Orchestrating high-throughput genomic analysis with Bioconductor

By Wolfgang Huber, Vincent J. Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S. Carvalho, Héctor Corrada Bravo, Sean R. Davis, Laurent Gatto, Thomas Girke and 16 more. Nature Methods, 2015

3,336 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/8a2fa093d26ac42002900ae0accfb6b7733add71

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

metaX: a flexible and comprehensive software for processing metabolomics data (2017, background): “At the present time, R [11] is a popular statistical programming environment and provides a convenient environment for statistical analysis of metabolomic and other -omics data [12, 13].”
Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi (2016, methodology): “The minﬁ package in R/Bioconductor (Gentleman et al., 2004; Huber et al., 2015) is a widely used software package for analyzing data from the Illumina HumanMethyla-tion450 array (Aryee et al., 2014).”
It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR (2016, methodology): “edgeR, Rsubread and other packages used in this article are publicly available as part of the Bioconductor project [12].”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “A NN D ATA is similar to R’s E XPRESSION S ET [26], but supports sparse data and allows HDF5-based backing of A NN D ATA objects on disk, a format independent of platform, framework, and language.”
Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants (2015, methodology): “We will also create an R package in Bioconductor (24) for accessing all Atlas data.”
csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows (2015, methodology): “Here we present csaw, a new window-based software package for de novo DB analyses of ChIP-seq data as part of the Bioconductor project (16,17).”
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2015, methodology): “Although RTCGAToolbox can download and analyze Firehose-generated data, neither tool can provide the downloaded data as a ‘Summarized-Experiment’ object, which is critical for allowing the full integration and use of other popular Bioconductor packages, an integral aspect of Bioconductor (21,22).”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Among command line platforms, Scater (McCarthy et al , 2017) and Seurat (Butler et al , 2018) easily interface with the large variety of analysis tools available via the R Bioconductor project (Huber et al , 2015).”
CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data (2015, background): “The fullydocumented R package “Chicago” and the tutorial data package “PCHiCdata” are publicly available [50] and are part of Bioconductor [57].”