Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Detecting allele-specific expression and chromatin modifications is crucial for understanding gene regulation.
• Existing methods often lack sensitivity or are prone to false positives, especially for lowly expressed genes.

Purpose of the Study:

• To develop and validate Allelome.PRO, an automated bioinformatics pipeline for comprehensive allelome analysis.
• To enhance sensitivity and minimize false positives in detecting allele-specific expression and chromatin modifications.

Main Methods:

• Utilized high-throughput sequencing data (RNA-seq and ChIP-seq) from reciprocal crosses of two distinct mouse strains.
• Developed an automated pipeline (Allelome.PRO) to categorize gene allelic expression (imprinted, strain-biased, biallelic, non-informative).
• Empirically calculated a robust false discovery rate and determined allelic ratio cutoffs using mouse embryonic fibroblast data.

Main Results:

• Allelome.PRO successfully categorized the allelic expression of all genes in mouse embryonic fibroblasts, defining the 'allelome'.
• The pipeline demonstrated increased sensitivity for lowly expressed transcripts and a robust false discovery rate.
• Validated RNA-seq findings by detecting differential H3K4me3 enrichment over promoters using ChIP-seq data.

Conclusions:

• Allelome.PRO provides a sensitive and user-friendly approach for comprehensive allelome analysis.
• The pipeline accurately detects allele-specific expression and chromatin modifications, aiding in the identification of cis-regulatory elements.
• This method can be extended to analyze various histone modifications and transcription factor binding sites for genome-wide regulatory element discovery.