title: WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis.
abstract: BACKGROUND
  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.
  
  
  METHODS
  The code is freely available under Apache 2.0 license at www.github.com/Ensembl/Wiggletools
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Zerbino
authors.forename: Nathan
authors.initials: N
authors.lastname: Johnson
authors.forename: Thomas
authors.initials: T
authors.lastname: Juettemann
authors.forename: Steven P
authors.initials: SP
authors.lastname: Wilder
authors.forename: Paul
authors.initials: P
authors.lastname: Flicek
mesh_terms: D016678:Genome; D015698:Genomic Library; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D020407:Internet; D012984:Software