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Record W4393823353 · doi:10.5281/zenodo.6562380

Western redcedar single nucleotide polymorphism (SNP) genotyping data for genomic selection and population genetics

2022· dataset· en· W4393823353 on OpenAlexaff
Tal J. Shalev, Omnia Gamal El‐Dien, Macaire M. S. Yuen, Lise van der Merwe, Jesse W. Breinholt, Leandro G. Neves, Matias Kirst, Alvin D. Yanchuk, Carol Ritland, John H. Russell, Jöerg Bohlmann

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGenotypingSingle-nucleotide polymorphismGeneticsSNPBiologySNP genotypingPolymorphism (computer science)Selection (genetic algorithm)GenotypeGeneComputer science

Abstract

fetched live from OpenAlex

Western redcedar (Thuja plicata) Single Nucleotide Polymorphism (SNP) data in Variant Call Format (VCF) for genomic selection training and target populations, genomic selection parents, and self-fertilized (selfing) lines, comprising 4,833 trees. Targeted sequencing-based genotyping was done by Capture-Seq methodology at Rapid Genomics (Neves est al. 2013). A set of 57,000 probes as designed for initial marker discovery, from which a panel of 20,858 probes was selected for genotyping. A set of transcriptomes (Shalev et al. 2018) (PRJNA704616) was aligned to the reference genome to identify SNPs. Candidate probes (120 nt) were initially designed in silico and 57,000 selected by removing candidates with poor base composition for hybridization (GC content <0.2 and >0.6, high G content >0.2 and long homopolymers >7), followed by removing probes aligning to more than one position on the reference genome (≥90% identity and length). The 57,000 probes represent 14,517 scaffolds (average 3.9 probes/scaffold), with 37,275 targeting at least one SNP and 19,725 mapping to intergenic regions not containing pre-identified SNPs. A set of 128 individuals were selected to validate the 57,000 probe panel and associated polymorphisms. Genomic DNA (0.5 ug) was fragmented (mean size 300 bp), followed by repair of ends, phosphorylation, adenylation, ligation of Illumina compatible adapters containing 8bp indexes and 5’ T-overhang, and 10 cycles PCR amplification with universal primers to produce sequencing-ready libraries. Libraries were quantified using PicoGreen. Libraries from 16 samples were pooled, hybridized to the 120 nt RNA probes following Agilent’s SureSelect Target Enrichment System (Agilent Technologies) and sequenced on an Illumina HiSeq X machine with paired-end 150bp cycle for an average sequencing depth per sample of 15X. Sequence data were aligned to the reference genome with BWA-MEM (http://arxiv.org/abs/1303.3997) and sets of four samples were combined to increase sequencing depth for identifying markers. Putative SNPs were identified using Freebayes (http://arxiv.org/abs/1207.3907) in 150bp on either side of the 57,000 probes and filtered probes that had more than 17 SNPs per 420 bp target region (150bp + 120bp + 150bp). The sequencing depth of the probes was used to select the final set of 20,885 probes, removing probes on both sides of the distribution (low and high sequencing depth), for Capture-Seq on the remainder of the samples.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.016

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 head0.049
GPT teacher head0.267
Teacher spread0.218 · 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
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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