Whole genome sequences from non-invasively collected samples
Bibliographic record
Abstract
Conservation genomics is an important tool to manage threatened species under current biodiversity loss. Recent advances in sequencing technology mean that we can now use whole genomes to investigate demographic history, local adaptation, inbreeding, and more in unprecedented detail. However, for many rare and elusive species only non-invasive samples such as faeces can be obtained, making it difficult to take advantage of whole genome data. We present a method to extract DNA from the mucosal layer of faecal samples to reconstruct high coverage whole genomes using standard laboratory techniques, therefore in a cost-effective and efficient way. We use wild collected faecal pellets collected from wild caribou (Rangifer tarandus), a species undergoing declines in many parts of its range in Canada and subject to comprehensive conservation and population monitoring measures. We compare four faecal genomes to two tissue genomes sequenced in the same run. Quality metrics were similar between faecal and tissue samples with the main difference being the alignment success of raw reads to the reference genome likely due to differences in endogenous DNA content, affecting overall coverage. One of our faecal genomes was only reconstructed at low coverage (1.6X), however the other three obtained between 7 and 15X, compared to 19 and 25X for the tissue samples. We successfully reconstructed high-quality whole genomes from faecal DNA and, to our knowledge, are the first to obtain genome-wide data from wildlife faecal DNA in a non-primate species, representing an important advancement for non-invasive conservation genomics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".