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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.

Source text:
(Zhang et al., 2010) HHMD: the human histone modification database. Nucleic Acids Res.

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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.
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.
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.
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