Genome-wide identification, evolutionary and expression analyses of putative Fe–S biogenesis genes in rice (<i>Oryza sativa</i>)
Bibliographic record
Abstract
Iron-sulfur (Fe-S) proteins are ubiquitous in nature and carry Fe-S clusters (ISCs) as prosthetic groups that are essential in maintaining basic biological processes such as photosynthesis, respiration, nitrogen fixation, and DNA repair. In the present investigation, a comprehensive genome-wide analysis was carried out to find all the genes involved in the formation of ISCs in rice ( Oryza sativa L.) through a systematic EST and genomic DNA sequence data mining. This analysis profiled 44 rice ISC genes (OsISCs) that were identified using in silico analysis. Multiple sequence alignment and phylogenetic analysis revealed that these genes were highly conserved among bacteria, fungi, animals, and plants. EST analysis and RT-PCR assays demonstrated that all OsISCs were active and that the transcript abundance of some OsISCs was tissue specific. The results of this study will assist further investigations to identify and elucidate the structural components involved in the assembly, biogenesis, and regulation of OsISCs. Thus, the outcome of the present study provides basic genomic information for the OsISC and will pave the way for elucidating the precise role of OsISCs in plant growth and development in the future. Also, it may enable us in the future to enhance the crop yield, uptake of Fe, and protection against abiotic and biotic stress.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".