Transcriptomic and epigenomic data integration bioinformatic tools

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.

1 - 5 of 21 results

MSR

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.

ngs.plot

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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.

HubPredictor

A computational model integrating Hi-C and histone mark ChIP-seq data to predict two important features of chromatin organization: chromatin interaction hubs and topologically associated domain (TAD) boundaries. HubPredictor accurately and robustly predicts these features across datasets and cell types. Cell-type specific histone mark information is required for prediction of chromatin interaction hubs but not for TAD boundaries. HubPredictor provides a useful guide for the exploration of chromatin organization.

MM-ChIP / Model-based Meta-analysis of ChIP data

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.

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

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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.

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