Identification of High-Confidence Structural Variants in Domesticated Rainbow Trout Using Whole-Genome Sequencing
Bibliographic record
Abstract
), an important aquaculture species of cold freshwater. The objectives of this study were 1) to identify and validate high-confidence SVs in rainbow trout using whole-genome re-sequencing; and 2) to examine the contribution of transposable elements (TEs) to SVs in rainbow trout. A total of 96 rainbow trout, including 11 homozygous lines and 85 outbred fish from three breeding populations, were whole-genome sequenced with an average genome coverage of 17.2×. Putative SVs were identified using the program Smoove which integrates LUMPY and other associated tools into one package. After rigorous filtering, 13,863 high-confidence SVs were identified. Pacific Biosciences long-reads of Arlee, one of the homozygous lines used for SV detection, validated 98% (3,948 of 4,030) of the high-confidence SVs identified in the Arlee homozygous line. Based on principal component analysis, the 85 outbred fish clustered into three groups consistent with their populations of origin, further indicating that the high-confidence SVs identified in this study are robust. The repetitive DNA content of the high-confidence SV sequences was 86.5%, which is much higher than the 57.1% repetitive DNA content of the reference genome, and is also higher than the repetitive DNA content of Atlantic salmon SVs reported previously. TEs thus contribute substantially to SVs in rainbow trout as TEs make up the majority of repetitive sequences. Hundreds of the high-confidence SVs were annotated as exon-loss or gene-fusion variants, and may have phenotypic effects. The high-confidence SVs reported in this study provide a foundation for further rainbow trout SV studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".