Comparison of <i>de novo</i> and reference genome-based transcriptome assembly pipelines for differential expression analysis of RNA sequencing data
Bibliographic record
Abstract
Abstract Objective As sequencing technologies become more accessible and bioinformatic tools improve, genomic resources are increasingly available for non-model species. Using a draft genome to guide transcriptome assembly from RNA sequencing data, rather than performing assembly de novo , affects downstream analyses. Yet, direct comparisons of these approaches are rare. Here, we compare the results of the standard de novo assembly pipeline (‘Trinity’) and two reference genome-based pipelines (‘Tuxedo’ and the ‘new Tuxedo’) for differential expression and gene ontology enrichment analysis of a companion study on Atlantic cod ( Gadus morhua ). Results The new Tuxedo pipeline produced a higher quality assembly than the Tuxedo suite. However, greater enrichment of Trinity-identified differentially expressed genes suggests that a higher proportion of them represent biologically meaningful differences in transcription, as opposed to transcriptional noise or false positives. Coupled with the ability to annotate novel loci, the increased sensitivity of the Trinity pipeline might make it preferable over the reference genome-based approaches for studies aimed at broadly characterizing variation in the magnitude of expression differences and biological processes. However, the ‘new Tuxedo’ pipeline might be appropriate when a more conservative approach is warranted, such as for the identification of candidate genes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".