csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows

By Aaron T. L. Lun, Gordon K. Smyth. Nucleic Acids Research, 2015

414 citations recorded by the source corpus.

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify binding sites for a target protein in the genome. An important scientific application is to identify changes in protein binding between different treatment conditions, i.e. to detect differential binding. This can reveal potential mechanisms through which changes in binding may contribute to the treatment effect. The csaw package provides a framework for the de novo detection of differentially bound genomic regions. It uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data. csaw is implemented as a R software package and is freely available from the open-source Bioconductor project.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bfc9e7c1057802fde3d2c7c6ecc766f4e234af2d

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Citations in the corpus

Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis (2018, methodology): “905 Read counting in specified regions Paired end reads were counted in specified regions using the regionCounts function implemented in the csaw Bioconductor package (Lun and Smyth, 2016).”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, positive): “It has become the underlying analysis engine for a wide range of sequencing technologies including ChIP-seq [ 11 ], Hi-C [ 12 ], BS-seq [ 13 ] and even proteomics [ 14–16 ].”
ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION (2016, methodology): “These functions integrate the robust EB strategy into analysis pipelines for gene expression microarrays and RNA-seq. glmQLFit is also called in analysis pipelines of the csaw and diffHic packages for the analysis of ChIP-seq and Hi-C sequencing data (Lun and Smyth, 2015a,b).”
From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis (2020, methodology): “In contrast, csaw was developed by extending edgeR framework to binned genomes [91].”