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

Motivation: 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. Results: 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 Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers.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. Availability and implementation: 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

Multi-omics data integration and computational approaches to enhance gene annotations and decipher function

The big-data analysis of multi-omics data associated with maize genomes is increasingly utilized to accelerate genetic research and improve agronomic traits. As a result, efforts have increased to integrate diverse datasets and extract meaning from these measurements. For my Ph.D. dissertation, I have evaluated the current pitfalls of multi-omics data integration and analysis and built platforms that automatically analyze these omics’ datasets. One such platform is qTeller, now designed to handle pan-genome level transcriptomics and proteomics datasets and extract meaningful interpretation from them by providing an interactive user interface. Although genomics and transcriptomics have been more extensively used, other omics technologies, such as epigenomics, variomics, and proteomics, are now often incorporated into standard research methodologies. Therefore, I designed a fully automated platform, called Maize Feature Store (MFS), that allows the integration of complex omics to construct models that can be used to predict complex gene traits or annotations. To demonstrate the utility of the MFS, I critically discussed the application of MFS in pan-genome analysis using only a single maize genome (B73v5) as a multi-omics utility case study. I also aim to utilize these large-scale omics data to solve several other complex biological problems associated with the maize genome and phenome. I aim to continue improving the tools and assisting users in implementing them