Orchestrating high-throughput genomic analysis with Bioconductor

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.

Figure 1

Example uses of the Ranges algebra. A GRanges object, g (top), represents two transcript isoforms of a gene, each with two exons. The coordinates of unspliced transcripts are identified with the function range(g). Calculating the gene region involves flattening the gene model into its constituent exons and reducing these to nonoverlapping ranges, reduce(unlist(g)). Ranges defining disjoint bins, disjoin(unlist(g)), are useful in counting operations, e.g., in RNA-seq analysis. Putative promoter ranges are found using strand-aware range extension, flank(range(g), width=100). Elementary operations can be composed to succinctly execute queries such as psetdiff(range(g), g) for computing the intron ranges.

Figure 2

The integrative data container SummarizedExperiment. Its assays component is one or several rectangular arrays of equivalent row and column dimensions. Rows correspond to features, and columns to samples. The component rowData stores metadata about the features, including their genomic ranges. The colData component keeps track of sample-level covariate data. The exptData component carries experiment-level information, including MIAME (minimum information about a microarray experiment)-structured metadata [21]. The R expressions exemplify how to access components. For instance, provided that these metadata were recorded, rowData(se)$entrezId returns the NCBI Entrez Gene identifiers of the features, and se$tissue returns the tissue descriptions for the samples. Range-based operations, such as %in%, act on the rowData to return a logical vector that selects the features lying within the regions specified by the data object CNVs. Together with the bracket operator, such expressions can be used to subset a SummarizedExperiment to a focused set of genes and tissues for downstream analysis.

Figure 3

Visualization along genomic coordinates with ggbio. The plot shows the gene Apoe alongside RNA-seq data from mouse hematopoietic stem cells (HSC) and four fractions of multipotent progenitor (MPP) cells [22]. The disjoint bins (center) were computed from the four transcript isoforms shown in the bottom panel. The y axis of the top panel shows the relative exon usage coefficients estimated with the DEXSeq method [23]. Regions detected as differentially used between the cell fractions are colored dark red in the center panel.