Dataset Information

The sequence and de novo assembly of hog deer genome.

ABSTRACT: Hog deer (Axis porcinus) is a small deer species in family Cervidae and has been undergoing a serious and global decline during the past decades. Chengdu Zoo currently holds a captive population of hog deer with sufficient genetic diversity in China. We sequenced and de novo assembled its genome sequence in the present study. A total of six different insert-size libraries were sequenced and generated 395?Gb of clean data in total. With aid of the linked reads of 10X Genomics, genome sequence was assembled to 2.72?Gb in length (contig N50, 66.04 Kb; scaffold N50, 20.55?Mb), in which 94.5% of expected genes were detected. We comprehensively annotated 22,473 protein-coding genes, 37,019 tRNAs, and 1,058?Mb repeated sequences. The newly generated reference genome is expected to significantly contribute to comparative analysis of genome biology and evolution within family Cervidae.

SUBMITTER: Wang W

PROVIDER: S-EPMC6326164 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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The sequence and de novo assembly of hog deer genome.

Wang Wei W Yan Hui-Juan HJ Chen Shi-Yi SY Li Zhen-Zhen ZZ Yi Jun J Niu Li-Li LL Deng Jia-Po JP Chen Wei-Gang WG Pu Yang Y Jia Xianbo X Qu Yu Y Chen Ang A Zhong Yan Y Yu Xin-Ming XM Pang Shuai S Huang Wan-Long WL Han Yue Y Liu Guang-Jian GJ Yu Jian-Qiu JQ

Scientific data 20190108

Hog deer (Axis porcinus) is a small deer species in family Cervidae and has been undergoing a serious and global decline during the past decades. Chengdu Zoo currently holds a captive population of hog deer with sufficient genetic diversity in China. We sequenced and de novo assembled its genome sequence in the present study. A total of six different insert-size libraries were sequenced and generated 395 Gb of clean data in total. With aid of the linked reads of 10X Genomics, genome sequence was ...[more]

PMID: 30620341

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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 hog deer genome.

Publications

The sequence and de novo assembly of hog deer genome.

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