Transcriptomic and epigenomic data integration software tools | RNA sequencing analysis
A variety of NGS-based techniques have been developed. For example, chromatin immunoprecipitation coupled with parallel sequencing (ChIP-seq) is widely used to assess the binding of proteins to the genome. RNA sequencing (RNA-seq) can estimate the abundance of whole transcripts and their isoforms. Genome-wide nucleosome positioning and open chromatin can be captured by MNase-seq and DNase-seq, respectively. As the demand for NGS has increased, several thousand NGS-based data sets have been deposited in public data repositories such as gene expression omnibus (GEO). Notably, novel findings frequently emerge from reanalyzing available NGS-based data sets. However, there is no easy way to access, download, and process a large set of original (raw) NGS-based data for comparative and integrative analysis, although some web-based applications have been developed to resolve the issue.
Allows data-mining and visualization of next-generation sequencing (NGS) samples such as enrichment patterns of DNA-interacting proteins at functional genomic regions. ngs.plot has a built-in database of functional elements that facilitates the management of genomic coordinates for users. This software supports large sequencing data and is available through the Galaxy tool shed.
Enables computational reconstruction of regulatory circuitry from high-throughput data. ISMARA uses motif activity response analysis to identify the key regulators, i.e., transcription factors (TFs) and microRNAs (miRNAs) driving gene expression/chromatin state changes across the samples, the activity profiles of these regulators, their target genes, and the sites on the genome through which these regulators act. The stand-alone client application (called the ISMARA client) automates the process of pre-processing the user’s raw data on her/his own computer.
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.
Allows the integrative analysis of ChIP-chip/seq data across platforms and between laboratories. MM-ChIP proceeds by modeling the characteristic fragment size of the sequenced ChIP-DNA library for each individual data source. It uses then the 3’ direction to represent the protein-DNA interaction sites. Finally, a sliding window is used to score the significance of signal enrichment in the ChIP samples by measuring and comparing tags within the same windows between ChIP.
Facilitates several typical operations related to the quantification of the sequencing signal in a set of genomic regions. compEpiTools provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. It allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms.
A general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab can improve performance of binding site predictions by allowing users to integrate several motif discovery tools (including AlignAce, BioProspector, ChIPMunk, MEME, MotifSampler, Priority and Weeder) as well as different types of data, for instance phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, TF-TF interactions, TF-expression and target gene expression.
Enables analysis and visualization of the information content of genomic signals. MSR is a method, adapted from an image segmentation algorithm and inspired by multiscale approaches for classifying image texture patterns. The software enables global analysis of genomic data in an unbiased manner with respect to the spatial scales on which biological information is encoded. It was used to analyze measurements of transcription factor binding, covalent histone modifications and DNA methylation, as well as genomic annotations and sequence-derived data.