title: Proteotyping of microbial communities by optimization of tandem mass spectrometry data interpretation.
abstract: We report the development of a novel high performance computing method for the identification of proteins from unknown (environmental) samples. The method uses computational optimization to provide an effective way to control the false discovery rate for environmental samples and complements de novo peptide sequencing. Furthermore, the method provides information based on the expressed protein in a microbial community, and thus complements DNA-based identification methods. Testing on blind samples demonstrates that the method provides 79-95% overlap with analogous results from searches involving only the correct genomes. We provide scaling and performance evaluations for the software that demonstrate the ability to carry out large-scale optimizations on 1258 genomes containing 4.2M proteins.
authors.affiliation: Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
authors.forename: Alys
authors.initials: A
authors.lastname: Hugo
authors.forename: Douglas J
authors.initials: DJ
authors.lastname: Baxter
authors.forename: William R
authors.initials: WR
authors.lastname: Cannon
authors.forename: Ananth
authors.initials: A
authors.lastname: Kalyanaraman
authors.forename: Gaurav
authors.initials: G
authors.lastname: Kulkarni
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Callister
mesh_terms: D019295:Computational Biology; D003205:Computing Methodologies; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D064307:Microbiota / Q000235:genetics; D011506:Proteins / Q000235:genetics / Q000302:isolation & purification; D020543:Proteome / Q000235:genetics / Q000302:isolation & purification; D040901:Proteomics / Q000706:statistics & numerical data*; D012984:Software; D053719:Tandem Mass Spectrometry / Q000706:statistics & numerical data*
chemical_list: D011506:Proteins; D020543:Proteome