawa, Ichigaku|Mamitsuka, Hiroshi', 'Belfast.|Belfast.|Belfast.|Belfast.', '-|-|-|-', 0, 'Breast Neoplasms|Humans|Mutation|Phenotype|Pilot Projects', 'Journal Article', '10.1093/bib/bbw040', '27197545', 'Triple-negative (TN) breast cancer (BC) patients have limited treatment options and poor prognosis even after extant treatments and standard chemotherapeutic regimens. Linking TN patients to clinically known phenotypes with appropriate treatments is vital. Location-specific sequence variants are expected to be useful for this purpose by identifying subgroups within a disease population. Single gene mutational signatures have been widely reported, with related phenotypes in literature. We thoroughly survey currently available mutations (and mutated genes), linked to BC phenotypes, to demonstrate their limited performance as sole predictors/biomarkers to assign phenotypes to patients. We then explore mutational combinations, as a pilot study, using The Cancer Genome Atlas Research Network mutational data of BC and three machine learning methods: association rules (limitless arity multiple procedure), decision tree and hierarchical disjoint clustering. The study results in a patient classification scheme through combinatorial mutations in Phosphatidylinositol-4,5-Bisphosphate 3-Kinase and tumor protein 53, being consistent with all three methods, implying its validity from a diverse viewpoint. However, it would warrant further research to select multi-gene signatures to identify phenotypes specifically and be clinically used routinely.'),
(69063, 2017, 'bib', 'Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins', 'Deng, Wankun|Wang, Yongbo|Ma, Lili|Zhang, Ying|Ullah, Shahid|Xue, Yu', 'Belfast.|Belfast.|Belfast.|Belfast.|Belfast.|Belfast.', '-|-|-|-|-|-', 2, 'Arginine|Lysine|Methylation|Protein Processing, Post-Translational|Proteins', 'Journal Article', '10.1093/bib/bbw041', '27241573', 'Protein methylation is an essential posttranslational modification (PTM) mostly occurs at lysine and arginine residues, and regulates a variety of cellular processes. Owing to the rapid progresses in the large-scale identification of methylation sites, the available data set was dramatically expanded, and more attention has been paid on the identification of specific methylation types of modification residues. Here, we briefly summarized the current progresses in computational prediction of methylation sites, which provided an accurate, rapid and efficient approach in contrast with labor-intensive experiments. We collected 5421 methyllysines and methylarginines in 2592 proteins from the literature, and classified most of the sites into different types. Data analyses demonstrated that different types of methylated proteins were preferentially involved in different biological processes and pathways, whereas a unique sequence preference was observed for each type of methylation sites. Thus, we developed a predictor of GPS-MSP, which can predict mono-, di- and tri-methylation types for specific lysines, and mono-, symmetric di- and asymmetrical di-methylation types for specific arginines. We critically evaluated the performance of GPS-MSP, and compared it with other existing tools. The satisfying results exhibited that the classification of methylation sites into different types for training can considerably improve the prediction accuracy. Taken together, we anticipate that our study provides a new lead for future computational analysis of protein methylation, and the prediction of methylation types of covalently modified lysine and arginine residues can generate more useful information for further experimental manipulation.'),
(69064, 2017, 'bib', 'Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned', 'Li, Gerald|Bankhead, Peter|Dunne, Philip D|OReilly, Paul G|James, Jacqueline A|Salto-Tellez, Manuel|Hamilton, Peter W|McArt, Darragh G', 'Belfast.|Belfast.|Belfast.|Belfast.|Belfast.|Belfast.|Belfast.|Belfast.', '-|-|-|-|-|-|-|-', 7, 'Biomarkers, Tumor|Biomedical Research|Computational Biology|Humans|Neoplasms|Precision Medicine', 'Journal Article', '10.1093/bib/bbw044', '27255914', 'Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating big data across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.'),
(69065, 2017, 'bib', 'Predicting disease trait with genomic data: a composite kernel approach', 'Yang, Haitao|Li, Shaoyu|Cao, Hongyan|Zhang, Chichen|Cui, Yuehua', 'Belfast.|Belfast.|Belfast.|Belfast.|Belfast.', '-|-|-|-|-', 3, 'Algorithms|Genome|Genomics|Humans|Least-Squares Analysis|Phenotype', 'Journal Article', '10.1093/bib/bbw043', '27255915', 'With the advancement of biotechniques, a vast amount of genomic data is generated with no limit. Predicting a disease trait based on these data offers a cost-effective and time-efficient way for early disease screening. Here we proposed a composite kernel partial least squares (CKPLS) regression model for quantitative disease trait prediction focusing on genomic data. It can efficiently capture nonlinear relationships among features compared with linear learning algorithms such as Least Absolute Shrinkage and Selection Operator or ridge regression. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). In addition to improved performance for parameter optimization, the proposed GA-CKPLS approach also has better learning capacity and generalization ability compared with single kernel-based KPLS method as well as other nonlinear prediction models such as the support vector regression. Extensive simulation studies demonstrated that GA-CKPLS had better prediction performance than its counterparts under different scenarios. The utility of the method was further demonstrated through two case studies. Our method provides an efficient quantitative platform for disease trait prediction based on increasing volume of omics data.'),
(69066, 2017, 'bib', 'Circular RNA: an emerging key player in RNA world', 'Meng, Xianwen|Li, Xue|Zhang, Peijing|Wang, Jingjing|Zhou, Yincong|Chen, Ming', 'Belfast.|Belfast.|Belfast.|Belfast.|Belfast.|Belfast.', '-|-|-|-|-|-', 99, 'Computational Biology|Gene Expression Regulation|Humans|RNA|Transcriptome', 'Journal Article', '10.1093/bib/bbw045', '27255916', 'Insights into the circular RNA (circRNA) exploration have revealed that they are abundant in eukaryotic transcriptomes. Diverse genomic regions can generate different types of RNA circles, implying their diversity. Covalently closed loop structures elevate the stability of this new type of noncoding RNA. High-throughput sequencing analyses suggest that circRNAs exhibit tissue- and developmental-specific expression, indicating that they may play crucial roles in multiple cellular processes. Strikingly, several circRNAs could function as microRNA sponges and regulate gene transcription, highlighting a new class of important regulators. Here, we review the recent advances in knowledge of endogenous circRNA biogenesis, properties and functions. We further discuss the current findings about circRNAs in human diseases. In plants, the roles of circRNAs remain a mystery. Online resources and bioinformatics identification of circRNAs are essential for the analysis of circRNA biology, although different strategies yield divergent results. The understanding of circRNA functions remains limited, however, circRNAs are enriching the RNA world, acting as an emerging key player.'),
(69067, 2017, 'bib', 'Computational methods for identifying miRNA sponge interactions', 'Le, Thuc Duy|Zhang, Junpeng|Liu, Lin|Li, Jiuyong', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Engineering, Dali University, Dali, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|China|Australia|Australia', 19, 'Breast Neoplasms|Humans|MicroRNAs|RNA, Messenger', 'Journal Article', '10.1093/bib/bbw042', '27273287', 'Recent findings show that coding genes are not the only targets that miRNAs interact with. In fact, there is a pool of different RNAs competing with each other to attract miRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The ceRNAs indirectly regulate each other via the titration mechanism, i.e. the increasing concentration of a ceRNA will decrease the number of miRNAs that are available for interacting with other targets. The cross-talks between ceRNAs, i.e. their interactions mediated by miRNAs, have been identified as the drivers in many disease conditions, including cancers. In recent years, some computational methods have emerged for identifying ceRNA-ceRNA interactions. However, there remain great challenges and opportunities for developing computational methods to provide new insights into ceRNA regulatory mechanisms.In this paper, we review the publically available databases of ceRNA-ceRNA interactions and the computational methods for identifying ceRNA-ceRNA interactions (also known as miRNA sponge interactions). We also conduct a comparison study of the methods with a breast cancer dataset. Our aim is to provide a current snapshot of the advances of the computational methods in identifying miRNA sponge interactions and to discuss the remaining challenges.'),
(69068, 2017, 'bib', 'The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions', 'Vilar, Santiago|Hripcsak, George', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 6, 'Databases, Factual|Drug Interactions|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions', 'Journal Article', '10.1093/bib/bbw048', '27273288', 'Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies.'),
(69069, 2016, 'bib', 'A review of ensemble methods for de novo motif discovery in ChIP-Seq data', 'Lihu, Andrei|Holban, Stefan', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 0, '-', 'Journal Article', '10.1093/bib/bbw047', '27273289', '-'),
(69070, 2017, 'bib', 'Structural database resources for biological macromolecules', 'Abriata, Luciano A', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia', 5, 'Databases, Protein|Proteins', 'Journal Article', '10.1093/bib/bbw049', '27273290', 'This Briefing reviews the widely used, currently active, up-to-date databases derived from the worldwide Protein Data Bank (PDB) to facilitate browsing, finding and exploring its entries. These databases contain visualization and analysis tools tailored to specific kinds of molecules and interactions, often including also complex metrics precomputed by experts or external programs, and connections to sequence and functional annotation databases. Importantly, updates of most of these databases involves steps of curation and error checks based on specific expertise about the subject molecules or interactions, and removal of sequence redundancy, both leading to better data sets for mining studies compared with the full list of raw PDB entries. The article presents the databases in groups such as those aimed to facilitate browsing through PDB entries, their molecules and their general information, those built to link protein structure with sequence and dynamics, those specific for transmembrane proteins, nucleic acids, interactions of biomacromolecules with each other and with small molecules or metal ions, and those concerning specific structural features or specific protein families. A few webservers directly connected to active databases, and a few databases that have been discontinued but would be important to have back, are also briefly commented on. Along the Briefing, sample cases where these databases have been used to aid structural studies or advance our knowledge about biological macromolecules are referenced. A few specific examples are also given where using these databases is easier and more informative than using raw PDB data.'),
(69071, 2017, 'bib', 'Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era', 'Cheng, Feixiong|Hong, Huixiao|Yang, Shengyong|Wei, Yuquan', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 9, 'Drug Repositioning|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Precision Medicine', 'Journal Article', '10.1093/bib/bbw051', '27296652', 'Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the undruggable cancer genome in the functional genomics era.'),
(69072, 2017, 'bib', 'Long non-coding RNAs and complex diseases: from experimental results to computational models', 'Chen, Xing|Yan, Chenggang Clarence|Zhang, Xu|You, Zhu-Hong', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 233, 'Algorithms|Computational Biology|Computer Simulation|Humans|RNA, Long Noncoding', 'Journal Article', '10.1093/bib/bbw060', '27345524', 'LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA-disease associations and predicting potential human lncRNA-disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.'),
(69073, 2017, 'bib', 'Estimation of random effects and identifying heterogeneous genes in meta-analysis of gene expression studies', 'Siangphoe, Umaporn|Archer, Kellie J', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 2, 'Gene Expression|Gene Expression Profiling|Genome|Genomics|Meta-Analysis as Topic|Research Design', 'Journal Article', '10.1093/bib/bbw050', '27345525', 'Combining effect sizes from individual studies using random-effects meta-analysis models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistencies in sample quality and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. In this study, we describe three hypothesis-testing frameworks for meta-analysis of microarray data, and review several existing meta-analytic techniques that have been used in the genomic setting. These include P-value-based methods, rank-based methods and effect-size-based methods. We then discuss limitations of some of these methods and describe random-effects-based methods in detail. We introduce two methods for estimating the inter-study variance in random-effects meta-analytic models and another method for identifying heterogeneous genes for gene expression data. We compared various methods with the standard and existing meta-analytic techniques in the genomic framework. We demonstrate our results through a series of simulations and application in Alzheimers gene expression data.'),
(69074, 2017, 'bib', 'Dynamic and modular gene regulatory networks drive the development of gametogenesis', 'Che, Dongxue|Wang, Yang|Bai, Weiyang|Li, Leijie|Liu, Guiyou|Zhang, Liangcai|Zuo, Yongchun|Tao, Shiheng|Hua, Jinlian|Liao, Mingzhi', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 2, 'Animals|Gametogenesis|Gene Regulatory Networks|Humans|Meiosis|Mice|Protein Interaction Maps|Systems Biology', 'Journal Article', '10.1093/bib/bbw056', '27373733', 'Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.'),
(69075, 2017, 'bib', 'A comprehensive overview of computational resources to aid in precision genome editing with engineered nucleases', 'Periwal, Vinita', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia', 7, 'Clustered Regularly Interspaced Short Palindromic Repeats|Endonucleases|Gene Editing|Genetic Engineering|Genome|Humans', 'Journal Article', '10.1093/bib/bbw052', '27373734', 'Genome editing with engineered nucleases (zinc finger nucleases, TAL effector nucleases s and Clustered regularly inter-spaced short palindromic repeats/CRISPR-associated) has recently been shown to have great promise in a variety of therapeutic and biotechnological applications. However, their exploitation in genetic analysis and clinical settings largely depends on their specificity for the intended genomic target. Large and complex genomes often contain highly homologous/repetitive sequences, which limits the specificity of genome editing tools and could result in off-target activity. Over the past few years, various computational approaches have been developed to assist the design process and predict/reduce the off-target activity of these nucleases. These tools could be efficiently used to guide the design of constructs for engineered nucleases and evaluate results after genome editing. This review provides a comprehensive overview of various databases, tools, web servers and resources for genome editing and compares their features and functionalities. Additionally, it also describes tools that have been developed to analyse post-genome editing results. The article also discusses important design parameters that could be considered while designing these nucleases. This review is intended to be a quick reference guide for experimentalists as well as computational biologists working in the field of genome editing with engineered nucleases.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69076, 2017, 'bib', 'Screening and validation of lncRNAs and circRNAs as miRNA sponges', 'Militello, Giuseppe|Weirick, Tyler|John, David|Döring, Claudia|Dimmeler, Stefanie|Uchida, Shizuka', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 36, 'Computational Biology|MicroRNAs|RNA, Long Noncoding', 'Journal Article', '10.1093/bib/bbw053', '27373735', 'Intensive research in past two decades has uncovered the presence and importance of noncoding RNAs (ncRNAs), which includes microRNAs (miRs) and long ncRNAs (lncRNAs). These two classes of ncRNAs interact to a certain extent, as some lncRNAs bind to miRs to sequester them. Such lncRNAs are collectively called competing endogenous RNAs or miRNA sponges. In this study, we screened for lncRNAs that may act as miRNA sponges using the publicly available data sets and databases. To uncover the roles of miRNA sponges, loss-of-function experiments were conducted, which revealed the biological roles as miRNA sponges. LINC00324 is important for the cell survival by binding to miR-615-5p leading to the de-repression of its target BTG2. LOC400043 controls several biological functions via sequestering miR-28-3p and miR-96-5p, thereby changing the expressions of transcriptional regulators. Finally, we also screened for circular RNAs (circRNAs) that may function as miRNA sponges. The results were negative at least for the selected circRNAs in this study. In conclusion, miRNA sponges can be identified by applying a series of bioinformatics techniques and validated with biological experiments.'),
(69077, 2017, 'bib', 'Comparison of methods to detect differentially expressed genes between single-cell populations', 'Jaakkola, Maria K|Seyednasrollah, Fatemeh|Mehmood, Arfa|Elo, Laura L', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 24, 'Gene Expression|Gene Expression Profiling|RNA|Reproducibility of Results|Sequence Analysis, RNA|Single-Cell Analysis', 'Comparative Study', '10.1093/bib/bbw057', '27373736', 'We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.'),
(69078, 2017, 'bib', 'BrowseVCF: a web-based application and workflow to quickly prioritize disease-causative variants in VCF files', 'Salatino, Silvia|Ramraj, Varun', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 7, 'Exome|Internet|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1093/bib/bbw054', '27373737', 'Following variant calling and annotation, accurate variant filtering is a crucial step to extract meaningful information from sequencing data and to investigate disease aetiology. However, the variant call format (VCF) used to store this information is not easy to handle for non-bioinformaticians. We present BrowseVCF, a flexible and intuitive software to enable researchers to browse and filter millions of variants in a few seconds. Key features include querying user-defined gene lists, grouping samples for family or tumour/normal studies and exporting results in spreadsheet format. BrowseVCFs significant advantages over most existing tools include the ability to process data from any DNA sequencing experiment (exome, whole-genome and amplicons) and to correctly parse files annotated with Variant Effect Predictor. BrowseVCF can be used either locally on personal computers or as part of automated pipelines. Its user interface has been carefully designed to minimize tunable parameters. BrowseVCF is freely available from https://github.com/BSGOxford/BrowseVCF/releases/latest.'),
(69079, 2017, 'bib', 'TBC2health: a database of experimentally validated health-beneficial effects of tea bioactive compounds', 'Zhang, Shihua|Xuan, Hongdong|Zhang, Liang|Fu, Sicong|Wang, Yijun|Yang, Hua|Tai, Yuling|Song, Youhong|Zhang, Jinsong|Ho, Chi-Tang|Li, Shaowen|Wan, Xiaochun', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 11, 'Computational Biology|Databases, Factual|Humans|Molecular Docking Simulation|Tea|User-Computer Interface', 'Journal Article', '10.1093/bib/bbw055', '27387194', 'Tea is one of the most consumed beverages in the world. Considerable studies show the exceptional health benefits (e.g. antioxidation, cancer prevention) of tea owing to its various bioactive components. However, data from these extensively published papers had not been made available in a central database. To lay a foundation in improving the understanding of healthy tea functions, we established a TBC2health database that currently documents 1338 relationships between 497 tea bioactive compounds and 206 diseases (or phenotypes) manually culled from over 300 published articles. Each entry in TBC2health contains comprehensive information about a bioactive relationship that can be accessed in three aspects: (i) compound information, (ii) disease (or phenotype) information and (iii) evidence and reference. Using the curated bioactive relationships, a bipartite network was reconstructed and the corresponding network (or sub-network) visualization and topological analyses are provided for users. This database has a user-friendly interface for entry browse, search and download. In addition, TBC2health provides a submission page and several useful tools (e.g. BLAST, molecular docking) to facilitate use of the database. Consequently, TBC2health can serve as a valuable bioinformatics platform for the exploration of beneficial effects of tea on human health. TBC2health is freely available at http://camellia.ahau.edu.cn/TBC2health.'),
