Multiplex PCR with minisequencing as an effective high‐throughput SNP typing method for formalin‐fixed tissue
Bibliographic record
Abstract
Extensive collections of formalin-fixed paraffin-embedded (FFPE) tissues exist that could be exploited for genetic analyses in order to provide important insights into the genetic basis of disease or host/pathogen cointeractions. We report here an evaluation of a 44 SNP multiplex genotyping method, multiplex PCR with minisequencing (MPMS), on 92 DNA extractions performed on six archival FFPE samples of variable DNA quality, which date between 9 and 25 years old. On the three extracts with highest quality, we found the assay efficiency to be near 100%. However, the efficiency of the lowest quality extracts varied significantly. In this study, we demonstrate that although direct measures of DNA concentration in the extracts provide no useful information with regard to subsequent MPMS success, the success of the assay can be determined to some degree a priori, through initial screening of the DNA quality using a simple quantitative real-time PCR (qPCR) assay for nuclear DNA, and/or an assay of the maximum PCR amplifiable size of nuclear DNA. MPMS promises to be of significant use in future genetic studies on FFPE material. It provides a streamlined approach for retrieving a large amount of genetic information using simple, single reactions and minute amounts of archival tissue/DNA. In the light of this evidence, we suggest that the systematic screening of FFPE collections may in the future provide valuable insights into the past.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".