Abstract
BackgroundTranscription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions.MethodsHere, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF.ResultsWe show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions.ConclusionThe interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.
Highlights
Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health
Because our method finds TF-TF interactions based on genomic colocation and is entirely focused on transcription factors, while STRING is focused on all protein-protein interactions and derives its interactions from very diverse data sources, it is expected that our method would produce many novel predictions when compared to STRING
For the ChIP-seq and Encyclopedia of DNA Elements (ENCODE)-motif datasets, we found that our method identified TF-TF interactions which were significantly (p < 0.05 and p < 0.001, respectively) more enriched in the Co-expression evidence category when compared to STRING interactions which were not predicted by our method
Summary
Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to cooperatively bind the genome as large complexes, or clusters, binding to the DNA, one another, or both [10, 11]. In these situations, one or more “anchor” TFs bind the DNA directly, and other TFs bind the anchors rather than the DNA. This creates a combinatorial problem, wherein a given anchor TF may be bound by several different other TFs depending on time, cellular conditions, etc., and a given association (non-anchor) TF may bind
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