MetaCurator: A hidden Markov model‐based toolkit for extracting and curating sequences from taxonomically‐informative genetic markers
Bibliographic record
Abstract
Abstract While metabarcoding and metagenomic approaches are increasingly popular, questions remain about how best to analyse and taxonomically characterize the sequence data produced by such methods. Due to a lack of software infrastructure, important reference sequence curation steps are often ignored. We present MetaCurator, a software package designed for automated reference sequence curation and highly generalizable across markers and study systems. MetaCurator contains two signature tools. IterRazor utilizes profile hidden Markov models and an iterative search framework to exhaustively identify and extract the precise marker of interest from available references. DerepByTaxonomy dereplicates sequences using a taxonomically aware approach, removing duplicates only when they belong to the same taxon. This is important for highly conserved markers, such as plant rbcL and trnL , which often display no sequence divergence across taxa, even at the genus level. Using MetaCurator, we produced reference sequence databases for a popular arthropod COI marker as well as four plant barcoding markers, trnL , rbcL , ITS2 and trnH . In comparing these databases to those produced by recent and comparable studies, we show that the Metacurator pipeline exhibits greater sensitivity during sequence extraction, especially for poorly conserved markers. Further, database taxonomic richness was not decreased following sequence dereplication, as observed in previous studies. MetaCurator is supported on OSX and Linux and is freely available under a GPL v3.0 license at https://github.com/RTRichar/MetaCurator . The reference databases produced in this work, and the commands used for curation, are available at https://github.com/RTRichar/MetabarcodeDBsV2 .
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".