Dataset Information

The sequence and de novo assembly of Oxygymnocypris stewartii genome.

ABSTRACT: Animal genomes in the Qinghai-Tibetan Plateau provide valuable resources for scientists to understand the molecular mechanism of environmental adaptation. Tibetan fish species play essential roles in the local ecology; however, the genomic information for native fishes was still insufficient. Oxygymnocypris stewartii, belonging to Oxygymnocypris genus, Schizothoracinae subfamily, is a native fish in the Tibetan plateau living within the elevation from roughly 3,000?m to 4,200?m. In this report, PacBio and Illumina sequencing platform were used to generate ~385.3?Gb genomic sequencing data. A genome of about 1,849.2?Mb was obtained with a contig N50 length of 257.1?kb. More than 44.5% of the genome were identified as repetitive elements, and 46,400 protein-coding genes were annotated in the genome. The assembled genome can be used as a reference for future population genetic studies of O. stewartii and will improve our understanding of high altitude adaptation of fishes in the Qinghai-Tibetan Plateau.

SUBMITTER: Liu HP

PROVIDER: S-EPMC6362891 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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The sequence and de novo assembly of Oxygymnocypris stewartii genome.

Liu Hai-Ping HP Xiao Shi-Jun SJ Wu Nan N Wang Di D Liu Yan-Chao YC Zhou Chao-Wei CW Liu Qi-Yong QY Yang Rui-Bin RB Jiang Wen-Kai WK Liang Qi-Qi QQ Wangjiu Zhang Chi C Gong Jun-Hua JH Yuan Xiao-Hui XH Mou Zhen-Bo ZB

Scientific data 20190205

Animal genomes in the Qinghai-Tibetan Plateau provide valuable resources for scientists to understand the molecular mechanism of environmental adaptation. Tibetan fish species play essential roles in the local ecology; however, the genomic information for native fishes was still insufficient. Oxygymnocypris stewartii, belonging to Oxygymnocypris genus, Schizothoracinae subfamily, is a native fish in the Tibetan plateau living within the elevation from roughly 3,000 m to 4,200 m. In this report, ...[more]

PMID: 30720802

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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 datasets, 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 datasets 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.Availability and implementationALGA is available at http://alga.put.poznan.pl.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

The sequence and de novo assembly of Oxygymnocypris stewartii genome.

Publications

The sequence and de novo assembly of Oxygymnocypris stewartii genome.

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