# CompMap: an allele-specific expression read counter based on competitive mapping

## TL;DR

CompMap is a new method for analyzing gene expression differences between species or populations by accurately counting allele-specific reads.

## Contribution

CompMap introduces a streamlined, self-contained approach to allele-specific expression analysis with reduced mapping bias and information loss.

## Key findings

- CompMap accurately sorts and counts reads in homologous regions using parental alignments.
- Ambiguous alignments are resolved using either proportional or statistical methods.
- Simulations show CompMap has low error rates in detecting regulatory divergence.

## Abstract

Gene regulatory changes acting
cis
and
trans
to a gene can be inferred with allele-specific expression (ASE) transcriptomes from interspecies and inter-population hybrids and their parents. Problems of mapping bias and excessive information loss, however, can arise unintentionally from cumbersome analysis pipelines. We introduce CompMap, a self-contained method in Python that generates allele-specific expression counts from genotype-specific alignments. CompMap sorts and counts reads, not just SNPs, by comparing read-mapping statistics to parental alignments within homologous regions. Ambiguous alignments resolve proportionally to allele-specific counts or statistically using a binomial distribution. Simulations with CompMap show low error rates in assessing regulatory divergence.