Histone modification detection software tools | DNA-protein interaction data analysis

Predicts histone modification and DNA methylation patterns from DNA motifs. Epigram is an analysis pipeline used to systematically identify DNA motifs that are predictive of epigenomic modifications. It reveals the cis-regulatory program that is read by the dynamic genetic network to shape the epigenome. In particular, Epigram’s motifs are significantly correlated with almost double the number of H3K27ac regions when compared to five times the number of known Transcription Factor (TF) motifs.

Provides a web platform to execute analysis of somatic DNA mutations with genome regulation elements and DNA sequence contexts. Mutalisk can correlate somatic mutations in query sample by using data such as physical location of the genome, regional DNA sequence contexts and functional elements in the genome (like DNA replication, RNA transcription and epigenome landmarks). This tool can be utilized for quality control of mutation calls.

Identifies cell-type-specific regulatory elements (CSREs) in the human genome. dCMA is a computational framework capable of identifying genomic regions, containing distinctive chromatin modifications that are unique to each cell type. This framework demonstrates the power of a differential comparative epigenomics strategy in deciphering aspects of the human genome and characterizing cell specificity.

Identifies histone modifications in genomes with large copy number alterations. HMCan is a tool for detection of histone modifications in cancer samples using ChIP-seq data. The software corrects for copy number bias and for GC-content bias and then uses hidden markov models (HMMs) to detect regions rich in histone modifications. It can also calculate posterior probabilities for each bin. HMCan was applied on both simulated and experimental data.

Handles all the bins together, capturing both neighboring range and long range interactions among input features, as well as automatically extract important features. DeepChrome is a unified Convolutional Neural Networks (CNNs) framework that automatically learns combinatorial interactions among histone modification marks to predict the gene expression. Through this model, DeepChrome incorporates representations of both local neighboring bins as well as the whole gene.

Allows the capture and comparison of ChIP-seq histone profiles. LPCHP can be used as an alternative to read intensities, its utility may extend beyond ChIP-seq to other next-generation sequencing (NGS) applications. It can be used in identification of enhancer or regulatory regions in the genome. The tool is robust against changes in p, including cases where it was customized to dataset. LPCHP can identify commonalities between different histone modifications.

Predicts differential gene expression using histone modifications (HMs). DeepDiff consists of an attention-based deep learning architecture developed to understand how different HMs work together to influence changes in expression patterns of a gene between two different cell types. The software utilizes a hierarchy of two levels of gene-specific attention mechanisms for determining salient features at both the bin level and the HM levels. It was evaluated using datasets from Roadmap Epigenome Project (REMC), on ten different pairs of cell types.

A tool to build a transcriptional regulatory network composing of histone modification and transcription factor binding in promoters and interactions between factors in these two fields. Given a set of categorized genes, EpiRegNet would find the factors which contribute most to the difference in the gene expression level based on the statistic tests on the enrichment of each factor in gene promoters. Furthermore, the tool perform correlation tests to study the relation between these factors, and draw the regulatory network to declare their cooperative or competitive roles in activation or repression of gene expression.

A non-negative matrix factorization method to discover combinatorial patterns of epigenetic marks that frequently co-occur in subsets of genomic regions. epicope discovers patterns of histone modifications from aligned sequence data. It also looks for combinations (subsets) of marks that tend to occur in sub-portions of the data. Alternatively, epicope identifies combinations of marks that change coordinately i.e. are "gained" or "lost" frequently at the same time.

A package based on an algorithm for the analysis of nucleosome positioning and histone modification ChIP-seq experiments. ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should be flexible enough to handle other types of experiments as well.

Allows the analysis and annotation of coverage islands within individual read alignment (BAM) files of histone modification ChIPseq datasets harboring broad chromatin domains. SUPERmerge allows flexible regulation of a variety of read pileup parameters, thereby revealing how read islands aggregate into areas of coverage across the genome and what annotation features they map to within individual biological replicates. It can be useful for investigating low sample size ChIP-seq experiments.

Integrative analyses of epigenetic data promise a deeper understanding of the epigenome. Epidaurus is a bioinformatics tool used to effectively reveal inter-dataset relevance and differences through data aggregation, integration and visualization.