Whole genome analysis (WGA) of five pea aphids biotypes for the identification and classification of SNPs
Bibliographic record
Abstract
Leguminous plants are the rich source of dietary proteins that account for 27% of the world primary crop production. Their high vulnerability to the pathogens resulted in the annual loss of ~2.0 -2.1 million tonnes of crops. Among the various pests, pea aphids represent the highly specialized and most devastating pathogens of the leguminous plants. During the evolution, they evolved themselves in such a manner that showed reduced performance when grown in the alternative host plant. In order to understand their host preference, genome level scan is required. Therefore, in this study, we used the genome-wide SNP scan of five pea aphid biotypes of >70 GB raw sequencing reads. We used the pea aphid genome as a reference having size of ~450 MB. Using quality filtering, BWA alignment, and GATK tool, we identified more than 1.5 million bi-allelic and around 1500 multiallelic nuclear SNPs with a Ts/Tv ratio of 1.42. A high rate of mutation was observed in T. pratense biotype as compared to the M. Sativa. Their functional classification revealed approximately 26K -30K missense and ~400 nonsense variation in their nuclear genome. Furthermore, the genome-wide scan revealed that an average of 5.42% of the identified SNPs located in the exon regions while a major portion ~80% were present in the intron region of the genome. This is the first report of genome-wide SNPs analysis on different pea aphid biotypes which in future could be used for host-pathogen interaction, and diversity analysis. Our future goal is the analysis of more biotypes and development of a unique resource of SNP's present biologically important class of genes playing role in immunity, olfactory response, etc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".