(69080, 2017, 'bib', 'Graphics processing units in bioinformatics, computational biology and systems biology', 'Nobile, Marco S|Cazzaniga, Paolo|Tangherloni, Andrea|Besozzi, Daniela', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 54, 'Algorithms|Computer Graphics|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbw058', '27402792', 'Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.'),
(69081, 2017, 'bib', 'OpenGeneMed: a portable, flexible and customizable informatics hub for the coordination of next-generation sequencing studies in support of precision medicine trials', 'Palmisano, Alida|Zhao, Yingdong|Li, Ming-Chung|Polley, Eric C|Simon, Richard M', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 2, 'Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Precision Medicine', 'Journal Article', '10.1093/bib/bbw059', '27422621', 'Trials involving genomic-driven treatment selection require the coordination of many teams interacting with a great variety of information. The need of better informatics support to manage this complex set of operations motivated the creation of OpenGeneMed. OpenGeneMed is a stand-alone and customizable version of GeneMed (Zhao et al. GeneMed: an informatics hub for the coordination of next-generation sequencing studies that support precision oncology clinical trials. Cancer Inform 2015,14(Suppl 2):45), a web-based interface developed for the National Cancer Institute Molecular Profiling-based Assignment of Cancer Therapy (NCI-MPACT) clinical trial coordinated by the NIH. OpenGeneMed streamlines clinical trial management and it can be used by clinicians, lab personnel, statisticians and researchers as a communication hub. It automates the annotation of genomic variants identified by sequencing tumor DNA, classifies the actionable mutations according to customizable rules and facilitates quality control in reviewing variants. The system generates summarized reports with detected genomic alterations that a treatment review team can use for treatment assignment. OpenGeneMed allows collaboration to happen seamlessly along the clinical pipeline, it helps reduce errors made transferring data between groups and facilitates clear documentation along the pipeline. OpenGeneMed is distributed as a stand-alone virtual machine, ready for deployment and use from a web browser, its code is customizable to address specific needs of different clinical trials and research teams. Examples on how to change the code are provided in the technical documentation distributed with the virtual machine. In summary, OpenGeneMed offers an initial set of features inspired by our experience with GeneMed, a system that has been proven to be efficient and successful for coordinating the application of next-generation sequencing in the NCI-MPACT trial.'),
(69082, 2017, 'bib', 'Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction', 'Zhou, Yao|Vales, M Isabel|Wang, Aoxue|Zhang, Zhiwu', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 4, 'Genome|Genomics|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbw064', '27436121', 'Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that the minimum value for prediction accuracy should hover around zero when randomly permuted data sets are analyzed. We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by the mathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as the mean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using a modified formula.'),
(69083, 2017, 'bib', 'Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns', 'Yang, Xiaofei|Gao, Lin|Zhang, Shihua', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 46, 'CpG Islands|DNA Methylation|Epigenomics|Genome|Humans|Neoplasms', 'Comparative Study', '10.1093/bib/bbw063', '27436122', 'Abnormal DNA methylation is an important epigenetic regulator involving tumorigenesis. Deciphering cancer common and specific DNA methylation patterns is essential for us to understand the mechanisms of tumor development. The Cancer Genome Atlas (TCGA) project provides a large number of samples of different cancers that enable a pan-cancer study of DNA methylation possible. Here we investigate cancer common and specific DNA methylation patterns among 5480 DNA methylation profiles of 15 cancer types from TCGA. We first define differentially methylated CpG sites (DMCs) in each cancer and then identify 5450 hyper- and 4433 hypomethylated pan-cancer DMCs (PDMCs). Intriguingly, three adjacent hypermethylated PDMC constitute an enhancer region, which potentially regulates two tumor suppressor genes BVES and PRDM1 negatively. Moreover, we identify six distinct motif clusters, which are enriched in hyper- or hypomethylated PDMCs and are associated with several well-known cancer hallmarks. We also observe that PDMCs relate to distinct transcriptional groups. Additionally, 55 hypermethylated and 7 hypomethylated PDMCs are significantly associated with patient survival. Lastly, we find that cancer-specific DMCs are enriched in known cancer genes and cell-type-specific super-enhancers. In summary, this study provides a comprehensive investigation and reveals meaningful cancer common and specific DNA methylation patterns.'),
(69084, 2017, 'bib', 'Identification and function annotation of long intervening noncoding RNAs', 'Luo, Haitao|Bu, Dechao|Sun, Liang|Fang, Shuangsang|Liu, Zhiyong|Zhao, Yi', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 11, 'Computational Biology|Proteins|RNA, Long Noncoding', 'Journal Article', '10.1093/bib/bbw046', '27439532', 'RNA-seq technology offers the promise of rapid comprehensive discovery of long intervening noncoding RNAs (lincRNAs). Basic tools such as Tophat and Cufflinks have been widely used for RNA-seq assembly. However, advanced bioinformatics methodologies that allow in-depth analysis of lincRNAs are lacking. Here, we describe a computational protocol that is especially designed for the identification of novel lincRNAs and the prediction of the function. The protocol mainly includes two open-access tools, CNCI and ncFANs. CNCI allows users to distinguish noncoding from protein-coding transcripts and to retrieve novel lincRNAs. ncFANs integrates expression profiles of protein-coding and lincRNA genes to construct coexpression networks. Such networks are subsequently used to perform function predictions of unknown lincRNAs. This protocol will allow users to apply these procedures without the need of additional training. All the tools in current protocol are available http://www.bioinfo.org/np/.'),
(69085, 2017, 'bib', 'Protein-protein interactions: detection, reliability assessment and applications', 'Peng, Xiaoqing|Wang, Jianxin|Peng, Wei|Wu, Fang-Xiang|Pan, Yi', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 26, 'Computational Biology|Humans|Protein Interaction Mapping|Proteins|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbw066', '27444371', 'Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.'),
(69086, 2017, 'bib', 'Computational models for predicting drug responses in cancer research', 'Azuaje, Francisco', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia', 20, 'Biomedical Research|Computational Biology|Humans|Neoplasms|Precision Medicine', 'Journal Article', '10.1093/bib/bbw065', '27444372', 'The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.'),
(69087, 2017, 'bib', 'Evo-Devo-EpiR: a genome-wide search platform for epistatic control on the evolution of development', 'Jiang, Libo|Zhang, Miaomiao|Sang, Mengmeng|Ye, Meixia|Wu, Rongling', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 4, 'Biological Evolution|Epistasis, Genetic|Evolution, Molecular|Genome-Wide Association Study|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bib/bbw062', '27473062', 'Evo-devo is a theory proposed to study how phenotypes evolve by comparing the developmental processes of different organisms or the same organism experiencing changing environments. It has been recognized that nonallelic interactions at different genes or quantitative trait loci, known as epistasis, may play a pivotal role in the evolution of development, but it has proven difficult to quantify and elucidate this role into a coherent picture. We implement a high-dimensional genome-wide association study model into the evo-devo paradigm and pack it into the R-based Evo-Devo-EpiR, aimed at facilitating the genome-wide landscaping of epistasis for the diversification of phenotypic development. By analyzing a high-throughput assay of DNA markers and their pairs simultaneously, Evo-Devo-EpiR is equipped with a capacity to systematically characterize various epistatic interactions that impact on the pattern and timing of development and its evolution. Enabling a global search for all possible genetic interactions for developmental processes throughout the whole genome, Evo-Devo-EpiR provides a computational tool to illustrate a precise genotype-phenotype map at interface between epistasis, development and evolution.'),
(69088, 2017, 'bib', 'Comparative assessment of differential network analysis methods', 'Lichtblau, Yvonne|Zimmermann, Karin|Haldemann, Berit|Lenze, Dido|Hummel, Michael|Leser, Ulf', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 17, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|MicroRNAs', 'Comparative Study', '10.1093/bib/bbw061', '27473063', 'Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.'),
(69089, 2017, 'bib', 'Deep learning in bioinformatics', 'Min, Seonwoo|Lee, Byunghan|Yoon, Sungroh', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 73, 'Computational Biology|Humans|Machine Learning|Neural Networks (Computer)', 'Journal Article', '10.1093/bib/bbw068', '27473064', 'In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.'),
(69090, 2017, 'bib', 'Indel detection from RNA-seq data: tool evaluation and strategies for accurate detection of actionable mutations', 'Sun, Zhifu|Bhagwate, Aditya|Prodduturi, Naresh|Yang, Ping|Kocher, Jean-Pierre A', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 17, 'Algorithms|Case-Control Studies|Computational Biology|ErbB Receptors|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Lung Neoplasms|Software|Whole Exome Sequencing', 'Evaluation Studies', '10.1093/bib/bbw069', '27473065', 'Driver somatic mutations are a hallmark of a tumor that can be used for diagnosis and targeted therapy. Mutations are primarily detected from tumor DNA. As dynamic molecules of gene activities, transcriptome profiling by RNA sequence (RNA-seq) is becoming increasingly popular, which not only measures gene expression but also structural variations such as mutations and fusion transcripts. Although single-nucleotide variants (SNVs) can be easily identified from RNA-seq, intermediate long insertions/deletions (indelsxe2x80x89 >xe2x80x892 bases and less than sequence reads) cause significant challenges and are ignored by most RNA-seq analysis tools. This study evaluates commonly used RNA-seq analysis programs along with variant and somatic mutation callers in a series of data sets with simulated and known indels. The aim is to develop strategies for accurate indel detection. Our results show that the RNA-seq alignment is the most important step for indel identification and the evaluated programs have a wide range of sensitivity to map sequence reads with indels, from not at all to decently sensitive. The sensitivity is impacted by sequence read lengths. Most variant calling programs rely on hard evidence indels marked in the alignment and the programs with realignment may use soft-clipped reads for indel inferencing. Based on the observations, we have provided practical recommendations for indel detection when different RNA-seq aligners are used and demonstrated the best option with highly reliable results. With careful customization of bioinformatics algorithms, RNA-seq can be reliably used for both SNV and indel mutation detection that can be used for clinical decision-making.'),
(69091, 2017, 'bib', 'Gene Ontology semantic similarity tools: survey on features and challenges for biological knowledge discovery', 'Mazandu, Gaston K|Chimusa, Emile R|Mulder, Nicola J', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 21, 'Gene Ontology|Humans|Molecular Sequence Annotation|Semantics|Surveys and Questionnaires', 'Journal Article', '10.1093/bib/bbw067', '27473066', 'Gene Ontology (GO) semantic similarity tools enable retrieval of semantic similarity scores, which incorporate biological knowledge embedded in the GO structure for comparing or classifying different proteins or list of proteins based on their GO annotations. This facilitates a better understanding of biological phenomena underlying the corresponding experiment and enables the identification of processes pertinent to different biological conditions. Currently, about 14 tools are available, which may play an important role in improving protein analyses at the functional level using different GO semantic similarity measures. Here we survey these tools to provide a comprehensive view of the challenges and advances made in this area to avoid redundant effort in developing features that already exist, or implementing ideas already proven to be obsolete in the context of GO. This helps researchers, tool developers, as well as end users, understand the underlying semantic similarity measures implemented through knowledge of pertinent features of, and issues related to, a particular tool. This should empower users to make appropriate choices for their biological applications and ensure effective knowledge discovery based on GO annotations.'),
(69092, 2017, 'bib', 'Benchmarking computational tools for polymorphic transposable element detection', 'Rishishwar, Lavanya|Mariño-Ramírez, Leonardo|Jordan, I King', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 29, 'Algorithms|Benchmarking|DNA Transposable Elements|Genome, Human|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbw072', '27524380', 'Transposable elements (TEs) are an important source of human genetic variation with demonstrable effects on phenotype. Recently, a number of computational methods for the detection of polymorphic TE (polyTE) insertion sites from next-generation sequence data have been developed. The use of such tools will become increasingly important as the pace of human genome sequencing accelerates. For this report, we performed a comparative benchmarking and validation analysis of polyTE detection tools in an effort to inform their selection and use by the TE research community. We analyzed a core set of seven tools with respect to ease of use and accessibility, polyTE detection performance and runtime parameters. An experimentally validated set of 893 human polyTE insertions was used for this purpose, along with a series of simulated data sets that allowed us to assess the impact of sequence coverage on tool performance. The recently developed tool MELT showed the best overall performance followed by Mobster and then RetroSeq. PolyTE detection tools can best detect Alu insertion events in the human genome with reduced reliability for L1 insertions and substantially lowered performance for SVA insertions. We also show evidence that different polyTE detection tools are complementary with respect to their ability to detect a complete set of insertion events. Accordingly, a combined approach, coupled with manual inspection of individual results, may yield the best overall performance. In addition to the benchmarking results, we also provide notes on tool installation and usage as well as suggestions for future polyTE detection algorithm development.'),
(69093, 2017, 'bib', 'Recording negative results of protein-protein interaction assays: an easy way to deal with the biases and errors of interactomic data sets', 'Alvarez-Ponce, David', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia', 2, 'Bias|Databases, Protein|Humans|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins|Species Specificity', 'Journal Article', '10.1093/bib/bbw075', '27542401', 'In recent years, it has become increasingly common to use assays that can test whether two proteins interact, such as yeast two-hybrid and tandem affinity purification followed by mass spectrometry. Such techniques, particularly when applied at a large scale, suffer from high rates of false positives and false negatives. In addition, interactomic data sets are subjected to a number of biases, which limits considerably their usefulness to address biological questions. Interactomic databases only keep track of the positive results of protein interaction assays (those indicating that the tested proteins interact). Despite their importance, negative results (those indicating that the tested proteins do not interact) are not recorded in interactomic databases. Indeed, current interactomic databases do not support negative results. Here, I argue that systematically recording not only positive but also negative results of protein interaction assays would help scientists identify errors and deal with biases, thus enormously increasing the value of interactomic data sets. The challenges of implementing this change, along with potential solutions, are discussed.'),
(69094, 2017, 'bib', 'A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks', 'Zhang, Peng|Tao, Lin|Zeng, Xian|Qin, Chu|Chen, Shangying|Zhu, Feng|Li, Zerong|Jiang, Yuyang|Chen, Weiping|Chen, Yu-Zong', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 19, 'Algorithms|Computational Biology|Databases, Protein|Disease|Gene Regulatory Networks|Humans|Internet|Metabolic Networks and Pathways|Pharmaceutical Preparations|Protein Interaction Mapping|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbw071', '27542402', 'The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.'),
(69095, 2017, 'bib', 'Comprehensive characterization of tissue-specific circular RNAs in the human and mouse genomes', 'Xia, Siyu|Feng, Jing|Lei, Lijun|Hu, Jun|Xia, Linjian|Wang, Jun|Xiang, Yu|Liu, Lingjun|Zhong, Shan|Han, Leng|He, Chunjiang', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 95, 'Animals|Fetus|Genome|Humans|Mice|Mice, Inbred C57BL|MicroRNAs|Organ Specificity|RNA|RNA-Binding Proteins', 'Journal Article', '10.1093/bib/bbw081', '27543790', 'Circular RNA (circRNA) is a group of RNA family generated by RNA circularization, which was discovered ubiquitously across different species and tissues. However, there is no global view of tissue specificity for circRNAs to date. Here we performed the comprehensive analysis to characterize the features of human and mouse tissue-specific (TS) circRNAs. We identified in total 302 853 TS circRNAs in the human and mouse genome, and showed that the brain has the highest abundance of TS circRNAs. We further confirmed the existence of circRNAs by reverse transcription polymerase chain reaction (RT-PCR). We also characterized the genomic location and conservation of these TS circRNAs and showed that the majority of TS circRNAs are generated from exonic regions. To further understand the potential functions of TS circRNAs, we identified microRNAs and RNA binding protein, which might bind to TS circRNAs. This process suggested their involvement in development and organ differentiation. Finally, we constructed an integrated database TSCD (Tissue-Specific CircRNA Database: http://gb.whu.edu.cn/TSCD) to deposit the features of TS circRNAs. This study is the first comprehensive view of TS circRNAs in human and mouse, which shed light on circRNA functions in organ development and disorders.'),
