Analysis of gene expression data in transgenic and non- transgenic soybean cultivars using bioinformatics tools
Bibliographic record
Abstract
Current safety assessment for novel crops, including transgenic crops, uses a targeted approach, which determines crop safeness by assessing the content of a few specific chemical components.However, microarray technology can simultaneously assess the whole transcriptome and can therefore be used to analyze target genes as well as unintended effects.In this study, we used this technique as a non-targeted approach.Gene expression data from a microarray experiment with five soybean cultivars was analyzed using bioinformatics.Two cultivars were transgenic (RoundUp®) and three were non-transgenic.We show that the variation in gene expression between transgenic and non-transgenic soybean is less than that between non-transgenic cultivars.A MySQL database coupled with CGI web interfaces was developed to store and present the results (http://thor.agrenv.mcgill.ca/cgi-bin/soy/soybean.cgi).By integrating the microarray data with gene annotations and other soybean data, a comprehensive view of differences in gene expression can be explored between cultivars.iii Résumé Les méthodes actuelles d'évaluation du risque pour des cultures nouvelles, incluant les cultures transgéniques, utilisent une approche ciblée; elles évaluent le contenu en composés chimiques spécifiques.La technologie des micropuces étant maintenant disponible, il est possible d'évaluer la totalité du transcriptome.Nous avons utilisé cette technologie comme approche non-ciblée.Dans la présente étude, les données d'expériences de micropuces comparant l'expression des gènes de cinq cultivars de soja sont analysées par des méthodes bioinformatiques.Deux de ces cultivars sont des soja transgéniques RoundUp® et trois sont non-transgéniques.Nous montrons que la variation de l'expression des gènes entre soja transgéniques et non-transgéniques est moins grande qu'entre des cultivars non-transgéniques.Une base de données MySQL et une interface web CGI ont été développées pour entreposer et récupérer les données.L'intégration avec d'autres données sur le soja a rendu possible l'exploration de données génétiques globales entre cultivars en terme de fonctions biologiques.iv Dedication This thesis is dedicated to my parents who offered me unconditional love, support and understanding throughout all these years of education.Thank you for everything.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".