Reference : Spectral Sequence Motif Discovery
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/21010
Spectral Sequence Motif Discovery
English
Colombo, Nicolo mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Vlassis, Nikos [> >]
2014
No
[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.
http://hdl.handle.net/10993/21010
http://arxiv.org/abs/1407.6125

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