Reference : FastMotif: Spectral Sequence Motif Discovery
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
FastMotif: Spectral Sequence Motif Discovery
Colombo, Nicolo mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Vlassis, Nikos [> >]
Oxford University Press - Journals Department
Yes (verified by ORBilu)
United Kingdom
[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.
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)

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