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Record W2915565085 · doi:10.21769/bioprotoc.3171

Rapid Multiplexed Reduced Representation Bisulfite Sequencing Library Prep (rRRBS)

2019· article· en· W2915565085 on OpenAlexafffund
Lisa‐Marie Legault, Donovan Chan, Serge McGraw

Bibliographic record

VenueBIO-PROTOCOL · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcGill University Health CentreUniversité de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsBisulfite sequencingMultiple displacement amplificationBisulfiteComputational biologyDNA methylationBiologyEpigeneticsDigital polymerase chain reactionDNA sequencingPolymerase chain reactionMolecular biologyDNAGeneticsDNA extractionGeneGene expression

Abstract

fetched live from OpenAlex

DNA methylation is a common mechanism of epigenetic regulation involved in transcriptional modulation and genome stability. With the evolution of next-generation sequencing technologies, establishing quantitative genome-wide DNA methylation profiles is becoming routine in many laboratories. However, many of these approaches take several days to accomplish and use subjective PCR methods to amplify sequencing libraries, which can induce amplification bias. Here we propose a rapid Reduced Representation Bisulfite Sequencing (rRRBS) protocol to minimize PCR amplification bias and reduce total time of multiplexed library construction. In this modified approach, the precise quantification of the final library amplification step is accomplished and monitored by qPCR, instead of using standard PCR and gel electrophoresis, to determine the appropriate number of cycles to perform. The main advantages of this rRRBS method are: i) Reduced amount of amplification enzyme used for library prep, ii) Reduced number of PCR cycles resulting in less PCR amplification bias, and iii) Preparation of quality multiplexed rRRBS libraries in only ~2 days.

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: Bench or experimental
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.259
Threshold uncertainty score0.747

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)0.0000.000

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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
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

Citations12
Published2019
Admission routes2
Has abstractyes

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