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Read assembly is usually an essential first step for the follow-up studies in metagenomic research. Understanding the power and limitations of various read assembly programs in practice is important for researchers to choose which programs to use in their investigations. Many studies evaluating different assembly programs used either simulated metagenomes or real metagenomes with unknown genome compositions. However, the simulated datasets may not reflect the real complexities of metagenomic samples and the estimated assembly accuracy could be misleading due to the unknown genomes in real metagenomes. Therefore, hybrid strategies are required to evaluate the various read assemblers for metagenomic studies. In this paper, we benchmark the metagenomic read assemblers by mixing reads from real metagenomic datasets with reads from known genomes and evaluating the integrity, contiguity and accuracy of the assembly using the reads from the known genomes. We selected four advanced metagenome assemblers, MEGAHIT, MetaSPAdes, IDBA-UD and Faucet, for evaluation. We showed the strengths and weaknesses of these assemblers in terms of integrity, contiguity and accuracy for different variables, including the genetic difference of the real genomes with the genome sequences in the real metagenomic datasets and the sequencing depth of the simulated datasets. Overall, MetaSPAdes performs best in terms of integrity and continuity at the species-level, followed by MEGAHIT. Faucet performs best in terms of accuracy at the cost of worst integrity and continuity, especially at low sequencing depth. MEGAHIT has the highest genome fractions at the strain-level and MetaSPAdes has the overall best performance at the strain-level. MEGAHIT is the most efficient in our experiments. Availability: The source code is available at https:\/\/github.com\/ziyewang\/MetaAssemblyEval.<\/jats:p>","DOI":"10.1093\/bib\/bbz025","type":"journal-article","created":{"date-parts":[[2019,2,18]],"date-time":"2019-02-18T04:19:45Z","timestamp":1550463585000},"page":"777-790","source":"Crossref","is-referenced-by-count":39,"title":["Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences"],"prefix":"10.1093","volume":"21","author":[{"given":"Ziye","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences and the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Automation, Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jed A","family":"Fuhrman","sequence":"first","affiliation":[{"name":"Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, California, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengzhu","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanfeng","family":"Zhu","sequence":"first","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, the School of Computer Science and the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,3,11]]},"reference":[{"key":"2020061712232669900_ref1","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1038\/4441022a","article-title":"Microbial ecology: human gut microbes associated with obesity","volume":"444","author":"Ley","year":"2006","journal-title":"Nature"},{"key":"2020061712232669900_ref2","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1038\/nature12506","article-title":"Richness of human gut microbiome correlates with metabolic markers","volume":"500","author":"Le Chatelier","year":"2013","journal-title":"Nature"},{"key":"2020061712232669900_ref3","doi-asserted-by":"crossref","first-page":"501","DOI":"10.3748\/wjg.v22.i2.501","article-title":"Gut microbiota imbalance and colorectal cancer","volume":"22","author":"Gagni\u00e8re","year":"2016","journal-title":"World J Gastroenterol"},{"key":"2020061712232669900_ref4","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/nature11450","article-title":"Li Yand Cai Z, et al. 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