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

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Allele-specific expression (ASE) analysis from RNA-sequencing (RNA-Seq) data is crucial for understanding genomic 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 end-to-end pipeline designed to streamline SNP-level ASE data generation, visualization, and parent-of-origin (PofO) effect testing.
• To provide a comprehensive and user-friendly solution for molecular and biomedical scientists.

Main Methods:

• ASET utilizes a modular Nextflow pipeline for ASE quantification from short-read transcriptome sequencing data.
• It incorporates an R library for data visualization and a Julia script for PofO testing.
• The pipeline includes read 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 testing.
• The toolkit addresses challenges in reproducibility and ease of use for complex ASE analyses.

Conclusions:

• ASET offers a complete, integrated solution for identifying and interpreting ASE patterns from RNA-Seq data.
• The toolkit empowers scientists to efficiently study genomic imprinting and transcription-related genetic variants.