Orchestrating high-throughput genomic analysis with Bioconductor
Harvard University1, Genentech2, Fred Hutchinson Cancer Research Center3, State University of Campinas4, University of Maryland, College Park5, National Institutes of Health6, University of Cambridge7, University of California, Riverside8, Novartis9, Johns Hopkins University10, University of Washington11, Walter and Eliza Hall Institute of Medical Research12, City University of New York13
TL;DR: An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.
Abstract: Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.
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Northern Arizona University1, National Institutes of Health2, University of Minnesota3, University of California, Davis4, Woods Hole Oceanographic Institution5, Massachusetts Institute of Technology6, University of Copenhagen7, University of Trento8, Chinese Academy of Sciences9, University of California, San Francisco10, University of Pennsylvania11, Pacific Northwest National Laboratory12, North Carolina State University13, University of California, San Diego14, Institute for Systems Biology15, Dalhousie University16, University of British Columbia17, Statens Serum Institut18, Anschutz Medical Campus19, University of Washington20, Michigan State University21, Stanford University22, Harvard University23, Broad Institute24, Australian National University25, University of Düsseldorf26, University of New South Wales27, Sookmyung Women's University28, San Diego State University29, Howard Hughes Medical Institute30, Cornell University31, Max Planck Society32, Colorado State University33, Google34, Syracuse University35, Webster University36, United States Department of Agriculture37, University of Arkansas for Medical Sciences38, Colorado School of Mines39, University of Southern Mississippi40, National Oceanic and Atmospheric Administration41, University of California, Merced42, Wageningen University and Research Centre43, University of Arizona44, Environment Agency45, University of Florida46, Merck & Co.47
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.
8,821 citations
TL;DR: This is a list of winners and nominees for the 2016 Paralympic Games in Rio de Janeiro, Brazil.
Abstract: Hadley Wickham1, Mara Averick1, Jennifer Bryan1, Winston Chang1, Lucy D’Agostino McGowan8, Romain François1, Garrett Grolemund1, Alex Hayes12, Lionel Henry1, Jim Hester1, Max Kuhn1, Thomas Lin Pedersen1, Evan Miller13, Stephan Milton Bache3, Kirill Müller2, Jeroen Ooms14, David Robinson5, Dana Paige Seidel10, Vitalie Spinu4, Kohske Takahashi9, Davis Vaughan1, Claus Wilke6, Kara Woo7, and Hiroaki Yutani11
7,298 citations
Cites background from "Orchestrating high-throughput genom..."
...The closest is perhaps Bioconductor (Gentleman et al., 2004; Huber et al., 2015), which provides an ecosystem of packages that support the analysis of high-throughput genomic data....
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TL;DR: The Perseus software platform was developed to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data and it is anticipated that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
Abstract: A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
5,165 citations
TL;DR: Efforts have been put to improve efficiency, flexibility, support for 'big data' (R's long vectors), ease of use and quality check before a new release of ape.
Abstract: Summary After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users The release of version 50 has marked a leap towards a modern software for evolutionary analyses Efforts have been put to improve efficiency, flexibility, support for 'big data' (R's long vectors), ease of use and quality check before a new release These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity Availability and implementation ape is distributed through the Comprehensive R Archive Network: http://cranr-projectorg/package=ape Further information may be found at http://ape-packageirdfr/
4,303 citations
TL;DR: This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts.
Abstract: High-throughput sequencing of mRNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate and flexible software to reduce the raw read data to comprehensible results. HISAT (hierarchical indexing for spliced alignment of transcripts), StringTie and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol's execution time depends on the computing resources, but it typically takes under 45 min of computer time. HISAT, StringTie and Ballgown are available from http://ccb.jhu.edu/software.shtml.
3,755 citations
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TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Availability: http://samtools.sourceforge.net
Contact: [email protected]
45,957 citations
"Orchestrating high-throughput genom..." refers methods in this paper
...This has allowed high-level code written in R to seamlessly use the functionality of the SAMtools software [19]....
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TL;DR: The Encyclopedia of DNA Elements project provides new insights into the organization and regulation of the authors' genes and genome, and is an expansive resource of functional annotations for biomedical research.
Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.
13,548 citations
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
Abstract: High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
13,356 citations
Harvard University1, Brigham and Women's Hospital2, University of Wisconsin-Madison3, University of California, Berkeley4, Technical University of Denmark5, Icahn School of Medicine at Mount Sinai6, Vienna University of Technology7, University of Erlangen-Nuremberg8, German Cancer Research Center9, University of Milan10, Johns Hopkins University11, University of Washington12, Scripps Research Institute13, Walter and Eliza Hall Institute of Medical Research14, University of Iowa15
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Abstract: The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
12,142 citations
"Orchestrating high-throughput genom..." refers methods in this paper
...Bioconductor [1] provides core data structures and methods that enable genome-scale analysis of high-throughput data in the context of the rich statistical programming environment offered by the R project [2]....
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European Bioinformatics Institute1, TigerLogic2, Stanford University3, University of California, Berkeley4, University of Pennsylvania5, Harvard University6, Imperial College London7, Rockefeller University8, Katholieke Universiteit Leuven9, Utrecht University10, University of Colorado Denver11, Max Planck Society12
TL;DR: The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools.
Abstract: Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.
4,030 citations