title: Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data.
abstract: BACKGROUND
  Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.
  
  
  RESULTS
  We first demonstrate that detecting such 'co-fit' gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast.
  
  
  METHODS
  Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx.
  
  
  BACKGROUND
  xin.gao@kaust.edu.sa.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMCE) Division, Thuwal, 23955-6900, Saudi Arabia.
authors.forename: Majed
authors.initials: M
authors.lastname: Alzahrani
authors.affiliation: King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMCE) Division, Thuwal, 23955-6900, Saudi Arabia.
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Kuwahara
authors.affiliation: Department of Computer Science, University of California, Los Angeles, CA 90095, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMCE) Division, Thuwal, 23955-6900, Saudi Arabia.
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D048708:Cell Growth Processes / Q000235:genetics* / Q000502:physiology; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism / Q000502:physiology; D053858:Metabolic Networks and Pathways; D009154:Mutation; D010641:Phenotype; D020560:Proteobacteria / Q000235:genetics / Q000378:metabolism / Q000502:physiology; D012984:Software; D013312:Stress, Physiological / Q000235:genetics*; D015003:Yeasts / Q000235:genetics / Q000378:metabolism / Q000502:physiology