(69096, 2017, 'bib', 'Robust logistic regression to narrow down the winners curse for rare and recessive susceptibility variants', 'Kesselmeier, Miriam|Lorenzo Bermejo, Justo', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 3, 'Adolescent|Adult|Age Factors|Aged|Aged, 80 and over|Algorithms|Case-Control Studies|Child|Child, Preschool|Computer Simulation|Gene Frequency|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Infant|Infant, Newborn|Logistic Models|Middle Aged|Polymorphism, Single Nucleotide|Software|Young Adult', 'Journal Article', '10.1093/bib/bbw074', '27543791', 'Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package robustbase with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69097, 2017, 'bib', 'miRNA-miRNA crosstalk: from genomics to phenomics', 'Xu, Juan|Shao, Tingting|Ding, Na|Li, Yongsheng|Li, Xia', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 9, 'Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Genomics|Humans|MicroRNAs|Phenotype|Transcriptome', 'Journal Article', '10.1093/bib/bbw073', '27551063', 'The discovery of microRNA (miRNA)-miRNA crosstalk has greatly improved our understanding of complex gene regulatory networks in normal and disease-specific physiological conditions. Numerous approaches have been proposed for modeling miRNA-miRNA networks based on genomic sequences, miRNA-mRNA regulation, functional information and phenomics alone, or by integrating heterogeneous data. In addition, it is expected that miRNA-miRNA crosstalk can be reprogrammed in different tissues or specific diseases. Thus, transcriptome data have also been integrated to construct context-specific miRNA-miRNA networks. In this review, we summarize the state-of-the-art miRNA-miRNA network modeling methods, which range from genomics to phenomics, where we focus on the need to integrate heterogeneous types of omics data. Finally, we suggest future directions for studies of crosstalk of noncoding RNAs. This comprehensive summarization and discussion elucidated in this work provide constructive insights into miRNA-miRNA crosstalk.'),
(69098, 2016, 'bib', 'A network medicine approach to build a comprehensive atlas for the prognosis of human cancer', 'Zhang, Fan|Ren, Chunyan|Lau, Kwun Kit|Zheng, Zihan|Lu, Geming|Yi, Zhengzi|Zhao, Yongzhong|Su, Fei|Zhang, Shaojun|Zhang, Bin|Sobie, Eric A|Zhang, Weijia|Walsh, Martin J', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 19, 'DNA Copy Number Variations|DNA Methylation|Genomics|Humans|Neoplasms|Prognosis', 'Journal Article', '10.1093/bib/bbw076', '27559151', 'The Cancer Genome Atlas project has generated multi-dimensional and highly integrated genomic data from a large number of patient samples with detailed clinical records across many cancer types, but it remains unclear how to best integrate the massive amount of genomic data into clinical practice. We report here our methodology to build a multi-dimensional subnetwork atlas for cancer prognosis to better investigate the potential impact of multiple genetic and epigenetic (gene expression, copy number variation, microRNA expression and DNA methylation) changes on the molecular states of networks that in turn affects complex cancer survivorship. We uncover an average of 38 novel subnetworks in the protein-protein interaction network that correlate with prognosis across four prominent cancer types. The clinical utility of these subnetwork biomarkers was further evaluated by prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumor stratification and independent validation. Some pathways including the dynactin, cohesion and pyruvate dehydrogenase-related subnetworks are identified as promising new targets for therapy in specific cancer types. In conclusion, this integrative analysis of existing protein interactome and cancer genomics data allows us to systematically dissect the molecular mechanisms that underlie unexpected outcomes for cancer, which could be used to better understand and predict clinical outcomes, optimize treatment and to provide new opportunities for developing therapeutics related to the subnetworks identified.'),
(69099, 2017, 'bib', 'Characterization of MinION nanopore data for resequencing analyses', 'Magi, Alberto|Giusti, Betti|Tattini, Lorenzo', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 17, 'DNA, Bacterial|Escherichia coli|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Nanopores|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbw077', '27559152', 'The Oxford Nanopore Technologies MinION is a new device, based on nanopore sequencing that is able to generate reads of tens of kilobases in length with faster sequencing time with respect to other platforms. To evaluate the capability of nanopore data to be exploited for resequencing analyses we used the largest MinION data set to date and we compared with Illumina and Pacific Biosciences technologies. By using five different mapping approaches we estimated that the global sequencing error rate of MinION reads, mainly caused by inserted and deleted bases, is around 11%. The study of error distribution showed that substituted, inserted and deleted bases are not randomly distributed along the reads, but mainly occur in specific nucleotide patterns, generating a significant number of genomic loci that can be misclassified as false-positive variants. With 40xc3x97 sequencing coverage, MinION data can produce at best around one false substitution and insertion every 10-50xe2x80x89kb, and one false deletion every 1000xe2x80x89bp, making use of this technology still challenging for small-sized variant discovery. We also analyzed depth of coverage distribution and we demonstrated that nanopore sequencing is a uniform process that generates sequences randomly and independently without classical sources of bias such as GC-content and mappability. Owing to these properties, the MinION data can be readily used to detect genomic regions involved in copy number variants with high accuracy, outperforming other state-of-the-art sequencing methods in terms of both sensitivity and specificity.'),
(69100, 2017, 'bib', 'Recent advances in sequence-based protein structure prediction', 'Kc, Dukka B', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia', 20, 'Algorithms|Databases, Protein|Humans|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/bbw070', '27562963', 'The most accurate characterizations of the structure of proteins are provided by structural biology experiments. However, because of the high cost and labor-intensive nature of the structural experiments, the gap between the number of protein sequences and solved structures is widening rapidly. Development of computational methods to accurately model protein structures from sequences is becoming increasingly important to the biological community. In this article, we highlight some important progress in the field of protein structure prediction, especially those related to free modeling (FM) methods that generate structure models without using homologous templates. We also provide a short synopsis of some of the recent advances in FM approaches as demonstrated in the recent Computational Assessment of Structure Prediction competition as well as recent trends and outlook for FM approaches in protein structure prediction.'),
(69101, 2017, 'bib', 'Protein side-chain packing problem: is there still room for improvement?', 'Colbes, José|Corona, Rosario I|Lezcano, Christian|Rodríguez, David|Brizuela, Carlos A', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 4, 'Algorithms|Computational Biology|Databases, Protein|Humans|Protein Conformation|Proteins', 'Journal Article', '10.1093/bib/bbw079', '27567382', 'The protein side-chain packing problem (PSCPP) is an important subproblem of both protein structure prediction and protein design. During the past two decades, a large number of methods have been proposed to tackle this problem. These methods consist of three main components: a rotamer library, a scoring function and a search strategy. The average overall accuracy level obtained by these methods is approximately 87%. Whether a better accuracy level could be achieved remains to be answered. To address this question, we calculated the maximum accuracy level attainable using a simple rotamer library, independently of the energy function or the search method. Using 2883 different structures from the Protein Data Bank, we compared this accuracy level with the accuracy level of five state-of-the-art methods. These comparisons indicated that, for buried residues in the protein, we are already close to the best possible accuracy results. In addition, for exposed residues, we found that a significant gap exists between the possible improvement and the maximum accuracy level achievable with current methods. After determining that an improvement is possible, the next step is to understand what limitations are preventing us from obtaining such an improvement. Previous works on protein structure prediction and protein design have shown that scoring function inaccuracies may represent the main obstacle to achieving better results for these problems. To show that the same is true for the PSCPP, we evaluated the quality of two scoring functions used by some state-of-the-art algorithms. Our results indicate that neither of these scoring functions can guide the search method correctly, thereby reinforcing the idea that efforts to solve the PSCPP must also focus on developing better scoring functions.'),
(69102, 2017, 'bib', 'Opportunities for community awareness platforms in personal genomics and bioinformatics education', 'Bianchi, Lucia|Liò, Pietro', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 3, 'Computational Biology|Computer Security|Electronic Health Records|Genetic Privacy|Genomics|Health Knowledge, Attitudes, Practice|Humans|Information Dissemination|Internet|Precision Medicine|Social Responsibility', 'Journal Article', '10.1093/bib/bbw078', '27580620', 'Precision and personalized medicine will be increasingly based on the integration of various type of information, particularly electronic health records and genome sequences. The availability of cheap genome sequencing services and the information interoperability will increase the role of online bioinformatics analysis. Being on the Internet poses constant threats to security and privacy. While we are connected and we share information, websites and internet services collect various types of personal data with or without the user consent. It is likely that genomics will merge with the internet culture of connectivity. This process will increase incidental findings, exposure and vulnerability. Here we discuss the social vulnerability owing to the genome and Internet combined security and privacy weaknesses. This urges more efforts in education and social awareness on how biomedical data are analysed and transferred through the internet and how inferential methods could integrate information from different sources. We propose that digital social platforms, used for raising collective awareness in different fields, could be developed for collaborative and bottom-up efforts in education. In this context, bioinformaticians could play a meaningful role in mitigating the future risk of digital-genomic divide.'),
(69103, 2017, 'bib', 'Exploring and visualizing multidimensional data in translational research platforms', 'Dunn, William|Burgun, Anita|Krebs, Marie-Odile|Rance, Bastien', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 12, 'Computational Biology|Computer Graphics|Databases, Factual|Genomics|Humans|Information Storage and Retrieval|Software|Statistics as Topic|Translational Medical Research', 'Journal Article', '10.1093/bib/bbw080', '27585944', 'The unprecedented advances in technology and scientific research over the past few years have provided the scientific community with new and more complex forms of data. Large data sets collected from single groups or cross-institution consortiums containing hundreds of omic and clinical variables corresponding to thousands of patients are becoming increasingly commonplace in the research setting. Before any core analyses are performed, visualization often plays a key role in the initial phases of research, especially for projects where no initial hypotheses are dominant. Proper visualization of data at a high level facilitates researchers abilities to find trends, identify outliers and perform quality checks. In addition, research has uncovered the important role of visualization in data analysis and its implied benefits facilitating our understanding of disease and ultimately improving patient care. In this work, we present a review of the current landscape of existing tools designed to facilitate the visualization of multidimensional data in translational research platforms. Specifically, we reviewed the biomedical literature for translational platforms allowing the visualization and exploration of clinical and omics data, and identified 11 platforms: cBioPortal, interactive genomics patient stratification explorer, Igloo-Plot, The Georgetown Database of Cancer Plus, tranSMART, an unnamed data-cube-based model supporting heterogeneous data, Papilio, Caleydo Domino, Qlucore Omics, Oracle Health Sciences Translational Research Center and OmicsOffice® powered by TIBCO Spotfire. In a health sector continuously witnessing an increase in data from multifarious sources, visualization tools used to better grasp these data will grow in their importance, and we believe our work will be useful in guiding investigators in similar situations.'),
(69104, 2017, 'bib', 'AlloMap6: an R package for genetic linkage analysis in allohexaploids', 'Zhu, Xuli|Li, Huan|Ye, Meixia|Jiang, Libo|Sang, Mengmeng|Wu, Rongling', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 1, 'Algorithms|Chromosome Mapping|Computational Biology|Computer Simulation|Genetic Linkage|Genome, Plant|Meiosis|Models, Genetic|Polyploidy', 'Journal Article', '10.1093/bib/bbw085', '27651481', 'Allopolyploids are a group of polyploids with more than two sets of chromosomes derived from different species. Previous linkage analysis of allopolyploids is based on the assumption that different chromosomes pair randomly during meiosis. A more sophisticated model to relax this assumption has been developed for allotetraploids by incorporating the preferential pairing behavior of homologous over homoeologous chromosomes. Here, we show that the basic principle of this model can be extended to perform linkage analysis of higher-ploidy allohexaploids, where multiple preferential pairing factors are used to characterize chromosomal-pairing meiotic features between different constituent species. We implemented the extended model into an R package, called AlloMap6, allowing the recombination fractions and preferential pairing factors to be estimated simultaneously. Allomap6 has two major functionalities, computer simulation and real-data analysis. By analyzing a real data from a full-sib family of allohexaploid persimmon, we tested and validated the usefulness and utility of this package. AlloMap6 lays a foundation for allohexaploid genetic mapping and provides a new horizon to explore the chromosomal kinship of allohexaploids.'),
(69105, 2017, 'bib', 'Rare variant association test in family-based sequencing studies', 'Wang, Xuefeng|Zhang, Zhenyu|Morris, Nathan|Cai, Tianxi|Lee, Seunggeun|Wang, Chaolong|Yu, Timothy W|Walsh, Christopher A|Lin, Xihong', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Algorithms|Autistic Disorder|Case-Control Studies|Computer Simulation|Family|Female|Genetic Association Studies|Genetic Variation|Humans|Male|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbw083', '27677958', 'The objective of this article is to introduce valid and robust methods for the analysis of rare variants for family-based exome chips, whole-exome sequencing or whole-genome sequencing data. Family-based designs provide unique opportunities to detect genetic variants that complement studies of unrelated individuals. Currently, limited methods and software tools have been developed to assist family-based association studies with rare variants, especially for analyzing binary traits. In this article, we address this gap by extending existing burden and kernel-based gene set association tests for population data to related samples, with a particular emphasis on binary phenotypes. The proposed approach blends the strengths of kernel machine methods and generalized estimating equations. Importantly, the efficient generalized kernel score test can be applied as a mega-analysis framework to combine studies with different designs. We illustrate the application of the proposed method using data from an exome sequencing study of autism. Methods discussed in this article are implemented in an R package gskat, which is available on CRAN and GitHub.'),
(69106, 2017, 'bib', 'A novel computational method for inferring competing endogenous interactions', 'Sardina, Davide S|Alaimo, Salvatore|Ferro, Alfredo|Pulvirenti, Alfredo|Giugno, Rosalba', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia', 19, 'Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Humans|MicroRNAs|RNA|RNA, Long Noncoding|RNA, Messenger|Response Elements', 'Journal Article', '10.1093/bib/bbw084', '27677959', 'Posttranscriptional cross talk and communication between genes mediated by microRNA response element (MREs) yield large regulatory competing endogenous RNA (ceRNA) networks. Their inference may improve the understanding of pathologies and shed new light on biological mechanisms. A variety of RNA: messenger RNA, transcribed pseudogenes, noncoding RNA, circular RNA and proteins related to RNA-induced silencing complex complex interacting with RNA transfer and ribosomal RNA have been experimentally proved to be ceRNAs. We retrace the ceRNA hypothesis of posttranscriptional regulation from its original formulation [Salmena L, Poliseno L, Tay Y, et al. Cell 2011,146:353-8] to the most recent experimental and computational validations. We experimentally analyze the methods in literature [Li J-H, Liu S, Zhou H, et al. Nucleic Acids Res 2013,42:D92-7, Sumazin P, Yang X, Chiu H-S, et al. Cell 2011,147:370-81, Sarver AL, Subramanian S. Bioinformation 2012,8:731-3] comparing them with a general machine learning approach, called ceRNA predIction Algorithm, evaluating the performance in predicting novel MRE-based ceRNAs.'),
(69107, 2017, 'bib', 'Unraveling chloroplast transcriptomes with ChloroSeq, an organelle RNA-Seq bioinformatics pipeline', 'Smith, David Roy|Sanitá Lima, Matheus', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 4, 'Arabidopsis|Chloroplasts|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbw088', '27677960', 'Online sequence repositories are teeming with RNA sequencing (RNA-Seq) data from a wide range of eukaryotes. Although most of these data sets contain large numbers of organelle-derived reads, researchers tend to ignore these data, focusing instead on the nuclear-derived transcripts. Consequently, GenBank contains massive amounts of organelle RNA-Seq data that are just waiting to be downloaded and analyzed. Recently, a team of scientists designed an open-source bioinformatics program called ChloroSeq, which systemically analyzes an organelle transcriptome using RNA-Seq. The ChloroSeq pipeline uses RNA-Seq alignment data to deliver detailed analyses of organelle transcriptomes, which can be fed into statistical software for further analysis and for generating graphical representations of the data. In addition to providing data on expression levels via coverage statistics, ChloroSeq can examine splicing efficiency and RNA editing profiles. Ultimately, ChloroSeq provides a well-needed avenue for researchers of all stripes to start exploring organelle transcription and could be a key step toward a more thorough understanding of organelle gene expression.'),
(69108, 2017, 'bib', 'What is the probability of replicating a statistically significant association in genome-wide association studies?', 'Jiang, Wei|Xue, Jing-Hao|Yu, Weichuan', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Bayes Theorem|Case-Control Studies|Computer Simulation|Diabetes Mellitus, Type 2|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbw091', '27687799', 'The goal of genome-wide association studies (GWASs) is to discover genetic variants associated with diseases/traits. Replication is a common validation method in GWASs. We regard an association as true finding when it shows significance in both primary and replication studies. A question worth pondering is what is the probability of a primary association (i.e. a statistically significant association in the primary study) being validated in the replication study? This article systematically reviews the answers to this question from different points of view. As Bayesian methods can help us integrate out the uncertainty about the underlying effect of the primary association, we will mainly focus on the Bayesian view in this article. We refer the Bayesian replication probability as the replication rate (RR). We further describe an estimation method for RR, which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study and to check the consistency between the results of the primary study and those of the replication study. We describe an R-package to estimate and apply RR in GWASs. Simulation and real data experiments show that the estimated RR has good prediction and calibration performance. We also use these data to demonstrate the usefulness of RR. The R-package is available at http://bioinformatics.ust.hk/RRate.html.'),
