Next-Generation Sequencing: A New Revolution in Molecular Diagnostics?
Bibliographic record
Abstract
In 1980, Fred Sanger and Walter Gilbert were awarded the Nobel Prize in Chemistry for discovering novel ways for sequencing nucleic acids. In 2003, the human genome sequence was published, an effort that involved more than 3000 scientists from 6 countries. The work took 13 years to complete, at a cost of nearly $3 billion. Only 6 years later, nucleic acid sequencing technologies have advanced to a stage in which a human genome can be sequenced within weeks at a cost of $50 000 or less. These new sequencing technologies are about a million times more efficient than standard Sanger sequencing. Now, people are talking about the $1000 genome, and there is an X Prize worth $10 million for sequencing 100 human genomes within 10 days at a cost of <$10 000 per genome. International organizations are sequencing thousands of cancer genomes to find novel genetic changes, and individuals with money are paying for genomewide association studies in hopes of preventing diseases to which they are predisposed. Although the technologies for high-throughput sequencing are here and although they are being perfected in terms of accuracy and reduced costs, many questions are being raised. Some of these questions are explored below with leading scientists from academia and industry. Karl V. Voelkerding2 : Calculating the cost for sequencing a human genome needs to incorporate the level of sequencing “completeness” or “coverage” that will be required to accurately characterize both sequence and structural variation. Reagent and wet bench labor costs for sequencing a human genome should approach $5000 or less within three to five years, depending on the technology. It is difficult to price the costs for bioinformatic analysis, currently a lengthy and extensive process that varies depending on the questions being asked. New computational algorithms will definitely streamline this process. Beyond …
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".