Biology subjects

Michael M. Hoffman

Publications and source records attributed to Michael M. Hoffman.

3 recordsLinked to original sources

bioRxiv2016

DNAmod: the DNA modification database

Covalent DNA modifications, such as 5-methylcytosine (5mC), are increasingly the focus of numerous research programs. In eukaryotes, both 5mC and 5-hydroxymethylcytosine (5hmC) are now recognized as stable epigenetic marks, with diverse functions. Bacteria, archaea, and viruses contain various other modified DNA nucleobases. Numerous databases describe RNA and histone modifications, but no database specifically catalogues DNA modifications, despite their broad importance in epigenetic regulation. To address this need, we have developed DNAmod: the DNA modification database.\n\nDNAmod is an open-source database (https://dnamod.hoffmanlab.org) that catalogues DNA modifications and provides a single source to learn about their properties. DNAmod provides a web interface to easily browse and search through these modifications. The database annotates the chemical properties and structures of all curated modified DNA bases, and a much larger list of candidate chemical entities. DNAmod includes manual annotations of available sequencing methods, descriptions of their occurrence in nature, and provides existing and suggested nomenclature. DNAmod enables researchers to rapidly review previous work, select mapping techniques, and track recent developments concerning modified bases of interest.

Bioinformatics↗

bioRxiv2016

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult. Here, we developed methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We developed Cytomod to create modified genomic sequences and enhanced the Multiple EM for Motif Elicitation (MEME) Suite by adding the capacity to handle custom alphabets. We adapted the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identified modification-sensitive transcription factor binding motifs. We confirmed established binding preferences, such as the preference of ZFP57 and C/EBP{beta} for methylated motifs and the preference of c-Myc for unmethylated E-box motifs. Using known binding preferences to tune model parameters, we discovered novel modified motifs for a wide array of transcription factors. Finally, we validated predicted binding preferences of OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

Bioinformatics↗

bioRxiv2015

Learning the human chromatin network from all ENCODE ChIP-seq data

Introduction: A cells epigenome arises from interactions among regulatory factors -- transcription factors, histone modifications, and other DNA-associated proteins -- co-localized at particular genomic regions. Identifying the network of interactions among regulatory factors, the chromatin network, is of paramount importance in understanding epigenome regulation.\n\nMethods: We developed a novel computational approach, ChromNet, to infer the chromatin network from a set of ChIP-seq datasets. ChromNet has four key features that enable its use on large collections of ChIP-seq data. First, rather than using pairwise co-localization of factors along the genome, ChromNet identifies conditional dependence relationships that better discriminate direct and indirect interactions. Second, our novel statistical technique, the group graphical model, improves inference of conditional dependence on highly correlated datasets. Such datasets are common because some transcription factors form a complex and the same transcription factor is often assayed in different laboratories or cell types. Third, ChromNets computationally efficient method and the group graphical model enable the learning of a joint network across all cell types, which greatly increases the scope of possible interactions. We have shown that this results in a significantly higher fold enrichment for validated protein interactions. Fourth, ChromNet provides an efficient way to identify the genomic context that drives a particular network edge, which provides a more comprehensive understanding of regulatory factor interactions.\n\nResults: We applied ChromNet to all available ChIP-seq data from the ENCODE Project, consisting of 1451 ChIP-seq datasets, which revealed previously known physical interactions better than alternative approaches. ChromNet also identified previously unreported regulatory factor interactions. We experimentally validated one of these interactions, between the MYC and HCFC1 transcription factors.\n\nDiscussion: ChromNet provides a useful tool for understanding the interactions among regulatory factors and identifying novel interactions. We have provided an interactive web-based visualization of the full ENCODE chromatin network and the ability to incorporate custom datasets at http://chromnet.cs.washington.edu.

Genomics↗