De novo assembly and characterization of fruit transcriptome in Litchi chinensis Sonn and analysis of differentially regulated genes in fruit in response to shading
Bibliographic record
Abstract
BACKGROUND: Litchi (Litchi chinensis Sonn.) is one of the most important fruit trees cultivated in tropical and subtropical areas. However, a lack of transcriptomic and genomic information hinders our understanding of the molecular mechanisms underlying fruit set and fruit development in litchi. Shading during early fruit development decreases fruit growth and induces fruit abscission. Here, high-throughput RNA sequencing (RNA-Seq) was employed for the de novo assembly and characterization of the fruit transcriptome in litchi, and differentially regulated genes, which are responsive to shading, were also investigated using digital transcript abundance(DTA)profiling. RESULTS: More than 53 million paired-end reads were generated and assembled into 57,050 unigenes with an average length of 601 bp. These unigenes were annotated by querying against various public databases, with 34,029 unigenes found to be homologous to genes in the NCBI GenBank database and 22,945 unigenes annotated based on known proteins in the Swiss-Prot database. In further orthologous analyses, 5,885 unigenes were assigned with one or more Gene Ontology terms, 10,234 hits were aligned to the 24 Clusters of Orthologous Groups classifications and 15,330 unigenes were classified into 266 Kyoto Encyclopedia of Genes and Genomes pathways. Based on the newly assembled transcriptome, the DTA profiling approach was applied to investigate the differentially expressed genes related to shading stress. A total of 3.6 million and 3.5 million high-quality tags were generated from shaded and non-shaded libraries, respectively. As many as 1,039 unigenes were shown to be significantly differentially regulated. Eleven of the 14 differentially regulated unigenes, which were randomly selected for more detailed expression comparison during the course of shading treatment, were identified as being likely to be involved in the process of fruitlet abscission in litchi. CONCLUSIONS: The assembled transcriptome of litchi fruit provides a global description of expressed genes in litchi fruit development, and could serve as an ideal repository for future functional characterization of specific genes. The DTA analysis revealed that more than 1000 differentially regulated unigenes respond to the shading signal, some of which might be involved in the fruitlet abscission process in litchi, shedding new light on the molecular mechanisms underlying organ abscission.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".