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FindPeaks 3.1: a Tool for Identifying Areas of Enrichment from Massively Parallel Short-read Sequencing Technology

Overview
Journal Bioinformatics
Specialty Biology
Date 2008 Jul 5
PMID 18599518
Citations 156
Authors
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Abstract

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.

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References
1.
Slater G, Birney E . Automated generation of heuristics for biological sequence comparison. BMC Bioinformatics. 2005; 6:31. PMC: 553969. DOI: 10.1186/1471-2105-6-31. View

2.
Morin R, OConnor M, Griffith M, Kuchenbauer F, Delaney A, Prabhu A . Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008; 18(4):610-21. PMC: 2279248. DOI: 10.1101/gr.7179508. View

3.
Lander E, Waterman M . Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics. 1988; 2(3):231-9. DOI: 10.1016/0888-7543(88)90007-9. View

4.
Taylor K, Kramer R, Davis J, Guo J, Duff D, Xu D . Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer Res. 2007; 67(18):8511-8. DOI: 10.1158/0008-5472.CAN-07-1016. View

5.
Barski A, Cuddapah S, Cui K, Roh T, Schones D, Wang Z . High-resolution profiling of histone methylations in the human genome. Cell. 2007; 129(4):823-37. DOI: 10.1016/j.cell.2007.05.009. View