Single-molecule optical genome mapping in nanochannels: multidisciplinarity at the nanoscale
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.
Bibliographic record
Abstract
The human genome contains multiple layers of information that extend beyond the genetic sequence. In fact, identical genetics do not necessarily yield identical phenotypes as evident for the case of two different cell types in the human body. The great variation in structure and function displayed by cells with identical genetic background is attributed to additional genomic information content. This includes large-scale genetic aberrations, as well as diverse epigenetic patterns that are crucial for regulating specific cell functions. These genetic and epigenetic patterns operate in concert in order to maintain specific cellular functions in health and disease. Single-molecule optical genome mapping is a high-throughput genome analysis method that is based on imaging long chromosomal fragments stretched in nanochannel arrays. The access to long DNA molecules coupled with fluorescent tagging of various genomic information presents a unique opportunity to study genetic and epigenetic patterns in the genome at a single-molecule level over large genomic distances. Optical mapping entwines synergistically chemical, physical, and computational advancements, to uncover invaluable biological insights, inaccessible by sequencing technologies. Here we describe the method's basic principles of operation, and review the various available mechanisms to fluorescently tag genomic information. We present some of the recent biological and clinical impact enabled by optical mapping and present recent approaches for increasing the method's resolution and accuracy. Finally, we discuss how multiple layers of genomic information may be mapped simultaneously on the same DNA molecule, thus paving the way for characterizing multiple genomic observables on individual DNA molecules.
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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 | 0.001 |
| 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