A Series of TA-Based and Zero-Background Vectors for Plant Functional Genomics
Bibliographic record
Abstract
With the sequencing of genomes from many organisms now complete and the development of high-throughput sequencing, life science research has entered the functional post-genome era. Therefore, deciphering the function of genes and how they interact is in greater demand. To study an unknown gene, the basic methods are either overexpression or gene knockout by creating transgenic plants, and gene construction is usually the first step. Although traditional cloning techniques using restriction enzymes or a site-specific recombination system (Gateway or Clontech cloning technology) are highly useful for efficiently transferring DNA fragments into destination plasmids, the process is time consuming and expensive. To facilitate the procedure of gene construction, we designed a TA-based cloning system in which only one step was needed to subclone a DNA fragment into vectors. Such a cloning system was developed from the pGreen binary vector, which has a minimal size and facilitates construction manipulation, combined with the negative selection marker gene ccdB, which has the advantages of eliminating the self-ligation background and directly enabling high-efficiency TA cloning technology. We previously developed a set of transient and stable transformation vectors for constitutive gene expression, gene silencing, protein tagging, subcellular localization analysis and promoter activity detection. Our results show that such a system is highly efficient and serves as a high-throughput platform for transient or stable transformation in plants for functional genome research.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".