Harnessing cross-species alignment to discover SNPs and generate a draft genome sequence of a bighorn sheep (Ovis canadensis)
Bibliographic record
Abstract
BACKGROUND: Whole genome sequences (WGS) have proliferated as sequencing technology continues to improve and costs decline. While many WGS of model or domestic organisms have been produced, a growing number of non-model species are also being sequenced. In the absence of a reference, construction of a genome sequence necessitates de novo assembly which may be beyond the ability of many labs due to the large volumes of raw sequence data and extensive bioinformatics required. In contrast, the presence of a reference WGS allows for alignment which is more tractable than assembly. Recent work has highlighted that the reference need not come from the same species, potentially enabling a wide array of species WGS to be constructed using cross-species alignment. Here we report on the creation a draft WGS from a single bighorn sheep (Ovis canadensis) using alignment to the closely related domestic sheep (Ovis aries). RESULTS: Two sequencing libraries on SOLiD platforms yielded over 865 million reads, and combined alignment to the domestic sheep reference resulted in a nearly complete sequence (95% coverage of the reference) at an average of 12x read depth (104 SD). From this we discovered over 15 million variants and annotated them relative to the domestic sheep reference. We then conducted an enrichment analysis of those SNPs showing fixed differences between the reference and sequenced individual and found significant differences in a number of gene ontology (GO) terms, including those associated with reproduction, muscle properties, and bone deposition. CONCLUSION: Our results demonstrate that cross-species alignment enables the creation of novel WGS for non-model organisms. The bighorn sheep WGS will provide a resource for future resequencing studies or comparative genomics.
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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 | 0.000 | 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".