Developing genomic resources in two <i>Linum</i> species via 454 pyrosequencing and genomic reduction
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
Recent advances in next-generation DNA sequencing (NGS) have enhanced the development of genomic resources such as contigs or single-nucleotide polymorphisms (SNPs) for evolutionary studies of a nonmodel species with a complex and unsequenced genome. This study presents an application of a NGS technique in combination with genomic reduction and advanced bioinformatics tools to identify contigs and SNPs from multiple samples of two Linum species. A full Roche 454 GS FLX run of 16 diverse Linum samples representing cultivated flax (Linum usitatissimum L.) and its wild progenitor (Linum bienne Mill.) generated approximately 1.6 million sequence reads with a total length of 498 Mbp. Application of the computational pipeline de novo identification of alleles identified 713 contigs and 1067 SNPs. A blast search revealed alignments of all 713 contigs with 491 existing Linum scaffolds and gene annotations associated with 512 contigs. Sanger sequencing confirmed 95% of 79 selected contigs and 94% of 272 SNPs and identified 211 new SNPs and 19 new indels. The scored 454 SNP data were highly imbalanced for assayed samples. These findings not only are useful for evolutionary studies of Linum species but also help to illustrate the utility of NGS technologies in SNP discovery for nonmodel organisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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