Massively Parallel Sequencing of Chikso (Korean Brindle Cattle) to Discover Genome-Wide SNPs and InDels
Bibliographic record
Abstract
Since the completion of the bovine sequencing projects, a substantial number of genetic variations such as single nucleotide polymorphisms have become available across the cattle genome. Recently, cataloguing such genetic variations has been accelerated using massively parallel sequencing technology. However, most of the recent studies have been concentrated on European Bos taurus cattle breeds, resulting in a severe lack of knowledge for valuable native cattle genetic resources worldwide. Here, we present the first whole-genome sequencing results for an endangered Korean native cattle breed, Chikso, using the Illumina HiSeq 2,000 sequencing platform. The genome of a Chikso bull was sequenced to approximately 25.3-fold coverage with 98.8% of the bovine reference genome sequence (UMD 3.1) covered. In total, 5,874,026 single nucleotide polymorphisms and 551,363 insertion/deletions were identified across all 29 autosomes and the X-chromosome, of which 45% and 75% were previously unknown, respectively. Most of the variations (92.7% of single nucleotide polymorphisms and 92.9% of insertion/deletions) were located in intergenic and intron regions. A total of 16,273 single nucleotide polymorphisms causing missense mutations were detected in 7,111 genes throughout the genome, which could potentially contribute to variation in economically important traits in Chikso. This study provides a valuable resource for further investigations of the genetic mechanisms underlying traits of interest in cattle, and for the development of improved genomics-based breeding tools.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".