Software for Computing and Annotating Genomic Ranges

By Michael F. Lawrence, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan, Vincent J. Carey. PLoS Comput. Biol., 2013

4,046 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f66932081e0847c30c40afbc6f5dc949afa9a308

How researchers see this paper

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

In Molecular Biology

Citations in the corpus

Tximeta: Reference sequence checksums for provenance identification in RNA-seq (2019, methodology): “The transcript or gene ranges can be easily manipulated using the GenomicRanges or plyranges packages in the Bioconductor ecosystem [30, 41].”
ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization (2015, methodology): “These functions are available via the GenomicFeatures package (Lawrence et al. , 2013).”
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps (2015, methodology): “We used the summarizeOverlaps function from the GenomicRanges Bioconductor package [47] to count the reads mapping per gene.”
Tissue-specific mutation accumulation in human adult stem cells during life (2016, positive): “To determine the replication bias of signatures, we determined whether the point mutations were located in an intermediate, early or late replicating region (as defined above) using GenomicRanges 37 and repeated the NMF on a 288 count matrix (96 trinucleotides × 3 replication timing regions).”
Orchestrating high-throughput genomic analysis with Bioconductor (2015, methodology): “Bioconductor supports representation and analysis of genomic intervals with a “Ranges” infrastructure that encompasses data structures, algorithms and utilities including arithmetic functions, set operations and summarization [4] (Fig.”
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2020, methodology): “Gene counts were computed using the R GenomicAlignments package (Lawrence et al., 2013) and summarizeOverlaps function in ‘IntersectionNotEmpty’ mode for exonic and intronic regions separately.”
QuasR: quantification and annotation of short reads in R (2014, neutral): “Availability and implementation: QuasR is implemented in R and C/Cþþ.”
De novo identification of differentially methylated regions in the human genome (2015, methodology): “We generated 200 GRanges [54] objects from the resultant lists of DMRs for the 200 values of α, each corresponding to a point on the precision–recall curve.”