Next Generation Sequencing Workshop at the Royal Society of Medicine (London, May 2022): how genomics is on the path to modernizing genetic toxicology
Bibliographic record
Abstract
The use of error-corrected Next Generation Sequencing (ecNG) to determine mutagenicity has been a subject of growing interest and potentially a disruptive technology that could supplement, and in time, replace current testing paradigms in preclinical safety assessment. Considering this, a Next Generation Sequencing Workshop was held at the Royal Society of Medicine in London in May 2022, supported by the United Kingdom Environmental Mutagen Society (UKEMS) and TwinStrand Biosciences (WA, USA), to discuss progress and future applications of this technology. In this meeting report, the invited speakers provide an overview of the Workshop topics covered and identify future directions for research. In the area of somatic mutagenesis, several speakers reviewed recent progress made with correlating ecNGS to classic in vivo transgenic rodent mutation assays as well as exploring the use of this technology directly in humans and animals, and in complex organoid models. Additionally, ecNGS has been used for detecting off-target effects of gene editing tools and emerging data suggest ecNGS potential to measure clonal expansion of cells carrying mutations in cancer driver genes as an early marker of carcinogenic potential and for direct human biomonitoring. As such, the workshop demonstrated the importance of raising awareness and support for advancing the science of ecNGS for mutagenesis, gene editing, and carcinogenesis research. Furthermore, the potential of this new technology to contribute to advances in drug and product development and improve safety assessment was extensively explored.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".