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Record W2334642566 · doi:10.1021/sb500045w

A Multiplexed Transcription Activator-like Effector System for Detecting Specific DNA Sequences

2014· article· en· W2334642566 on OpenAlexafffund
Ali Honarmand, Robert M. Mayall, Iain George, Lisa Oberding, Himika Dastidar, Jamie E. Fegan, Somshukla Chaudhuri, Justin Dole, Sharon Feng, Denny Hoang, Ruth Moges, Julie Osgood, Taylor Remondini, Wm. Keith van der Meulen, Su Wang, Chris Wintersinger, Amanda Z. Zucoloto, Kate Chatfield‐Reed, Mayi Arcellana‐Panlilio, Anders Nygren

Bibliographic record

VenueACS Synthetic Biology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Calgary
FundersResearch Services, University of CalgaryGenome AlbertaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsDNAEffectorBiologyDNA binding siteTranscription (linguistics)DNA-binding proteinDNA sequencingMolecular biologyGeneticsComputational biologyTranscription factorPromoterGeneCell biologyGene expression

Abstract

fetched live from OpenAlex

Transcription activator-like effectors (TALEs), originating from the Xanthomonas genus of bacteria, bind to specific DNA sequences based on amino acid sequence in the repeat-variable diresidue (RVD) positions of the protein. By altering these RVDs, it has been shown that a TALE protein can be engineered to bind virtually any DNA sequence of interest. The possibility of multiplexing TALEs for the purposes of identifying specific DNA sequences has yet to be explored. Here, we demonstrate a system in which a TALE protein bound to a nitrocellulose strip has been utilized to capture purified DNA, which is then detected using the binding of a second distinct TALE protein conjugated to a protein tag that is then detected by a dot blot. This system provides a signal only when both TALEs bind to their respective sequences, further demonstrating the specificity of the TALE binding.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.012
GPT teacher head0.275
Teacher spread0.264 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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