Single-molecule real-time transcript sequencing facilitates common wheat genome annotation and grain transcriptome research
Bibliographic record
Abstract
BACKGROUND: The large and complex hexaploid genome has greatly hindered genomics studies of common wheat (Triticum aestivum, AABBDD). Here, we investigated transcripts in common wheat developing caryopses using the emerging single-molecule real-time (SMRT) sequencing technology PacBio RSII, and assessed the resultant data for improving common wheat genome annotation and grain transcriptome research. RESULTS: We obtained 197,709 full-length non-chimeric (FLNC) reads, 74.6 % of which were estimated to carry complete open reading frame. A total of 91,881 high-quality FLNC reads were identified and mapped to 16,188 chromosomal loci, corresponding to 13,162 known genes and 3026 new genes not annotated previously. Although some FLNC reads could not be unambiguously mapped to the current draft genome sequence, many of them are likely useful for studying highly similar homoeologous or paralogous loci or for improving chromosomal contig assembly in further research. The 91,881 high-quality FLNC reads represented 22,768 unique transcripts, 9591 of which were newly discovered. We found 180 transcripts each spanning two or three previously annotated adjacent loci, suggesting that they should be merged to form correct gene models. Finally, our data facilitated the identification of 6030 genes differentially regulated during caryopsis development, and full-length transcripts for 72 transcribed gluten gene members that are important for the end-use quality control of common wheat. CONCLUSIONS: Our work demonstrated the value of PacBio transcript sequencing for improving common wheat genome annotation through uncovering the loci and full-length transcripts not discovered previously. The resource obtained may aid further structural genomics and grain transcriptome studies of common wheat.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".