Impacts of degraded <scp>DNA</scp> on restriction enzyme associated <scp>DNA</scp> sequencing (<scp>RADS</scp>eq)
Bibliographic record
Abstract
Degraded DNA from suboptimal field sampling is common in molecular ecology. However, its impact on techniques that use restriction site associated next-generation DNA sequencing (RADSeq, GBS) is unknown. We experimentally examined the effects of in situDNA degradation on data generation for a modified double-digest RADSeq approach (3RAD). We generated libraries using genomic DNA serially extracted from the muscle tissue of 8 individual lake whitefish (Coregonus clupeaformis) following 0-, 12-, 48- and 96-h incubation at room temperature posteuthanasia. This treatment of the tissue resulted in input DNA that ranged in quality from nearly intact to highly sheared. All samples were sequenced as a multiplexed pool on an Illumina MiSeq. Libraries created from low to moderately degraded DNA (12-48 h) performed well. In contrast, the number of RADtags per individual, number of variable sites, and percentage of identical RADtags retained were all dramatically reduced when libraries were made using highly degraded DNA (96-h group). This reduction in performance was largely due to a significant and unexpected loss of raw reads as a result of poor quality scores. Our findings remained consistent after changes in restriction enzymes, modified fold coverage values (2- to 16-fold), and additional read-length trimming. We conclude that starting DNA quality is an important consideration for RADSeq; however, the approach remains robust until genomic DNA is extensively degraded.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".