Biology subjects

Purcaro, M.

Publications and source records attributed to Purcaro, M..

3 recordsLinked to original sources

bioRxiv2020

Annotation of Chromatin States in 66 Complete Mouse Epigenomes During Development

The morphologically and functionally distinct cell types of a multicellular organism are maintained by epigenomes and gene expression programs. Phase III of the ENCODE Project profiled 66 mouse epigenomes across twelve tissues at daily intervals from embryonic day 10.5 to birth. Applying the ChromHMM algorithm to these epigenomes, we annotated eighteen chromatin states with characteristics of promoters, enhancers, transcribed regions, repressed regions, and quiescent regions throughout the developmental time course. Our integrative analyses delineate the tissue specificity and developmental trajectory of the loci in these chromatin states. Approximately 0.3% of each epigenome is assigned to a bivalent chromatin state, which harbors both active marks and the repressive mark H3K27me3. Highly evolutionarily conserved, these loci are enriched in silencers bound by Polycomb Repressive Complex proteins and the transcription start sites of their silenced target genes. This collection of chromatin state assignments provides a useful resource for studying mammalian development.

bioinformatics↗

bioRxiv2019

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods

Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. To facilitate the development of computational methods for predicting target genes, we developed a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the Registry of cCREs we developed recently with experimentally-derived genomic interactions. We used BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the supervised learning methods TargetFinder and PEP. We found that while TargetFinder was the best performing method, it was modestly better than a baseline distance method for most benchmark datasets while trained and tested within the same cell type and that TargetFinder often did not outperform the distance method when applied across cell types. Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

bioinformatics↗

bioRxiv2016

LR-DNase: Predicting TF binding from DNase-seq data

Transcription factors play a key role in the regulation of gene expression. Hypersensitivity to DNase I cleavage has long been used to gauge the accessibility of genomic DNA for transcription factor binding and as an indicator of regulatory genomic locations. An increasing amount of ChIP-seq data on a large number of TFs is being generated, mostly in a small number of cell types. DNase-seq data are being produced for hundreds of cell types. We aimed to develop a computational method that could combine ChIP-seq and DNase-seq data to predict TF binding sites in a wide variety of cell types. We trained and tested a logistic regression model, called LR-DNase, to predict binding sites for a specific TF using seven features derived from DNase-seq and genomic sequence. We calculated the area under the precision-recall curve at a false discovery rate cutoff of 0.5 for the LR-DNase model, a number of logistic regression models with fewer features, and several existing state-of-the-art TF binding prediction methods. The LR-DNase model outperformed existing unsupervised and supervised methods. Additionally, for many TFs, a model that uses only two features, DNase-seq reads and motif score, was sufficient to match the performance of the best existing methods.

bioinformatics↗