PlasmaDNA: a free, cross-platform plasmid manipulation program for molecular biology laboratories
Bibliographic record
Abstract
BACKGROUND: Most molecular biology experiments, and the techniques associated with this field of study, involve a great deal of engineering in the form of molecular cloning. Like all forms of engineering, perfect information about the starting material is crucial for successful completion of design and strategies. RESULTS: We have generated a program that allows complete in silico simulation of the cloning experiment. Starting with a primary DNA sequence, PlasmaDNA looks for restriction sites, open reading frames, primer annealing sequences, and various common domains. The databases are easily expandable by the user to fit his most common cloning needs. PlasmaDNA can manage and graphically represent multiple sequences at the same time, and keeps in memory the overhangs at the end of the sequences if any. This means that it is possible to virtually digest fragments, to add the digestion products to the project, and to ligate together fragments with compatible ends to generate the new sequences. Polymerase Chain Reaction (PCR) fragments can also be virtually generated using the primer database, automatically adding to the fragments any 5' extra sequences present in the primers. CONCLUSION: PlasmaDNA is a program available both on Windows and Apple operating systems, designed to facilitate molecular cloning experiments by building a visual map of the DNA. It then allows the complete planning and simulation of the cloning experiment. It also automatically updates the new sequences generated in the process, which is an important help in practice. The capacity to maintain multiple sequences in the same file can also be used to archive the various steps and strategies involved in the cloning of each construct. The program is freely available for download without charge or restriction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.039 |
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".