Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,616 bioRxiv papers from 308,268 authors.

K-mer Motif Multinomial Mixtures

By Brian L Trippe, Sandhya Prabhakaran, Harmen J. Bussemaker

Posted 24 Dec 2016
bioRxiv DOI: 10.1101/096735

Motivation: The advent of inexpensive high-throughput sequencing (HTS) places new demands on motif discovery algorithms. To confront the challenges and embrace the opportunities presented by the growing wealth of information tied up in HTS datasets, we developed K-mer motif multinomial mixtures (KMMMs), a flexible class of Bayesian models for identifying multiple motifs in sequence sets using K-mer tables. Advantages of this framework are inference with time and space complexities that only scale with K, and the ability to be incorporated into larger Bayesian models. Results: We derived a class of probabilistic models of K-mer tables generated from sequence containing multiple motifs. KMMMs model the K-mer table as a multinomial mixture, with motif and background components, which are distributions over K-mers overlapping with each of the latent motifs and over K-mers that do not overlap with any motif, respectively. The framework casts motif discovery as a posterior inference problem, and we present several approximate inference methods that provide accurate reconstructions of motifs in synthetic data. Finally we apply the method to discover motifs in DNAse hypersensitive sites and ChIP-seq peaks obtained from the ENCODE project.

Download data

  • Downloaded 717 times
  • Download rankings, all-time:
    • Site-wide: 13,841 out of 70,616
    • In bioinformatics: 2,225 out of 6,917
  • Year to date:
    • Site-wide: 49,830 out of 70,616
  • Since beginning of last month:
    • Site-wide: 64,303 out of 70,616

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)