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Record W1912609739 · doi:10.1093/nar/gkv1111

Automated design of hammerhead ribozymes and validation by targeting the PABPN1 gene transcript

2015· article· en· W1912609739 on OpenAlexafffund
Nawwaf Kharma, Luc Varin, Aida Abu‐Baker, Jonathan Ouellet, Sabrine Najeh, Mohammad-Reza Ehdaeivand, Gabriel Belmonte, Anas Ambri, Guy A. Rouleau, Jonathan Perreault

Bibliographic record

VenueNucleic Acids Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsInstitut National de la Recherche ScientifiqueMontreal Neurological Institute and HospitalConcordia University
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsRibozymeHammerhead ribozymeBiologyLigase ribozymeComputational biologyCleaveGeneGeneticsMammalian CPEB3 ribozymeSequence (biology)MutantRNADNA

Abstract

fetched live from OpenAlex

We present a new publicly accessible web-service, RiboSoft, which implements a comprehensive hammerhead ribozyme design procedure. It accepts as input a target sequence (and some design parameters) then generates a set of ranked hammerhead ribozymes, which target the input sequence. This paper describes the implemented procedure, which takes into consideration multiple objectives leading to a multi-objective ranking of the computer-generated ribozymes. Many ribozymes were assayed and validated, including four ribozymes targeting the transcript of a disease-causing gene (a mutant version of PABPN1). These four ribozymes were successfully tested in vitro and in vivo, for their ability to cleave the targeted transcript. The wet-lab positive results of the test are presented here demonstrating the real-world potential of both hammerhead ribozymes and RiboSoft. RiboSoft is freely available at the website http://ribosoft.fungalgenomics.ca/ribosoft/.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.067
GPT teacher head0.327
Teacher spread0.260 · 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.

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

Citations28
Published2015
Admission routes2
Has abstractyes

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