1 - 6 of 6 results

Allelome.PRO / Allelome Profiler

A user-friendly pipeline to investigate allele specific features in high-throughput data using any compatible annotation and SNP file. Allelome.PRO extends approaches used in previous studies that exclusively analyzed 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 non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data.


A computational toolbox for allele-specific epigenomics analysis, which incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. ALEA provides a customizable pipeline of command line tools for allele-specific analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. The pipeline has been validated using human and hybrid mouse ChIP-seq and RNA-seq data.


With the evolution of DNA sequencing technology, it is now possible to study expression and DNA binding differences between pairs of sequence alleles on the maternally- and paternally-derived chromosomes within an individual, phenomena known as allele-specific expression (ASE) and allele specific binding (ASB). AlleleSeq constructs a diploid personal genome sequence using genomic sequence variants, and then identifies allele-specific events with significant differences in the number of mapped reads between maternal and paternal alleles.

BaalChIP / Bayesian analysis of allele-specific transcription factor binding in cancer genomes

A package to process multiple ChIP-seq BAM files and detect allele-specific events. BaalChIP computes allele counts at individual variants (SNPs/SNVs), implements extensive quality control steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.