Genotyping of Tomato Cultivars and Hybrids using ddRAD
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it