PigVar: a database of pig variations and positive selection signatures
Bibliographic record
Abstract
Pigs are excellent large-animal models for medical research and a promising organ donor source for transplant patients. Next-generation sequencing technology has yielded a dramatic increase in the volume of genomic data for pigs. However, the limited amount of variation data provided by dbSNP, and non-congruent criteria used for calling variation, present considerable hindrances to the utility of this data. We used a uniform pipeline, based on GATK, to identify non-redundant, high-quality, whole-genome SNPs from 280 pigs and 6 outgroup species. A total of 64.6 million SNPs were identified in 280 pigs and 36.8 million in the outgroups. We then used LUMPY to identify a total of 7 236 813 structural variations (SVs) in 211 pigs. Positively selected loci were identified through five statistical tests of different evolutionary attributes of the SNPs. Combining the non-redundant variations and the evolutionary selective scores, we built the first pig-specific variation database, PigVar (http://www.ibiomedical.net/pigvar/), which is a web-based open-access resource. PigVar collects parameters of the variations including summary lists of the locations of the variations within protein-coding and long intergenic non-coding RNA (lincRNA) genes, whether the SNPs are synonymous or non-synonymous, their ancestral and derived states, geographic sampling locations, as well as breed information. The PigVar database will be kept operational and updated to facilitate medical research using the pig as model and agricultural research including pig breeding. Database URL: http://www.ibiomedical.net/pigvar/
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".