Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification
Bibliographic record
Abstract
Bioinformatic analysis of eDNA metabarcoding data is a crucial step toward rigorously assessing biodiversity. Many programs are now available for each step of the required analyses, but their relative abilities at providing fast and accurate species lists have seldom been evaluated. We used simulated mock communities and real fish eDNA metabarcoding data to evaluate the performance of 13 bioinformatic programs and pipelines to retrieve fish occurrence and read abundance using the 12S mt rRNA gene marker. We used four indices to compare the outputs of each program with the simulated samples: sensitivity, F-measure, root-mean-square error (RMSE) on read relative abundances, and execution time. We found marked differences among programs only for the taxonomic assignment step, both in terms of sensitivity, F-measure and RMSE. Running time was highly different between programs for each step. The fastest programs with best indices for each step were assembled into a pipeline. We compared this pipeline to pipelines constructed from existing toolboxes (OBITools, Barque, and QIIME 2). Our pipeline and Barque obtained the best performance for all indices and appear to be better alternatives to highly used pipelines for analysing fish eDNA metabarcoding data when a complete reference database is available. Analysis on real eDNA metabarcoding data also indicated differences for taxonomic assignment and execution time only. This study reveals major differences between programs during the taxonomic assignment step. The choice of algorithm for the taxonomic assignment can have a significant impact on diversity estimates and should be made according to the objectives of the study.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".