The sequence and de novo assembly of the giant panda genome
Bibliographic record
Abstract
Using next-generation sequencing technology alone, we have successfully generated and assembled a draft sequence of the giant panda genome. The assembled contigs (2.25 gigabases (Gb)) cover approximately 94% of the whole genome, and the remaining gaps (0.05 Gb) seem to contain carnivore-specific repeats and tandem repeats. Comparisons with the dog and human showed that the panda genome has a lower divergence rate. The assessment of panda genes potentially underlying some of its unique traits indicated that its bamboo diet might be more dependent on its gut microbiome than its own genetic composition. We also identified more than 2.7 million heterozygous single nucleotide polymorphisms in the diploid genome. Our data and analyses provide a foundation for promoting mammalian genetic research, and demonstrate the feasibility for using next-generation sequencing technologies for accurate, cost-effective and rapid de novo assembly of large eukaryotic genomes. The genome of the giant panda — specifically of the female Beijing Olympics mascot Jingjing — has been determined using short-read sequencing technology, a first for such a complex genome. It consists of some 2.4 billion DNA base pairs, compared to 3 billion in humans, and contains around 21,000 protein-encoding genes, similar to the human genome. Genomic diversity reflected in the sequence is high, raising hopes that despite a population of only about 2,500, conservation efforts can keep the species from extinction. Intriguingly, the panda appears to have all the genes needed for a carnivorous digestive system but lacks digestive cellulase genes. It may therefore depend on its gut microbiome to handle its famously limited bamboo diet. Taste may be a diet-limiting factor: loss of function of the T1R1 gene means that pandas may not experience the umami taste associated with high-protein foods. Technical aspects of this work pave the way for the use of next-generation sequencing for rapid de novo assembly of large eukaryotic genomes. Here, a draft sequence of the giant panda genome is assembled using next-generation sequencing technology alone. Genome analysis reveals a low divergence rate in comparison with dog and human genomes and insights into panda-specific traits; for example, the giant panda's bamboo diet may be more dependent on its gut microbiome than its own genetic composition.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".