Dataset Information

SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler.

ABSTRACT: BACKGROUND:There is a rapidly increasing amount of de novo genome assembly using next-generation sequencing (NGS) short reads; however, several big challenges remain to be overcome in order for this to be efficient and accurate. SOAPdenovo has been successfully applied to assemble many published genomes, but it still needs improvement in continuity, accuracy and coverage, especially in repeat regions. FINDINGS:To overcome these challenges, we have developed its successor, SOAPdenovo2, which has the advantage of a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome. CONCLUSIONS:Benchmark using the Assemblathon1 and GAGE datasets showed that SOAPdenovo2 greatly surpasses its predecessor SOAPdenovo and is competitive to other assemblers on both assembly length and accuracy. We also provide an updated assembly version of the 2008 Asian (YH) genome using SOAPdenovo2. Here, the contig and scaffold N50 of the YH genome were ~20.9 kbp and ~22 Mbp, respectively, which is 3-fold and 50-fold longer than the first published version. The genome coverage increased from 81.16% to 93.91%, and memory consumption was ~2/3 lower during the point of largest memory consumption.

SUBMITTER: Luo R

PROVIDER: S-EPMC3626529 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler.

Luo Ruibang R Liu Binghang B Xie Yinlong Y Li Zhenyu Z Huang Weihua W Yuan Jianying J He Guangzhu G Chen Yanxiang Y Pan Qi Q Liu Yunjie Y Tang Jingbo J Wu Gengxiong G Zhang Hao H Shi Yujian Y Liu Yong Y Yu Chang C Wang Bo B Lu Yao Y Han Changlei C Cheung David W DW Yiu Siu-Ming SM Peng Shaoliang S Xiaoqian Zhu Z Liu Guangming G Liao Xiangke X Li Yingrui Y Yang Huanming H Wang Jian J Lam Tak-Wah TW Wang Jun J

GigaScience 20121227 1

<h4>Background</h4>There is a rapidly increasing amount of de novo genome assembly using next-generation sequencing (NGS) short reads; however, several big challenges remain to be overcome in order for this to be efficient and accurate. SOAPdenovo has been successfully applied to assemble many published genomes, but it still needs improvement in continuity, accuracy and coverage, especially in repeat regions.<h4>Findings</h4>To overcome these challenges, we have developed its successor, SOAPdeno ...[more]

PMID: 23587118

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Project description:With the exponential growth of sequence information stored over the last decade, including that of de novo assembled contigs from RNA-Seq experiments, quantification of chimeric sequences has become essential when assembling read data. In transcriptomics, de novo assembled chimeras can closely resemble underlying transcripts, but patterns such as those seen between co-evolving sites, or mapped read counts, become obscured. We have created a de Bruijn based de novo assembler for RNA-Seq data that utilizes a classification system to describe the complexity of underlying graphs from which contigs are created. Each contig is labelled with one of three levels, indicating whether or not ambiguous paths exist. A by-product of this is information on the range of complexity of the underlying gene families present. As a demonstration of CStones ability to assemble high-quality contigs, and to label them in this manner, both simulated and real data were used. For simulated data, ten million read pairs were generated from cDNA libraries representing four species, Drosophila melanogaster, Panthera pardus, Rattus norvegicus and Serinus canaria. These were assembled using CStone, Trinity and rnaSPAdes; the latter two being high-quality, well established, de novo assembers. For real data, two RNA-Seq datasets, each consisting of ≈30 million read pairs, representing two adult D. melanogaster whole-body samples were used. The contigs that CStone produced were comparable in quality to those of Trinity and rnaSPAdes in terms of length, sequence identity of aligned regions and the range of cDNA transcripts represented, whilst providing additional information on chimerism. Here we describe the details of CStones assembly and classification process, and propose that similar classification systems can be incorporated into other de novo assembly tools. Within a related side study, we explore the effects that chimera's within reference sets have on the identification of differentially expression genes. CStone is available at: https://sourceforge.net/projects/cstone/.

Project description:BACKGROUND:The challenges when developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize a de Bruijn graph, with which complexity grows linearly with data size while suffering from errors and repeats. Although one can correct the errors by inspecting the topological structure of the graph, this is not an easy task when there are too many branches. Two research directions are to improve either the graph reliability or the path search precision, and in this study, we focused on the former. RESULTS:We present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. In the proposed approach, we built contigs by direct read alignment within a distance margin and performed a junction search to construct splicing graphs. While doing so, a contig of length l was represented by a 4 × l matrix (called a consensus matrix), in which each element was the base count of the aligned reads so far. A representative sequence was obtained by taking the majority in each column of the consensus matrix to be used for further read alignment. Once the splicing graphs had been obtained, we used IsoLasso to find paths with a noticeable read depth. The experiments using real and simulated reads show that the method provided considerable improvement in sensitivity and moderately better performance when comparing sensitivity and precision. This was achieved by the error-aware graph construction using the consensus matrix, with which the reads having errors were made usable for the graph construction (otherwise, they might have been eventually discarded). This improved the quality of the coverage depth information used in the subsequent path search step and finally the reliability of the graph. CONCLUSIONS:De novo assembly is mainly used to explore undiscovered isoforms and must be able to represent as many reads as possible in an efficient way. In this sense, TraRECo provides us with a potential alternative for improving graph reliability even though the computational burden is much higher than the single k-mer in the de Bruijn graph approach.

Dataset Information

SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler.

Publications

SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler.

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