FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

By Anthony P. Fejes, Gordon Robertson, M. Bilenky, R. Varhol, M. Bainbridge, Steven J. M. Jones. Bioinform., 2008

306 citations recorded by the source corpus.

Summary: Next-generation sequencing can provide insight into protein–DNA association events on a genome-wide scale, and is being applied in an increasing number of applications in genomics and meta-genomics research. However, few software applications are available for interpreting these experiments. We present here an efficient application for use with chromatin-immunoprecipitation (ChIP-Seq) experimental data that includes novel functionality for identifying areas of gene enrichment and transcription factor binding site locations, as well as for estimating DNA fragment size distributions in enriched areas. The FindPeaks application can generate UCSC compatible custom ‘WIG’ track files from aligned-read files for short-read sequencing technology. The software application can be executed on any platform capable of running a Java Runtime Environment. Memory requirements are proportional to the number of sequencing reads analyzed; typically 4 GB permits processing of up to 40 million reads. Availability: The FindPeaks 3.1 package and manual, containing algorithm descriptions, usage instructions and examples, are available at http://www.bcgsc.ca/platform/bioinfo/software/findpeaks Source files for FindPeaks 3.1 are available for academic use. Contact: afejes@bcgsc.ca

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1a01e2de435091cb169cb55313663cdb07689c8d

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Citations in the corpus

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data (2010, methodology): “A number of algorithms and tools have been developed for analyzing the large datasets generated by ChIP-chip (reviewed in [4]) and ChIP-seq experiments [1,5-10].”
Quantitative interaction proteomics and genome-wide profiling of epigenetic histone marks and their readers. (2010, unknown): “Enriched regions were identified by FindPeaks (Fejes et al., 2008).”
oPOSSUM-3: Advanced Analysis of Regulatory Motif Over-Representation Across Genes or ChIP-Seq Datasets (2012, methodology): “…obtained as BED files from Gene Expression Omnibus (GEO accession GSE11431) (Edgar et al. 2002) and processed into peaks (putative TF bound regions) using the FindPeaks algorithm (Fejes et al. 2008) with the following settings: -control -dist_type 1 200 -subpeaks 0.6 -trim 0.2 -duplicatefilter.”
Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data (2011, background): “Received on July 16, 2011; revised on September 20, 2011; accepted on October 6, 2011”
A clustering approach for identification of enriched domains from histone modification ChIP-Seq data (2009, unknown): “FindPeaks (3.2.2.3) and F-Seq (1.8.3) do not use a control library.”