(69109, 2017, 'bib', 'RNAEditor: easy detection of RNA editing events and the introduction of editing islands', 'John, David|Weirick, Tyler|Dimmeler, Stefanie|Uchida, Shizuka', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 24, 'Algorithms|Animals|B-Lymphocytes|Cluster Analysis|Computational Biology|Genome|High-Throughput Nucleotide Sequencing|Humans|Mice|RNA Editing|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbw087', '27694136', 'RNA editing of adenosine residues to inosine (A-to-I editing) is the most common RNA modification event detectible with RNA sequencing (RNA-seq). While not directly detectable, inosine is read by next-generation sequencers as guanine. Therefore, mapping RNA-seq reads to their corresponding reference genome can detect potential editing events by identifying A-to-G conversions. However, one must exercise caution when searching for editing sites, as A-to-G conversions also arise from sequencing errors as well as mutations. To address these complexities, several algorithms and software products have been developed to accurately identify editing events. Here, we survey currently available methods to analyze RNA editing events and introduce a new easy-to-use bioinformatics tool RNAEditor for the detection of RNA editing events. During the development of RNAEditor, we noticed editing often happened in clusters, which we named editing islands. We developed a clustering algorithm to find editing islands and included it in RNAEditor. RNAEditor is freely available at http://rnaeditor.uni-frankfurt.de. We anticipate that RNAEditor will provide biologists with an easy-to-use tool for studying RNA editing events and the newly defined editing islands.'),
(69110, 2017, 'bib', 'Optimization of cell lines as tumour models by integrating multi-omics data', 'Zhao, Ning|Liu, Yongjing|Wei, Yunzhen|Yan, Zichuang|Zhang, Qiang|Wu, Cheng|Chang, Zhiqiang|Xu, Yan', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Cell Line, Tumor|Gene Expression|Gene Expression Profiling|Genomics|Humans|Mutation', 'Journal Article', '10.1093/bib/bbw082', '27694350', 'Cell lines are widely used as in vitro models of tumorigenesis. However, an increasing number of researchers have found that cell lines differ from their sourced tumour samples after long-term cell culture. The application of unsuitable cell lines in experiments will affect the experimental accuracy and the treatment of patients. Therefore, it is imperative to identify optimal cell lines for each cancer type. Here, we review the methods used to evaluate cell lines since 2005. Furthermore, gene expression, copy number and mutation profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia are used to calculate similarity between tumours and cell lines. Then, the ideal cell lines to use for experiments for eight types of cancers are found by combining the results with Gene Ontology functional similarity. After verification, the optimal cell lines have the same genomic characteristics as their homologous tumour samples. The contaminated cell lines identified in previous research are also determined to be unsuitable in vitro cancer models here. Moreover, our study suggests that some of the commonly used cell lines are not suitable cancer models. In summary, we provide a reference for ideal cell lines to use in in vitro experiments and contribute to improving the accuracy of future cancer research. Furthermore, this research provides a foundation for identifying more effective treatment strategies.'),
(69111, 2018, 'bib', 'A systematic evaluation of normalization methods in quantitative label-free proteomics', 'Välikangas, Tommi|Suomi, Tomi|Elo, Laura L', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia', 54, 'Animals|Databases, Protein|Humans|Mice|Models, Statistical|Peptide Mapping|Proteome|Proteomics|Reproducibility of Results', 'Evaluation Studies', '10.1093/bib/bbw095', '27694351', 'To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.'),
(69112, 2018, 'bib', 'The present and future of de novo whole-genome assembly', 'Sohn, Jang-Il|Nam, Jin-Wu', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 12, 'Algorithms|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bib/bbw096', '27742661', 'As the advent of next-generation sequencing (NGS) technology, various de novo assembly algorithms based on the de Bruijn graph have been developed to construct chromosome-level sequences. However, numerous technical or computational challenges in de novo assembly still remain, although many bright ideas and heuristics have been suggested to tackle the challenges in both experimental and computational settings. In this review, we categorize de novo assemblers on the basis of the type of de Bruijn graphs (Hamiltonian and Eulerian) and discuss the challenges of de novo assembly for short NGS reads regarding computational complexity and assembly ambiguity. Then, we discuss how the limitations of the short reads can be overcome by using a single-molecule sequencing platform that generates long reads of up to several kilobases. In fact, the long read assembly has caused a paradigm shift in whole-genome assembly in terms of algorithms and supporting steps. We also summarize (i) hybrid assemblies using both short and long reads and (ii) overlap-based assemblies for long reads and discuss their challenges and future prospects. This review provides guidelines to determine the optimal approach for a given input data type, computational budget or genome.'),
(69113, 2018, 'bib', 'Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis', 'Rigaill, Guillem|Balzergue, Sandrine|Brunaud, Véronique|Blondet, Eddy|Rau, Andrea|Rogier, Odile|Caius, José|Maugis-Rabusseau, Cathy|Soubigou-Taconnat, Ludivine|Aubourg, Sébastien|Lurin, Claire|Martin-Magniette, Marie-Laure|Delannoy, Etienne', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 3, 'Arabidopsis|Arabidopsis Proteins|Computer Simulation|Datasets as Topic|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|RNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbw092', '27742662', 'Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes. Hence, it provides an evaluation of the key ingredients of the differential analysis, free of the biases associated with the simulation of data using parametric models. Our results show the relevance of a proper modeling of the mean by using linear or generalized linear modeling. Once the mean is properly modeled, the impact of the other parameters on the performance of the test is much less important. Finally, we propose to use the simple visualization of the raw P-value histogram as a practical evaluation criterion of the performance of differential analysis methods on real data sets.'),
(69114, 2018, 'bib', 'Combinatorial epigenetic regulation of non-coding RNAs has profound effects on oncogenic pathways in breast cancer subtypes', 'Xu, Juan|Wang, ZiShan|Li, Shengli|Chen, Juan|Zhang, Jinwen|Jiang, Chunjie|Zhao, Zheng|Li, Jing|Li, Yongsheng|Li, Xia', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 3, 'Biomarkers, Tumor|Breast Neoplasms|Carcinogenesis|Case-Control Studies|DNA Methylation|Epigenesis, Genetic|Female|Gene Expression Regulation, Neoplastic|Histones|Humans|MicroRNAs|RNA, Untranslated|Signal Transduction|Transcriptome', 'Journal Article', '10.1093/bib/bbw099', '27742663', 'Although systematic genomic studies have identified a broad spectrum of non-coding RNAs (ncRNAs) that are involved in breast cancer, our understanding of the epigenetic dysregulation of those ncRNAs remains limited. Here, we systematically analysed the epigenetic alterations of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in two breast cancer subtypes (luminal and basal). Widespread epigenetic alterations of miRNAs and lncRNAs were observed in both cancer subtypes. In contrast to protein-coding genes, the majority of epigenetically dysregulated ncRNAs were shared between subtypes, but a subset of transcriptomic and corresponding epigenetic changes occurred in a subtype-specific manner. In addition, our findings suggested that various types of epi-modifications might synergistically modulate ncRNA transcription. Our observations further highlighted the complementary dysregulation of epi-modifications, particularly of miRNA members within the same family, which produced the same directed alterations as a result of diverse epi-modifications. Functional enrichment analysis revealed that epigenetically dysregulated ncRNAs were significantly involved in several hallmarks of cancers. Finally, our analysis of epigenetic modification-mediated miRNA regulatory networks revealed that cancer progression was associated with specific miRNA-gene modules in two subtypes. This study enhances understanding of the aberrant epigenetic patterns of ncRNA expression and provides new insights into the functions of ncRNAs in breast cancer subtypes.'),
(69115, 2018, 'bib', 'A bioimage informatics platform for high-throughput embryo phenotyping', 'Brown, James M|Horner, Neil R|Lawson, Thomas N|Fiegel, Tanja|Greenaway, Simon|Morgan, Hugh|Ring, Natalie|Santos, Luis|Sneddon, Duncan|Teboul, Lydia|Vibert, Jennifer|Yaikhom, Gagarine|Westerberg, Henrik|Mallon, Ann-Marie', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 3, 'Animals|Automation|Embryo, Mammalian|High-Throughput Screening Assays|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Mice|Mice, Inbred C57BL|Mice, Mutant Strains|Molecular Imaging|Phenotype|Software', 'Journal Article', '10.1093/bib/bbw101', '27742664', 'High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene-phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encompass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for themselves without the need for highly specialized software or technical expertise. In this article, we present a platform of software tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC. Over 750 reconstructions from 80 embryonic lethal and subviable lines have been captured to date, all of which are openly accessible at mousephenotype.org. Although designed for the IMPC, all software is available under an open-source licence for others to use and develop further. Ongoing developments aim to increase throughput and improve the analysis and dissemination of image data. Furthermore, we aim to ensure that images are searchable so that users can locate relevant images associated with genes, phenotypes or human diseases of interest.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69116, 2018, 'bib', 'Notions of similarity for systems biology models', 'Henkel, Ron|Hoehndorf, Robert|Kacprowski, Tim|Knüpfer, Christian|Liebermeister, Wolfram|Waltemath, Dagmar', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Animals|Computational Biology|Computer Simulation|Databases, Factual|Humans|Models, Biological|Signal Transduction|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bib/bbw090', '27742665', 'Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here we survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users intuition about model similarity, and to support complex model searches in databases.'),
(69117, 2018, 'bib', 'Inter-kingdom prediction certainty evaluation of protein subcellular localization tools: microbial pathogenesis approach for deciphering host microbe interaction', 'Khan, Abdul Arif|Khan, Zakir|Kalam, Mohd Abul|Khan, Azmat Ali', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Animals|Bacteria|Bacterial Proteins|Cell Membrane|Computational Biology|Cytoplasm|Databases, Protein|Host-Pathogen Interactions|Humans|Models, Biological|Proteome|Software|Subcellular Fractions', 'Evaluation Studies', '10.1093/bib/bbw093', '27758808', 'Microbial pathogenesis involves several aspects of host-pathogen interactions, including microbial proteins targeting host subcellular compartments and subsequent effects on host physiology. Such studies are supported by experimental data, but recent detection of bacterial proteins localization through computational eukaryotic subcellular protein targeting prediction tools has also come into practice. We evaluated inter-kingdom prediction certainty of these tools. The bacterial proteins experimentally known to target host subcellular compartments were predicted with eukaryotic subcellular targeting prediction tools, and prediction certainty was assessed. The results indicate that these tools alone are not sufficient for inter-kingdom protein targeting prediction. The correct prediction of pathogens protein subcellular targeting depends on several factors, including presence of localization signal, transmembrane domain and molecular weight, etc., in addition to approach for subcellular targeting prediction. The detection of protein targeting in endomembrane system is comparatively difficult, as the proteins in this location are channelized to different compartments. In addition, the high specificity of training data set also creates low inter-kingdom prediction accuracy. Current data can help to suggest strategy for correct prediction of bacterial proteins subcellular localization in host cell.'),
(69118, 2018, 'bib', 'Genome-wide DNA methylation analysis identifies candidate epigenetic markers and drivers of hepatocellular carcinoma', 'Zheng, Yongchang|Huang, Qianqian|Ding, Zijian|Liu, Tingting|Xue, Chenghai|Sang, Xinting|Gu, Jin', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 22, 'Biomarkers, Tumor|Carcinoma, Hepatocellular|CpG Islands|DNA Methylation|Epigenesis, Genetic|Gene Expression Regulation, Neoplastic|Genome, Human|Humans|Liver Neoplasms|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/bbw094', '27760737', 'The alteration of DNA methylation landscape is a key epigenetic event in cancer. As the accumulation of large-scale genome-wide DNA methylation data from clinical samples, we are able to characterize the patterns of DNA methylation alterations for identifying candidate epigenetic markers and drivers. In this survey, we take hepatocellular carcinoma (HCC) as an example to show the basic steps of analyzing the DNA methylation patterns in cancer across multiple data sets. We collected three genome-wide DNA methylation data sets with xe2x88xbc800 clinical samples and the corresponding gene expression data sets. First, by quantitatively analyzing two global methylation alterations, it is found that about 90% tumors acquire either genome-wide DNA hypo-methylation or CpG island methylator phenotype. Second, probe-level analysis identified 267, 228 and 197 hyper-methylated sites in promoter regions for the three data sets, respectively. These local hyper-methylated patterns are highly consistent: 84 sites (from 61 promoters) are hyper-methylated in all the three studied data sets, including many previously reported genes, such as CDKL2, TBX15 and NKX6-2. Then, these hyper-methylated sites were used as candidate markers to classify tumor and non-tumor samples. The classifiers based on only 10 selected probes can achieve high discriminative ability across different data sets. Finally, by integrative analyzing DNA methylation and gene expression data, we identified 222 candidate epigenetic drivers, which are enriched in inflammatory response and multiple metabolic pathways. A set of high-confidence candidates, including SFN, SPP1 and TKT, are significantly associated with patients overall survivals. In summary, this study systematically characterized the DNA methylation alterations and their impacts on gene expressions in HCCs based on multiple data sets.'),
(69119, 2018, 'bib', 'The framework for population epigenetic study', 'Zhao, Linna|Liu, Di|Xu, Jing|Wang, Zhaoyang|Chen, Yang|Lei, Changgui|Li, Ying|Liu, Guiyou|Jiang, Yongshuai', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'African Continental Ancestry Group|Alleles|Cells, Cultured|DNA Methylation|Epigenesis, Genetic|European Continental Ancestry Group|Genetics, Population|HapMap Project|Haplotypes|Humans|Linkage Disequilibrium|Lymphocytes|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbw098', '27760738', 'At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block and methylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and shared many MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study.'),
(69120, 2018, 'bib', 'Computational pan-genomics: status, promises and challenges', 'Computational Pan-Genomics Consortium', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia', 21, 'Algorithms|Computational Biology|Genome, Human|Genomics|Humans|Software', 'Journal Article', '10.1093/bib/bbw089', '27769991', 'Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.'),
(69121, 2018, 'bib', 'An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq', 'Xu, Maoqi|Chen, Liang', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 0, 'Biomarkers, Tumor|Case-Control Studies|Computer Simulation|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Lung Neoplasms|Male|Models, Statistical|Prostatic Neoplasms|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbw103', '27769992', 'The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without making any assumption about read-count distribution. It also incorporates a constraint for the read-count overdispersion, which is widely observed in RNA-seq data. ELTSeq demonstrates a significant improvement over existing methods such as edgeR, DESeq, t-tests, Wilcoxon tests and the classic empirical likelihood-ratio test when handling heterogeneous groups. It will significantly advance the transcriptomics studies of cancers and other complex disease.'),
(69122, 2018, 'bib', 'Transferring entropy to the realm of GxG interactions', 'Ferrario, Paola G|König, Inke R', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia', 0, 'Entropy|Epistasis, Genetic|Genetic Variation|Genome-Wide Association Study|Genomics|Humans|Information Theory|Models, Genetic|Models, Statistical', 'Journal Article', '10.1093/bib/bbw086', '27769993', 'Genome-wide association studies are moving to genome-wide interaction studies, as the genetic background of many diseases appears to be more complex than previously supposed. Thus, many statistical approaches have been proposed to detect gene-gene (GxG) interactions, among them numerous information theory-based methods, inspired by the concept of entropy. These are suggested as particularly powerful and, because of their nonlinearity, as better able to capture nonlinear relationships between genetic variants and/or variables. However, the introduced entropy-based estimators differ to a surprising extent in their construction and even with respect to the basic definition of interactions. Also, not every entropy-based measure for interaction is accompanied by a proper statistical test. To shed light on this, a systematic review of the literature is presented answering the following questions: (1) How are GxG interactions defined within the framework of information theory? (2) Which entropy-based test statistics are available? (3) Which underlying distribution do the test statistics follow? (4) What are the given strengths and limitations of these test statistics?'),
(69123, 2018, 'bib', 'Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI', 'An, Yi|Wang, Jiawei|Li, Chen|Leier, André|Marquez-Lago, Tatiana|Wilksch, Jonathan|Zhang, Yang|Webb, Geoffrey I|Song, Jiangning|Lithgow, Trevor', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 26, 'Algorithms|Amino Acid Sequence|Amino Acids|Bacteria|Bacterial Proteins|Bacterial Secretion Systems|Gene Expression Regulation, Bacterial|Genome, Bacterial|Host-Pathogen Interactions|Humans|Position-Specific Scoring Matrices|Software', 'Journal Article', '10.1093/bib/bbw100', '27777222', 'Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS.'),
(69124, 2018, 'bib', 'A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles', 'Ng, Felicia S L|Ruau, David|Wernisch, Lorenz|Göttgens, Berthold', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.', 'Australia|Australia|Australia|Australia', 1, 'Algorithms|Animals|Binding Sites|Cells, Cultured|Chromatin Immunoprecipitation|Computational Biology|Computer Graphics|Gene Expression Regulation|Genome|Hematopoietic Stem Cells|Leukemia|Mice|Models, Statistical|Protein Binding|Transcription Factors', 'Journal Article', '10.1093/bib/bbw102', '27780826', 'Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish direct versus indirect TF interactions. We applied four algorithms to measure direct dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq samples and obtained a consensus network known as a TF association network where edges in the network corresponded to likely causal pairwise relationships between TFs. The TF association network illustrates the role of TFs in developmental pathways, is reminiscent of combinatorial TF regulation, corresponds to known protein-protein interactions and indicates substantial TF-binding reorganization in leukemic cell types. With the rapid increase in TF ChIP-Seq data sets, the approach presented here will be a powerful tool to study transcriptional programmes across a wide range of biological systems.'),
