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Record W2883706068 · doi:10.1038/protex.2015.048

BAC ends library generation for Illumina sequencing

2015· article· en· W2883706068 on OpenAlexaff
Adriana Alberti, Laura Briñas, Céline Orvain, Caroline Belser, Corinne Cruaud, Karine Labadie, Laurie Bertrand, Valérie Barbe, Jean‐Marc Aury, Patrick Wincker

Bibliographic record

VenueProtocol Exchange · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsL'Alliance Boviteq
FundersAgence Nationale de la Recherche
KeywordsIllumina dye sequencingComputational biologyDNA sequencingBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Bacterial arti cial chromosome \(BAC) libraries are still a valuable tool for _de novo_ assembly of complex genomes, such as many plants genomes.Shotgun sequencing of BACs, individually or by pools, produces rst assemblies which usually need further improvement towards nished quality.We developed a new approach to obtain BAC ends libraries for Illumina sequencing \(BES), overcoming the expensive and time consuming BAC ends Sanger sequencing.This new method could be useful for improving _de novo_ assembly, especially in the case of highly repeated genomes.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0430.053

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 head0.089
GPT teacher head0.304
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations0
Published2015
Admission routes1
Has abstractyes

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