Application of comparative genomics to narrow-leafed lupin (<i>Lupinus angustifolius</i>L.) using sequence information from soybean and<i>Arabidopsis</i>
Bibliographic record
Abstract
The completion of genome-sequencing initiatives for model plants and EST databases for major crop species provides a large resource for gaining fundamental knowledge of complex gene interactions and the functional significance of proteins. There are increasingly numerous opportunities to transfer this information to other plant species with uncharacterized genomes and make advances in genome analysis, gene expression, and predicted protein function. In this study, we have used DNA sequences from soybean and Arabidopsis to determine the feasibility of applying comparative genomics to narrow-leafed lupin. We have used transcribed sequences from soybean and showed that a high proportion cross hybridize to lupin DNA, identifying similar genes and providing landmarks for estimating the degree of chromosomal synteny between species. To further investigate comparative relationships in this study, a detailed analysis of three lupin genes and comparison of orthologs from soybean and Arabidopsis shows that, in some cases, gene structure and expression are highly conserved and their proteins may have similar function. In other cases, genes show variation in expression profiles indicating alternative functions across species. The advantages and limitation of using soybean and Arabidopsis sequences for comparative genomics in lupins are discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".