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Record W2572935401 · doi:10.1186/s12864-017-3489-9

Medium throughput bisulfite sequencing for accurate detection of 5-methylcytosine and 5-hydroxymethylcytosine

2017· article· en· W2572935401 on OpenAlexafffund
Gary G. Chen, Jeffrey Gross, Pierre-Éric Lutz, Kathryn Vaillancourt, Gilles Maussion, Alexandre Bramoullé, Jean‐François Théroux, Elena Gardini, Ulrike Ehlert, Geneviève Bourret, Aurélie Masurel, Patricia Lepage, Naguib Mechawar, Gustavo Turecki, Carl Ernst

Bibliographic record

VenueBMC Genomics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill University and Génome Québec Innovation CentreDouglas Mental Health University InstituteMcGill University
FundersCanadian Institutes of Health ResearchNational Institutes of HealthFondation Bettencourt SchuellerFondation pour la Recherche MédicaleNational Institute on Drug AbuseFondation FyssenCanada Research ChairsFondation Pierre Deniker pour la Recherche et la Prévention en Santé MentaleScottish Rite Charitable Foundation of CanadaAmerican Foundation for Suicide Prevention
KeywordsBisulfiteBisulfite sequencing5-MethylcytosineDNA methylationBiologyMethylated DNA immunoprecipitation5-HydroxymethylcytosineComputational biologyEpigeneticsDNADNA microarraygenomic DNADNA nanoball sequencingDNA sequencingSequencing by ligationGenomeCytosineIllumina Methylation AssayGeneticsGeneGenomic libraryBase sequenceGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Epigenetic modifications of DNA, such as 5-methylcytosine and 5-hydroxymethycytosine, play important roles in development and disease. Here, we present a cost-effective and versatile methodology for the analysis of DNA methylation in targeted genomic regions, which comprises multiplexed, PCR-based preparation of bisulfite DNA libraries followed by customized MiSeq sequencing. RESULTS: Using bisulfite and oxidative bisulfite conversion of DNA, we have performed multiplexed targeted sequencing to analyse several kilobases of genomic DNA in up to 478 samples, and achieved high coverage data of 5-methylcytosine and 5-hydroxymethycytosine at single-base resolution. Our results demonstrate the ability of this methodology to detect all levels of cytosine modifications at greater than 100× coverage in large sample sets at low cost compared to other targeted methods. CONCLUSIONS: This approach can be applied to multiple settings, from candidate gene to clinical studies, and is especially useful for validation of differentially methylated or hydroxymethylated regions following whole-genome analyses.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.603

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 head0.038
GPT teacher head0.297
Teacher spread0.259 · 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

Citations37
Published2017
Admission routes2
Has abstractyes

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