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2013 | OriginalPaper | Buchkapitel

CMF: A Combinatorial Tool to Find Composite Motifs

verfasst von : Mauro Leoncini, Manuela Montangero, Marco Pellegrini, Karina Panucia Tillán

Erschienen in: Learning and Intelligent Optimization

Verlag: Springer Berlin Heidelberg

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Abstract

Controlling the differential expression of many thousands genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks. Cis-Regulatory Modules are fundamental units of such networks. To detect Cis-Regulatory Modules “in-silico” a key step is the discovery of recurrent clusters of DNA binding sites for sets of cooperating Transcription Factors. Composite motif is the term often adopted to refer to these clusters of sites. In this paper we describe CMF, a new efficient combinatorial method for the problem of detecting composite motifs, given in input a description of the binding affinities for a set of transcription factors. Testing with known benchmark data, we attain statistically significant better performance against nine state-of-the-art competing methods.

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Assuming the number \(N\) of sequences is clearly understood, we silently equate the fraction \(q\in (0,1]\) and the absolute number of sequences \(\lceil q\cdot N\rceil \).

Even if not taken into consideration in this paper, CMF is also able to run a number of third-party motif discovery tools to “synthesize” PWMs.

Currently, CMF invokes RSAT’s utility compare-matrices for this purpose [21], which uses pairwise normalized correlation

Sometimes referred to as weak signals in the literature.

In the following, we refer to [8] as to the assessment paper.

Note that in the already cited paper by Tompa et al. [31], true negative predictions at the motif level are not considered.

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Titel: CMF: A Combinatorial Tool to Find Composite Motifs
verfasst von: Mauro Leoncini
Manuela Montangero
Marco Pellegrini
Karina Panucia Tillán
Verlag: Springer Berlin Heidelberg
Buch: Learning and Intelligent Optimization
Print ISBN: 978-3-642-44972-7

Electronic ISBN: 978-3-642-44973-4

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-3-642-44973-4_21

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