qTeller: A tool for comparative multi-genomic gene expression analysis

Authors: Woodhouse, Margaret; SEN, SHATABDI - Iowa State University; SCHOTT, DAVID - Iowa State University; Portwood, John; FREELING, MICHAEL - University Of California; WALLEY, JUSTIN - Iowa State University; Andorf, Carson; SCHNABLE, JAMES - University Of Nebraska

Submitted to: Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/17/2021
Publication Date: 8/18/2021
Citation: Woodhouse, M.H., Sen, S., Schott, D., Portwood II, J.L., Freeling, M., Walley, J.W., Andorf, C.M., Schnable, J.C. 2021. qTeller: A tool for comparative multi-genomic gene expression analysis. Bioinformatics. 38(1): 236-242. https://doi.org/10.1093/bioinformatics/btab604.
DOI: https://doi.org/10.1093/bioinformatics/btab604

Interpretive Summary: One critical method of studying the functions of genes in plants and other organisms is to study gene RNA expression, which is where an organism converts gene sequence to RNA, which then is converted to proteins and enzymes, which power the cells of the organism. Understanding where and how a gene is expressed, such as across different conditions, tissues, and cell types or between different cultivars, is key to understanding gene function. Although many tools exist to examine and compare gene expression, few web-based tools are dedicated to making data originally generated for individual genome analysis accessible and reusable for comparative analysis between genes or across different genomes. To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public gene expression datasets. Though previously unpublished, qTeller has been cited hundreds of times in the scientific literature, demonstrating its importance to researchers. We now present a new version of qTeller that has been updated in several useful ways. qTeller now supports data from multiple maize cultivars, and both RNA and protein abundance data. Other new features include: support for additional data formats, modernized interface, and optimized framework to promote adoption by other organisms’ databases. A working instance of qTeller is available for the maize research community where we have mapped over 200 unique RNA and protein datasets from across 27 maize genomes and made them available through the Maize Genetics and Genomics database.

Technical Abstract: Over the last decade, whole-genome-level expression sequencing using RNA-Seq 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. Common applications for RNA-Seq data include differential gene expression, gene identification, and splicing analysis. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to make data originally generated for individual genomic analysis accessible and reusable at a gene-level scale to allow 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 was originally designed as an RNA-Seq processing pipeline that allowed 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 hundreds of times in the scientific literature, demonstrating its importance to researchers. We now present this version of qTeller that has been updated in several useful ways. qTeller is no longer reference-based, and supports data from multiple genomes and allows for intergenomic comparisons. qTeller’s functionality has been expanded to allow for both mRNA and protein abundance datasets. Other new features include: support for additional data formats, modernized interface and back-end database, and optimized framework for adoption by other model organisms’ databases. A working instance of qTeller is available for the maize research community where we have mapped over 200 unique datasets from GenBank across 27 maize genomes and made them available through the Maize Genetics and Genomics database.