Novel Informatic Tools to Support Functional Annotation of the Durum Wheat Genome
Bibliographic record
Abstract
Seed mutagenesis is one strategy to create a population with thousands of useful mutations for the direct selection of desirable traits, to introduce diversity into varietal improvement programs, or to generate a mutant collection to support gene functional analysis. However, phenotyping such large collections, where each individual may carry many mutations, is a bottleneck for downstream analysis. Targeting Induced Local Lesions in Genomes (TILLinG), when coupled with next-generation sequencing allows high-throughput mutation discovery and selection by genotyping. We mutagenized an advanced durum breeding line, UAD0951096_F2:5 and performed short-read (2x125 bp) Illumina sequencing of the exome of 100 lines using an available exome capture platform. To improve variant calling, we generated a consolidated exome reference using the recently available genome sequences of the cultivars Svevo and Kronos to facilitate the alignment of reads from the UAD0951096_F2:5 derived mutants. The resulting exome reference was 484.4 Mbp. We also developed a user-friendly, searchable database and bioinformatic analysis pipeline that allowed us to predict zygosity of the mutations discovered and extracts flanking sequences for rapid marker development. Here, we present these tools with the aim of allowing researchers fast and accurate downstream selection of mutations discovered by TILLinG by sequencing to support functional annotation of the durum wheat genome.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.013 | 0.008 |
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".