The Experts below are selected from a list of 7515 Experts worldwide ranked by ideXlab platform

Wolfgang Huber - One of the best experts on this subject based on the ideXlab platform.

  • BioC 2012: Analyzing RNA-seq data for differential exon usage with the DEXSeq package
    2020
    Co-Authors: Alejandro Reyes, Simon Anders, Wolfgang Huber
    Abstract:

    Abstract This vignette describes how to use the Bioconductor package DEXSeq to detect quantitatively different usage of exons from shotgun RNA sequence (RNA-seq) data. The statistical model is based on generalised linear models of the Negative Binomial family (NB-GLMs) and aims to detect changes between experimental conditions of interest that are significantly larger than the technical and biological variability among replicates. The method is described in Here, the method is demonstrated on the data from the package pasilla. To cite this software, please refer to citation("DEXSeq")

  • RNA-Seq workflow: gene-level exploratory analysis and differential expression
    F1000Research, 2016
    Co-Authors: Michael I Love, Vladislav Kim, Simon Anders, Wolfgang Huber
    Abstract:

    Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

  • orchestrating high throughput genomic analysis with Bioconductor
    Nature Methods, 2015
    Co-Authors: Wolfgang Huber, Simon Anders, Vincent J. Carey, Robert Gentleman, Sean Davis, Héctor Corrada Bravo, Benilton S Carvalho, Marc R J Carlson, Laurent Gatto, Thomas Girke
    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.

  • Printer: Opaque thisv Preface
    2013
    Co-Authors: Robert Gentleman, Wolfgang Huber, Rafael A Irizarry, Vince Carey, Rine Dudoit
    Abstract:

    During the past few years, there have been enormous advances in genomics and molecular biology, which carry the promise of understanding the functioning of whole genomes in a systematic manner. The challenge of interpreting the vast amounts of data from microarrays and other high throughput technologies has led to the development of new tools in the fields of computational biology and bioinformatics, and opened exciting new connections to areas such as chemometrics, exploratory data analysis, statistics, machine learning, and graph theory. The Bioconductor project is an open source and open development software project for the analysis and comprehension of genomic data. It is rooted in the open source statistical computing environment R. This book’s coverage is broad and ranges across most of the key capabilities of the Bioconductor project. Thanks to the hard work and dedication of many developers, a responsive and enthusiastic user community has formed. Although this book is self-contained with respect to the data processing an

  • Software for Computing and Annotating Genomic Ranges
    PLoS Computational Biology, 2013
    Co-Authors: Michael Lawrence, Patrick Aboyoun, Marc Carlson, Hervé Pagès, Martin T Morgan, Wolfgang Huber, Robert Gentleman, Vincent J. Carey
    Abstract:

    We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.

Robert Gentleman - One of the best experts on this subject based on the ideXlab platform.

  • orchestrating high throughput genomic analysis with Bioconductor
    Nature Methods, 2015
    Co-Authors: Wolfgang Huber, Simon Anders, Vincent J. Carey, Robert Gentleman, Sean Davis, Héctor Corrada Bravo, Benilton S Carvalho, Marc R J Carlson, Laurent Gatto, Thomas Girke
    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.

  • Printer: Opaque thisv Preface
    2013
    Co-Authors: Robert Gentleman, Wolfgang Huber, Rafael A Irizarry, Vince Carey, Rine Dudoit
    Abstract:

    During the past few years, there have been enormous advances in genomics and molecular biology, which carry the promise of understanding the functioning of whole genomes in a systematic manner. The challenge of interpreting the vast amounts of data from microarrays and other high throughput technologies has led to the development of new tools in the fields of computational biology and bioinformatics, and opened exciting new connections to areas such as chemometrics, exploratory data analysis, statistics, machine learning, and graph theory. The Bioconductor project is an open source and open development software project for the analysis and comprehension of genomic data. It is rooted in the open source statistical computing environment R. This book’s coverage is broad and ranges across most of the key capabilities of the Bioconductor project. Thanks to the hard work and dedication of many developers, a responsive and enthusiastic user community has formed. Although this book is self-contained with respect to the data processing an

  • Software for Computing and Annotating Genomic Ranges
    PLoS Computational Biology, 2013
    Co-Authors: Michael Lawrence, Patrick Aboyoun, Marc Carlson, Hervé Pagès, Martin T Morgan, Wolfgang Huber, Robert Gentleman, Vincent J. Carey
    Abstract:

    We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.

