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.
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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.
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Nature
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| uniprot:author |
Bai Y.,
Fan W.,
Zhang H.,
Wang Y.,
Wang W.,
Li Z.,
Yu C.,
Xu L.,
Wang J.,
Wang X.,
Li Y.,
Huang Y.,
Wang G.,
Liu B.,
Li Q.,
Li H.,
Zhao J.,
Zhang J.,
Zhang Z.,
Wang M.,
Lu Z.,
Liu X.,
Liu Q.,
Cao J.,
Cheng S.,
Yang Z.,
Liu H.,
Wang B.,
Zhang Y.,
Yang H.,
Lin R.,
Li S.,
Wang H.,
Li J.,
Zhu H.,
Zhou Y.,
Liu S.,
Wu Q.,
Xie X.,
Li G.,
Li L.,
Chen Y.,
Fu Y.,
Kristiansen K.,
Li B.,
Zheng H.,
Fang X.,
Guo X.,
Zhang G.,
Guo Y.,
Sun X.,
Huang Q.,
Lin S.,
He L.,
Zhang Q.,
Li R.,
Shi Y.,
Jiang Z.,
Gu W.,
Ma L.,
Xu A.,
Yang G.,
Bolund L.,
Zheng Y.,
Hu Y.,
Li D.,
Zhang X.,
Zhao S.,
Ryder O.A.,
Hu W.,
Tian G.,
Zhang D.,
Min J.,
Qin N.,
Wu Z.,
Cai J.,
Cai Q.,
Tian J.,
Shen F.,
Fang L.,
Liang H.,
Shi Z.,
Ye C.,
Wen M.,
Lam T.T.,
Qian W.,
Ren Y.,
Ruan J.,
Ren X.,
Bruford M.W.,
Wang X.',
Jian M.,
Tian F.,
Olson M.,
Nie W.,
Nielsen R.
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