Dataset Information

The sequence and de novo assembly of the giant panda genome.

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.

SUBMITTER: Li R

PROVIDER: S-EPMC3951497 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The sequence and de novo assembly of the giant panda genome.

Li Ruiqiang R Fan Wei W Tian Geng G Zhu Hongmei H He Lin L Cai Jing J Huang Quanfei Q Cai Qingle Q Li Bo B Bai Yinqi Y Zhang Zhihe Z Zhang Yaping Y Wang Wen W Li Jun J Wei Fuwen F Li Heng H Jian Min M Li Jianwen J Zhang Zhaolei Z Nielsen Rasmus R Li Dawei D Gu Wanjun W Yang Zhentao Z Xuan Zhaoling Z Ryder Oliver A OA Leung Frederick Chi-Ching FC Zhou Yan Y Cao Jianjun J Sun Xiao X Fu Yonggui Y Fang Xiaodong X Guo Xiaosen X Wang Bo B Hou Rong R Shen Fujun F Mu Bo B Ni Peixiang P Lin Runmao R Qian Wubin W Wang Guodong G Yu Chang C Nie Wenhui W Wang Jinhuan J Wu Zhigang Z Liang Huiqing H Min Jiumeng J Wu Qi Q Cheng Shifeng S Ruan Jue J Wang Mingwei M Shi Zhongbin Z Wen Ming M Liu Binghang B Ren Xiaoli X Zheng Huisong H Dong Dong D Cook Kathleen K Shan Gao G Zhang Hao H Kosiol Carolin C Xie Xueying X Lu Zuhong Z Zheng Hancheng H Li Yingrui Y Steiner Cynthia C CC Lam Tommy Tsan-Yuk TT Lin Siyuan S Zhang Qinghui Q Li Guoqing G Tian Jing J Gong Timing T Liu Hongde H Zhang Dejin D Fang Lin L Ye Chen C Zhang Juanbin J Hu Wenbo W Xu Anlong A Ren Yuanyuan Y Zhang Guojie G Bruford Michael W MW Li Qibin Q Ma Lijia L Guo Yiran Y An Na N Hu Yujie Y Zheng Yang Y Shi Yongyong Y Li Zhiqiang Z Liu Qing Q Chen Yanling Y Zhao Jing J Qu Ning N Zhao Shancen S Tian Feng F Wang Xiaoling X Wang Haiyin H Xu Lizhi L Liu Xiao X Vinar Tomas T Wang Yajun Y Lam Tak-Wah TW Yiu Siu-Ming SM Liu Shiping S Zhang Hemin H Li Desheng D Huang Yan Y Wang Xia X Yang Guohua G Jiang Zhi Z Wang Junyi J Qin Nan N Li Li L Li Jingxiang J Bolund Lars L Kristiansen Karsten K Wong Gane Ka-Shu GK Olson Maynard M Zhang Xiuqing X Li Songgang S Yang Huanming H Wang Jian J Wang Jun J

Nature 20091213 7279

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 ind ...[more]

PMID: 20010809

Similar Datasets

Project description:The whole-genome sequence assembly (WGSA) problem is among one of the most studied problems in computational biology. Despite the availability of a plethora of tools (i.e., assemblers), all claiming to have solved the WGSA problem, little has been done to systematically compare their accuracy and power. Traditional methods rely on standard metrics and read simulation: while on the one hand, metrics like N50 and number of contigs focus only on size without proportionately emphasizing the information about the correctness of the assembly, comparisons performed on simulated dataset, on the other hand, can be highly biased by the non-realistic assumptions in the underlying read generator. Recently the Feature Response Curve (FRC) method was proposed to assess the overall assembly quality and correctness: FRC transparently captures the trade-offs between contigs' quality against their sizes. Nevertheless, the relationship among the different features and their relative importance remains unknown. In particular, FRC cannot account for the correlation among the different features. We analyzed the correlation among different features in order to better describe their relationships and their importance in gauging assembly quality and correctness. In particular, using multivariate techniques like principal and independent component analysis we were able to estimate the "excess-dimensionality" of the feature space. Moreover, principal component analysis allowed us to show how poorly the acclaimed N50 metric describes the assembly quality. Applying independent component analysis we identified a subset of features that better describe the assemblers performances. We demonstrated that by focusing on a reduced set of highly informative features we can use the FRC curve to better describe and compare the performances of different assemblers. Moreover, as a by-product of our analysis, we discovered how often evaluation based on simulated data, obtained with state of the art simulators, lead to not-so-realistic results.

Project description:MotivationThere are very few methods for de novo genome assembly based on the overlap graph approach. It is considered as giving more exact results than the so-called de Bruijn graph approach but in much greater time and of much higher memory usage. It is not uncommon that assembly methods involving the overlap graph model are not able to successfully compute greater data sets, mainly due to memory limitation of a computer. This was the reason for developing in last decades mainly de Bruijn-based assembly methods, fast and fairly accurate. However, the latter methods can fail for longer or more repetitive genomes, as they decompose reads to shorter fragments and lose a part of information. An efficient assembler for processing big data sets and using the overlap graph model is still looked out.ResultsWe propose a new genome-scale de novo assembler based on the overlap graph approach, designed for short-read sequencing data. The method, ALGA, incorporates several new ideas resulting in more exact contigs produced in short time. Among these ideas we have creation of a sparse but quite informative graph, reduction of the graph including a procedure referring to the problem of minimum spanning tree of a local subgraph, and graph traversal connected with simultaneous analysis of contigs stored so far. What is rare in genome assembly, the algorithm is almost parameter-free, with only one optional parameter to be set by a user. ALGA was compared with nine state-of-the-art assemblers in tests on genome-scale sequencing data obtained from real experiments on six organisms, differing in size, coverage, GC content, and repetition rate. ALGA produced best results in the sense of overall quality of genome reconstruction, understood as a good balance between genome coverage, accuracy, and length of resulting sequences. The algorithm is one of tools involved in processing data in currently realized national project Genomic Map of Poland.AvailabilityALGA is available at http://alga.put.poznan.pl.Supplementary informationSupplementary material is available at Bioinformatics online.

Dataset Information

The sequence and de novo assembly of the giant panda genome.

Publications

The sequence and de novo assembly of the giant panda genome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets