1 - 23 of 23 results


Evaluates evidence of allelic imbalance and asymmetry of DNA and RNA sequencing datasets. RNA2DNAlign identifies allelic distributions corresponding to the following events: RNA editing (RNAed), variant-specific expression/loss (VSE/VSL), somatic mutagenesis (SOM), and loss of heterozygosity (LOH). RNA2DNAlign possesses several important advantages. First, the simultaneous assessment of a position in multiple matching datasets supports novel nucleotide-resolution analyses. Second, this is the first tool to simultaneously produce eight different outputs of SNVs associated with major molecular events. Third, the read-count output supports numerical operations towards fine quantitation of allelic abundance, including the reference allele-count for positions with no variant.


Allows users to quantify Allele-specific expression (ASE) at both the gene and gene isoform levels. IDP-ASE integrates third generation-sequencing (TGS) and second-generation sequencing (SGS) data with a Bayesian model to determine haplotypes and measure ASE. It is useful to identify and to estimate the abundance of allele-specific isoforms. It can also calculate ASE at the gene isoform level. This tool can be applied to human breast cancer cells (MCF-7 cell line) and human embryonic stem cells (hESCs, H1 cell line).

MBASED / Meta-analysis Based Allele-Specific Expression Detection

Allows users to detect allele-specific expression (ASE) in cancer tissues and cell lines. MBASED assesses allele-specific expression by combining information across individual heterozygous single nucleotide variants (SNVs) within a gene without requiring a priori knowledge of haplotype phasing. This tool can be applied to a wide array of existing RNA-Seq data sets. Moreover, it can serve for investigating allele-specific expression, both within an individual sample and in the context of differential ASE.

QuASAR / Quantitative Allele Specific Analysis of Reads

Jointly detects heterozygous genotypes and infers allele-specific expression (ASE). QuASAR is a statistical learning method that starts from a single or multiple RNA-seq experiments from the same individual and can directly identify heterozygous single-nucleotides polymorphism (SNPs) and assess ASE by taking into account base-calling errors and over dispersion in the ASE ratio. It can be applied to RNAseq, and other data types (ChIP-seq, DNase-seq, ATAC-seq and others).


A tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. This tool can also be applied to investigate eQTL. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines.

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.


A Bayesian approach to estimate allele-specific expression (ASE) from RNA sequencing data with diploid genome sequences. In the statistical framework, the haploid choice is modeled as a hidden variable and estimated simultaneously with isoform expression levels by variational Bayesian inference. Through the simulation data analysis, we demonstrate the effectiveness of the proposed approach in terms of identifying ASE compared to the existing approach. We also show that our approach enables better quantification of isoform expression levels compared to the existing methods, TIGAR2, RSEM and Cufflinks.


A computer program for the analysis and interpretation of genomics data with an emphasis on understanding the genetic basis of biomedical traits. In MAMBA, statistical methods for the analysis of multiple study designs including case-control, continuous trait, cross-disorder, and cross-phenotype analysis are implemented. Three approaches for ASE analysis are also available in MAMBA, using a Bayesian model comparison framework and computation done via a Gibbs sampler: Independent Tissue Model (ITM), Grouped Tissue Model (GTM) and the Hierarchical Grouped Tissue Model (GTM*).

EMASE / Expectation-Maximization algorithm for Allele Specific Expression

Simultaneously quantifies allele-specific expression and gene expression from RNA-seq data. EMASE is the implementation of an expectation maximization (EM) algorithm that accounts for the hierarchical structure of the transcriptome. The application of the software was demonstrated analyzing liver RNA-Seq data from a reciprocal F1 hybrid cross between two inbred mouse strains. The EMASE algorithm is readily adaptable to other contexts. All that is required is an alignment target composed of discrete sequence elements and a hierarchy mouse strains.


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.