Copy Number Variant Analysis of Human Embryonic Stem Cells
Why this work is in the frame
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Bibliographic record
Abstract
Differences between individual DNA sequences provide the basis for human genetic variability. Forms of genetic variation include single-nucleotide polymorphisms, insertions/duplications, deletions, and inversions/translocations. The genome of human embryonic stem cells (hESCs) has been characterized mainly by karyotyping and comparative genomic hybridization (CGH), techniques whose relatively low resolution at 2-10 megabases (Mb) cannot accurately determine most copy number variability, which is estimated to involve 10%-20% of the genome. In this brief technical study, we examined HSF1 and HSF6 hESCs using array-comparative genomic hybridization (aCGH) to determine copy number variants (CNVs) as a higher-resolution method for characterizing hESCs. Our approach used five samples for each hESC line and showed four consistent CNVs for HSF1 and five consistent CNVs for HSF6. These consistent CNVs included amplifications and deletions that ranged in size from 20 kilobases to 1.48 megabases, involved seven different chromosomes, were both shared and unique between hESCs, and were maintained during neuronal stem/progenitor cell differentiation or drug selection. Thirty HSF1 and 40 HSF6 less consistently scored but still highly significant candidate CNVs were also identified. Overall, aCGH provides a promising approach for uniquely identifying hESCs and their derivatives and highlights a potential genomic source for distinct differentiation and functional potentials that lower-resolution karyotype and CGH techniques could miss. Disclosure of potential conflicts of interest is found at the end of this article.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it