(69125, 2018, 'bib', 'Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening', 'Paricharak, Shardul|Méndez-Lucio, Oscar|Chavan Ravindranath, Aakash|Bender, Andreas|IJzerman, Adriaan P|van Westen, Gerard J P', 'Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom.|Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, RA Leiden, The Netherlands.|Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, RA Leiden, The Netherlands.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Netherlands|Netherlands', 1, 'Animals|Data Collection|Drug Design|Drug Discovery|High-Throughput Screening Assays|Humans|Models, Molecular|Small Molecule Libraries|Structure-Activity Relationship', 'Journal Article', '10.1093/bib/bbw105', '27789427', 'High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.'),
(69126, 2018, 'bib', 'A large-scale comparative assessment of methods for residue-residue contact prediction', 'Wuyun, Qiqige|Zheng, Wei|Peng, Zhenling|Yang, Jianyi', 'School of Mathematical Sciences, Nankai University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.|Center for Applied Mathematics, Tianjin University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|China|China', 9, 'Algorithms|Computational Biology|Humans|Models, Molecular|Protein Conformation|Protein Folding|Protein Interaction Domains and Motifs|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bib/bbw106', '27802931', 'Sequence-based prediction of residue-residue contact in proteins becomes increasingly more important for improving protein structure prediction in the big data era. In this study, we performed a large-scale comparative assessment of 15 locally installed contact predictors. To assess these methods, we collected a big data set consisting of 680 nonredundant proteins covering different structural classes and target difficulties. We investigated a wide range of factors that may influence the precision of contact prediction, including target difficulty, structural class, the alignment depth and distribution of contact pairs in a protein structure. We found that: (1) the machine learning-based methods outperform the direct-coupling-based methods for short-range contact prediction, while the latter are significantly better for long-range contact prediction. The consensus-based methods, which combine machine learning and direct-coupling methods, perform the best. (2) The target difficulty does not have clear influence on the machine learning-based methods, while it does affect the direct-coupling and consensus-based methods significantly. (3) The alignment depth has relatively weak effect on the machine learning-based methods. However, for the direct-coupling-based methods and consensus-based methods, the predicted contacts for targets with deeper alignment tend to be more accurate. (4) All methods perform relatively better on xcexb2 and xcexb1xe2x80x89+xe2x80x89xcexb2 proteins than on xcexb1 proteins. (5) Residues buried in the core of protein structure are more prone to be in contact than residues on the surface (22 versus 6%). We believe these are useful results for guiding future development of new approach to contact prediction.'),
(69127, 2018, 'bib', 'Evaluation of computational programs to predict HLA genotypes from genomic sequencing data', 'Bauer, Denis C|Zadoorian, Armella|Wilson, Laurence O W|Melbourne Genomics Health Alliance|Thorne, Natalie P', 'CSIRO, Sydney, Australia.|CSIRO, Sydney, Australia.|CSIRO, Sydney, Australia.|CSIRO, Sydney, Australia.|Murdoch Childrens Research Institute, Royal Childrens Hospital, Parkville, Australia.', 'Australia|Australia|Australia|Australia|Australia', 9, 'Algorithms|Computational Biology|Exome|Genotype|HLA Antigens|Histocompatibility Testing|Humans|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bib/bbw097', '27802932', 'Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative.'),
(69128, 2018, 'bib', 'Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences', 'Manzoni, Claudia|Kia, Demis A|Vandrovcova, Jana|Hardy, John|Wood, Nicholas W|Lewis, Patrick A|Ferrari, Raffaele', 'School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.|Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.|Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.|Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.|Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.|School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.|Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kin', 24, 'Animals|Biomedical Research|Genome, Human|Humans|Proteome|Systems Biology|Transcriptome', 'Journal Article', '10.1093/bib/bbw114', '27881428', 'Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.'),
(69129, 2018, 'bib', 'A review of validation strategies for computational drug repositioning', 'Brown, Adam S|Patel, Chirag J', 'Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.|Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.', 'United Kingdom|United Kingdom', 18, 'Computational Biology|Computer Simulation|Databases, Factual|Drug Repositioning|Humans|Validation Studies as Topic', 'Journal Article', '10.1093/bib/bbw110', '27881429', 'Repositioning of previously approved drugs is a promising methodology because it reduces the cost and duration of the drug development pipeline and reduces the likelihood of unforeseen adverse events. Computational repositioning is especially appealing because of the ability to rapidly screen candidates in silico and to reduce the number of possible repositioning candidates. What is unclear, however, is how useful such methods are in producing clinically efficacious repositioning hypotheses. Furthermore, there is no agreement in the field over the proper way to perform validation of in silico predictions, and in fact no systematic review of repositioning validation methodologies. To address this unmet need, we review the computational repositioning literature and capture studies in which authors claimed to have validated their work. Our analysis reveals widespread variation in the types of strategies, predictions made and databases used as gold standards. We highlight a key weakness of the most commonly used strategy and propose a path forward for the consistent analytic validation of repositioning techniques.'),
(69130, 2018, 'bib', 'A comprehensive review and comparison of different computational methods for protein remote homology detection', 'Chen, Junjie|Guo, Mingyue|Wang, Xiaolong|Liu, Bin', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.', 'China|China|China|China', 44, 'Computational Biology|Humans|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bib/bbw108', '27881430', 'Protein remote homology detection is one of the most fundamental and central problems for the studies of protein structures and functions, aiming to detect the distantly evolutionary relationships among proteins via computational methods. During the past decades, many computational approaches have been proposed to solve this important task. These methods have made a substantial contribution to protein remote homology detection. Therefore, it is necessary to give a comprehensive review and comparison on these computational methods. In this article, we divide these computational approaches into three categories, including alignment methods, discriminative methods and ranking methods. Their advantages and disadvantages are discussed in a comprehensive perspective, and their performance is compared on widely used benchmark data sets. Finally, some open questions in this field are further explored and discussed.'),
(69131, 2018, 'bib', 'Predictive approaches for drug combination discovery in cancer', 'Madani Tonekaboni, Seyed Ali|Soltan Ghoraie, Laleh|Manem, Venkata Satya Kumar|Haibe-Kains, Benjamin', 'Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.', 'Canada|Canada|Canada|Canada', 6, 'Animals|Antineoplastic Combined Chemotherapy Protocols|Computational Biology|Drug Discovery|Humans|Neoplasms', 'Journal Article', '10.1093/bib/bbw104', '27881431', 'Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.'),
(69132, 2018, 'bib', 'What (not) to expect when classifying rare events', 'Blagus, Rok|Goeman, Jelle J', 'Univerza v Ljubljani Medicinska Fakulteta, Institute for Biostatistics and Medical Informatics, Leiden, The Netherlands.|Leiden University Medical Center, Department of Medical Statistics and Bioinformatics, Leiden, The Netherlands.', 'Netherlands|Netherlands', 4, 'Computer Simulation|Databases, Factual|Diagnosis, Computer-Assisted|Humans|Models, Biological|Software', 'Journal Article', '10.1093/bib/bbw107', '27881432', 'When building classifiers, it is natural to require that the classifier correctly estimates the event probability (Constraint 1), that it has equal sensitivity and specificity (Constraint 2) or that it has equal positive and negative predictive values (Constraint 3). We prove that in the balanced case, where there is equal proportion of events and non-events, any classifier that satisfies one of these constraints will always satisfy all. Such unbiasedness of events and non-events is much more difficult to achieve in the case of rare events, i.e. the situation in which the proportion of events is (much) smaller than 0.5. Here, we prove that it is impossible to meet all three constraints unless the classifier achieves perfect predictions. Any non-perfect classifier can only satisfy at most one constraint, and satisfying one constraint implies violating the other two constraints in a specific direction. Our results have implications for classifiers optimized using g-means or F1-measure, which tend to satisfy Constraints 2 and 1, respectively. Our results are derived from basic probability theory and illustrated with simulations based on some frequently used classifiers.'),
(69133, 2018, 'bib', 'Computational modelling folate metabolism and DNA methylation: implications for understanding health and ageing', 'Mc Auley, Mark T|Mooney, Kathleen M|Salcedo-Sora, J Enrique', 'Department of Chemical Engineering, Thornton Science Park, University of Chester, UK.|Faculty of Health and Social Care, Edge Hill University, Ormskirk, Lancashire, UK.|School of Health Science, Liverpool Hope University, Hope Park, Liverpool, UK.', 'United Kingdom|United Kingdom|United Kingdom', 1, 'Aging|Animals|Computer Simulation|DNA Methylation|Epigenesis, Genetic|Folic Acid|Health Status|Humans|Models, Biological', 'Journal Article', '10.1093/bib/bbw116', '28007697', 'Dietary folates have a key role to play in health, as deficiencies in the intake of these B vitamins have been implicated in a wide variety of clinical conditions. The reason for this is folates function as single carbon donors in the synthesis of methionine and nucleotides. Moreover, folates have a vital role to play in the epigenetics of mammalian cells by supplying methyl groups for DNA methylation reactions. Intriguingly, a growing body of experimental evidence suggests that DNA methylation status could be a central modulator of the ageing process. This has important health implications because the methylation status of the human genome could be used to infer age-related disease risk. Thus, it is imperative we further our understanding of the processes which underpin DNA methylation and how these intersect with folate metabolism and ageing. The biochemical and molecular mechanisms, which underpin these processes, are complex. However, computational modelling offers an ideal framework for handling this complexity. A number of computational models have been assembled over the years, but to date, no model has represented the full scope of the interaction between the folate cycle and the reactions, which governs the DNA methylation cycle. In this review, we will discuss several of the models, which have been developed to represent these systems. In addition, we will present a rationale for developing a combined model of folate metabolism and the DNA methylation cycle.'),
(69134, 2018, 'bib', 'Visualizing and comparing results of different peptide identification methods', 'Mohammed, Yassene|Palmblad, Magnus', 'Center for Proteomics and Metabolomics, Leiden University Medical Center, the Netherlands.|Center for Proteomics and Metabolomics, Leiden University Medical Center, the Netherlands.', 'Netherlands|Netherlands', 0, 'Computer Graphics|Escherichia coli|Escherichia coli Proteins|Peptide Fragments|Proteomics|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bib/bbw115', '28011752', 'In mass spectrometry-based proteomics, peptides are typically identified from tandem mass spectra using spectrum comparison. A sequence search engine compares experimentally obtained spectra with those predicted from protein sequences, applying enzyme cleavage and fragmentation rules. To this, there are two main alternatives: spectral libraries and de novo sequencing. The former compares measured spectra with a collection of previously acquired and identified spectra in a library. De novo attempts to sequence peptides from the tandem mass spectra alone. We here present a theoretical framework and a data processing workflow for visualizing and comparing the results of these different types of algorithms. The method considers the three search strategies as different dimensions, identifies distinct agreement classes and visualizes the complementarity of the search strategies. We have included X! Tandem, SpectraST and PepNovo, as they are in common use and representative for algorithms of each type. Our method allows advanced investigation of how the three search methods perform relatively to each other and shows the impact of the currently used decoy sequences for evaluating the false discovery rates.'),
(69135, 2018, 'bib', 'A review on machine learning principles for multi-view biological data integration', 'Li, Yifeng|Wu, Fang-Xiang|Ngom, Alioune', 'Information and Communications Technologies, National Research Council Canada, Ottawa, Ontario, Canada.|Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.|School of Computer Science, University of Windsor, Windsor, Ontario, Canada.', 'Canada|Canada|Canada', 23, 'Animals|Gene Regulatory Networks|Humans|Machine Learning|Models, Biological|Systems Biology', 'Journal Article', '10.1093/bib/bbw113', '28011753', 'Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions, tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view, kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model, network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network, matrix factorization models have potential to learn interactions among features from different views, and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.'),
(69136, 2018, 'bib', 'Logic programming to infer complex RNA expression patterns from RNA-seq data', 'Weirick, Tyler|Militello, Giuseppe|Ponomareva, Yuliya|John, David|Döring, Claudia|Dimmeler, Stefanie|Uchida, Shizuka', 'Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.|Dr. Senckenberg Institute of Pathology, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 2, 'Gene Expression Profiling|Gene Expression Regulation|HEK293 Cells|High-Throughput Nucleotide Sequencing|Humans|RNA, Long Noncoding|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbw117', '28011754', 'To meet the increasing demand in the field, numerous long noncoding RNA (lncRNA) databases are available. Given many lncRNAs are specifically expressed in certain cell types and/or time-dependent manners, most lncRNA databases fall short of providing such profiles. We developed a strategy using logic programming to handle the complex organization of organs, their tissues and cell types as well as gender and developmental time points. To showcase this strategy, we introduce RenalDB (http://renaldb.uni-frankfurt.de), a database providing expression profiles of RNAs in major organs focusing on kidney tissues and cells. RenalDB uses logic programming to describe complex anatomy, sample metadata and logical relationships defining expression, enrichment or specificity. We validated the content of RenalDB with biological experiments and functionally characterized two long intergenic noncoding RNAs: LOC440173 is important for cell growth or cell survival, whereas PAXIP1-AS1 is a regulator of cell death. We anticipate RenalDB will be used as a first step toward functional studies of lncRNAs in the kidney.'),
(69137, 2018, 'bib', 'Computational modeling of brain pathologies: the case of multiple sclerosis', 'Pappalardo, Francesco|Rajput, Abdul-Mateen|Motta, Santo', 'Department of Drug Sciences, University of Catania, Catania, Italy.|University of Bonn, Bonn, Germany.|Istitute for Applied Calculus (IAC) M. Picone, National Research Council of Italy (CNR), Italy.', 'Italy|Germany|Italy', 0, 'Animals|Brain Diseases|Computer Simulation|Humans|Models, Biological|Multiple Sclerosis', 'Journal Article', '10.1093/bib/bbw123', '28011755', 'The central nervous system is the most complex network of the human body. The existence and functionality of a large number of molecular species in human brain are still ambiguous and mostly unknown, thus posing a challenge to Science and Medicine. Neurological diseases inherit the same level of complexity, making effective treatments difficult to be found. Multiple sclerosis (MS) is a major neurological disease that causes severe inabilities and also a significant social burden on health care system: between 2 and 2.5 million people are affected by it, and the cost associated with it is significantly higher as compared with other neurological diseases because of the chronic nature of the disease and to the partial efficacy of current therapies. Despite difficulties in understanding and treating MS, many computational models have been developed to help neurologists. In the present work, we briefly review the main characteristics of MS and present a selection criteria of modeling approaches.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69138, 2018, 'bib', 'MicroRNAs and their variants in an RNA world: implications for complex interactions and diverse roles in an RNA regulatory network', 'Guo, Li|Liang, Tingming', 'Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China.|Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Science, Nanjing Normal University, Nanjing, China.', 'China|China', 2, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Genetic Variation|Humans|MicroRNAs|Transcriptome', 'Journal Article', '10.1093/bib/bbw124', '28011756', 'Multiple microRNA (miRNA) variant (isomiR) sequences have been identified at miRNA loci, suggesting that the miRNA sequence is not a single sequence but a series of isomiR sequences with sequence and expression heterogeneities. These isomiRs may be considered a large gene family with diverse expression patterns or a mini-gene cluster with high levels of sequence similarity. Although the isomiRs are diverse and have potentially coordinated relationships in regulatory networks via isomiR-isomiR interactions, they are largely unstudied. External interactions with other RNAs also enrich the cross-talk across different RNA molecules. In the present study, we describe miRNAs/isomiRs and their interactions, and methods and platforms. Interactions with small RNAs may be an internal regulatory pattern and an effective means of achieving synergistic regulation, which provides a new angle to explore the small RNA world.'),
(69139, 2018, 'bib', 'Kinase impact assessment in the landscape of fusion genes that retain kinase domains: a pan-cancer study', 'Kim, Pora|Jia, Peilin|Zhao, Zhongming', 'Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.', 'United States|United States|United States', 5, 'Humans|Neoplasms|Oncogene Fusion|Oncogene Proteins, Fusion|Protein Domains|Protein-Tyrosine Kinases|Receptor Protein-Tyrosine Kinases', 'Journal Article', '10.1093/bib/bbw127', '28013235', 'Assessing the impact of kinase in gene fusion is essential for both identifying driver fusion genes (FGs) and developing molecular targeted therapies. Kinase domain retention is a crucial factor in kinase fusion genes (KFGs), but such a systematic investigation has not been done yet. To this end, we analyzed kinase domain retention (KDR) status in chimeric protein sequences of 914 KFGs covering 312 kinases across 13 major cancer types. Based on 171 kinase domain-retained KFGs including 101 kinases, we studied their recurrence, kinase groups, fusion partners, exon-based expression depth, short DNA motifs around the break points and networks. Our results, such as more KDR than 5-kinase fusion genes, combinatorial effects between 3-KDR kinases and their 5-partners and a signal transduction-specific DNA sequence motif in the break point intronic sequences, supported positive selection on 3-kinase fusion genes in cancer. We introduced a degree-of-frequency (DoF) score to measure the possible number of KFGs of a kinase. Interestingly, kinases with high DoF scores tended to undergo strong gene expression alteration at the break points. Furthermore, our KDR gene fusion network analysis revealed six of the seven kinases with the highest DoF scores (ALK, BRAF, MET, NTRK1, NTRK3 and RET) were all observed in thyroid carcinoma. Finally, we summarized common features of effective (highly recurrent) kinases in gene fusions such as expression alteration at break point, redundant usage in multiple cancer types and 3-location tendency. Collectively, our findings are useful for prioritizing driver kinases and FGs and provided insights into KFGs clinical implications.'),
