Minimizing loss of sequence information in SAGE ditags by modulating the temperature dependent 3′ → 5′ exonuclease activity of DNA polymerases on 3′‐terminal isoheptyl amino groups
Bibliographic record
Abstract
Numerous steps are required to prepare a sequencing library for serial analysis of gene expression (or SAGE) from an original mRNA sample. The presence of inefficiencies, however, can lead to a cumulative loss of sample during processing which can yield a library of short sequence tags (SSTs) that represents only a minute fraction of the original starting sample, potentially compromising the quality of the analysis and necessitating relatively large amounts of starting material. We show here that commonly observed higher molecular weight (HMW) amplification products observed following the PCR amplification of ditags are a direct result of the presence of HMW ligation products created during ditag formation. Using model tags, we demonstrate that the formation of these HMW ligation products becomes permissible following the release of the 3'-terminal isoheptyl amine (3'-IHA) from the SST during the fill-in reaction with the Klenow fragment (KF) of DNA polymerase (DNAP) I and is mediated by its 3' --> 5' exonuclease activity. We further show that the incorporation of SSTs into HMW ligation products can lead to a loss of sequence information from SAGE analysis, potentially skewing sequencing results away from the true distribution in the original sample. By modifying fill-in conditions through the use of Vent DNAP at 12 degrees C and by including terminal phosphorothioate linkages within the SAGE adaptors to specifically inhibit exonucleolytic removal of the 3'-terminal amine, we are able to maximize the yield of ditags and bypass the need for gel purification via PAGE following PCR. The modifications described here, combined with the modifications described previously by our group for adaptor ligation, ensure that the full sequence information content in SSTs derived from the transcriptome is preserved in the pool of amplified ditags prior to the creation of a SAGE library.
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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 | 0.000 |
| 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".