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A software tool for large-scale systematic epigenome imputation. ChromImpute takes an existing compendium of epigenomic data and uses it to predict signal tracks for mark-sample combinations not experimentally mapped or to generate a potentially more robust version of data sets that have been mapped experimentally. ChromImpute bases its predictions on features from signal tracks of other marks that have been mapped in the target sample and the target mark in other samples with these features combined using an ensemble of regression trees.
Contains a method for analyzing multiple tracks of functional genomics data. Segway uses a dynamic Bayesian network (DBN) model, which enables it to analyze the entire genome at 1-bp resolution even in the face of heterogeneous patterns of missing data. This method is the first application of DBN techniques to genome-scale data and the first genomic segmentation method designed for use with the maximum resolution data available from ChIP-seq experiments without downsampling. Segway uses the Graphical Model Toolkit for efficient DBN inference.
A software program for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types.
GATE / Genomic Annotation from Time-couse Epigenomic data
Enables chromatin states prediction based on time-course epigenetic marks data. GATE is a hierarchical model with two layers: (1) a FMM, in which each component of the mixture represents a cluster of genomic segments that share temporal epigenomic patterns and (2) a bottom layer that models the time-course epigenomic data in each cluster. The software models epigenomic data as a dynamic continuum and directly utilizes temporal information to annotate epigenomic states. It was tested with simulated cell differentiation processes.
CHROMATRA / CHROmatin Mapping Across TRAnscripts
Provides a visualization tool available as plug-in for the Galaxy platform. CHROMATRA allows detailed yet concise presentations of data derived from ChIP-chip or ChIP-seq experiments by visualizing enrichment scores across genes or other genomic features while accounting for their length and additional characteristics such as gene expression. It integrates into typical analysis workflows and enables rapid graphical assessment and comparison of genome-wide data at a glance.
An original algorithm that approximate the genomic distribution of given DNA features (e.g. promoter, enhancer, viral integration) by identifying relevant ChIPSeq profiles of post-translational histone marks or DNA binding proteins and combining them in a supermark. EMdeCODE kernel is essentially a two-step procedure: (i) an expectation-maximization process calculates the mixture of epigenetic factors that maximize the Sensitivity (recall) of the association with the feature under study; (ii) the approximated density is then recursively trimmed with respect to a control dataset to increase the precision by reducing the number of false positives. EMdeCODE densities improve significantly the prediction of enhancer loci and retroviral integration sites with respect to previous methods.
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