An efficient strategy for large-scale high-throughput transposon-mediated sequencing of cDNA clones
Bibliographic record
Abstract
We describe an efficient high-throughput method for accurate DNA sequencing of entire cDNA clones. Developed as part of our involvement in the Mammalian Gene Collection full-length cDNA sequencing initiative, the method has been used and refined in our laboratory since September 2000. Amenable to large scale projects, we have used the method to generate >7 Mb of accurate sequence from 3695 candidate full-length cDNAs. Sequencing is accomplished through the insertion of Mu transposon into cDNAs, followed by sequencing reactions primed with Mu-specific sequencing primers. Transposon insertion reactions are not performed with individual cDNAs but rather on pools of up to 96 clones. This pooling strategy reduces the number of transposon insertion sequencing libraries that would otherwise be required, reducing the costs and enhancing the efficiency of the transposon library construction procedure. Sequences generated using transposon-specific sequencing primers are assembled to yield the full-length cDNA sequence, with sequence editing and other sequence finishing activities performed as required to resolve sequence ambiguities. Although analysis of the many thousands (22 785) of sequenced Mu transposon insertion events revealed a weak sequence preference for Mu insertion, we observed insertion of the Mu transposon into 1015 of the possible 1024 5mer candidate insertion sites.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".