Strategy in wheat- <i>Fusarium</i> dual-genome RNA-seq data processing
Bibliographic record
Abstract
Abstract In RNA-seq data processing, short read alignments are usually of one species against its own genome; however, in plant-microbe interaction systems, reads from both host and pathogen samples are blended together. In contrast to single-genome, both pathogen and host reference genomes are involved in the alignment process. In such circumstances, the order of alignment to the host, the pathogen, or simultaneously to both genomes results in differences in read counts of certain genes, especially at the advanced infection stage. It is crucial to have an appropriate strategy for aligning the reads to their respective genomes, yet the existing strategies of either sequential or parallel alignment become a problem when mapping mixed reads to their corresponding reference genomes. The challenge lies in the determination of which reads belong to which species, especially when homology exists between the two genomes. This study proposes a combo-genome alignment strategy after comparing three alignment scenarios. Simulation results demonstrated that the degree of discrepancy in the results is correlated with phylogenetic distance of the two species in the mixture which was attributable to the extent of homology between the two genomes involved. This correlation was also found in the analysis using two real RNA-seq datasets of Fusarium -challenged wheat plants. Comparisons of the three RNA-seq processing strategies on three simulation datasets and two real Fusarium -infected wheat datasets showed that an alignment to a combo-genome, consisting of both host and pathogen genomes, improves mapping quality as compared to sequential alignment procedures.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".