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Abstract
BackgroundTranscriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features.ResultsWe describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression.ConclusionsOur models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.
Highlights
Dysregulation of transcription and gene expression has been linked to conditions such as diabetes [1], different subtypes of cancer [2] and neurological [3], autoimmune [4]and developmental disorders [5]
We first identified : Transcription factors (TF) interacting with the cis-regulatory region of each protein coding : Target gene (TG) by isolating the TF ChIP-seq peaks occurring within the regulatory window demarcated by the most upstream and downstream occurring CTCF ChIP-seq peaks within a 50 Kb region surrounding the gene body (Fig. 1)
We further filtered these positional TF binding sites (TFBS) based on statistical significance using the : Find individual motif occurences (FIMO) algorithm and TF binding affinity using the TEPIC algorithm
Summary
Dysregulation of transcription and gene expression has been linked to conditions such as diabetes [1], different subtypes of cancer [2] and neurological [3], autoimmune [4]and developmental disorders [5]. Patel and Bush BMC Bioinformatics (2021) 22:200 transcriptional regulation in eukaryotes, the mechanistic underpinnings of many of these diseases are yet unknown Databases such as the Encyclopedia of DNA elements (ENCODE) [6], FANTOM5 [7] and gene expression omnibus (GEO) [8] have provided researchers with the opportunity to explore gene expression regulation using computational methods. Despite the availability of the epigenomic, transcriptomic, and genomic data, there is a dearth of integrative algorithms that consolidate these data types into models of regulatory impact on gene expression Such models would provide relative weights of TF influence over gene expression, and could be used to annotate and prioritize regulatory variants within genetic association tests for several diseases. We create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features
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