ASET: An end-to-end pipeline for quantification and visualization of allele specific expression

Area of Science:

• Genomics
• Transcriptomics
• Bioinformatics

Background:

• Allele-specific expression (ASE) analysis from RNA-Seq data offers insights into imprinting and genetic variants impacting transcription.
• Current ASE analysis involves complex, multi-step computational processes, posing challenges in reproducibility, scalability, and user-friendliness.

Purpose of the Study:

• To present ASE Toolkit (ASET), an integrated pipeline designed to streamline SNP-level ASE data generation, visualization, and parent-of-origin (PofO) effect testing.
• To provide a user-friendly and reproducible solution for molecular and biomedical scientists analyzing ASE.

Main Methods:

• ASET utilizes a modular Nextflow pipeline for ASE quantification from short-read transcriptome sequencing data.
• An R library is incorporated for data visualization, and a Julia script handles PofO testing.
• The pipeline includes quality control, SNP-tolerant alignment, allele- and strand-resolved read counting, gene/exon annotation, and contamination estimation.

Main Results:

• ASET successfully generates SNP-level ASE data, visualizes results, and performs PofO effect testing.
• The toolkit offers a comprehensive solution for analyzing ASE patterns from RNA-Seq data.
• Key features include robust read processing, accurate quantification, and integrated statistical testing.

Conclusions:

• ASET provides a complete, easy-to-use solution for ASE analysis.
• The toolkit enhances the ability of scientists to identify and interpret PofO effects and other ASE patterns.
• ASET addresses the need for reproducible and scalable ASE analysis in molecular and biomedical research.