Bioinformatic Analyses for Nitrogen Responsive Transcriptome and De Novo Regulatory Motif Discovery in Potato «Solanum tuberosum»
Bibliographic record
Abstract
Nitrogen is an essential nutrient for plant growth and development, but high usage of nitrogen-based fertilizers can have adverse effects on crops, the environment, and the economy.To discover nitrogen responsive molecular mechanisms that could be used to develop nitrogen efficient potato varieties and optimal nitrogen fertilizer management strategies, differentially expressed genes were identified in the transcriptomes of tuber and leaf tissue from three commercial cultivars of potato S. tuberosum L. Solanaceae (Russet Burbank, Atlantic and Shepody).The upstream sequences of the differentially expressed genes were used with two de novo regulatory motif discovery algorithms (Seeder and Homer2), to pinpoint potential cis-regulatory element(s) most likely to be responsible for nitrogen response.The results confirm findings from an earlier study on the leaf as well as identifies novel tuberspecific genes and cis-regulatory motifs that are involved in response mechanisms, as well as nitrogen metabolism expressed in both tissue types.advice provided by all of you not only allowed me to finish this project but grounded my understanding of this field, which I will always be thankful for.I would also like to acknowledge the help provided from our collaborator Dr. Helen Tai, not only for her valuable contributions, but also the guidance from her involvement in the preliminary studies associated with this project.To all my fellow lab members, Ilayda, Sai and Juan, thank you for your all the help and insight you provided me.And finally, to my parents and to my partner, I cannot thank you enough for the support you have given me over the past two years, for which I would not have been able to go through these times without it.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".