Allele Identification for Transcriptome-Based Population Genomics in the Invasive Plant<i>Centaurea solstitialis</i>
Bibliographic record
Abstract
Transcriptome sequences are becoming more broadly available for multiple individuals of the same species, providing opportunities to derive population genomic information from these datasets. Using the 454 Life Science Genome Sequencer FLX and FLX-Titanium next-generation platforms, we generated 11-430 Mbp of sequence for normalized cDNA for 40 wild genotypes of the invasive plant Centaurea solstitialis, yellow starthistle, from across its worldwide distribution. We examined the impact of sequencing effort on transcriptome recovery and overlap among individuals. To do this, we developed two novel publicly available software pipelines: SnoWhite for read cleaning before assembly, and AllelePipe for clustering of loci and allele identification in assembled datasets with or without a reference genome. AllelePipe is designed specifically for cases in which read depth information is not appropriate or available to assist with disentangling closely related paralogs from allelic variation, as in transcriptome or previously assembled libraries. We find that modest applications of sequencing effort recover most of the novel sequences present in the transcriptome of this species, including single-copy loci and a representative distribution of functional groups. In contrast, the coverage of variable sites, observation of heterozygosity, and overlap among different libraries are all highly dependent on sequencing effort. Nevertheless, the information gained from overlapping regions was informative regarding coarse population structure and variation across our small number of population samples, providing the first genetic evidence in support of hypothesized invasion scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".