IWGSC Sequence Repository: Moving towards tools to facilitate data integration for the reference sequence of wheat
Bibliographic record
Abstract
URGI is a genomics and bioinformatics research unit at INRA (French National institute for Agricultural Research), dedicated to plants and crop parasites. We develop and maintain a genomic and genetic Information System called GnpIS that manages multiple types of wheat data. Under the umbrella of the IWGSC (International Wheat Genome Sequencing Consortium), we have set up a Sequence Repository on the Wheat@URGI website to store, browse and BLAST the data being generated by the wheat genome project: http://wheat-urgi.versailles.inra.fr/Seq-Repository. The repository holds the wheat physical maps, the chromosome survey sequence data for the individual chromosomes of breadwheat, draft sequences for diploid and tetraploid wheats and provides browsable access to the BAC-based reference sequence for chromosome 3B, the first of the chromosomes to be completed by the consortium. I will highlight the new features and data available in the Sequence Repository (e.g., new BLAST functionalities) and, in particular, present what we have done to address needs and concerns raised during the IWGSC S&P workshop last year. In addition, I will open the discussion about the future needs for tools to facilitate the integration of data to produce the reference sequence.
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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 | 0.044 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.015 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.046 |
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".