Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets

Thumbnail Image

Access rights

openAccess
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)

Authors

Date

2018-05-04

Department

Department of Computer Science

Major/Subject

Mcode

Degree programme

Language

en

Pages

16

Series

Nucleic Acids Research, Volume 46, issue 8

Abstract

In some dimeric cases of transcription factor (TF) binding, the specificity of dimeric motifs has been observed to differ notably from what would be expected were the two factors to bind to DNA independently of each other. Current motif discovery methods are unable to learn monomeric and dimeric motifs in modular fashion such that deviations from the expected motif would become explicit and the noise from dimeric occurrences would not corrupt monomeric models. We propose a novel modeling technique and an expectation maximization algorithm, implemented as software tool MODER, for discovering monomeric TF binding motifs and their dimeric combinations. Given training data and seeds for monomeric motifs, the algorithm learns in the same probabilistic framework a mixture model which represents monomeric motifs as standard position-specific probability matrices (PPMs), and dimeric motifs as pairs of monomeric PPMs, with associated orientation and spacing preferences. For dimers the model represents deviations from pure modular model of two independent monomers, thus making co-operative binding effects explicit. MODER can analyze in reasonable time tens of Mbps of training data. We validated the tool on HT-SELEX and ChIP-seq data. Our findings include some TFs whose expected model has palindromic symmetry but the observed model is directional.

Description

Keywords

CHIP-SEQ DATA, EXPECTATION MAXIMIZATION ALGORITHM, EM ALGORITHM, DNA-BINDING, HUMAN GENOME, SITES, SEQUENCE, IDENTIFICATION, SPECIFICITIES, ALIGNMENT

Other note

DOI

10.1093/nar/gky027

Citation

Toivonen, J, Kivioja, T, Jolma, A, Yin, Y, Taipale, J & Ukkonen, E 2018, 'Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets', Nucleic Acids Research, vol. 46, no. 8. https://doi.org/10.1093/nar/gky027

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201809064985

Collections

[article-cris] Perustieteiden korkeakoulu / SCI