Gene Slider: sequence logo interactive data-visualization for education and research
Bibliographic record
Abstract
Gene Slider helps visualize the conservation and entropy of orthologous DNA and protein sequences by presenting them as one long sequence logo that can be zoomed in and out of, from an overview of the entire sequence down to just a few residues at a time. A search function enables users to find motifs such as cis-elements in promoter regions by simply 'drawing' a sequence logo representation of the desired motif as a query. In addition to displaying user-supplied FASTA files, our demonstration version of Gene Slider loads and displays a rich database of 90 000+ conserved non-coding regions across the Brassicaceae indexed to the TAIR10 Col-0 Arabidopsis thaliana sequence. It also displays transcription factor binding sites, enabling easy identification of regions that are both conserved across multiple species and may contain transcription factor binding sites. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at: http://www.bar.utoronto.ca/GeneSlider and also as an app on http://araport.org Website implemented in JavaScript and Processing.js with all major browsers supported. Source code available under GNU GPLv2 at SourceForge: https://sourceforge.net/projects/geneslider/ CONTACT: nicholas.provart@utoronto.ca.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.253 | 0.101 |
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".