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Spectral Sequence Motif Discovery
Colombo, Nicolo; Vlassis, Nikos
2014
 

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Abstract :
[en] Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, motif finding algorithms of increasingly high performance are required to process the big datasets produced by new high-throughput sequencing technologies. Most existing algorithms are computationally demanding and often cannot support the large size of new experimental data. We present a new motif discovery algorithm that is built on a recent machine learning technique, referred to as Method of Moments. Based on spectral decompositions, this method is robust under model misspecification 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. In a few minutes, we can process datasets of hundreds of thousand sequences and extract motif profiles that match those computed by various state-of-the-art algorithms.
Disciplines :
Computer science
Author, co-author :
Colombo, Nicolo ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Vlassis, Nikos 
Language :
English
Title :
Spectral Sequence Motif Discovery
Publication date :
2014
Available on ORBilu :
since 10 May 2015

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