A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

• Published in: PLoS computational biology Vol. 14; no. 10; p. e1006434
• Main Authors: Aun, Erki, Brauer, Age, Kisand, Veljo, Tenson, Tanel, Remm, Maido
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Antibiotics; Antimicrobial agents; Artificial intelligence; Azithromycin; Bioinformatics; Biology; Biology and Life Sciences; Biomarkers; Biosynthesis; Ciprofloxacin; Datasets; Drug resistance; Gene sequencing; Genes; Genomes; Genotype & phenotype; Internet; Klebsiella; Klebsiella pneumoniae; Machine learning; Mathematical models; Medicine and Health Sciences; Mutation; Phenotypes; Physical Sciences; Prediction models; Programming languages; Pseudomonas aeruginosa; Research and Analysis Methods; Software; Source code; Statistical models; Virulence; Estonia
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006434

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).