Development of an InDel polymorphism database for jute via comparative transcriptome analysis
Bibliographic record
Abstract
Jute (Corchorus spp.) is one of the most commercially important bast fiber crops in the world. However, molecular markers and high-density genetic maps are still lacking on jute compared with other crops. Insertion/deletion (InDel) markers, one of the most abundant sources of DNA/RNA variations in plant genomes, can easily be distinguished among different accessions using high-throughput sequencing. Using three transcriptome datasets, we identified and developed InDel markers. Altogether, 51 172 InDel sites in 18 800 unigenes were discovered, and the number of InDel loci per unigene varied from 1 to 31. Further, we found 94 InDel types, varying from 1 to 159 bp; the most common were single-nucleotide (23 028), binucleotide (9824), and trinucleotide (9182). In total, 49 563 InDels in 18 445 transcripts were discovered in the comparison between TC and YG, followed by 48 934 InDels in 18 408 transcripts between NY and YG, and 3570 InDels in 2701 unigenes between NY and TC. Additionally, there were 1273 InDel sites in 1129 unigenes with polymorphisms between any two of the three accessions. Twenty-nine (58%) primer pairs represented polymorphisms when compared to the jute accessions, and PIC varied from 0.340 to 0.680, with an average of 0.491.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".