  • shortread a Bioconductor package for input quality assessment and exploration of high throughput sequence data
    Bioinformatics, 2009
    Co-Authors: Martin Morgan, Patrick Aboyoun, Hervé Pagès, Simon Anders, Michael V Lawrence, Robert Gentleman
    Abstract:

    Summary: ShortRead is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources. Availability and Implementation: This package is implemented in R and available at the Bioconductor web site; the package contains a ‘vignette’ outlining typical work flows. Contact: mtmorgan@fhcrc.org

  • arrayqualitymetrics a Bioconductor package for quality assessment of microarray data
    Bioinformatics, 2009
    Co-Authors: Audrey Kauffmann, Robert Gentleman, Wolfgang Huber
    Abstract:

    Summary:: The assessment of data quality is a major concern in microarray analysis. arrayQualityMetrics is a Bioconductor package that provides a report with diagnostic plots for one or two colour microarray data. The quality metrics assess reproducibility, identify apparent outlier arrays and compute measures of signal-to-noise ratio. The tool handles most current microarray technologies and is amenable to use in automated analysis pipelines or for automatic report generation, as well as for use by individuals. The diagnosis of quality remains, in principle, a context-dependent judgement, but our tool provides powerful, automated, objective and comprehensive instruments on which to base a decision. Availability:: arrayQualityMetrics is a free and open source package, under LGPL license, available from the Bioconductor project at www.Bioconductor.org. A users guide and examples are provided with the package. Some examples of HTML reports generated by arrayQualityMetrics can be found at http://www.microarray-quality.org Contact:: ku.ca.ibe@yerdua Supplementary information:: Supplementary data are available at Bioinformatics online.

Simon Anders - One of the best experts on this subject based on the ideXlab platform.

  • BioC 2012: Analyzing RNA-seq data for differential exon usage with the DEXSeq package
    2020
    Co-Authors: Alejandro Reyes, Simon Anders, Wolfgang Huber
    Abstract:

    Abstract This vignette describes how to use the Bioconductor package DEXSeq to detect quantitatively different usage of exons from shotgun RNA sequence (RNA-seq) data. The statistical model is based on generalised linear models of the Negative Binomial family (NB-GLMs) and aims to detect changes between experimental conditions of interest that are significantly larger than the technical and biological variability among replicates. The method is described in Here, the method is demonstrated on the data from the package pasilla. To cite this software, please refer to citation("DEXSeq")

  • RNA-Seq workflow: gene-level exploratory analysis and differential expression
    F1000Research, 2016
    Co-Authors: Michael I Love, Vladislav Kim, Simon Anders, Wolfgang Huber
    Abstract:

    Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

  • orchestrating high throughput genomic analysis with Bioconductor
    Nature Methods, 2015
    Co-Authors: Wolfgang Huber, Simon Anders, Vincent J. Carey, Robert Gentleman, Sean Davis, Héctor Corrada Bravo, Benilton S Carvalho, Marc R J Carlson, Laurent Gatto, Thomas Girke
    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.

  • Differential expression analysis for sequence count data
    Genome Biology, 2010
    Co-Authors: Simone Anders, Stephan Anders, Simon Anders, Wolfgang Huber
    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.

  • shortread a Bioconductor package for input quality assessment and exploration of high throughput sequence data
    Bioinformatics, 2009
    Co-Authors: Martin Morgan, Patrick Aboyoun, Hervé Pagès, Simon Anders, Michael V Lawrence, Robert Gentleman
    Abstract:

    Summary: ShortRead is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources. Availability and Implementation: This package is implemented in R and available at the Bioconductor web site; the package contains a ‘vignette’ outlining typical work flows. Contact: mtmorgan@fhcrc.org

Gordon K Smyth - One of the best experts on this subject based on the ideXlab platform.

