A Functional Genomics Approach to Identifying Potential Candidates Underlying the E7 Maturity Locus in Soybean (Glycine max)
Bibliographic record
Abstract
Soybean [Glycine max (L.) Merr.] is a valuable crop, with benefits attributed to its high protein and oil content, in addition to its nitrogen fixating capabilities.To expand production across Canada, breeding programs will need the availability of markers linked to desired traits, including time of flowering and maturity.The E7 locus is among the 10 maturity loci identified in this pathway, whose underlying gene remains unknown.Using functional genomics resources, and a computational approach utilizing PIPE (Protein-protein Interaction Prediction Engine), this region was narrowed to a short-list of candidates, including 3 promising candidates: Glyma.06G200400,Glyma.06G200800, and Glyma.06G220000.From the expression analysis performed to date, Glyma.06G199800 and Glyma.06G233300, were found to have significant change in expression between the E7/e7 lines in contrasting flowering stages during long-day (LD) photoperiod.However, additional expression analysis on the remaining candidate's, along with further experimentation needs to be performed to reveal the underlying gene for E7.First, and foremost, I would like to thank my direct supervisor Dr. Bahram Samanfar for his valuable guidance, encouragement, and providing an inspirational work environment that
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".