De novo transcriptome assembly and hepatic gene expression analyses in overfed and ad libitum fed Muscovy and Pekin ducks and their reciprocal inter-specific Mule and Hinny hybrids
Bibliographic record
Abstract
Abstract Background Common Pekin and Muscovy ducks and their intergeneric hinny and mule hybrids have different abilities for fatty liver production. RNA-Seq analyses from the liver of these different genetic types fed ad libitum or overfed would help to identify genes with different response to overfeeding between them. However RNA-seq analyses from different species and comparison is challenging. The goal of this study was develop a relevant strategy for transcriptome analysis and comparison between different species. Results Transcriptomes were first assembled with a reference-based approach. Important mapping biases were observed when heterologous mapping were conducted on common duck reference genome, suggesting that this reference-based strategy was not suited to compare the four different genetic types. De novo transcriptome assemblies were then performed using Trinity and Oases. Assemblies of transcriptomes were not relevant when more than a single genetic type was considered. Finally, single genetic type transcriptomes were assembled with DRAP in a mega-transcriptome. No bias was observed when reads from the different genetic types were mapped on this mega-transcriptome and differences in gene expression between the four genetic types could be identified. Conclusions Analyses using both reference-based and de novo transcriptome assemblies point out a good performance of the de novo approach for the analysis of gene expression in different species. It also allowed the identification of differences in responses to overfeeding between Pekin and Muscovy ducks and hinny and mule hybrids.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".