Automating high-throughput screening for anthracnose resistance in common bean using allele specific PCR
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Common beans (Phaseolus vulgaris L.) provide important protein and calories globally. Anthracnose (Colletotrichum lindemuthianum (Sacc. & Magnus) Briosi & Cavara, 1889) is a major disease in common bean and causes significant yield losses in bean production areas. Screening for markers linked to known disease resistance genes provides useful information for plant breeders to develop improved common bean varieties. The Kompetitive Allele Specific PCR (KASP) assay is an affordable genetic screening technique that can be used to accelerate breeding programs, but manual DNA extraction and KASP assay preparation are time-consuming. Several KASP markers have been developed for genes involved in resistance to bean anthracnose, which can reduce yield by up to 100%, but their usefulness is hindered by the labor required to screen a significant number of bean lines. Our research objective was to develop publicly available protocols for DNA extraction and KASP assaying using a liquid handling robot (LHR) which would facilitate high-throughput genetic screening with less active human time required. Anthracnose resistance markers were used to compare manual and automated results. RESULTS: both by hand and with the use of an LHR. A protocol was written for DNA extraction and KASP assay thermocycling to implement the LHR. The LHR protocol reduced the active human screening time of 24 samples from 3h44 to 1h23. KASP calls were consistent across replicates but not always accurate for their known linked resistance genes, suggesting more specific markers still need to be developed. Using an LHR, information from KASP assays can be accumulated with little active human time. CONCLUSION: Results suggest that LHRs can be used to expedite time-consuming and tedious lab work such as DNA extraction or PCR plate filling. Notably, LHRs can be used to prepare KASP assays for large sample sizes, facilitating higher throughput use of genetic marker screening tools.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it