Exploring the potential of Raman and resonance Raman spectroscopy for quantitative analysis of duplex DNA
Bibliographic record
Abstract
Advances in DNA microarray fabrication technologies, expanding probe libraries, and new bioinformatics methods and resources have firmly established array-based techniques as mainstream bioanalytical tools and the application space is proliferating rapidly. However, the capability of these tools to yield truly quantitative information remains limited, primarily due to problems inherent to the use of fluorescence imaging for reading the hybridized arrays. The obvious advantages of fluorescence are the unrivaled sensitivity and simplicity of the instrumentation. There are disadvantages of this approach, however, such as difficulties in achieving optimal labeling of targets and reproducible signals (due to quenching, resonance energy transfer, photobleaching effects, etc.) that undermine precision. We are exploring alternative approaches, based mainly on Raman and resonance Raman spectroscopy, that in principle permit direct analysis of structural differences between hybridized and unhybridized probes, thereby eliminating the need for labeling the target analytes. We report here on the status of efforts to evaluate the potential of these methods based on a combination of measured data and simulated experiments involving short (12-mer) ssDNA oligomer probes with varying degrees of hybridized target DNA. Preliminary results suggest that it may be possible to determine the fraction of duplex probes within a single register on a DNA microarray from 100% down to 10% (or possibly less) with a precision of ±2 5%. Details of the methods used, their implementation, and their potential advantages and limitations are presented, along with discussion of the utility of using 2DCOS methods to emphasize small spectral changes sensitive to interstrand H bonding, backbone flexibility, hypochromicity due to base-stacking in duplex structures and solvation effects.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".