(69140, 2018, 'bib', 'Comparing genome versus proteome-based identification of clinical bacterial isolates', 'Galata, Valentina|Backes, Christina|Laczny, Cxc3xa9dric Christian|Hemmrich-Stanisak, Georg|Li, Howard|Smoot, Laura|Posch, Andreas Emanuel|Schmolke, Susanne|Bischoff, Markus|von Mxc3xbcller, Lutz|Plum, Achim|Franke, Andre|Keller, Andreas', 'Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.|Siemens Healthcare, R&D, 725 Potter Street, Berkeley, CA 94710, USA.|Siemens Healthcare, R&D, 1584 Enterprise Blvd., West Sacramento, CA 95691, USA.|Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.|Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.|Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straxc3x9fe, Campus Building 43, 66421 Homburg/Saar, Germany.|Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straxc3x9fe, Campus Building 43, 66421 Homburg/Saar, Germany.|Curetis GmbH, Max-Eyth-Str. 42, 71088 Holzgeringen, Germany.|Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.', 'Germany|Germany|Germany|Germany|United States|United States|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 1, 'Bacterial Proteins|Genome, Bacterial|Gram-Negative Bacteria|Humans|Proteome|Whole Genome Sequencing', 'Comparative Study', '10.1093/bib/bbw122', '28013236', 'Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful gold standard, the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with kxe2x80x89=31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.'),
(69141, 2017, 'bib', 'Optimization of cell lines as tumour models by integrating multi-omics data', 'Ning, Zhao|Yongjing, Liu|Yunzhen, Wei|Zichuang, Yan|Qiang, Zhang|Cheng, Wu|Zhiqiang, Chang|Yan, Xu', 'Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.|Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrxc3xbccken, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bib/bbw121', '28013237', '-'),
(69142, 2018, 'bib', 'MTGIpick allows robust identification of genomic islands from a single genome', 'Dai, Qi|Bao, Chaohui|Hai, Yabing|Ma, Sheng|Zhou, Tao|Wang, Cong|Wang, Yunfei|Huo, Wenwen|Liu, Xiaoqing|Yao, Yuhua|Xuan, Zhenyu|Chen, Min|Zhang, Michael Q', 'College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA.|Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA.|College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China.|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.|Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA.|Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.|Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA.', 'China|China|China|China|China|China|United States|United States|China|China|United States|United States|United States', 4, 'Algorithms|Genome, Bacterial|Genomic Islands|Genomics|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bib/bbw118', '28025178', 'Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showing multiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizes missed by existing methods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.'),
(69143, 2018, 'bib', 'Measuring the diversity of the human microbiota with targeted next-generation sequencing', 'Finotello, Francesca|Mastrorilli, Eleonora|Di Camillo, Barbara', 'Biocenter, Division of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.|Department of Food Safety, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy.|Department of Information Engineering, University of Padova, Padova, Italy.', 'Austria|Italy|Italy', 3, 'Bacteria|Computational Biology|DNA, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Metagenome|Microbiota|Phylogeny|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bib/bbw119', '28025179', 'The human microbiota is a complex ecological community of commensal, symbiotic and pathogenic microorganisms harboured by the human body. Next-generation sequencing (NGS) technologies, in particular targeted amplicon sequencing of the 16S ribosomal RNA gene (16S-seq), are enabling the identification and quantification of human-resident microorganisms at unprecedented resolution, providing novel insights into the role of the microbiota in health and disease. Once microbial abundances are quantified through NGS data analysis, diversity indices provide valuable mathematical tools to describe the ecological complexity of a single sample or to detect species differences between samples. However, diversity is not a determined physical quantity for which a consensus definition and unit of measure have been established, and several diversity indices are currently available. Furthermore, they were originally developed for macroecology and their robustness to the possible bias introduced by sequencing has not been characterized so far. To assist the reader with the selection and interpretation of diversity measures, we review a panel of broadly used indices, describing their mathematical formulations, purposes and properties, and characterize their behaviour and criticalities in dependence of the data features using simulated data as ground truth. In addition, we make available an R package, DiversitySeq, which implements in a unified framework the full panel of diversity indices and a simulator of 16S-seq data, and thus represents a valuable resource for the analysis of diversity from NGS count data and for the benchmarking of computational methods for 16S-seq.'),
(69144, 2018, 'bib', 'Identification and analysis of the human sex-biased genes', 'Guo, Sisi|Zhou, Yuan|Zeng, Pan|Xu, Guoheng|Wang, Guoqing|Cui, Qinghua', 'Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China.|Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China.|Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China.|Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China.|Department of Pathogenobiology, College of Basic Medicine, Jilin University, Changchun, Jilin, China.|Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, China.', 'China|China|China|China|China|China', 5, 'Evolution, Molecular|Female|Gene Expression Profiling|Gene Expression Regulation|Genes, X-Linked|Genome, Human|Humans|Male|Organ Specificity|Sex Characteristics|Transcriptome', 'Journal Article', '10.1093/bib/bbw125', '28028006', 'Tremendous differences between human sexes are universally observed. Therefore, identifying and analyzing the sex-biased genes are becoming basically important for uncovering the mystery of sex differences and personalized medicine. Here, we presented a computational method to identify sex-biased genes from public gene expression databases. We obtained 1407 female-biased genes (FGs) and 1096 male-biased genes (MGs) across 14 different tissues. Bioinformatics analysis revealed that compared with MGs, FGs have higher evolutionary rate, higher single-nucleotide polymorphism density, less homologous gene numbers and smaller phyletic age. FGs have lower expression level, higher tissue specificity and later expressed stage in body development. Moreover, FGs are highly involved in immune-related functions, whereas MGs are more enriched in metabolic process. In addition, cellular network analysis revealed that MGs have higher degree, more cellular activating signaling and tend to be located in cellular inner space, whereas FGs have lower degree, more cellular repressing signaling and tend to be located in cellular outer space. Finally, the identified sex-biased genes and the discovered biological insights together can be a valuable resource helpful for investigating sex-biased physiology and medicine, for example sex-biased disease diagnosis and therapy, which represents one important aspect of personalized and precision medicine.'),
(69145, 2018, 'bib', 'Sixty-five years of the long march in protein secondary structure prediction: the final stretch?', 'Yang, Yuedong|Gao, Jianzhao|Wang, Jihua|Heffernan, Rhys|Hanson, Jack|Paliwal, Kuldip|Zhou, Yaoqi', 'Insitute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD 4222, Australia.|School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.|Signal Processing Laboratory, Griffith University, Brisbane, 4122, Australia.|Signal Processing Laboratory, Griffith University, Brisbane, 4122, Australia.|Signal Processing Laboratory, Griffith University, Brisbane, 4122, Australia.|Insitute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD 4222, Australia.', 'Australia|China|China|Australia|Australia|Australia|Australia', 13, 'Algorithms|Computational Biology|Databases, Protein|Humans|Models, Theoretical|Neural Networks (Computer)|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1093/bib/bbw129', '28040746', 'Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cxcexb1-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6xc2xa0xc3x85 root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.'),
(69146, 2018, 'bib', 'Statistical method evaluation for differentially methylated CpGs in base resolution next-generation DNA sequencing data', 'Zhang, Yun|Baheti, Saurabh|Sun, Zhifu', 'Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.', '-|-|-', 9, 'Computational Biology|CpG Islands|DNA Methylation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Leukemia, Myelomonocytic, Chronic|Models, Statistical|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bib/bbw133', '28040747', 'High-throughput bisulfite methylation sequencing such as reduced representation bisulfite sequencing (RRBS), Agilent SureSelect Human Methyl-Seq (Methyl-seq) or whole-genome bisulfite sequencing is commonly used for base resolution methylome research. These data are represented either by the ratio of methylated cytosine versus total coverage at a CpG site or numbers of methylated and unmethylated cytosines. Multiple statistical methods can be used to detect differentially methylated CpGs (DMCs) between conditions, and these methods are often the base for the next step of differentially methylated region identification. The ratio data have a flexibility of fitting to many linear models, but the raw count data take consideration of coverage information. There is an array of options in each datatype for DMC detection, however, it is not clear which is an optimal statistical method. In this study, we systematically evaluated four statistic methods on methylation ratio data and four methods on count-based data and compared their performances with regard to type I error control, sensitivity and specificity of DMC detection and computational resource demands using real RRBS data along with simulation. Our results show that the ratio-based tests are generally more conservative (less sensitive) than the count-based tests. However, some count-based methods have high false-positive rates and should be avoided. The beta-binomial model gives a good balance between sensitivity and specificity and is preferred method. Selection of methods in different settings, signal versus noise and sample size estimation are also discussed.'),
(69147, 2018, 'bib', 'How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications', 'Khomtchouk, Bohdan B|Weitz, Edmund|Karp, Peter D|Wahlestedt, Claes', 'Center for Therapeutic Innovation and Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1120 NW 14th St., Miami, FL, USA 33136.|Center for Therapeutic Innovation and Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1120 NW 14th St., Miami, FL, USA 33136.|Center for Therapeutic Innovation and Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1120 NW 14th St., Miami, FL, USA 33136.|Center for Therapeutic Innovation and Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1120 NW 14th St., Miami, FL, USA 33136.', '-|-|-|-', 1, 'Artificial Intelligence|Computational Biology|Programming Languages|Software', 'Journal Article', '10.1093/bib/bbw130', '28040748', 'We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the programmable programming language. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.'),
(69148, 2018, 'bib', 'miRandb: a resource of online services for miRNA research', 'Aghaee-Bakhtiari, Seyed Hamid|Arefian, Ehsan|Lau, Pierre', 'Mashhad University of Medical Sciences, Faculty of Medicine, Department of Medical Biotechnology, Mashhad, Razavi Khorasan, Iran.|University of Tehran, College of Science, School of Biology, Department of Microbiology, Tehran, Tehran, Iran and Stem Cell Technology Research Center, Molecular Biology and Genetic Engineering Department, Tehran, Tehran, Iran.|KU Leuven, VIB Center for the Biology of Disease, Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders, Leuven, Belgium.', '-|-|Belgium', 13, 'Computational Biology|Databases, Factual|Humans|MicroRNAs|Online Systems|Software', 'Journal Article', '10.1093/bib/bbw109', '28049134', 'Recent discovery of thousands of small and large noncoding RNAs, in parallel to technical improvements enabling scientists to study the transcriptome in much higher depth, has resulted in massive data generation. This burst of information prompts the development of easily accessible resources for storage, retrieval and analysis of raw and processed data, and hundreds of Web-based tools dedicated to these tasks have been made available. However, the increasing number and diversity of bioinformatics tools, each covering a specific and specialized area, as well as their redundancies, represent potential sources of complication for end users. To overcome these issues, we are introducing an easy-to-follow classification of microRNA (miRNA)-related bioinformatics tools for biologists interested in studying this important class of small noncoding RNAs. We also developed our miRNA database miRNA algorithmic network database (miRandb) that is a meta-database, which presents a survey ofxe2x80x89>xe2x80x89180 Web-based miRNA databases. These include miRNA sequence, discovery, target prediction, target validation, expression and regulation, functions and their roles in diseases, interactions in cellular pathways and networks and deep sequencing. miRandb recapitulates the diverse possibilities and facilitates that access to the different categories of miRNA resources. Researchers can easily select the category of miRNA information and desired organism, in result eligible databases with their features are presented. This database introducing an easy-to-follow classification of available resources that can facilitate selection of appropriate resources for miRNA-related bioinformatics tools. Finally, we described current shortages and future necessities that assist researchers to use these tools easily. Our database is accessible at http://mirandb.ir.'),
(69149, 2018, 'bib', 'Design of RNAs: comparing programs for inverse RNA folding', 'Churkin, Alexander|Retwitzer, Matan Drory|Reinharz, Vladimir|Ponty, Yann|Waldispühl, Jérôme|Barash, Danny', 'Shamoon College of Engineering and Physics Department at Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Laboratoire dinformatique, École Polytechnique, Palaiseau, France.|School of Computer Science, McGill University, Montréal QC, Canada.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.', 'Israel|Israel|Israel|France|Canada|Israel', 16, 'Algorithms|Animals|Computational Biology|Humans|Models, Molecular|Nucleic Acid Conformation|RNA|RNA Folding|Software', 'Journal Article', '10.1093/bib/bbw120', '28049135', 'Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.'),
(69150, 2018, 'bib', 'Mapping morphological shape as a high-dimensional functional curve', 'Fu, Guifang|Huang, Mian|Bo, Wenhao|Hao, Han|Wu, Rongling', 'Department of Math and Statistics, Utah State University, Logan, Utah, USA 84321.|Data Engineering Center, Shanghai University of Finance and Economics, Shanghai, China 200433.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China 100083.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China 100083.', '-|China|China|-|China', 1, 'Chromosome Mapping|Chromosomes, Plant|Computer Simulation|Genes, Plant|Models, Statistical|Phenotype|Plant Leaves|Populus|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbw111', '28062411', 'Detecting how genes regulate biological shape has become a multidisciplinary research interest because of its wide application in many disciplines. Despite its fundamental importance, the challenges of accurately extracting information from an image, statistically modeling the high-dimensional shape and meticulously locating shape quantitative trait loci (QTL) affect the progress of this research. In this article, we propose a novel integrated framework that incorporates shape analysis, statistical curve modeling and genetic mapping to detect significant QTLs regulating variation of biological shape traits. After quantifying morphological shape via a radius centroid contour approach, each shape, as a phenotype, was characterized as a high-dimensional curve, varying as angle xcexb8 runs clockwise with the first point starting from angle zero. We then modeled the dynamic trajectories of three mean curves and variation patterns as functions of xcexb8. Our framework led to the detection of a few significant QTLs regulating the variation of leaf shape collected from a natural population of poplar, Populus szechuanica var tibetica. This population, distributed at altitudes 2000-4500xc2xa0m above sea level, is an evolutionarily important plant species. This is the first work in the quantitative genetic shape mapping area that emphasizes a sense of function instead of decomposing the shape into a few discrete principal components, as the majority of shape studies do.'),
(69151, 2018, 'bib', 'Plant genome and transcriptome annotations: from misconceptions to simple solutions', 'Bolger, Marie E|Arsova, Borjana|Usadel, Björn', 'Forschungszentrum Jülich, Wilhelm Johnen Str, 52428, Jülich, Germany.|Forschungszentrum Jülich, Wilhelm Johnen Str, 52428, Jülich, Germany.|Forschungszentrum Jülich, Wilhelm Johnen Str, 52428, Jülich, Germany.', 'Germany|Germany|Germany', 21, 'Genome, Plant|Genomics|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Plant Proteins|Plants|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbw135', '28062412', 'Next-generation sequencing has triggered an explosion of available genomic and transcriptomic resources in the plant sciences. Although genome and transcriptome sequencing has become orders of magnitudes cheaper and more efficient, often the functional annotation process is lagging behind. This might be hampered by the lack of a comprehensive enumeration of simple-to-use tools available to the plant researcher. In this comprehensive review, we present (i) typical ontologies to be used in the plant sciences, (ii) useful databases and resources used for functional annotation, (iii) what to expect from an annotated plant genome, (iv) an automated annotation pipeline and (v) a recipe and reference chart outlining typical steps used to annotate plant genomes/transcriptomes using publicly available resources.'),
(69152, 2018, 'bib', 'Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin', 'Guzzi, Pietro Hiram|Milenkovic, Tijana', 'Department of Surgical and Medical Sciences, University Magna Graecia, Catanzaro, 88100 Italy.|Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications (iCeNSA), ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.', 'Italy|United States', 25, 'Algorithms|Computational Biology|Humans|Protein Interaction Mapping|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbw132', '28062413', 'Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.'),
(69153, 2018, 'bib', 'Transformation and model choice for RNA-seq co-expression analysis', 'Rau, Andrea|Maugis-Rabusseau, Cathy', 'GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.|Institut de Mathématiques de Toulouse, INSA de Toulouse, Université de Toulouse, 31400 Toulouse, France.', 'France|France', 19, 'Animals|Computational Biology|Drosophila melanogaster|Embryo, Mammalian|Female|Fetus|High-Throughput Nucleotide Sequencing|Humans|Intestine, Small|Male|Mice|Models, Statistical|Neocortex|Swine', 'Journal Article', '10.1093/bib/bbw128', '28065917', 'Although a large number of clustering algorithms have been proposed to identify groups of co-expressed genes from microarray data, the question of if and how such methods may be applied to RNA sequencing (RNA-seq) data remains unaddressed. In this work, we investigate the use of data transformations in conjunction with Gaussian mixture models for RNA-seq co-expression analyses, as well as a penalized model selection criterion to select both an appropriate transformation and number of clusters present in the data. This approach has the advantage of accounting for per-cluster correlation structures among samples, which can be strong in RNA-seq data. In addition, it provides a rigorous statistical framework for parameter estimation, an objective assessment of data transformations and number of clusters and the possibility of performing diagnostic checks on the quality and homogeneity of the identified clusters. We analyze four varied RNA-seq data sets to illustrate the use of transformations and model selection in conjunction with Gaussian mixture models. Finally, we propose a Bioconductor package coseq (co-expression of RNA-seq data) to facilitate implementation and visualization of the recommended RNA-seq co-expression analyses.'),
