A general integrative genomic feature transcription factor binding site prediction method applied to analysis of USF1 binding in cardiovascular disease

Proficiency of data interpretation: identification of signaling SNPs/specific loci for coronary artery disease

Database URLs

http://www.regulomedb.org/;https://www.broadinstitute.org/mpg/snap/.

Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites

Motivation: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation.

Results: We tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by ∼50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01.

Availability: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac.

Contact:aderem@systemsbiology.org; ishmulevich@systemsbiology.org

Supplementary information:Supplementary data are available at Bioinformatics online.

Comparative analysis of MTF-1 binding sites between human and mouse

MTF-1 is a crucial transcription factor involved in the cellular response to heavy-metal load and other stresses by specifically binding to metal response elements (MREs). Thus far only a handful of direct target genes are known for this transcription factor, limiting our understanding of the biological network it governs. In this article we try to employ a computational strategy based on the generation of literature-based positional weight matrices (PWM) and log-likelihood scoring of the candidate binding sites (BSs) for identification of direct targets of the transcription factor MTF-1 in human and mouse. Through comparisons, we explore the conservation and unique characteristics between two species. Our results show that the numbers of MREs differ dramatically between species and their positions relative to their cognate promoter is also flexible. Importantly, we identify a set of target genes generally well conserved between human and mouse. Finally, by combining expression analysis we provide two putative targets (HMGCR and CYP51A), which regulate lipid metabolism conserved in human and mouse. Overall, interspecies comparison from our study may provide some valuable information for further studying human Wilson disease (WD) using mouse model systems.