title: FastMotif: spectral sequence motif discovery.
abstract: BACKGROUND
  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.
  
  
  METHODS
  The Matlab code of FastMotif is available from http://lcsb-portal.uni.lu/bioinformatics.
  
  
  BACKGROUND
  vlassis@adobe.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg and.
authors.forename: Nicoló
authors.initials: N
authors.lastname: Colombo
authors.affiliation: Adobe Research, San Jose, CA, USA.
authors.forename: Nikos
authors.initials: N
authors.lastname: Vlassis
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D059372:Nucleotide Motifs / Q000235:genetics*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors