minMLST: machine learning for optimization of bacterial strain typing
| Autor: | Yair Motro, Jacob Moran-Gilad, Shani Attias Cohen, Isana Veksler-Lublinsky, Lior Rokach |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Computer science Locus (genetics) Ontology (information science) Machine learning computer.software_genre Biochemistry Genome Disease Outbreaks Discriminatory power Machine Learning 03 medical and health sciences Molecular Biology Gene Phylogeny 030304 developmental biology 0303 health sciences 030306 microbiology business.industry Strain typing Computer Science Applications Bacterial Typing Techniques Computational Mathematics Identification (information) Computational Theory and Mathematics Multilocus sequence typing Artificial intelligence business computer Genome Bacterial Multilocus Sequence Typing |
| Zdroj: | Bioinformatics (Oxford, England). 37(3) |
| ISSN: | 1367-4811 |
| Popis: | Motivation High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. Results We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. Availability and implementation The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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