Full chloroplast sequencing using genome skimming for novel plant DNA barcode discovery in Amaranths
Bibliographic record
Abstract
DNA barcoding has been established as an efficient, sensitive and reliable methodology for plant identification. However, in spite of efforts to find a universal DNA plant barcode, some taxa are not sufficiently resolved by typical plant barcoding genes like matK or rbcL. We have used a technique known as genome skimming, which relies on the empiric low coverage sequencing of a full plant genome, resulting in high coverage of the high copy genome fractions such as chloroplast and rDNA. Phylogenetic studies show that these regions are a reservoir of variability which could be further exploited for DNA barcode discovery. We sequenced eight amaranth species using Illumina Next Generation Sequencing technology to test the feasibility of this technique. Amaranths were chosen due to their increasing impact as invasive species bearing multiple herbicide resistance mechanisms. Our results showed that complete chloroplast genomes could be assembled for all of the eight species tested. We obtained an average of 47 million reads for each one of the amaranth nuclear genomes, which range in size between 400-700Mb approximately. These reads provide an average theoretical coverage of 10-15X for each nuclear genome, but resulted in an average chloroplast genome coverage in the range of 500-8000X due to multiple chloroplast genome copies per cell. Alignment of the eight chloroplast genomes shows variability in the single copy regions (Fig. 1), especially on intergenic sections (Fig. 2). Additional preliminary analyses also show variation among different populations of the same species, demonstrating the importance of studying both inter and intraspecific diversity to design reliable and accurate DNA barcodes that can be used in species identification.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".