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
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

3650-3651

Subventions

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.

Auteurs

Ales Varabyou (A)

Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211, USA.

Geo Pertea (G)

Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins University, Baltimore, MD 21211, USA.

Christopher Pockrandt (C)

Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.

Mihaela Pertea (M)

Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.

Classifications MeSH