Bioinformatics Analysis of Cellulose Synthase <i>CesA</i> Gene from <i>Miscanthus lutarioriparius</i>
Bibliographic record
Abstract
Based on 3 generations transcriptome sequencing technology-PacBio SMRT single-molecule real-time sequencing method, supplemented with 2 generation sequencing method, a complete and accurate unigene library was constructed. After comparing with closely related species, the cellulose synthase gene ( CesA ) 4 , 7 , 9 gene sequences of Miscanthus lutarioriparius were spliced and named as MlCesA4 , MlCesA7 and MlCesA9 . Then, Bioinformatics analysis software was used to construct phylogenetic trees of proteins, to predict and analyze the phosphorylation sites of post-translational modifications of amino acids, and to predict and analyze conserved domains of proteins.The analysis results showed that the sequence lengths of MlCesA 4, 7, 9 genes were 2980 bp, 3310 bp, and 3208 bp, respectively, which were closely related to Sorghum bicolor and Zea mays ; cellulose synthase of Miscanthus lutarioriparius (MlCesA) MlCesA4 and MlCesA9 have 43 phosphorylation sites, MlCesA7 has 48 phosphorylation sites; MlCesA4 is an unstable protein with 6 transmembrane domains, 4 domains are outside the membrane and 3 are inside the membrane.; MlCesA7 and MlCesA9 are stable proteins, both of which have 8 transmembrane domains, 5 domains are outside the membrane and 4 are inside the membrane. The three are all hydrophilic proteins, and the secondary structure is dominated by α-helix and random coils.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".