Allele-specific expression identification software tools | RNA sequencing data analysis
RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants.
Provides an integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. paired-end, stranded, spliced and allele-specific), and is integrated in Bioconductor so that its output can be directly processed for statistical analysis and visualization.
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.
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).
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.
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.
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.
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).