Biology subjects

Awdeh, A.

Publications and source records attributed to Awdeh, A..

3 recordsLinked to original sources

bioRxiv2022

Cell Type Specific DNA Signatures of Transcription Factor Binding

Transcription factors (TFs) bind to different parts of the genome in different types of cells. These differences may be due to alterations in the DNA-binding preferences of a TF itself, or mechanisms such as chromatin accessibility, steric hindrance, or competitive binding, that result in a DNA "signature" of differential binding. We propose a method called SigTFB (Signatures of TF Binding), based on deep learning, to detect and quantify cell type specificity in a TFs DNA-binding signature. We conduct a wide scale investigation of 194 distinct TFs across various cell types. We demonstrate the existence of cell type specificity in approximately 30% of the TFs. We stratify our analysis by different antibodies for the same TF, to rule out the possibility of certain technical artifacts, yet we find that cell type specificity estimates are largely consistent when the same TF is assayed with different antibodies. To further explain the biology behind a TFs cell type specificity, or lack thereof, we conduct a wide scale motif enrichment analysis of all TFs in question. We show that the presence of alternate motifs correlates with a higher degree of cell type specificity in TFs, such as ATF7, while finding consistent motifs throughout is usually associated with the absence of cell type specificity in a TF, such as CTCF. In particular, we observe that several important TFs show distinct DNA binding signatures in different cancer cell types, which may point to important differences in modes of action. Moreover, we find that motif enrichment sometimes correlates with gene expression in TFs with higher cell type specificity. Our comprehensive investigation provides a basis for further study of the mechanisms behind differences in TF-DNA binding in different cell types.

bioinformatics↗

bioRxiv2019

WACS: Improving Peak Calling by Optimally Weighting Controls

MotivationChromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), initially introduced more than a decade ago, is widely used by the scientific community to detect protein/DNA binding and histone modifications across the genome. Every experiment is prone to noise and bias, and ChIP-seq experiments are no exception. To alleviate bias, the incorporation of control datasets in ChIP-seq analysis is an essential step. The controls are used to account for the background signal, while the remainder of the ChIP-seq signal captures true binding or histone modification. However, a recurrent issue is different types of bias in different ChIP-seq experiments. Depending on which controls are used, different aspects of ChIP-seq bias are better or worse accounted for, and peak calling can produce different results for the same ChIP-seq experiment. Consequently, generating "smart" controls, which model the non-signal effect for a specific ChIP-seq experiment, could enhance contrast and increase the reliability and reproducibility of the results. ResultsWe propose a peak calling algorithm, Weighted Analysis of ChIP-seq (WACS), which is an extension of the well-known peak caller MACS2. There are two main steps in WACS: First, weights are estimated for each control using non-negative least squares regression. The goal is to customize controls to model the noise distribution for each ChIP-seq experiment. This is then followed by peak calling. We demonstrate that WACS significantly outperforms MACS2 and AIControl, another recent algorithm for generating smart controls, in the detection of enriched regions along the genome, in terms of motif enrichment and reproducibility analyses. ConclusionThis ultimately improves our understanding of ChIP-seq controls and their biases, and shows that WACS results in a better approximation of the noise distribution in controls.

bioinformatics↗

bioRxiv2018

RECAP reveals the true statistical significance of ChIP-seq peak calls

MotivationChlP-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. A key step in ChIP-seq analysis is peak calling, where genomic regions enriched for ChIP versus control reads are identified. Many programs have been designed to solve this task, but nearly all fall into the statistical trap of using the data twice--once to determine candidate enriched regions, and again to assess enrichment by classical statistical hypothesis testing. This double use of the data invalidates the statistical significance assigned to enriched regions, and as a consequence, invalidates false discovery rate estimates. Thus, the true significance or reliability of peak calls remains unknown.\n\nResultsUsing simulated and real ChIP-seq data sets, we show that three well-known peak callers, MACS, SICER and diffReps, output optimistically biased p-values, and therefore optimistic false discovery rate estimates--in some cases, many orders of magnitude too optimistic. We propose a wrapper algorithm, RECAP, that uses resampling of ChIP-seq and control data to estimate and correct for biases built into peak calling algorithms. P-values recalibrated by RECAP are approximately uniformly distributed when applied to null hypothesis data, in which ChIP-seq and control come from the same genomic distributions. When applied to non-null data, RECAP p-values give a better estimate of the true statistical significance of candidate peaks and better false discovery rate estimates, which correlate better with empirical reproducibility. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls.\n\nAvailabilityThe RECAP software is available on github at https://github.com/theodorejperkins/RECAP.\n\nContacttperkins@ohri.ca

bioinformatics↗