Software


  • ChromHMM: Chromatin state discovery and characterization

  • ChromHMM is software 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. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

    Citation:
    Ernst J and Kellis M.
    ChromHMM: automating chromatin-state discovery and characterization.
    Nature Methods, 9:215-216, 2012.

    Papers using ChromHMM


  • DREM: Dynamic Regulatory Events Miner

  • DREM map of heat 
shock response in yeast Example bifurcation

    The Dynamic Regulatory Events Miner (DREM) allows one to model, analyze, and visualize transcriptional gene regulation dynamics. The method of DREM takes as input time series gene expression data and static or dynamic transcription factor-gene interaction data (e.g. ChIP-seq, ChIP-chip data), and produces as output a dynamic regulatory map. The dynamic regulatory map highlights major bifurcation events in the time series expression data and transcription factors potentially responsible for them.

    Citation:
    Ernst J, Vainas O, Harbison CT, Simon I, and Bar-Joseph Z.
    Reconstructing dynamic regulatory maps.
    Nature-EMBO Molecular Systems Biology, 3:74, 2007.

    Papers using DREM


  • STEM: Short Time-series Expression Miner


  • The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.)

    Citation:
    Ernst J, Bar-Joseph Z
    STEM: a tool for the analysis of short time series gene expression data
    .
    BMC Bioinformatics, 7:191, 2006.

    Papers using STEM



  • SEREND: SEmi-supervised REgulatory Network Discoverer

  • SEREND image

    The SEmi-supervised REgulatory Network Discoverer (SEREND) is a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene.

    Citation:
    Ernst J, Beg QK, Kay KA, Balazsi G, Oltvai ZN, Bar-Joseph Z.
    A Semi-Supervised Method for Predicting Transcription Factor-Gene Interactions in Escherichia coli.
    PLoS Computational Biology 4: e1000044, 2008.