Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 60,222 bioRxiv papers from 267,718 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 688 times
  • Download rankings, all-time:
    • Site-wide: 12,025 out of 60,222
    • In bioinformatics: 1,996 out of 6,078
  • Year to date:
    • Site-wide: 34,590 out of 60,222
  • Since beginning of last month:
    • Site-wide: 24,226 out of 60,222

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide

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