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.
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).
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.
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.
A method to identify genetically influenced mRNA processing events using transcriptome sequencing (RNA-Seq) data. The method examines RNA-Seq data at both single-nucleotide and whole-gene/isoform levels to identify allele-specific expression (ASE) and existence of allele-specific regulation of mRNA processing.
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).
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.
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.
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.
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.
Provides an integrated pipeline for estimating the allele specific gene expression and allelic imbalance tests. Allim is an open-source application which estimates allele-specific gene expression in F1 crosses. It provides a range of additional features and allows for a wide range of input options. It also includes a correction of the residual mapping bias and can take advantage of replicate data.
Assists users in studying allele-specific expression (ASE) within individual organisms. RPASE is a computational pipeline that uses physical phasing and a statistical model that accounts for non -independence of read counts within haplotypes. It can be applied both to a wide range of existing RNA seq data sets and new data sets from little studied, non-model organisms.
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.
Allows an accurate single nucleotide polymorphisms (SNPs)-aware alignment for allele-specific expression (ASE) analysis. ASElux is an approach that focuses on SNP-overlapping reads and combines the alignment and estimation of allelic expression into one step. It builds a personal allelic reference genome by using the individual’s existing genotype information to generate all possible ASE reads and pre-screen the RNA-seq data.
Assesses transcript abundance at both gene- and haplotype-level in genomic regions. AltHapAlignR is a three-steps pipeline providing a method for RNA-seq read mapping that considers alternate haplotypes. It uses the available information to generate less biased estimates of gene expression for highly polymorphic genes. The software can be applied to the study of multiple RNA sequencing datasets.
Enables analysis of allelic imbalance. This approach consists of a Bayesian PoissonGamma (PG) model. It can be used when DNA controls are not available through use of a parameter representing bias which is incorporated into the structure of the model. This method is flexible and can accommodate differences in experimental design and bias estimation.
Uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze allele-specific protein-DNA binding.
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 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.
Reduces allele bias by creating a variant masked genome. Allele workbench provides both heterozygous single nucleotide polymorphisms (SNP) coverage and isoform-aware read counts. It utilizes the binomial test to determine the allele imbalance (AI) for the genes, transcripts and SNPs. It contains a graphical user interface (GUI) allowing users to query the AI by SNP or transcript.
Detects, manages and visualizes allelic imbalances. AllelicImbalance allows users to ask genetic questions in any RNA sequencing experiment. It contains a visualization mode assisting users in notice of non-trivial allelic imbalance behavior over specific regions such as exons. This tool can detect and quantify alleles in introns of the precursor mRNA.