Bibliographic record
Abstract
Many gene-cloning strategies and gene survey often provide partial sequence data. To exploit the information from these partial sequences numerous PCR-based approaches have been developed to clone full-length open reading frames. These approaches can be successful using small quantities of cDNA or genomic DNA as starting material and avoid the need to go through the complex and tedious process of constructing and screening gene libraries. Here we present two of these approaches, called RACE and RAGE, we used to successfully clone partial and full-length ORFs from amitochondriate parasitic microbial eukaryotes. The RACE approach uses cDNA as template for PCR cloning whereas RAGE uses genomic DNA. These two approaches were used to complement each other to provide full-length genes. The amitochondriate microbial eukaryotes we are investigating are of interest from both evolutionary and biomedical perspectives. We have investigated genes of mitochondrial origins in the obligate intracellular parasite called microsporidia. In these organisms spores are the only source of material that can be isolated from host cells and typically yield small amount of mRNA and genomic DNA for cloning. A full-length mitochondrial Hsp70 could be cloned and sequenced and specific antibody raised against a fusion protein. The highly specific antibody allowed us to demonstrate for the first time the presence of mitochondrial-like organelles in microsporidia.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".