Identification and phylogenetic analysis of sequences of<i>Bean pod mottle virus</i>,<i>Soybean mosaic virus</i>, and<i>Cowpea chlorotic mottle virus</i>in expressed sequence tag data from soybean
Bibliographic record
Abstract
Field- or greenhouse-grown plants are naturally exposed to microbes. Sequence data derived from these plants can therefore inadvertently include microbe sequences. We have examined the extent of viral sequences in public soybean (Glycine max and Glycine soja) sequence data. A collection of 303 149 soybean expressed sequence tags (ESTs) was computationally analyzed for sequences from 12 viruses infecting soybean: Alfalfa mosaic virus (AMV), Bean pod mottle virus (BPMV), Bean yellow mosaic virus (BYMV), Cowpea chlorotic mottle virus (CCMV), Cowpea mosaic virus (CPMV), Cowpea severe mosaic virus (CPSMV), Cucumber mosaic virus (CMV), Peanut mottle virus (PeMoV), Peanut stunt virus (PSV), Soybean mosaic virus (SMV), Tobacco ringspot virus (TRSV), and Tobacco streak virus (TSV). A total of 1652 sequences matching BPMV (1097 sequences), SMV (132 sequences), and CCMV (423 sequences) were discovered in the soybean EST collection. The viral sequences were assembled into contiguous sequences, and sequence tracts in common were used in a parsimony analysis of the phylogenetic relationship of putative viral genotypes. Our results include representative sequences from two BPMV subgroups and, surprisingly, many variants of BPMV nucleotide sequences. Furthermore, the results suggest that BPMV viral strains may be mixes of different BPMV RNA-1 and RNA-2 genotypes. This study presents the largest collection of sequence data available to date for the BPMV, SMV, and CCMV viruses and represents the first report of this magnitude of varieties of BPMV nucleotide 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".