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.

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.

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.