title: SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles.
abstract: BACKGROUND
  Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-Seq) measures the genome-wide occupancy of transcription factors in vivo. Different combinations of DNA-binding protein occupancies may result in a gene being expressed in different tissues or at different developmental stages. To fully understand the functions of genes, it is essential to develop probabilistic models on multiple ChIP-Seq profiles to decipher the combinatorial regulatory mechanisms by multiple transcription factors.
  
  
  RESULTS
  In this work, we describe a probabilistic model (SignalSpider) to decipher the combinatorial binding events of multiple transcription factors. Comparing with similar existing methods, we found SignalSpider performs better in clustering promoter and enhancer regions. Notably, SignalSpider can learn higher-order combinatorial patterns from multiple ChIP-Seq profiles. We have applied SignalSpider on the normalized ChIP-Seq profiles from the ENCODE consortium and learned model instances. We observed different higher-order enrichment and depletion patterns across sets of proteins. Those clustering patterns are supported by Gene Ontology (GO) enrichment, evolutionary conservation and chromatin interaction enrichment, offering biological insights for further focused studies. We also proposed a specific enrichment map visualization method to reveal the genome-wide transcription factor combinatorial patterns from the models built, which extend our existing fine-scale knowledge on gene regulation to a genome-wide level.
  
  
  METHODS
  The matrix-algebra-optimized executables and source codes are available at the authors' websites: http://www.cs.toronto.edu/∼wkc/SignalSpider.
authors.affiliation: Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Ka-Chun
authors.initials: KC
authors.lastname: Wong
authors.affiliation: Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Yue
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Chengbin
authors.initials: C
authors.lastname: Peng
authors.affiliation: Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Zhaolei
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000235:genetics / Q000378:metabolism*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated; D012045:Regulatory Sequences, Nucleic Acid; D012984:Software; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D004268:DNA-Binding Proteins; D014157:Transcription Factors