Cloning a Chloroplast Genome in <em>Saccharomyces cerevisiae</em> and <em>Escherichia coli</em>
Bibliographic record
Abstract
Chloroplast genomes present an alternative strategy for large-scale engineering of photosynthetic eukaryotes. Prior to our work, the chloroplast genomes of Chlamydomonas reinhardtii (204 kb) and Zea mays (140 kb) had been cloned using bacterial and yeast artificial chromosome (BAC/YAC) libraries, respectively. These methods lack design flexibility as they are reliant upon the random capture of genomic fragments during BAC/YAC library creation; additionally, both demonstrated a low efficiency (≤ 10%) for correct assembly of the genome in yeast. With this in mind, we sought to create a highly flexible and efficient approach for assembling the 117 kb chloroplast genome of Phaeodactylum tricornutum, a photosynthetic marine diatom. Our original article demonstrated a PCR-based approach for cloning the P. tricornutum chloroplast genome that had 90%–100% efficiency when screening as few as 10 yeast colonies following assembly. In this article, we will discuss this approach in greater depth as we believe this technique could be extrapolated to other species, particularly those with a similar chloroplast genome size and architecture. Key features • Large fragments of the chloroplast genome can be readily amplified through PCR from total algal DNA isolate. • Assembly protocol can be completed within a day, and yeast colonies harboring chloroplast genomes can be obtained in as few as 4–5 days. • Cloned genomes isolated from yeast transformants can be moved to Escherichia coli through electroporation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.011 | 0.009 |
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".