  • the r package rsubread is easier faster cheaper and better for alignment and quantification of rna sequencing reads
    Nucleic Acids Research, 2019
    Co-Authors: Gordon K Smyth, Yang Liao, Wei Shi
    Abstract:

    We present Rsubread, a Bioconductor software package that provides high-performance alignment and read counting functions for RNA-seq reads. Rsubread is based on the successful Subread suite with the added ease-of-use of the R programming environment, creating a matrix of read counts directly as an R object ready for downstream analysis. It integrates read mapping and quantification in a single package and has no software dependencies other than R itself. We demonstrate Rsubread's ability to detect exon-exon junctions de novo and to quantify expression at the level of either genes, exons or exon junctions. The resulting read counts can be input directly into a wide range of downstream statistical analyses using other Bioconductor packages. Using SEQC data and simulations, we compare Rsubread to TopHat2, STAR and HTSeq as well as to counting functions in the Bioconductor infrastructure packages. We consider the performance of these tools on the combined quantification task starting from raw sequence reads through to summary counts, and in particular evaluate the performance of different combinations of alignment and counting algorithms. We show that Rsubread is faster and uses less memory than competitor tools and produces read count summaries that more accurately correlate with true values.

  • the r package rsubread is easier faster cheaper and better for alignment and quantification of rna sequencing reads
    bioRxiv, 2018
    Co-Authors: Yang Liao, Gordon K Smyth, Wei Shi
    Abstract:

    The first steps in the analysis of RNA sequencing (RNA-seq) data are usually to map the reads to a reference genome and then to count reads by gene, by exon or by exon-exon junction. These two steps are at once the most common and also typically the most expensive computational steps in an RNA-seq analysis. These steps are typically undertaken using Unix command-line or Python software tools, even when downstream analysis is to be undertaken using R. We present Rsubread, a Bioconductor software package that provides high-performance alignment and counting functions for RNA-seq reads. Rsubread provides the ease-of-use of the R programming environment, creating a matrix of read counts directly as an R object ready for downstream analysis. It has no software dependencies other than R itself. Using SEQC data and simulations, we compare Rsubread to the popular non-R tools TopHat2, STAR and HTSeq. We also compare to counting functions provided in the Bioconductor infrastructure packages. We show that Rsubread is faster, uses less memory and produces read count summaries that more accurately correlate with true values.

  • rna seq analysis is easy as 1 2 3 with limma glimma and edger
    F1000Research, 2016
    Co-Authors: Gordon K Smyth, Charity W Law, Monther Alhamdoosh, Xueyi Dong, Luyi Tian, Matthew E Ritchie
    Abstract:

    The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

  • edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
    Bioinformatics (Oxford England), 2010
    Co-Authors: Mark D Robinson, Davis J. Mccarthy, Gordon K Smyth
    Abstract:

    SUMMARY It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. AVAILABILITY The package is freely available under the LGPL licence from the Bioconductor web site (http://Bioconductor.org).

Ka Yee Yeung - One of the best experts on this subject based on the ideXlab platform.

  • reproducible Bioconductor workflows using browser based interactive notebooks and containers
    Journal of the American Medical Informatics Association, 2018
    Co-Authors: Reem Almugbel, Linghong Hung, Jiaming Hu, Abeer M Almutairy, Nicole E Ortogero, Yashaswi Tamta, Ka Yee Yeung
    Abstract:

    Objective:Bioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, that allows users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server. Materials and methods:We present four different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets, process RNA-seq data and KinomeScan data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need to install any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder. Results:BiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods. Conclusion:Given the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols, and as ubiquitous.

  • reproducible Bioconductor workflows using browser based interactive notebooks and containers
    bioRxiv, 2017
    Co-Authors: Reem Almugbel, Linghong Hung, Jiaming Hu, Abeer M Almutairy, Nicole E Ortogero, Yashaswi Tamta, Ka Yee Yeung
    Abstract:

    Objective: Bioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, to allow users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server. Materials and methods: We present three different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets and process RNA-seq data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need for installation of any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder. Results: BiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods. Conclusion: Given the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as ubiquitous and necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols.

  • mev r using mev as a graphical user interface for Bioconductor applications in microarray analysis
    Genome Biology, 2008
    Co-Authors: Vu T Chu, Raphael Gottardo, Adrian E Raftery, Roger E Bumgarner, Ka Yee Yeung
    Abstract:

    We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA.