Clone-array pooled shotgun mapping and sequencing: design and analysis of experiments
Bibliographic record
Abstract
Abstract. This paper studies sequencing and mapping methods that rely solely on pooling and shotgun sequencing of clones. First, we scrutinize and improve the recently proposed Clone-Array Pooled Shotgun Sequencing (CAPSS) method, which delivers a BAC-linked assembly of a whole genome sequence. Secondly, we introduce a novel physical mapping method, called Clone-Array Pooled Shotgun Mapping (CAPS-MAP), which computes the physical ordering of BACs in a random library. Both CAPSS and CAPS-MAP construct subclone libraries from pooled genomic BAC clones. We propose algorithmic and experimental improvements that make CAPSS a viable option for sequencing a set of BACs. We provide the first probabilistic model of CAPSS sequencing progress. The model leads to theoretical results supporting previous, less formal arguments on the practicality of CAPSS. We demonstrate the usefulness of CAPS-MAP for clone overlap detection with a probabilistic analysis, and a simulated assembly of the Drosophila melanogaster genome. Our analysis indicates that CAPS-MAP is well-suited for detecting BAC overlaps in a highly redundant library, relying on a low amount of shotgun sequence information. Consequently, it is a practical method for computing the physical ordering of clones in a random library, without requiring additional clone fingerprinting. Since CAPS-MAP requires only shotgun sequence reads, it can be seamlessly incorporated into a sequencing project with almost no experimental overhead. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".