(69154, 2018, 'bib', 'Exploiting next-generation sequencing to solve the haplotyping puzzle in polyploids: a simulation study', 'Motazedi, Ehsan|Finkers, Richard|Maliepaard, Chris|de Ridder, Dick', 'Bioinformatics Group, Wageningen University and Research, The Netherlands.|Wageningen UR Plant Breeding, The Netherlands.|Wageningen UR Plant Breeding, The Netherlands.|Bioinformatics Group, Wageningen University and Research, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 20, 'Algorithms|Computer Simulation|Genome, Plant|Genomics|Haplotypes|High-Throughput Nucleotide Sequencing|Polyploidy|Sequence Analysis, DNA|Solanum tuberosum', 'Journal Article', '10.1093/bib/bbw126', '28065918', 'Haplotypes are the units of inheritance in an organism, and many genetic analyses depend on their precise determination. Methods for haplotyping single individuals use the phasing information available in next-generation sequencing reads, by matching overlapping single-nucleotide polymorphisms while penalizing post hoc nucleotide corrections made. Haplotyping diploids is relatively easy, but the complexity of the problem increases drastically for polyploid genomes, which are found in both model organisms and in economically relevant plant and animal species. Although a number of tools are available for haplotyping polyploids, the effects of the genomic makeup and the sequencing strategy followed on the accuracy of these methods have hitherto not been thoroughly evaluated.We developed the simulation pipeline haplosim to evaluate the performance of three haplotype estimation algorithms for polyploids: HapCompass, HapTree and SDhaP, in settings varying in sequencing approach, ploidy levels and genomic diversity, using tetraploid potato as the model. Our results show that sequencing depth is the major determinant of haplotype estimation quality, that 1xe2x80x89kb PacBio circular consensus sequencing reads and Illumina reads with large insert-sizes are competitive and that all methods fail to produce good haplotypes when ploidy levels increase. Comparing the three methods, HapTree produces the most accurate estimates, but also consumes the most resources. There is clearly room for improvement in polyploid haplotyping algorithms.'),
(69155, 2018, 'bib', 'A review of connectivity map and computational approaches in pharmacogenomics', 'Musa, Aliyu|Ghoraie, Laleh Soltan|Zhang, Shu-Dong|Glazko, Galina|Yli-Harja, Olli|Dehmer, Matthias|Haibe-Kains, Benjamin|Emmert-Streib, Frank', 'Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada.|Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry BT47 6SB, Northern Ireland, UK.|University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York 14642, USA.|Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria.|Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada.|Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.', 'Finland|Canada|Ireland|United Kingdom|Finland|Austria|Canada|Finland', 41, 'Computational Biology|Databases, Factual|Gene Expression Profiling|Gene Regulatory Networks|Humans|Pharmacogenetics|Small Molecule Libraries|Transcriptome', 'Journal Article', '10.1093/bib/bbw112', '28069634', 'Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.'),
(69156, 2018, 'bib', 'Evaluating approaches to find exon chains based on long reads', 'Kuosmanen, Anna|Norri, Tuukka|Mäkinen, Veli', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland', 1, 'Chromosomes, Human, Pair 2|Computational Biology|Exons|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA Splicing|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bib/bbw137', '28069635', 'Transcript prediction can be modeled as a graph problem where exons are modeled as nodes and reads spanning two or more exons are modeled as exon chains. Pacific Biosciences third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions. We survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity/precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long-read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy.'),
(69157, 2018, 'bib', 'miRDis: a Web tool for endogenous and exogenous microRNA discovery based on deep-sequencing data analysis', 'Zhang, Hanyuan|Vieira Resende E Silva, Bruno|Cui, Juan', 'Systems Biology and Biomedical Informatics (SBBI) Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.|Systems Biology and Biomedical Informatics (SBBI) Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.|Systems Biology and Biomedical Informatics (SBBI) Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.', 'United States|United States|United States', 5, 'Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|Molecular Sequence Annotation|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbw140', '28073746', 'Small RNA sequencing is the most widely used tool for microRNA (miRNA) discovery, and shows great potential for the efficient study of miRNA cross-species transport, i.e., by detecting the presence of exogenous miRNA sequences in the host species. Because of the increased appreciation of dietary miRNAs and their far-reaching implication in human health, research interests are currently growing with regard to exogenous miRNAs bioavailability, mechanisms of cross-species transport and miRNA function in cellular biological processes. In this article, we present microRNA Discovery (miRDis), a new small RNA sequencing data analysis pipeline for both endogenous and exogenous miRNA detection. Specifically, we developed and deployed a Web service that supports the annotation and expression profiling data of known host miRNAs and the detection of novel miRNAs, other noncoding RNAs, and the exogenous miRNAs from dietary species. As a proof-of-concept, we analyzed a set of human plasma sequencing data from a milk-feeding study where 225 human miRNAs were detected in the plasma samples and 44 show elevated expression after milk intake. By examining the bovine-specific sequences, data indicate that three bovine miRNAs (bta-miR-378, -181* and -150) are present in human plasma possibly because of the dietary uptake. Further evaluation based on different sets of public data demonstrates that miRDis outperforms other state-of-the-art tools in both detection and quantification of miRNA from either animal or plant sources. The miRDis Web server is available at: http://sbbi.unl.edu/miRDis/index.php.'),
(69158, 2018, 'bib', 'Elemental metabolomics', 'Zhang, Ping|Georgiou, Constantinos A|Brusic, Vladimir', 'Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD Australia.|Chemistry Laboratory, Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece.|Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD Australia.', 'Australia|Greece|Australia', 11, 'Elements|Food Analysis|Food Technology|Humans|Mass Spectrometry|Metabolomics|Proteins', 'Journal Article', '10.1093/bib/bbw131', '28077402', 'Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations ofxe2x80x89>xe2x80x8970 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration ofxe2x80x89>xe2x80x8940 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69159, 2018, 'bib', 'Gene co-expression analysis for functional classification and gene-disease predictions', 'van Dam, Sipko|Võsa, Urmo|van der Graaf, Adriaan|Franke, Lude|de Magalhães, João Pedro', 'Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands.|Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands.|Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands.|Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands.|Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.', 'Netherlands|Netherlands|Netherlands|Netherlands|United Kingdom', 178, 'Computational Biology|Disease|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genes|Humans|Phenotype', 'Journal Article', '10.1093/bib/bbw139', '28077403', 'Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.'),
(69160, 2018, 'bib', 'High-throughput sequencing of the T-cell receptor repertoire: pitfalls and opportunities', 'Heather, James M|Ismail, Mazlina|Oakes, Theres|Chain, Benny', 'Massachusetts General Hospital, Boston, MA.|University College of London, Bloomsbury, UK.|University College of London, Bloomsbury, UK.|Division of Infection and Immunity, University College of London, Bloomsbury, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 39, 'Algorithms|Antibody Diversity|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Immunoglobulin Variable Region|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Software|T-Lymphocytes', 'Journal Article', '10.1093/bib/bbw138', '28077404', 'T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information. The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error-free representation of the non-templated additions and deletions that occur. High-level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T-cell receptors. Finally, we discuss the major challenge of linking T-cell receptor sequence to function, and specifically to antigen recognition. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross-reactivity of individual T-cell receptors with the specificity of the overall T-cell immune response. Computational analysis will provide the key to unlock the potential of the T-cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease.'),
(69161, 2018, 'bib', 'VRprofile: gene-cluster-detection-based profiling of virulence and antibiotic resistance traits encoded within genome sequences of pathogenic bacteria', 'Li, Jun|Tai, Cui|Deng, Zixin|Zhong, Weihong|He, Yongqun|Ou, Hong-Yu', 'College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, P.R.China.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.|College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, P.R.China.|Department of microbiology and immunology research, University of Michigan Medical School, Ann Arbor, Michigan, USA.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China|United States|China', 39, 'Bacteria|Chromosome Mapping|Drug Resistance, Microbial|Gene Expression Profiling|Genes, Bacterial|Genome, Bacterial|Humans|Multigene Family|Software|Virulence', 'Journal Article', '10.1093/bib/bbw141', '28077405', 'VRprofile is a Web server that facilitates rapid investigation of virulence and antibiotic resistance genes, as well as extends these trait transfer-related genetic contexts, in newly sequenced pathogenic bacterial genomes. The used backend database MobilomeDB was firstly built on sets of known gene cluster loci of bacterial type III/IV/VI/VII secretion systems and mobile genetic elements, including integrative and conjugative elements, prophages, class I integrons, IS elements and pathogenicity/antibiotic resistance islands. VRprofile is thus able to co-localize the homologs of these conserved gene clusters using HMMer or BLASTp searches. With the integration of the homologous gene cluster search module with a sequence composition module, VRprofile has exhibited better performance for island-like region predictions than the other widely used methods. In addition, VRprofile also provides an integrated Web interface for aligning and visualizing identified gene clusters with MobilomeDB-archived gene clusters, or a variety set of bacterial genomes. VRprofile might contribute to meet the increasing demands of re-annotations of bacterial variable regions, and aid in the real-time definitions of disease-relevant gene clusters in pathogenic bacteria of interest. VRprofile is freely available at http://bioinfo-mml.sjtu.edu.cn/VRprofile.'),
(69162, 2018, 'bib', 'Top considerations for creating bioinformatics software documentation', 'Karimzadeh, Mehran|Hoffman, Michael M', 'Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.', 'Canada|Canada', 6, 'Computational Biology|Documentation|Guidelines as Topic|Humans|Software', 'Journal Article', '10.1093/bib/bbw134', '28088754', 'Investing in documenting your bioinformatics software well can increase its impact and save your time. To maximize the effectiveness of your documentation, we suggest following a few guidelines we propose here. We recommend providing multiple avenues for users to use your research software, including a navigable HTML interface with a quick start, useful help messages with detailed explanation and thorough examples for each feature of your software. By following these guidelines, you can assure that your hard work maximally benefits yourself and others.'),
(69163, 2017, 'bib', 'Notions of similarity for systems biology models', 'Henkel, Ron|Hoehndorf, Robert|Kacprowski, Tim|Knüpfer, Christian|Liebermeister, Wolfram|Waltemath, Dagmar', 'Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bib/bbw146', '28096074', '-'),
(69164, 2018, 'bib', 'hppRNA-a Snakemake-based handy parameter-free pipeline for RNA-Seq analysis of numerous samples', 'Wang, Dapeng', 'Department of Plant Sciences, University of Oxford, Oxford, UK.', 'United Kingdom', 8, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA, Messenger|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbw143', '28096075', 'RNA-Seq technology has been gradually becoming a routine approach for characterizing the properties of transcriptome in terms of organisms, cell types and conditions and consequently a big burden has been put on the facet of data analysis, which calls for an easy-to-learn workflow to cope with the increased demands from a large number of laboratories across the world. We report a one-in-all solution called hppRNA, composed of four scenarios such as pre-mapping, core-workflow, post-mapping and sequence variation detection, written by a series of individual Perl and R scripts, counting on well-established and preinstalled software, irrespective of single-end or paired-end, unstranded or stranded sequencing method. It features six independent core-workflows comprising the state-of-the-art technology with dozens of popular cutting-edge tools such as Tophat-Cufflink-Cuffdiff, Subread-featureCounts-DESeq2, STAR-RSEM-EBSeq, Bowtie-eXpress-edgeR, kallisto-sleuth, HISAT-StringTie-Ballgown, and embeds itself in Snakemake, which is a modern pipeline management system. The core function of this pipeline is turning the raw fastq files into gene/isoform expression matrix and differentially expressed genes or isoforms as well as the identification of fusion genes, single nucleotide polymorphisms, long noncoding RNAs and circular RNAs. Last but not least, this pipeline is specifically designed for performing the systematic analysis on a huge set of samples in one go, ideally for the researchers who intend to deploy the pipeline on their local servers. The scripts as well as the user manual are freely available at https://sourceforge.net/projects/hpprna/.'),
(69165, 2018, 'bib', 'A landscape of synthetic viable interactions in cancer', 'Gu, Yunyan|Wang, Ruiping|Han, Yue|Zhou, Wenbin|Zhao, Zhangxiang|Chen, Tingting|Zhang, Yuanyuan|Peng, Fuduan|Liang, Haihai|Qi, Lishuang|Zhao, Wenyuan|Yang, Da|Guo, Zheng', 'Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Pharmacology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Pharmaceutical Sciences and Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|China|China|United States|China', 2, 'Computational Biology|Drug Resistance, Neoplasm|Gene Expression Profiling|Genes, Lethal|Humans|Mutation|Neoplasm Proteins|Neoplasms|Pharmacogenetics', 'Journal Article', '10.1093/bib/bbw142', '28096076', 'Synthetic viability, which is defined as the combination of gene alterations that can rescue the lethal effects of a single gene alteration, may represent a mechanism by which cancer cells resist targeted drugs. Approaches to detect synthetic viable (SV) interactions in cancer genome to investigate drug resistance are still scarce. Here, we present a computational method to detect synthetic viability-induced drug resistance (SVDR) by integrating the multidimensional data sets, including copy number alteration, whole-exome mutation, expression profile and clinical data. SVDR comprehensively characterized the landscape of SV interactions across 8580 tumors in 32 cancer types by integrating The Cancer Genome Atlas data, small hairpin RNA-based functional experimental data and yeast genetic interaction data. We revealed that the SV interactions are favorable to cells and can predict clinical prognosis for cancer patients, which were robustly observed in an independent data set. By integrating the cancer pharmacogenomics data sets from Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal, we have demonstrated that SVDR enables drug resistance prediction and exhibits high reliability between two databases. To our knowledge, SVDR is the first genome-scale data-driven approach for the identification of SV interactions related to drug resistance in cancer cells. This data-driven approach lays the foundation for identifying the genomic markers to predict drug resistance and successfully infers the potential drug combination for anti-cancer therapy.'),
(69166, 2018, 'bib', 'Feasibility of sample size calculation for RNA-seq studies', 'Poplawski, Alicia|Binder, Harald', 'Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Langenbeckstr, Mainz, Germany.|Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Langenbeckstr, Mainz, Germany.', 'Germany|Germany', 4, 'Feasibility Studies|High-Throughput Nucleotide Sequencing|Humans|Research Design|Sample Size|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bib/bbw144', '28100468', 'Sample size calculation is a crucial step in study design but is not yet fully established for RNA sequencing (RNA-seq) analyses. To evaluate feasibility and provide guidance, we evaluated RNA-seq sample size tools identified from a systematic search. The focus was on whether real pilot data would be needed for reliable results and on identifying tools that would perform well in scenarios with different levels of biological heterogeneity and fold changes (FCs) between conditions. We used simulations based on real data for tool evaluation. In all settings, the six evaluated tools provided widely different answers, which were strongly affected by FC. Although all tools failed for small FCs, some tools can at least be recommended when closely matching pilot data are available and relatively large FCs are anticipated.'),
(69167, 2018, 'bib', 'SmProt: a database of small proteins encoded by annotated coding and non-coding RNA loci', 'Hao, Yajing|Zhang, Lili|Niu, Yiwei|Cai, Tanxi|Luo, Jianjun|He, Shunmin|Zhang, Bao|Zhang, Dejiu|Qin, Yan|Yang, Fuquan|Chen, Runsheng', 'Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of Protein and Peptide Pharmaceuticals and Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of Protein and Peptide Pharmaceuticals and Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China|China|China|China|China', 27, 'Codon|Databases, Factual|Humans|Molecular Sequence Annotation|Proteins|RNA|RNA, Untranslated|Software', 'Journal Article', '10.1093/bib/bbx005', '28137767', 'Small proteins is the general term for proteins with length shorter than 100 amino acids. Identification and functional studies of small proteins have advanced rapidly in recent years, and several studies have shown that small proteins play important roles in diverse functions including development, muscle contraction and DNA repair. Identification and characterization of previously unrecognized small proteins may contribute in important ways to cell biology and human health. Current databases are generally somewhat deficient in that they have either not collected small proteins systematically, or contain only predictions of small proteins in a limited number of tissues and species. Here, we present a specifically designed web-accessible database, small proteins database (SmProt, http://bioinfo.ibp.ac.cn/SmProt), which is a database documenting small proteins. The current release of SmProt incorporates 255 010 small proteins computationally or experimentally identified in 291 cell lines/tissues derived from eight popular species. The database provides a variety of data including basic information (sequence, location, gene name, organism, etc.) as well as specific information (experiment, function, disease type, etc.). To facilitate data extraction, SmProt supports multiple search options, including species, genome location, gene name and their aliases, cell lines/tissues, ORF type, gene type, PubMed ID and SmProt ID. SmProt also incorporates a service for the BLAST alignment search and provides a local UCSC Genome Browser. Additionally, SmProt defines a high-confidence set of small proteins and predicts the functions of the small proteins.'),
