Genome Sequence-Based Curation of PubMLST Data Challenges Interspecies Recombination in the <i>Burkholderia</i> <i>Cepacia</i> Complex
Bibliographic record
Abstract
Multilocus sequence typing (MLST) has been the gold standard for typing and identification of a wide range of bacteria for two decades [1][2][3].Public MLST databases allow researchers worldwide to analyze and deposit data, and enable the study of the global prevalence and epidemiology of a broad range of bacteria [3,4].A Burkholderia cepacia complex (Bcc) MLST scheme based on partial atpD, gltB, gyrB, recA, lepA, phaC and trpB gene sequence analysis was developed for both species and strain level differentiation [5][6][7].PCR primers were subsequently improved to reliably amplify the target loci from both Bcc and non-Bcc Burkholderia bacteria and to enable the use of a single primer set for amplification and sequencing [8].The large number of recent publications that used MLST and the accompanying Bcc PubMLST database as a tool for epidemiological studies shows that MLST is a well-established method that enabled outbreak surveillance, shed light on the global distribution of strains and elucidated Bcc epidemiology and population structure [9].While the first few hundred sequence types were primarily originating from European, American and Canadian isolates, there has been an increase in submissions from Australia and countries in Asia and South-America.Reproducibility and portability have been considered major advantages of MLST over earlier typing and identification methods [1,2].In today's genomics era, traditional Sanger sequencing of MLST loci is gradually replaced by the extraction of MLST alleles from next-generation sequencing data, thus sustaining the continued use of the same MLST schemes [2,10].As curators of the Bcc PubMLST database [3,11] we observed that genome sequence derived MLST data revealed several types of conflicts with earlier MLST data that were generated through Sanger sequencing.We generated genome sequences from high-coverage Illumina data for 113 Bcc isolates (method as previously described by Peeters et al. [12], [unpublished data]) for which Sanger sequencing based MLST data were available; for 34 of these isolates (30%) there was a conflict between the genome sequence derived and earlier MLST data.Generally, two types of conflict were found.In one type of conflict the genome sequence derived MLST alleles revealed one or a few single nucleotide polymorphisms compared with the earlier MLST data.An example of this type of error was found for Burkholderia multivorans outbreak strain C1576 [13], for which the initial MLST analysis yielded lepA-8 and trpB-6 [7], while more recent analyses uncovered lepA-224 [14] (1/397 nucleotide differences) and trpB-415 (GenBank/ENA accession number ERS784904) (3/301 nucleotide differences), changing the sequence type from ST-27 into ST-899.Because the coverage of Illumina data by far exceeds that of traditional Sanger sequencing, it is not unexpected to find a few false single nucleotide polymorphisms in the original MLST data [15,16].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".