Increasing the efficiency of SAGE adaptor ligation bydirected ligation chemistry
Bibliographic record
Abstract
The ability of Serial Analysis of Gene Expression (SAGE) to provide a quantitative picture of global gene expression relies not only on the depth and accuracy of sequencing into the SAGE library, but also on the efficiency of each step required to generate the SAGE library from the starting mRNA material. The first critical step is the ligation of adaptors containing a Type IIS recognition sequence to the anchored 3' end cDNA population that permits the release of short sequence tags (SSTs) from defined sites within the 3' end of each transcript. Using an in vitro transcript as a template, we observed that only a small fraction of anchored 3' end cDNA are successfully ligated with added SAGE adaptors under typical reaction conditions currently used in the SAGE protocol. Although the introduction of approximately 500-fold molar excess of adaptor or the inclusion of 15% (w/v) PEG-8000 increased the yield of the adaptor-modified product, complete conversion to the desired adaptor:cDNA hetero-ligation product is not achieved. An alternative method of ligation, termed as directed ligation, is described which exploits a favourable mass-action condition created by the presence of NlaIII during ligation in combination with a novel SAGE adaptor containing a methylated base within the ligation site. Using this strategy, we were able to achieve near complete conversion of the anchored 3' end cDNA into the desired adaptor-modified product. This new protocol therefore greatly increases the probability that a SST will be generated from every transcript, greatly enhancing the fidelity of SAGE. Directed ligation also provides a powerful means to achieve near-complete ligation of any appropriately designed adaptor to its respective target.
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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 | 0.004 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".