Abstract LB503: Using JBrowse 2 plugins to visualize cancer genomic data
Bibliographic record
Abstract
Abstract Genome browsers are essential tools for integrating, exploring, and visualizing data from large cancer genomics datasets. JBrowse 2 is a JavaScript genome browser with novel features for visualizing structural variants, syntenic alignments, and multiple types of genomic data. Although JBrowse 2 is available as a web-based or desktop application, the capabilities and features are not fixed and can be expanded through a comprehensive plugin system. To help address users' needs, we launched the new JBrowse 2 Plugin Store (https://jbrowse.org/jb2/plugin_store/). The overarching goals of the Plugin Store are to showcase the crucial features plugins add to JBrowse 2, to enable researchers to search for plugins they need, and for developers to highlight their plugins. The plugin system spans all aspects of the JBrowse 2 application and enables new track types (e.g. Manhattan plots, Hi-C data, ideograms), data adapters (e.g. API endpoint adapters for NCI Genomic Data Commons and the International Cancer Genome Consortium), and views (e.g. dot plots and multiple sequence alignments). Here, we present the capabilities of JBrowse 2 plugins and describe usage scenarios for cancer biologists and bioinformaticians, and software developers. Citation Format: Robin Andrew Haw, Colin Diesh, Caroline Bridge, Rob Buels, Garrett Stevens, Peter Xie, Teresa Martinez, Elliot Hershberg, Junjun Zhang, Shihab Dider, Scott Cain, Lincoln Stein, Ian Holmes. Using JBrowse 2 plugins to visualize cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB503.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.028 |
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".