On the Feasibility of Using the Intrinsic Fluorescence of Nucleotides for DNA Sequencing
Bibliographic record
Abstract
There is presently a worldwide effort to increase the speed and decrease the cost of DNA sequencing as exemplified by the goal of the National Human Genome Research Institute (NHGRI) to sequence a human genome for under $1000. Several high throughput technologies are under development. Among these, single strand sequencing using exonuclease appear very promising. However, this approach requires complete labeling of at least two bases at a time, with extrinsic high quantum yield probes. This is necessary because nucleotides absorb in the deep ultra-violet (UV) and emit with extremely low quantum yields. Hence intrinsic emission from DNA and nucleotides is not being exploited for DNA sequencing. In the present paper we consider the possibility of identifying single nucleotides using their intrinsic emission. We used the finite-difference time-domain (FDTD) method to calculate the effects of aluminum nanoparticles on nearby fluorophores that emit in the UV. We find that the radiated power of UV fluorophores is significantly increased when they are in close proximity to aluminum nanostructures. We show that there will be increased localized excitation near aluminum particles at wavelengths used to excite intrinsic nucleotide emission. Using FDTD simulation we show that a typical DNA base when coupled to appropriate aluminum nanostructures leads to highly directional emission. Additionally we present experimental results showing that a thin film of nucleotides show enhanced emission when in close proximity to aluminum nanostructures. Finally we provide Monte Carlo simulations that predict high levels of base calling accuracy for an assumed number of photons that is derived from the emission spectra of the intrinsic fluorescence of the bases. Our results suggest that single nucleotides can be detected and identified using aluminum nanostructures that enhance their intrinsic emission. This capability would be valuable for the ongoing efforts towards the $1000 genome.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".