Bioinformatic prediction of upstream microRNAs of PPO and novel microRNAs in potato
Bibliographic record
Abstract
Chi, M., Liu, C., Su, Y., Tong, Y. and Liu, H. 2015. Bioinformatic prediction of upstream microRNAs of PPO and novel microRNAs in potato. Can. J. Plant Sci. 95: 871–877. MicroRNAs (miRNAs) are a class of endogenous small non-coding RNAs that play roles in many biological processes of plants. This study aimed to identify novel miRNAs and miRNAs targeting polyphenol oxidase (PPO) in potato. Small RNA-seq data (GSE32471 and GSE52599) including sequencing data of flower, leaf, stem, root, stolon and tuber tissue of potato were downloaded from Gene Expression Omnibus. After quality control and data cleaning of the raw data, the clean data were then mapped to Rfam to screen the reads corresponding to miRNA rather than other types of small RNA by Bowtie. Furthermore, the screened high-quality reads were mapped to known miRNAs in miRBase to identify and predict the novel miRNAs by miRDeep2. Finally, target gene prediction was performed for all identified miRNAs using psRNATarget and their roles in stress responses and brown spot of potato tubers through PPO genes were analyzed. A total of 18 novel potato miRNAs were identified and all of them had their specific expression patterns in different tissues. Targets prediction showed that some novel miRNAs (e.g., ST4.03ch03_9018, ST4.03ch05_15199 and ST4.03ch11_31208) could regulate the expression of potato resistance genes. Moreover, eight known miRNAs were found to target 3 PPO encoding genes, while they expressed at a low level in tuber tissue. Novel miRNAs might be associated with stress resistance of potato, and upstream miRNAs of PPO encoding genes might be important in suppression of potato brown spot.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".