Bioinformatics 2017 Dec;33(24):3929-3937
Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
Motivation: Transcription factors play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized by a transcription factor can be described in terms of position frequency matrices. When scanning a sequence for matches to a position frequency matrix, one needs to determine a cut-off, which then in turn results in a certain number of hits. In this paper we describe how to compute the distribution of match scores and of the number of motif hits, which are the prerequisites to perform motif hit enrichment analysis.
Results: We put forward an improved compound Poisson model that supports general order-d Markov background models and which computes the number of motif-hits more accurately than earlier models. We compared the accuracy of the improved compound Poisson model with previously proposed models across a range of parameters and motifs, demonstrating the improvement. The importance of the order-d model is supported in a case study using CpG-island sequences.
Availability And Implementation: The method is available as a Bioconductor package named 'motifcounter' https://bioconductor.org/packages/motifcounter.
Supplementary Information: Supplementary data are available at Bioinformatics online.