qTeller: a tool for comparative multi-genomic gene expression analysis.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
22 12 2021
Historique:
received: 18 03 2021
revised: 23 07 2021
accepted: 17 08 2021
pubmed: 19 8 2021
medline: 3 2 2023
entrez: 18 8 2021
Statut: ppublish

Résumé

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases. The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 34406385
pii: 6354355
doi: 10.1093/bioinformatics/btab604
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

236-242

Subventions

Organisme : US. Department of Agriculture, Agricultural Research Service
ID : 5030-21000-068-00-D
Organisme : Crop Genetics Research Unit in Ames, Iowa
Organisme : Department of Agriculture, Agricultural Research Service
ID : 58-5030-0-036

Informations de copyright

Published by Oxford University Press 2021. This work is written by US Government employees and is in the public domain in the US.

Auteurs

Margaret R Woodhouse (MR)

USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA.

Shatabdi Sen (S)

Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA.

David Schott (D)

Department of Computer Science, Iowa State University, Ames, IA 50011, USA.

John L Portwood (JL)

USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA.

Michael Freeling (M)

Department of Plant & Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

Justin W Walley (JW)

Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA.

Carson M Andorf (CM)

USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA.
Department of Computer Science, Iowa State University, Ames, IA 50011, USA.

James C Schnable (JC)

Center for Plant Science Innovation & Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.

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Classifications MeSH