Efficiency of RAPD and ISSR markers in assessing genetic diversity and relationships in black gram (<i>Vigna mungo</i> L. Hepper) vari
Bibliographic record
Abstract
The DNA fingerprinting methodologies, random amplified polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR), were used to estimate genetic diversity and relationships among 20 black gram (Vigna mungo L. Hepper) varieties. Thirty selected RAPD primers amplified 255 bands, 168 of which were polymorphic (66.5%). On average, these primers produced 8.5 bands, 5.6 of which were polymorphic. Polymorphic band number varied from 2 (A-05) to 10 (OPA-02), with sizes ranging from 100 to 2550 bp. Twenty-four selected ISSR primers produced 238 amplified products, 184 of which were polymorphic (77.8%). On average, these primers generated 9.8 bands, with 7.7 polymorphic bands ranging in number from 4 (ISSR-13) to 11 (ISSR-03), and size from 100-2650 bp. Genetic relationships were estimated using similarity coefficient (Jaccard’s) values between different accession pairs; these varied from 30.7 to 85.0 for RAPD, and from 37.2 to 88.4 with ISSR. UPGMA analysis indicated that the varieties ranged in similarity from 0.50 to 1.00 (mean of 0.75) for RAPD, and from 0.47 to 1.00 (mean of 0.76) with ISSR. Cluster analysis of RAPD and ISSR results identified three clusters with significant bootstrap values, which revealed greater homology between the varieties. Principal coordinates analysis also supported this conclusion. Among the black gram varieties, WBU-108 and RBU-38 were highly divergent, whereas LBG-648 and LBG-623 were genetically similar. The markers generated by RAPD and ISSR assays can provide practical information for the management of genetic resources and these results will also provide useful information for the molecular classification and breeding of new black gram varieties.Key words: Black gram, cluster analysis, genetic diversity, ISSR, molecular markers, RAPD
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".