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Record W3007907037 · doi:10.1186/s12920-020-0664-7

Quality of whole genome sequencing from blood versus saliva derived DNA in cardiac patients

2020· article· en· W3007907037 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Medical Genomics · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSickKids FoundationCanada Research ChairsHospital for Sick Children
FundersHospital for Sick ChildrenHeart and Stroke Foundation of Canada
KeywordsSalivaBiologyWhole genome sequencingHuman genomeDNA sequencingGeneticsGenomeConcordancePersonal genomicsMicrobiomeComputational biologygenomic DNADNAGene

Abstract

fetched live from OpenAlex

BACKGROUND: Whole-genome sequencing (WGS) is becoming an increasingly important tool for detecting genomic variation. Blood derived DNA is the current standard for WGS for research or clinical purposes but may not always be feasible to acquire. The usability of DNA from saliva for WGS is not known. We compared the quality of WGS between blood versus saliva derived DNA. METHODS: WGS was performed in DNA from 531 blood and 502 saliva samples (including 5 paired samples) from participants enrolled in a heart disease biorepository. We compared the proportion of sequencing reads that mapped to non-human sources (microbiome), the sequencing coverage, and the yield and concordance of single nucleotide variant (SNV) and copy number variant (CNV) calls between blood and saliva genomes. RESULTS: Of 531 blood and 502 saliva samples, 46% saliva DNA failed quality control (QC) requirements for WGS compared to 6% QC failure for blood DNA. An average of 10.7% WGS reads in the saliva samples mapped to the human oral microbiome compared to 0.09% WGS reads in blood samples. However, these reads were readily excluded by excluding reads that did not map to the human reference genome. Sequencing coverage met or exceeded the target sequencing depth of 30x in all the blood samples and 4 of the 5 saliva samples; the fifth saliva sample had an average sequencing depth of 22.6x. Over 95% of SNVs identified in saliva were concordant with those identified in blood across the genome, within all gene coding regions, and within cardiovascular disease-related gene coding regions. Rare SNVs, defined as those with a minor allele frequency of less than 1% in the Genome Aggregation Database, had a lower concordance of 90% between blood and saliva genomes. CNVs had only 76% concordance between blood and saliva samples. CONCLUSIONS: High quality saliva samples that meet stringent QC criteria can be used for WGS when blood-derived DNA is not available or is not suitable. Saliva DNA provides an acceptable yield of SNV calls but has a lower yield for CNV calls compared to blood DNA.

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.

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)0.001
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 score0.384
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.268
Teacher spread0.235 · 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