Genotyping By Sequencing development for Salmo salar: A simulation-based predictive approach using the R package SimRAD.
Bibliographic record
Abstract
Application of Next Generation Sequencing platform (NGS) for genotyping purpose in the field of biotechnology, ecology or evolutionary biology is developing quickly. The introduction of efficient methods to reduce genome complexity allows making the most of the huge number of sequences generated by analyzing several individuals in a single run. As a result, numerous approaches for genome complexity reduction have been recently developed using different combinations of restriction enzymes, library construction protocols and fragments size selection. Therefore, the choice of which strategy to use may become cumbersome because it is difficult to anticipate the number of loci resulting from each method and no tool was available to provide guidance. To fill this methodological gap, we developed the R package SimRAD (available on the CRAN at http://cran.r-project.org/ web/packages/SimRAD) for simulation-based prediction of the number of loci expected from alternative Genotyping by Sequencing (GBS) or Restriction Associated DNA (RAD) protocols. This package can be used for non-model species for which no reference genome sequence is available, or for species with a draft or a full reference genome sequence released. We illustrated the practical use of SimRAD by comparing the number of loci expected under different GBS approaches applied in Atlantic salmon. We performed our simulations based on a randomly DNA sequence generated following CG content of 42.6% characteristics of Atlantic salmon and the draft genome sequence (AGKD00000000.1) available as yet for this species. Based on these estimations, we selected a GBS protocol that provided a good compromise between number of loci and potential for individual multiplexing in a single run. We then implemented the GBS method on three individuals using the two restriction enzymes PstI and MseI and a fragment size selection step on the Ion Torrent PGM. This preliminary run resulted in a total of 100000 loci which was within the range of the prediction performed using SimRAD (68000 using the simulated sequence and 135000 using the draft genome sequence). This GBS approach will be scaled up on the Ion Torrent Proton platform enabling analyzing up to 72 individuals per run. The imminent release of the Atlantic salmon reference genome sequence will greatly improve GBS outcome predictions allowing an easier balancing of the tradeoff between the number of loci and the number of individual needed for each GBS application
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".