Cargando…

WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis

Motivation: 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 p...

Descripción completa

Detalles Bibliográficos
Autores principales: Zerbino, Daniel R., Johnson, Nathan, Juettemann, Thomas, Wilder, Steven P., Flicek, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3967112/
https://www.ncbi.nlm.nih.gov/pubmed/24363377
http://dx.doi.org/10.1093/bioinformatics/btt737
Descripción
Sumario:Motivation: 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. Availability and Implementation: The code is freely available under Apache 2.0 license at www.github.com/Ensembl/Wiggletools Contact: zerbino@ebi.ac.uk or flicek@ebi.ac.uk