Integrating De Novo Transcriptome Assembly and Cloning to Obtain Chicken Ovocleidin-17 Full-Length cDNA
Bibliographic record
Abstract
Efficiently obtaining full-length cDNA for a target gene is the key step for functional studies and probing genetic variations. However, almost all sequenced domestic animal genomes are not 'finished'. Many functionally important genes are located in these gapped regions. It can be difficult to obtain full-length cDNA for which only partial amino acid/EST sequences exist. In this study we report a general pipeline to obtain full-length cDNA, and illustrate this approach for one important gene (Ovocleidin-17, OC-17) that is associated with chicken eggshell biomineralization. Chicken OC-17 is one of the best candidates to control and regulate the deposition of calcium carbonate in the calcified eggshell layer. OC-17 protein has been purified, sequenced, and has had its three-dimensional structure solved. However, researchers still cannot conduct OC-17 mRNA related studies because the mRNA sequence is unknown and the gene is absent from the current chicken genome. We used RNA-Seq to obtain the entire transcriptome of the adult hen uterus, and then conducted de novo transcriptome assembling with bioinformatics analysis to obtain candidate OC-17 transcripts. Based on this sequence, we used RACE and PCR cloning methods to successfully obtain the full-length OC-17 cDNA. Temporal and spatial OC-17 mRNA expression analyses were also performed to demonstrate that OC-17 is predominantly expressed in the adult hen uterus during the laying cycle and barely at immature developmental stages. Differential uterine expression of OC-17 was observed in hens laying eggs with weak versus strong eggshell, confirming its important role in the regulation of eggshell mineralization and providing a new tool for genetic selection for eggshell quality parameters. This study is the first one to report the full-length OC-17 cDNA sequence, and builds a foundation for OC-17 mRNA related studies. We provide a general method for biologists experiencing difficulty in obtaining candidate gene full-length cDNA sequences.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".