A proximity-based graph clustering method for the identification and application of transcription factor clusters.

ABSTRACT:

Background

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 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.

Methods

Here, 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

SUBMITTER: Spadafore M 

PROVIDER: S-EPMC5706350 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature