FastMLST: A Multi-core Tool for Multilocus Sequence Typing of Draft Genome Assemblies

Enzo Guerrero-Araya, Marina Muñoz, César Rodríguez, Daniel Paredes-Sabja

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Multilocus Sequence Typing (MLST) is a precise microbial typing approach at the intra-species level for epidemiologic and evolutionary purposes. It operates by assigning a sequence type (ST) identifier to each specimen, based on a combination of alleles of multiple housekeeping genes included in a defined scheme. The use of MLST has multiplied due to the availability of large numbers of genomic sequences and epidemiologic data in public repositories. However, data processing speed has become problematic due to the massive size of modern datasets. Here, we present FastMLST, a tool that is designed to perform PubMLST searches using BLASTn and a divide-and-conquer approach that processes each genome assembly in parallel. The output offered by FastMLST includes a table with the ST, allelic profile, and clonal complex or clade (when available), detected for a query, as well as a multi-FASTA file or a series of FASTA files with the concatenated or single allele sequences detected, respectively. FastMLST was validated with 91 different species, with a wide range of guanine-cytosine content (%GC), genome sizes, and fragmentation levels, and a speed test was performed on 3 datasets with varying genome sizes. Compared with other tools such as mlst, CGE/MLST, MLSTar, and PubMLST, FastMLST takes advantage of multiple processors to simultaneously type up to 28 000 genomes in less than 10 minutes, reducing processing times by at least 3-fold with 100% concordance to PubMLST, if contaminated genomes are excluded from the analysis. The source code, installation instructions, and documentation of FastMLST are available at https://github.com/EnzoAndree/FastMLST.

Original languageEnglish (US)
JournalBioinformatics and Biology Insights
Volume15
DOIs
StatePublished - Nov 2021

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

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