Abstract
BackgroundDevelopment of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. The consequential growth of sequence motif data sets makes it important to systematically group and categorise regulatory motifs. It has been shown that there are familial tendencies in DNA sequence motifs that are predictive of the family of factors that binds them. Further development of methods that detect and describe familial motif trends has the potential to help in measuring the similarity of novel computational motif predictions to previously known data and sensitively detecting regulatory motifs similar to previously known ones from novel sequence.ResultsWe propose a probabilistic model for position weight matrix (PWM) sequence motif families. The model, which we call the 'metamotif' describes recurring familial patterns in a set of motifs. The metamotif framework models variation within a family of sequence motifs. It allows for simultaneous estimation of a series of independent metamotifs from input position weight matrix (PWM) motif data and does not assume that all input motif columns contribute to a familial pattern. We describe an algorithm for inferring metamotifs from weight matrix data. We then demonstrate the use of the model in two practical tasks: in the Bayesian NestedMICA model inference algorithm as a PWM prior to enhance motif inference sensitivity, and in a motif classification task where motifs are labelled according to their interacting DNA binding domain.ConclusionsWe show that metamotifs can be used as PWM priors in the NestedMICA motif inference algorithm to dramatically increase the sensitivity to infer motifs. Metamotifs were also successfully applied to a motif classification problem where sequence motif features were used to predict the family of protein DNA binding domains that would interact with it. The metamotif based classifier is shown to compare favourably to previous related methods. The metamotif has great potential for further use in machine learning tasks related to especially de novo computational sequence motif inference. The metamotif methods presented have been incorporated into the NestedMICA suite.
Highlights
Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs
A metamotif is a generative model for position weight matrix (PWM) motif columns that can be used to represent a gapless alignment of position weight matrices
This is analogous to computing a probability score for a sequence k-mer to measure the probability of the k-mer having been generated by a PWM, and drawing k-mer samples from a PWM
Summary
Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. A central goal in modelling genome regulation is the identification of transcription factors (TFs) and their target DNA binding sites, expressed as short nucleotide sequence motif models. This goal is becoming tractable even for higher eukaryotic genomes due to the availability of annotated reference genomes for numerous organisms, large scale protein-DNA interaction and gene expression studies, and advances in regulatory binding site motif inference algorithms. These include bacterial one-hybrid assays [12] and protein binding microarrays [13]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have