CompMap

CompMap performs allele-specific expression (ASE) analysis by competitive mapping to quantify allele-specific read counts from RNA-seq reads mapped to two parental reference sequences.

Key Features:

• Allele-specific read counting: Generates allele-specific expression counts by leveraging genotype-specific map alignments rather than relying solely on individual SNPs.
• Competitive mapping: Compares reads mapped to two distinct reference sequences and sorts reads based on mapping statistics from each parental alignment.
• Ambiguous read resolution: Resolves ambiguously aligned reads either by proportional distribution according to allele-specific matching read counts or by statistical modeling with a binomial framework.
• Simulation-based error assessment: Simulation studies indicate low error rates in assessing regulatory divergence.

Scientific Applications:

• Evolutionary and population genetics: Analyzes ASE in inter-species and inter-population hybrids alongside parental expression data to infer regulatory changes across the genome.
• Regulatory divergence detection: Detects regulatory sequence variation manifesting as allele-specific expression in heterozygote individuals to study genetic bases of phenotypic differences.

Methodology:

Compares reads mapped to two reference sequences, sorts reads by mapping statistics from each parental alignment, generates allele-specific counts from genotype-specific map alignments, and resolves ambiguous reads by proportional allocation or binomial statistical modeling; implemented in Python.