TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
25 Oct 2021
25 Oct 2021
Historique:
received:
23
11
2020
revised:
12
03
2021
accepted:
03
05
2021
medline:
9
5
2021
pubmed:
9
5
2021
entrez:
8
5
2021
Statut:
ppublish
Résumé
Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input. TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush). Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 33964128
pii: 6272575
doi: 10.1093/bioinformatics/btab342
pmc: PMC8545345
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3650-3651Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG006677
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH123567
Pays : United States
Organisme : NSF
ID : DBI-1759518
Organisme : NIH
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.