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Record W2976918065 · doi:10.4103/ijmy.ijmy_106_19

A multilocus sequence typing scheme for Mycobacterium abscessus complex (MAB-multilocus sequence typing) using whole-genome sequencing data

2019· article· en· W2976918065 on OpenAlexaff
MeenuKaushal Sharma, Michelle Wuzinski, AnetaK Bak, Aaron Petkau, Walter Demczuk, Hafid Soualhine

Bibliographic record

VenueInternational Journal of Mycobacteriology · 2019
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
Fundersnot available
KeywordsMultilocus sequence typingMycobacterium abscessusrpoBBiologyGeneticsGenomeTypingWhole genome sequencingSubtypingNontuberculous mycobacteriaComputational biologyGeneMycobacteriumGenotypeComputer science

Abstract

fetched live from OpenAlex

Background: Mycobacterium abscessus is a rapid growing nontuberculous mycobacteria (NTM) and a clinically significant pathogen capable of causing variable infections in humans that are difficult to treat and may require months of therapy/surgical interventions. Like other NTMs, M. abscessus can be associated with outbreaks leading to complex investigations and treatment of affected cases. Typing schemes for bacterial pathogens provide numerous applications; including identifying chain of transmission and tracking genomic evolution, are lacking or limited for many NTMs including M. abscessus. Methods: We extended the existing scheme from PubMLST using whole-genome data for M. abscessus by extracting data for 15 genetic regions within the M. abscessus genome. A total of 168 whole genomes and 11 gene sequences were used to build this scheme (MAB-multilocus sequence typing [MLST]). Results: All seven genes from the PubMLST scheme, namely argH, cya, gnd, murC, pta, purH, and rpoB, were expanded by 10, 14, 13, 10, 13, 10, and 9 alleles, respectively. Another eight novel genes were added including hsp 65, erm(41), arr, rrs, rrl, gyrA, gyrB, and recA with 16, 16, 25, 7, 32, 35, 29, and 15 alleles, respectively, with 85 unique sequence types identified among all isolates. Conclusion: MAB-MLST can provide identification of M. abscessus complex to the subspecies level based on three genes and can provide antimicrobial resistance susceptibility prediction based on results from seven genes. MAB-MLST generated a total of 85 STs, resulting in subtyping of 90 additional isolates that could not be genotyped using PubMLST and yielding results comparable to whole-genome sequencing (WGS). Implementation of a Galaxy-based data analysis tool, MAB-MLST, that simplifies the WGS data and yet maintains a high discriminatory index that can aid in deciphering an outbreak has vast applicability for routine diagnostics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.190
GPT teacher head0.408
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2019
Admission routes1
Has abstractyes

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