Histone modification detection software tools | DNA-protein interaction data analysis
Histone modifications play important roles in chromatin remodeling, gene transcriptional regulation, stem cell maintenance and differentiation. Alterations in histone modifications may be linked to human diseases especially cancer. Histone modifications including methylation, acetylation and ubiquitylation probed by ChIP-seq, ChIP-chip and qChIP have become widely available.
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.
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.
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.
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.
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.
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.