Nurk Sergey, Meleshko Dmitry, Korobeynikov Anton, Pevzner Pavel A
Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia 199004.
Department of Statistical Modelling, St. Petersburg State University, St. Petersburg, Russia 198515.
Genome Res. 2017 May;27(5):824-834. doi: 10.1101/gr.213959.116. Epub 2017 Mar 15.
While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging, thus stifling biological discoveries. Moreover, recent studies revealed that complex bacterial populations may be composed from dozens of related strains, thus further amplifying the challenge of metagenomic assembly. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse data sets.
虽然宏基因组学已成为分析细菌群体的首选技术,但宏基因组数据的组装仍然具有挑战性,从而阻碍了生物学发现。此外,最近的研究表明,复杂的细菌群体可能由数十种相关菌株组成,这进一步加大了宏基因组组装的难度。metaSPAdes通过利用在单细胞和高度多态二倍体基因组组装中被证明有用的计算思路,解决了宏基因组组装的各种挑战。我们将metaSPAdes与其他最先进的宏基因组组装工具进行了基准测试,并证明它能在各种数据集上产生高质量的组装结果。