Article (Scientific journals)
FastMotif: Spectral Sequence Motif Discovery
Colombo, Nicolo; Vlassis, Nikos
2015In Bioinformatics
Peer reviewed
 

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Abstract :
[en] Motivation: Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms are computationally demanding, and they may not be able to support the increasingly large datasets produced by modern high-throughput sequencing technologies. Results: We present FastMotif, a new motif discovery algorithm that is built on a recent machine learning technique referred to as Method of Moments. Based on spectral decompositions, our method is robust to model misspecifications and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. On HT-Selex data, FastMotif extracts motif profiles that match those computed by various state-of- the-art algorithms, but one order of magnitude faster. We provide a theoretical and numerical analysis of the algorithm’s robustness and discuss its sensitivity with respect to the free parameters.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Colombo, Nicolo ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Vlassis, Nikos 
External co-authors :
yes
Language :
English
Title :
FastMotif: Spectral Sequence Motif Discovery
Publication date :
16 April 2015
Journal title :
Bioinformatics
ISSN :
1367-4803
eISSN :
1460-2059
Publisher :
Oxford University Press - Journals Department, Oxford, United Kingdom
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 22 May 2015

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