Data from: Genomics of Compositae weeds: EST libraries, microarrays, and evidence of introgression
Bibliographic record
Abstract
PREMISE OF THE STUDY: Weeds cause considerable environmental and economic damage. However, genomic characterization of weeds has lagged behind that of model plants and crop species. Here we report on the development of genomic tools and resources for 11 weeds from the Compositae family that will serve as a basis for subsequent population and comparative genomic analyses. Because hybridization has been suggested as a stimulus for the evolution of invasiveness, we also analyze these genomic data for evidence of hybridization. METHODS: We generated 22 expressed sequence tag (EST) libraries for the 11 targeted weeds using Sanger, 454, and Illumina sequencing, compared the coverage and quality of sequence assemblies, and developed NimbleGen microarrays for expression analyses in five taxa. Where possible, we also compared the distributions of Ks values between orthologs of congeneric taxa to detect and quantify hybridization and introgression. RESULTS: Gene discovery was enhanced by sequencing from multiple tissues, normalization of cDNA libraries, and especially greater sequencing depth. However, assemblies from short sequence reads sometimes failed to resolve close paralogs. Substantial introgression was detected in Centaurea and Helianthus, but not in Ambrosia and Lactuca. CONCLUSIONS: Transcriptome sequencing using next generation platforms has greatly reduced the cost of genomic studies of non-model organisms, and the ESTs and microarrays reported here will accelerate evolutionary and molecular investigations of Compositae weeds. Our study also shows how ortholog comparisons can be used to quantify the genome-wide extent of introgression and to identify genes that have been exchanged between hybridizing taxa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".