An improved method for RNA isolation and cDNA library construction from immature seeds of Jatropha curcas L
Bibliographic record
Abstract
BACKGROUND: RNA quality and quantity is sometimes unsuitable for cDNA library construction, from plant seeds rich in oil, polysaccharides and other secondary metabolites. Seeds of jatropha (Jatropha curcas L.) are rich in fatty acids/lipids, storage proteins, polysaccharides, and a number of other secondary metabolites that could either bind and/or co-precipitate with RNA, making it unsuitable for downstream applications. Existing RNA isolation methods and commercial kits often fail to deliver high-quality total RNA from immature jatropha seeds for poly(A)+ RNA purification and cDNA synthesis. FINDINGS: A protocol has been developed for isolating good quality total RNA from immature jatropha seeds, whereby a combination of the CTAB based RNA extraction method and a silica column of a commercial plant RNA extraction kit is used. The extraction time was reduced from two days to about 3 hours and the RNA was suitable for poly(A)+ RNA purification, cDNA synthesis, cDNA library construction, RT-PCR, and Northern hybridization. Based on sequence information from selected clones and amplified PCR product, the cDNA library seems to be a good source of full-length jatropha genes. The method was equally effective for isolating RNA from mustard and rice seeds. CONCLUSIONS: This is a simple CTAB + silica column method to extract high quality RNA from oil rich immature jatropha seeds that is suitable for several downstream applications. This method takes less time for RNA extraction and is equally effective for other tissues where the quality and quantity of RNA is highly interfered by the presence of fatty acids, polysaccharides and polyphenols.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".