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Record W2515651902 · doi:10.1093/bioinformatics/btw525

Gene Slider: sequence logo interactive data-visualization for education and research

2016· article· en· W2515651902 on OpenAlexaff
Jamie Waese, Asher Pasha, Ting Ting Wang, Anna van Weringh, David S. Guttman, Nicholas J. Provart

Bibliographic record

VenueBioinformatics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceSource codeJavaScriptVisualizationComputational biologyBiologyWorld Wide WebProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.375
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2016
Admission routes1
Has abstractyes

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