(69168, 2018, 'bib', 'How trees allocate carbon for optimal growth: insight from a game-theoretic model', 'Fu, Liyong|Sun, Lidan|Hao, Han|Jiang, Libo|Zhu, Sheng|Ye, Meixia|Tang, Shouzheng|Huang, Minren|Wu, Rongling', 'Center for Computational Biology at Beijing Forestry University, China.|Beijing Key Laboratory of Ornamental Plants Germplasm Innovation & Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture at Beijing Forestry University, Beijing, China.|Department of Statistics at The Pennsylvania State University, USA.|Center for Computational Biology at Beijing Forestry University, Beijing, China.|Jiangsu Key Laboratory for Poplar Germplasm Enhancement and Variety Improvement at Nanjing Forestry University, Nanjing, China.|Center for Computational Biology at Beijing Forestry University, Beijing, China.|Forest Management in the Institute of Forest Resource Information Techniques at Chinese Academy of Forestry, Beijing, China.|Jiangsu Key Laboratory for Poplar Germplasm Enhancement and Variety Improvement at Nanjing Forestry University, Nanjing, China.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.', 'China|China|United States|China|China|China|China|China|China', 0, 'Carbon|Game Theory|Models, Biological|Population Dynamics|Quantitative Trait Loci|Seasons|Trees', 'Journal Article', '10.1093/bib/bbx003', '28158473', 'How trees allocate photosynthetic products to primary height growth and secondary radial growth reflects their capacity to best use environmental resources. Despite substantial efforts to explore tree height-diameter relationship empirically and through theoretical modeling, our understanding of the biological mechanisms that govern this phenomenon is still limited. By thinking of stem woody biomass production as an ecological system of apical and lateral growth components, we implement game theory to model and discern how these two components cooperate symbiotically with each other or compete for resources to determine the size of a tree stem. This resulting allometry game theory is further embedded within a genetic mapping and association paradigm, allowing the genetic loci mediating the carbon allocation of stemwood growth to be characterized and mapped throughout the genome. Allometry game theory was validated by analyzing a mapping data of stem height and diameter growth over perennial seasons in a poplar tree. Several key quantitative trait loci were found to interpret the process and pattern of stemwood growth through regulating the ecological interactions of stem apical and lateral growth. The application of allometry game theory enables the prediction of the situations in which the cooperation, competition or altruism is an optimal decision of a tree to fully use the environmental resources it owns.'),
(69169, 2018, 'bib', 'Methodological implementation of mixed linear models in multi-locus genome-wide association studies', 'Wen, Yang-Jun|Zhang, Hanwen|Ni, Yuan-Li|Huang, Bo|Zhang, Jin|Feng, Jian-Ying|Wang, Shi-Bo|Dunwell, Jim M|Zhang, Yuan-Ming|Wu, Rongling', 'State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|Applied Science, University of British Columbia, Columbia, Canada.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.|School of Agriculture, Policy and Development, University of Reading, Berkshire, UK.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|Public Health Sciences and Statistics and Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA.', 'China|Canada|China|China|China|China|China|United Kingdom|China|United Kingdom', 25, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Bayes Theorem|Computer Simulation|Genome-Wide Association Study|Linear Models|Models, Genetic|Multifactorial Inheritance|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbw145', '28158525', 'The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) withxe2x80x89xe2x89xa4xe2x80x890.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.'),
(69170, 2018, 'bib', 'DNA copy number profiling using single-cell sequencing', 'Wang, Xuefeng|Chen, Hao|Zhang, Nancy R', 'H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.|Department of Statistics, University of California, Davis, CA.|Wharton School Statistics Department, University of Pennsylvania.', 'United States|United States|United States', 0, 'Computational Biology|DNA Copy Number Variations|DNA, Neoplasm|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Ploidies|Sequence Analysis, DNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bib/bbx004', '28159966', 'Currently, there is a lack of software for detecting copy number variations and constructing copy number profile for the whole genome from single-cell DNA sequencing data, which are often of low coverage and high technical noises. Here we introduce a new toolkit, SCNV, which features an efficient bin-free segmentation approach and provides the highest resolution possible for breakpoint detection and the subsequent copy number calling. SCNV can auto-tune parameters based on a set of normal cells from the same batch to adjust for the technical noise level of the data, facilitating its application to data gathered from different platforms and different studies.'),
(69171, 2018, 'bib', 'Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning', 'Shameer, Khader|Glicksberg, Benjamin S|Hodos, Rachel|Johnson, Kipp W|Badgeley, Marcus A|Readhead, Ben|Tomlinson, Max S|OConnor, Timothy|Miotto, Riccardo|Kidd, Brian A|Chen, Rong|Maayan, Avi|Dudley, Joel T', 'Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.|Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.|Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.|Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.|Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.|Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.|Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.|Boston College, Boston, MA, USA.|Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.|Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.|Clinical Genome Informatics, Icahn Institute of Genetics and Multiscale Biology, Mount Sinai Health System, New York, NY.|Mount Sinai Center for Bioinformatics, Mount Sinai Health System, New York, NY.|Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 24, 'Computational Biology|Databases, Factual|Disease|Drug Discovery|Drug Repositioning|Humans|Molecular Epidemiology|Proteins', 'Journal Article', '10.1093/bib/bbw136', '28200013', 'Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.'),
(69172, 2018, 'bib', 'Probabilistic natural mapping of gene-level tests for genome-wide association studies', 'Bao, Feng|Deng, Yue|Du, Mulong|Ren, Zhiquan|Zhang, Qingzhao|Zhao, Yanyu|Suo, Jinli|Zhang, Zhengdong|Wang, Meilin|Dai, Qionghai', 'Department of Automation, Tsinghua University, China.|School of Pharmacy, University of California, San Francisco, USA.|Department of Environmental Genomics, Nanjing Medical University, China.|Department of Automation, Tsinghua University, China.|School of Economics and Wang Yanan Institute for Studies in Economics, Xiamen University, China.|Department of Biomedical Engineering, Boston University, USA.|Department of Automation, Tsinghua University, China.|Department of Genetic Toxicology, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, and a PI in Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center For Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.|Department of Genetic Toxicology, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, and a PI in Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center For Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.|Department of Automation, Tsinghua University, China.', 'China|United States|China|China|China|United States|China|China|China|China', 2, 'Biomarkers, Tumor|Genome-Wide Association Study|Genomics|Genotype|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Stomach Neoplasms', 'Journal Article', '10.1093/bib/bbx002', '28200018', 'Genome-wide association studies (GWASs) generally focus on a single marker, which limits the elucidation of the genetic architecture of complex traits. Herein, we present a new computational framework, termed probabilistic natural mapping (PALM), for performing gene-level association tests. PALM robustly reveals the inherent genomic structures of genes and generates feature representations that can be seamlessly incorporated into conventional statistic tests. Our approach substantially improves the effectiveness of uncovering associations derived from a subgroup of variants with weak effects, which represents a known challenge associated with existing methods. We applied PALM in a gastric cancer GWAS and identified two additional gastric cancer-associated susceptibility genes, NOC3L and RUNDC2A. The robust susceptibility discoveries of PALM are widely supported by existing studies from other biological perspectives. PALM will be useful for further GWAS analytical strategies that use gene-level analyses.'),
(69173, 2018, 'bib', 'A simple way to detect disease-associated cellular molecular alterations from mixed-cell blood samples', 'Hong, Guini|Li, Hongdong|Li, Mengyao|Zheng, Weicheng|Li, Jing|Chi, Meirong|Cheng, Jun|Guo, Zheng', 'Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.|Department of Bioinformatics, Fujian Medical University, Fuzhou, China.', 'China|China|China|China|China|China|China|China', 2, 'Biomarkers|Computer Simulation|Gene Expression Profiling|Humans|Lung Neoplasms|Lupus Erythematosus, Systemic', 'Journal Article', '10.1093/bib/bbx009', '28200092', 'Blood is a promising surrogate for solid tissue to investigate disease-associated molecular biomarkers. However, proportion changes of the constituent cells in the often-used peripheral whole blood (PWB) or peripheral blood mononuclear cell (PBMC) samples may influence the detection of cell-specific alterations under disease states. We propose a simple method, Ref-REO, to detect molecular alterations in leukocytes using the mixed-cell blood samples. The method is based on the predetermined within-sample relative expression orderings (REOs) of genes in purified leukocytes of healthy people. Both the simulated and real mixed-cell blood gene expression profiles were used to evaluate the method. Approximately 99% of the differentially expressed genes (DEGs) detected by Ref-REO in the simulated mixed-cell data are owing to the transcriptional alterations in leukocytes rather than the proportion changes of leukocytes. For the real mixed-cell data, the DEGs detected by Ref-REO in the PBMCs expression data for systemic lupus erythematosus (SLE) patients overlap significantly with the DEGs detected in the expression data of SLE CD4xe2x80x89+xe2x80x89T cells and B cells and they are mainly enriched with mRNA editing and interferon-associated genes. The detected DEGs in the PWB data for lung carcinoma patients are significantly enriched with coagulation-associated functional categories that are closely associated with cancer progression. In conclusion, the proposed method is capable of detecting the disease-associated leukocyte-specific molecular alterations, using mixed-cell blood samples, which provides simple, transferable and easy-to-use candidates for disease biomarkers.'),
(69174, 2018, 'bib', 'Benchmarking CRISPR on-target sgRNA design', 'Yan, Jifang|Chuai, Guohui|Zhou, Chi|Zhu, Chenyu|Yang, Jing|Zhang, Chao|Gu, Feng|Xu, Han|Wei, Jia|Liu, Qi', 'School of Life Sciences, Tongji University, China.|School of Life Sciences, Tongji University, China.|School of Life Sciences, Tongji University, China.|School of Life Sciences, Tongji University, China.|School of Life Sciences, Tongji University, China.|School of Life Sciences, Tongji University, China.|School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, China.|Dana-Farber Cancer Institute, Harvard School of Public Health, USA.|R&D Information, Innovation Center China, AstraZeneca, Shanghai, China.|School of Life Sciences, Tongji University, China.', 'China|China|China|China|China|China|China|United States|China|China', 6, 'Benchmarking|CRISPR-Cas Systems|Computer Simulation|Gene Editing|Humans|RNA, Guide', 'Journal Article', '10.1093/bib/bbx001', '28203699', 'CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based gene editing has been widely implemented in various cell types and organisms. A major challenge in the effective application of the CRISPR system is the need to design highly efficient single-guide RNA (sgRNA) with minimal off-target cleavage. Several tools are available for sgRNA design, while limited tools were compared. In our opinion, benchmarking the performance of the available tools and indicating their applicable scenarios are important issues. Moreover, whether the reported sgRNA design rules are reproducible across different sgRNA libraries, cell types and organisms remains unclear. In our study, a systematic and unbiased benchmark of the sgRNA predicting efficacy was performed on nine representative on-target design tools, based on six benchmark data sets covering five different cell types. The benchmark study presented here provides novel quantitative insights into the available CRISPR tools.'),
(69175, 2018, 'bib', 'Genome-wide characterization of lncRNAs in acute myeloid leukemia', 'Lei, Lijun|Xia, Siyu|Liu, Dan|Li, Xiaoqing|Feng, Jing|Zhu, Yaqi|Hu, Jun|Xia, Linjian|Guo, Lieping|Chen, Fei|Cheng, Hui|Chen, Ke|Hu, Hanyang|Chen, Xiaohua|Li, Feng|Zhong, Shan|Mittal, Nupur|Yang, Guohua|Qian, Zhijian|Han, Leng|He, Chunjiang', 'School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, China.|International School of Software, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|Physician in Eastern Hepatobiliary Hospital, Hospital of Second Military Medical University, Wuhan, Hubei, China.|Zhongnan hospital of Wuhan University, Wuhan, China.|Changhai Hospital, Second Military Medical University, Shanghai, China.|Department of Medicine and Cancer Research Center, University of Illinois at Chicago, Chicago, USA.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|Department of Medicine and Cancer Research Center, University of Illinois at Chicago, Chicago, USA.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.|Department of Medicine and Cancer Research Center, University of Illinois at Chicago, Chicago, USA.|Department of Biochemistry and Molecular Biology, University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA.|School of Basic Medical Sciences, Wuhan University, Wuhan, China.', 'China|China|China|China|China|China|China|China|China|China|China|United States|China|China|China|China|United States|China|', 20, 'Case-Control Studies|Gene Expression Regulation, Neoplastic|Histone Deacetylase 2|Humans|Leukemia, Myeloid, Acute|RNA, Long Noncoding|Transcriptome|Tumor Cells, Cultured', 'Journal Article', '10.1093/bib/bbx007', '28203711', 'Long noncoding RNAs (lncRNAs) are a large family of noncoding RNAs that play a critical role in various normal bioprocesses as well as tumorigenesis. However, the expression patterns and biological functions of lncRNAs in acute leukemia have not been well studied. Here, we performed transcriptome-wide lncRNA expression profiling of acute myeloid leukemia (AML) patient samples, along with non-leukemia control hematopoietic samples. We found that lncRNAs were differentially expressed in AML samples relative to control samples. Notably, we identified that lncRNAs upregulated in AML (relative to the control samples) are associated with a lower degree of DNA methylation and a higher ratio of being bound by transcription factors such as SP1, STAT4, ATF-2 and ELK-1 compared with those downregulated in AML. Moreover, an enrichment of H3K4me3 and a depletion of H3K27me3 were observed in upregulated lncRNAs in AML. Expression patterns of three types of lncRNAs (antisense, enhancer and intergenic lncRNAs) have previously been characterized. Of the identified lncRNAs, we found that high expression level lncRNA LOC285758 is associated with the poor prognosis in AML patients. Furthermore, we found that LOC285758 regulates proliferation of AML cell lines by enhancing the expression of HDAC2, a key factor in carcinogenesis. Collectively, our study depicts a landscape of important lncRNAs in AML and provides novel potential therapeutic targets and prognostic markers for AML treatment.'),
(69176, 2018, 'bib', 'HpQTL: a geometric morphometric platform to compute the genetic architecture of heterophylly', 'Sun, Lidan|Wang, Jing|Zhu, Xuli|Jiang, Libo|Gosik, Kirk|Sang, Mengmeng|Sun, Fengsuo|Cheng, Tangren|Zhang, Qixiang|Wu, Rongling', 'Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, Beijing Forestry University, Beijing, China.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.|Statistical Genetics, Department of Public Health Sciences, Pennsylvania State University, Pennsylvania, USA.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.|Ornamental Genetics, National Engineering Research, Center for Floriculture, Beijing Forestry University Beijing, China.|Ornamental Genetics, National Engineering Research, Center for Floriculture, Beijing Forestry University Beijing, China.|Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.', 'China|China|China|China|United States|China|China|China|China|China', 0, 'Computer Simulation|Environmental Exposure|Plant Leaves|Plant Proteins|Prunus|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bib/bbx011', '28203720', 'Heterophylly, i.e. morphological changes in leaves along the axis of an individual plant, is regarded as a strategy used by plants to cope with environmental change. However, little is known of the extent to which heterophylly is controlled by genes and how each underlying gene exerts its effect on heterophyllous variation. We described a geometric morphometric model that can quantify heterophylly in plants and further constructed an R-based computing platform by integrating this model into a genetic mapping and association setting. The platform, named HpQTL, allows specific quantitative trait loci mediating heterophyllous variation to be mapped throughout the genome. The statistical properties of HpQTL were examined and validated via computer simulation. Its biological relevance was demonstrated by results from a real data analysis of heterophylly in a wood plant, mei (Prunus mume). HpQTL provides a powerful tool to analyze heterophylly and its underlying genetic architecture in a quantitative manner. It also contributes a new approach for genome-wide association studies aimed to dissect the programmed regulation of plant development and evolution.'),
(69177, 2018, 'bib', 'Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media', 'Vilar, Santiago|Friedman, Carol|Hripcsak, George', 'Department of Biomedical Informatics, Columbia University, New York, USA.|Department of Biomedical Informatics, Columbia University, New York, USA.|Department of Biomedical Informatics, Columbia University, New York, USA.', 'United States|United States|United States', 7, 'Computational Biology|Data Mining|Drug Interactions|Drug-Related Side Effects and Adverse Reactions|Electronic Health Records|Humans|Pharmacovigilance|Publications|Social Media|United States|United States Food and Drug Administration', 'Journal Article', '10.1093/bib/bbx010', '28334070', 'Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.'),
(69178, 2018, 'bib', 'Genome-wide analysis reveals that exon methylation facilitates its selective usage in the human transcriptome', 'Li, Shengli|Zhang, Jiwei|Huang, Shenglin|He, Xianghuo', 'Shanghai Medical College, Fudan University, Shanghai, China.|Shanghai Medical College, Fudan University, Shanghai, China.|Shanghai Medical College, Fudan University, Shanghai, China.|Shanghai Medical College, Fudan University, Shanghai, China.', 'China|China|China|China', 12, 'Computational Biology|CpG Islands|DNA Methylation|DNA, Neoplasm|Exons|Genome, Human|Genome-Wide Association Study|High-Throughput Nucleotide Sequencing|Humans|Lung Neoplasms|Prognosis|Promoter Regions, Genetic|Sequence Analysis, RNA|Tissue Distribution|Transcriptome', 'Journal Article', '10.1093/bib/bbx019', '28334074', 'DNA methylation, especially in promoter regions, is a well-characterized epigenetic marker related to gene expression regulation in eukaryotes. However, the role of intragenic DNA methylation in the usage of corresponding exons still remains elusive. In this study, we described the DNA methylome across 10 human tissues. The human genome showed both conserved and varied methylation levels among these tissues. We found that the methylation densities in promoters and first exons were negatively correlated with the corresponding gene expression level. Nevertheless, the methylation densities within introns, internal exons and down 1xe2x80x89kb regions showed weak correlation with gene expression levels. Importantly, we observed a remarkably positive relationship between methylation density and exon expression level of intragenic exons. Notably, skip-in exons were much more methylated than skip-out exons. We also identified 260 exons that showed both differential methylation levels and differential expression levels in lung cancer. Genes harboring these differentially regulated exons were significantly enriched in the cancer hallmark-related biological process. Moreover, a 10-exon signature was identified as a promising prognostic predictor for lung cancer. Our study illuminates the DNA methylome, describes its relationship with gene expression across human tissues and provides new insights into intragenic DNA methylation and exon usage during the transcriptional/alternative splicing process and in cancer.');
