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Record W4282981142 · doi:10.1158/1538-7445.am2022-6400

Abstract 6400: JBrowse 2: An extensible open-source platform for modern genome analysis

2022· article· en· W4282981142 on OpenAlexaff
Scott Cain, Robin Haw, Caroline Bridge, Junjun Zhang, Robert Buels, Colin Diesh, Garrett Stevens, Teresa De Jesus Martinez, Peter Xie, Elliot A. Hershberg, Shihab Dider, Lincoln Stein, Ian Holmes

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsPlug-inComputer scienceGenomeContigReference genomeWorld Wide WebSequence assemblySyntenyVisualizationComputational biologyOpen sourceData scienceData miningBiologySoftwareProgramming languageGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction: Genome browsers are useful tools for research in human genetics, able to display and integrate biological information such as long and short read sequencing data, variant calls, and annotations. While traditional linear genome browsers have demonstrated broad utility for many applications, there is an increasing need for visualizing complex structural variants (SVs) beyond the linear view. To meet these challenges, we created JBrowse 2: an extensible open-source platform for visualizing and integrating genomic data. Results: JBrowse 2 is a flexible platform that provides the foundation for the development of applications and web dashboards that combine novel views and representations extending beyond linear displays in genomic reference coordinates. For example, a flagship JBrowse 2 application is the SV Inspector, which lets users open a list of structural variants in a data table and see the results in a whole-genome circular view. Clicking on a given variant in the table or circular view opens up a linear view that displays the read evidence supporting the SV, even across complex breakpoints like translocations. Dotplot and synteny views are built-in, enabling “long read vs reference” dotplot visualizations, or alignments of de-novo assembled contigs to the genome. These views are integrated with each other, so that (for example) a long read is just a few clicks away from the corresponding dotplot. In addition to including these new views, the JBrowse 2 platform is designed from the ground up to enable third-party plugins to add new views, data adapters, and track types, which has facilitated the creation of new plugins that address some very specific use cases. These include our MSA view plugin for viewing multiple sequence alignments, data adapters that download data from UCSC and CIVIC APIs, and the Quantseq plugin for viewing quantitative motif scores as a genome browser track. JBrowse 2 is available as both a web app or a local desktop app. We also offer re-usable components on NPM, and users of R can programmatically create an instance of JBrowse 2 with the JBrowseR package on CRAN. JBrowse 2 can also generate high quality SVG snapshots from inside the app, and we also created a CLI tool called jb2export to perform automated or bulk exports of JBrowse 2 visualizations. We anticipate that JBrowse 2 will better serve genome scientists with its structural variant visualization capabilities, and will provide the flexibility to adapt to new visualization and analysis challenges as they emerge in the coming years. Citation Format: Scott Cain, Robin Haw, Caroline Bridge, Junjun Zhang, Robert Buels, Colin Diesh, Garrett Stevens, Teresa Martinez, Peter K. Xie, Elliot Hershberg, Shihab Dider, Lincoln Stein, Ian Holmes. JBrowse 2: An extensible open-source platform for modern genome analysis [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 6400.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.994
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0060.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0880.166

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 head0.110
GPT teacher head0.401
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreSoftware

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

Citations2
Published2022
Admission routes1
Has abstractyes

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