Allelome.PRO

Detecting allelic biases from high-throughput sequencing data requires an approach that maximises
sensitivity while minimizing false positives. Here we present Allelome.PRO, an automated userfriendly
bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses
of two genetically distinct mouse strains to detect allele-specific expression and chromatin
modifications. Allelome.PRO extends approaches used in previous studies that exclusively analysed
imprinted expression to give a complete picture of the “allelome” by automatically categorising the
allelic expression of all genes in a given cell type into imprinted, strain-biased biallelic or noninformative.
Allelome.PRO offers increased sensitivity to analyse lowly expressed transcripts, together
with a robust false discovery rate empirically calculated from variation in the sequencing data.