WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis
Author(s) -
Daniel R. Zerbino,
Nathan Johnson,
Thomas Juettemann,
Steven P. Wilder,
Paul Flicek
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt737
Subject(s) - computer science , ensembl , mit license , genome , visualization , genome browser , computational biology , license , data mining , genomics , biology , genetics , operating system , gene
Using high-throughput sequencing, researchers are now generating hundreds of whole-genome assays to measure various features such as transcription factor binding, histone marks, DNA methylation or RNA transcription. Displaying so much data generally leads to a confusing accumulation of plots. We describe here a multithreaded library that computes statistics on large numbers of datasets (Wiggle, BigWig, Bed, BigBed and BAM), generating statistical summaries within minutes with limited memory requirements, whether on the whole genome or on selected regions.
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