mtGrasp: Streamlined reference‐grade mitochondrial genome assembly and standardization to enhance metazoan mitogenome resources
Bibliographic record
Abstract
Abstract High‐quality, accessible mitogenome sequences are crucial for comparative genomics, phylogenetic constructions and environmental DNA (eDNA) applications. With advancements in sequencing technologies, genomic sequencing reads are available for a wide variety of species. Although these data sets contain mitochondrial sequences, this information remains largely untapped due to the limited specialized tools for assembling mitogenomes from short‐read libraries. The present study introduces the Mitochondrial Genome Reference‐grade Assembly and Standardization Pipeline (mtGrasp), a streamlined and memory‐efficient pipeline for assembling complete and standardized mitogenomes from short‐read metazoan DNA libraries ( https://github.com/bcgsc/mtGrasp ). We assembled 23 short‐read libraries and found that mtGrasp ran 2–7 times faster and used 3–4 times less memory, on average, compared to the state‐of‐the‐art mitogenome assembly pipelines GetOrganelle and MitoZ. Additionally, mtGrasp successfully assembled mitogenomes of varying completeness across different animal taxa. We illustrate that standardizing existing mitogenome annotations with mtGrasp would improve the accuracy of downstream phylogenetic analysis and demonstrate the utility of mtGrasp in designing robust targeted quantitative polymerase chain reaction‐based eDNA assays using mitogenomes assembled from museum voucher specimens. We expect the reference‐grade mitogenomes generated with freely available mtGrasp to advance the development of robust, high‐quality eDNA tools and aid in a variety of comparative genomic analyses.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.015 |
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".