MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.

ABSTRACT:

Motivation

Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing.

Results

We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a

SUBMITTER: Toivonen J 

PROVIDER: S-EPMC7203737 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature