MiRiQ Database: A Platform for In Silico Rice Mutant Screening
Bibliographic record
Abstract
Genetic studies using mutant resources have significantly contributed to elucidating plant gene function. Massive mutant libraries sequenced by next-generation sequencing technology facilitate mutant identification and functional analysis of genes of interest. Here, we report the creation and release of an open-access database (https://miriq.agr.kyushu-u.ac.jp/index.php), called Mutation-induced Rice in Kyushu University (MiRiQ), designed for in silico mutant screening based on a whole-genome-sequenced mutant library. This database allows any user to easily find mutants of interest without laborious efforts such as large-scale screening by PCR. The initial version of the MiRiQ database (version 1.0) harbors a total of 1.6 million single-nucleotide variants (SNVs) and InDels of 721 M1 plants that were mutagenized by N-methyl-N-nitrosourea treatment of the rice cultivar Nipponbare (Oryza sativa ssp. japonica). The SNVs were distributed among 87% of all 35,630 annotated protein-coding genes of the Nipponbare genome and were predicted to induce missense and nonsense mutations. The MiRiQ database provides built-in tools, such as a search tool by keywords and JBrowse for mutation searches. Users can request mutant seeds in the M2 or M3 generations from a request form linked to this database. We believe that the availability of a wide range of gene mutations in this database will benefit the plant science community and breeders worldwide by accelerating functional genomic research and crop improvement.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.019 |
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".