RAD in the realm of next-generation sequencing technologies
Bibliographic record
Abstract
The first North American RAD Sequencing and Genomics Symposium, sponsored by Floragenex (http://www.floragenex.com/radmeeting/), took place in Portland, Oregon (USA) on 19 April 2011. This symposium was convened to promote and discuss the use of restriction-site-associated DNA (RAD) sequencing technologies. RAD sequencing is one of several strategies recently developed to increase the power of data generated via short-read sequencing technologies by reducing their complexity (Baird et al. 2008; Huang et al. 2009; Andolfatto et al. 2011; Elshire et al. 2011). RAD sequencing, as a form of genotyping by sequencing, has been effectively applied in genetic mapping and quantitative trait loci (QTL) analyses in a range of organisms including nonmodel, genetically highly heterogeneous organisms (Table 1; Baird et al. 2008; Baxter et al. 2011; Chutimanitsakun et al. 2011; Pfender et al. 2011). RAD sequencing has recently found applications in phylogeography (Emerson et al. 2010) and population genomics (Hohenlohe et al. 2010). Considering the diversity of talks presented during this meeting, more developments are to be expected in the very near future. Table 1. Published studies using RAD sequencing Organism by application References Genetic mapping Three-spined stickleback (Gasterosteus aculeatus) Baird et al. (2008) Diamondback moth (Plutella xylostella) Baxter et al. (2011) Barley (Hordeum vulgare) Chutimanitsakun et al. (2011) Perennial Ryegrass (Lolium perenne) Pfender et al. (2011) Neurospora crassa Baird et al. (2008) Population genomics Three-spined stickleback (Gasterosteus aculeatus) Hohenlohe et al. (2010) Phylogeography Pitcher plant mosquito (Wyeomyia smithii) Emerson et al. (2010) Whole-genome sequencing Escherichia coli Etter et al. (2011)
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".