True single-molecule DNA sequencing of a pleistocene horse bone
Bibliographic record
Abstract
Second-generation sequencing platforms have revolutionized the field of ancient DNA, opening access to complete genomes of past individuals and extinct species. However, these platforms are dependent on library construction and amplification steps that may result in sequences that do not reflect the original DNA template composition. This is particularly true for ancient DNA, where templates have undergone extensive damage post-mortem. Here, we report the results of the first "true single molecule sequencing" of ancient DNA. We generated 115.9 Mb and 76.9 Mb of DNA sequences from a permafrost-preserved Pleistocene horse bone using the Helicos HeliScope and Illumina GAIIx platforms, respectively. We find that the percentage of endogenous DNA sequences derived from the horse is higher among the Helicos data than Illumina data. This result indicates that the molecular biology tools used to generate sequencing libraries of ancient DNA molecules, as required for second-generation sequencing, introduce biases into the data that reduce the efficiency of the sequencing process and limit our ability to fully explore the molecular complexity of ancient DNA extracts. We demonstrate that simple modifications to the standard Helicos DNA template preparation protocol further increase the proportion of horse DNA for this sample by threefold. Comparison of Helicos-specific biases and sequence errors in modern DNA with those in ancient DNA also reveals extensive cytosine deamination damage at the 3' ends of ancient templates, indicating the presence of 3'-sequence overhangs. Our results suggest that paleogenomes could be sequenced in an unprecedented manner by combining current second- and third-generation sequencing approaches.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".