regulAS: A Bioinformatics Tool for the Integrative Analysis of Alternative Splicing Regulome using RNA-Seq data

TL;DR

regulAS is a comprehensive bioinformatics tool that automates and customizes the analysis of alternative splicing mechanisms using large-scale RNA-Seq data from cancer and healthy tissues, facilitating research in cancer biology.

Contribution

It introduces an extensible, user-friendly software package that integrates data retrieval, predictive modeling, and reporting for alternative splicing analysis in a reproducible workflow.

Findings

Automates large-scale RNA-Seq data analysis workflows.

Supports integration of multi-omics data for splicing studies.

Enhances reproducibility and customization in splicing research.

Abstract

The regulAS software package is a bioinformatics tool designed to support computational biology researchers in investigating regulatory mechanisms of splicing alterations through integrative analysis of large-scale RNA-Seq data from cancer and healthy human donors, characterized by TCGA and GTEx projects. This technical report provides a comprehensive overview of regulAS, focusing on its core functionality, basic modules, experiment configuration, further extensibility and customisation. The core functionality of regulAS enables the automation of computational experiments, efficient results storage and processing, and streamlined workflow management. Integrated basic modules extend regulAS with features such as RNA-Seq data retrieval from the public multi-omics UCSC Xena data repository, predictive modeling and feature ranking capabilities using the scikit-learn package, and flexible reporting generation for analysing gene expression profiles and relevant modulations of alternative splicing aberrations across tissues and cancer types. Experiment configuration is handled through YAML files with the Hydra and OmegaConf libraries, offering a user-friendly approach. Additionally, regulAS allows for the development and integration of custom modules to handle specialized tasks. In conclusion, regulAS provides an automated solution for alternative splicing and cancer biology studies, enhancing efficiency, reproducibility, and customization of experimental design, while the extensibility of the pipeline enables researchers to further tailor the software package to their specific needs. Source code is available under the MIT license at https://github.com/slipnitskaya/regulAS.