Tissue sampling methods and standards for vertebrate genomics
Bibliographic record
Abstract
The recent rise in speed and efficiency of new sequencing technologies have facilitated high-throughput sequencing, assembly and analyses of genomes, advancing ongoing efforts to analyze genetic sequences across major vertebrate groups. Standardized procedures in acquiring high quality DNA and RNA and establishing cell lines from target species will facilitate these initiatives. We provide a legal and methodological guide according to four standards of acquiring and storing tissue for the Genome 10K Project and similar initiatives as follows: four-star (banked tissue/cell cultures, RNA from multiple types of tissue for transcriptomes, and sufficient flash-frozen tissue for 1 mg of DNA, all from a single individual); three-star (RNA as above and frozen tissue for 1 mg of DNA); two-star (frozen tissue for at least 700 μg of DNA); and one-star (ethanol-preserved tissue for 700 μg of DNA or less of mixed quality). At a minimum, all tissues collected for the Genome 10K and other genomic projects should consider each species' natural history and follow institutional and legal requirements. Associated documentation should detail as much information as possible about provenance to ensure representative sampling and subsequent sequencing. Hopefully, the procedures outlined here will not only encourage success in the Genome 10K Project but also inspire the adaptation of standards by other genomic projects, including those involving other biota.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.034 |
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".