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RNAget: an API to securely retrieve RNA quantifications

2023· article· en· W4323810601 on OpenAlexafffund
Sean Upchurch, Emilio Palumbo, Jeremy Adams, David Bujold, Guillaume Bourque, Jared L. Nedzel, Keenan Graham, Meenakshi S. Kagda, Pedro Assis, Benjamin C. Hitz, Emilio Righi, Roderic Guigó, B Wold, Jeremy Adams, Alvis Brāzma, Julia Burchard, Joe Capka, M. L. Cherry, Laura Clarke, Brian Craft, Manolis Dermitzakis, Mark Diekhans, John Dursi, Michael Fitzsimons, Zac Flaming, Romina Garrido, Alfred Gil, Paul Godden, Matt Green, Mitch Guttman, Brian J. Haas, Max Haeussler, Bo Li, Sten Linnarsson, Adam Lipski, David Liu, Simonne Longerich, David R. Lougheed, Jonathan Manning, John C. Marioni, C. Meyer, Stephen B. Montgomery, Alyssa Morrow, Alfonso Munoz-Power Fuentes, David H. Nguyen, Kevin Osborn, B. F. Francis Ouellette, Irene Papatheodorou, Dmitri D. Pervouchine, Arun Ramani, Jordi Rambla, Bashir Sadjad, David Steinberg, Jeremiah Talkar, Timothy L. Tickle, Kathy Tzeng, Sean A. Upchurch, Saman Vaisipour, Sean Watford, Zhenyu Zhang, Jing Zhu

Bibliographic record

VenueBioinformatics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsOntario GenomicsMcGill UniversityOntario Institute for Cancer Research
FundersNational Human Genome Research InstituteEuropean Regional Development FundMedical Research CouncilNational Institutes of HealthGeneralitat de CatalunyaMinisterio de Ciencia e InnovaciónCentres de Recerca de CatalunyaCanadian Institutes of Health ResearchGenome Canada
KeywordsComputer scienceGenomicsSlicingDNA microarrayComputational biologyData miningWorld Wide WebGenomeBiologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

SUMMARY: Large-scale sharing of genomic quantification data requires standardized access interfaces. In this Global Alliance for Genomics and Health project, we developed RNAget, an API for secure access to genomic quantification data in matrix form. RNAget provides for slicing matrices to extract desired subsets of data and is applicable to all expression matrix-format data, including RNA sequencing and microarrays. Further, it generalizes to quantification matrices of other sequence-based genomics such as ATAC-seq and ChIP-seq. AVAILABILITY AND IMPLEMENTATION: https://ga4gh-rnaseq.github.io/schema/docs/index.html.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.736

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)0.0000.001

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.

Opus teacher head0.035
GPT teacher head0.306
Teacher spread0.271 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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