Biology subjects

Guertin, M. J.

Publications and source records attributed to Guertin, M. J..

4 recordsLinked to original sources

bioRxiv2018

Genetic and Epigenetic Determinants Establish a Continuum of Hsf1 Occupancy and Activity Across the Yeast Genome

Heat Shock Factor 1 (Hsf1) is the master transcriptional regulator of molecular chaperones and binds to the same cis-acting element - Heat Shock Element (HSE) - across the eukaryotic lineage. In budding yeast, Hsf1 drives transcription of ~20 genes essential to maintain proteostasis under basal conditions, yet its specific targets and extent of inducible binding during heat shock remain unclear. Here we combine Hsf1 ChIP-seq, nascent RNA-seq and Hsf1 nuclear depletion to quantify Hsf1 binding and transcription across the yeast genome. Hsf1 binds 74 loci during acute heat shock, 46 of which are linked to genes with strong Hsf1-dependent transcription. Most of these targets show detectable Hsf1 binding under basal conditions, but basal occupancy and heat shock-inducible binding both vary over two orders of magnitude. Notably, Hsf1s induced DNA binding leads to a disproportionate (up to 50-fold) increase in nascent transcription. While variation in basal Hsf1 occupancy poorly correlates with the strength of the HSE, promoters with high basal Hsf1 occupancy have nucleosome-depleted regions due to the presence of pioneer factors. Such accessible chromatin may be critical for Hsf1 occupancy of its genomic sites as the activator is incapable of binding HSEs embedded within a stable nucleosome in vitro. In response to heat shock, however, Hsf1 is able to gain access to nucleosomal sites and promotes chromatin remodeling with the RSC complex playing a key role. We propose that the interplay between nucleosome occupancy, HSE strength and active Hsf1 levels allows cells to precisely tune expression of the proteostasis network.

genomics↗

bioRxiv2018

BART: a transcription factor prediction tool with query gene sets or epigenomic profiles

SummaryIdentification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive to detect factors binding at distal enhancers. Here we present Binding Analysis for Regulation of Transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6,000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research.\n\nAvailabilityBART is implemented in Python and available at http://faculty.virginia.edu/zanglab/bart\n\nContact: zang@virginia.edu

bioinformatics↗

bioRxiv2017

Novel Quantitative ChIP-seq Methods Measure Absolute Fold-Change in ER Binding Upon Fulvestrant Treatment

A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes in occupancy. Analysis-based normalisation methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor binding, these assumptions do not hold true. The challenges in normalisation are confounded by experimental variability during sample preparation, processing, and recovery.\n\nWe present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalisation. We compare our approach to normalisation by total read depth and two alternative methods that utilise external experimental controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in Patient-Derived Xenographs. This is supported by an adaptable pipeline to normalise and quantify differential transcription factor binding genome-wide and generate metrics for differential binding at individual sites.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC=\"FIGDIR/small/182261_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@177285borg.highwire.dtl.DTLVardef@1fa7d79org.highwire.dtl.DTLVardef@43d17forg.highwire.dtl.DTLVardef@6eff8d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

genomics↗

bioRxiv2017

Universal correction of enzymatic sequence bias

Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA.

genomics↗