title: Mapping genomic features to functional traits through microbial whole genome sequences.
abstract: Recently, the utility of trait-based approaches for microbial communities has been identified. Increasing availability of whole genome sequences provide the opportunity to explore the genetic foundations of a variety of functional traits. We proposed a machine learning framework to quantitatively link the genomic features with functional traits. Genes from bacteria genomes belonging to different functional traits were grouped to Cluster of Orthologs (COGs), and were used as features. Then, TF-IDF technique from the text mining domain was applied to transform the data to accommodate the abundance and importance of each COG. After TF-IDF processing, COGs were ranked using feature selection methods to identify their relevance to the functional trait of interest. Extensive experimental results demonstrated that functional trait related genes can be detected using our method. Further, the method has the potential to provide novel biological insights.
authors.affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: Erliang
authors.initials: E
authors.lastname: Zeng
authors.affiliation: Department of Biological Science, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: Dan
authors.initials: D
authors.lastname: Liu
authors.affiliation: Department of Biological Science, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: Stuart E
authors.initials: SE
authors.lastname: Jones
authors.affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Emrich
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D003195:Computer Communication Networks; D056726:Genetic Association Studies; D064349:Genome, Microbial / Q000235:genetics*; D023281:Genomics; D011381:Programming Languages; D012372:ROC Curve; D017422:Sequence Analysis, DNA / Q000379:methods*; D060388:Support Vector Machine
keywords: bacteria genomes; bioinformatics; feature mapping; feature selection; functional genomics; functional traits; genes; genome sequences; genomic features; genomic signatures; machine learning; microbial diversity; phenotype–genotype association; sporulation