Genotyping of Tomato Cultivars and Hybrids using ddRAD
Bibliographic record
Abstract
Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera and includes annual and perennial plants from diverse habitats. ddRAD-seq is one of the most cost-effective methods in next generation sequencing (NGS) for generating robust genotyping data which permits high throughput simultaneous discovery and genotyping of sequence polymorphism either with or without an existing reference genome. Advantage of ddRAD technique was investigated by performing data analysis of sequence obtained through low pass whole genome sequencing and ddRAD protocol. Here we present a high-quality reduced represented genome sequence of domesticated tomato with the aim of understanding genetic variations in cultivated tomato; single nucleotide polymorphism (SNP) markers covering the whole genome of eight cultivars and four F1 hybrids were developed through Genotyping-By-Sequencing. We have sequenced twelve tomato varieties using Illumina HiSeq 4000, next generation sequencing platform. The raw data was subjected to preprocessing and aligned with reference tomato genome downloaded from ensembl release 36. The SNPs/INDELs were identified for each of the tomato varieties. A total of 30746 SNPs and 913 INDELs were identified. We investigated for homozygous polymorphic markers between PKM-1 and Arka Abha and found 745 markers which can be used as markers for fingerprinting.The homozygous polymorphic markers will be utilized for genetic mapping and trait association in a mapping population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".