Chromatin-state identification software tools | DNA-protein interaction data analysis
Readout of genetic information in eukaryotic genomes is mediated by the dynamic chromatin environment, which regulates DNA accessibility for the gene expression machinery through chromatin compaction, associated histone modifications and incorporation of histone variants.
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.
Combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.
Deduces chromatin states from genome-wide profiling data. GenoSTAN models read counts without the need to conduct data transformation. It is useful to understand genotype-phenotype relationships and genetic disease. This tool serves in the biochemical characterization of enhancers and promoters. It assists users to characterize the genomic context of the binding of further transcription factors (TFs).
A Bayesian non-parametric method to jointly infer chromatin state maps in multiple genomes (different species, cell types, and developmental stages) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them.
An algorithm for the computational inference of combinatorial chromatin state dynamics across an arbitrary number of conditions. ChromstaR uses a multivariate Hidden Markov Model to determine the number of discrete combinatorial chromatin states using multiple ChIP-seq experiments as input and assigns every genomic region to a state based on the presence/absence of each modification in every condition. chromstaR is a versatile computational tool that facilitates a deeper biological understanding of chromatin organization and dynamics.
Identifies chromatin modules in multiple cell types simultaneously. CMINT is a generative probabilistic graphical model-based approach for multitask clustering. It was developed to enable systematic characterization of chromatin state dynamics across multiple related cell types. CMINT is motivated by the hierarchical structure of developmental lineages, where a new cell type arises from a predecessor through several intermediate states.
Aims to identify accurate normalization controls for assay for transposase-accessible chromatin (ATAC) –quantitative polymerase chain reaction (qPCR). APT is a program assisting researchers to detect optimal regions for ATAC-qPCR primers within peaks by comparing the number of spanning fragments in overlapping windows to the normalized peak height across samples. Moreover, it includes functionalities for the identification of custom normalization controls based on user-supplied ATAC-seq data.