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Updated: Jan 23, 2026

Metagenomic Analysis of Silage
Published on: January 13, 2017
Comparison of de-novo assembly tools for plasmid metagenome analysis
Sachin Kumar Gupta1, Shahbaz Raza1, Tatsuya Unno2,3
1Faculty of Biotechnology, College of Applied Life Sciences, SARI, Jeju National University, Jeju, 63243, Republic of Korea.
Background:
With the advent of next-generation sequencing techniques, culture-independent metagenome approaches have now made it possible to predict possible presence of genes in the environmental bacteria most of which may be non-cultivable. Short reads obtained from the deep sequencing can be assembled into long contigs some of which include plasmids. Plasmids are the circular double stranded DNA in bacteria and known as one of the major carriers of antibiotic resistance genes.
Objective:
Metagenomic analyses, especially focused on plasmids, could help us predict dissemination mechanisms of antibiotic resistance genes in the environment. However, with the availability of a myriad of metagenomic assemblers, the selection of the most appropriate metagenome assembler for the plasmid metagenome study might be challenging. Therefore, in this study, we compared five open source assemblers to suggest most effective way of plasmid metagenome analysis.
Methods:
IDBA-UD, MEGAHIT, SPAdes, SOAPdenovo2, and Velvet are compared for conducting plasmid metagenome analyses using two water samples.
Results:
Our results clearly showed that abundance and types of antibiotic resistance genes on plasmids varied depending on the selection of assembly tools. IDBA-UD and MEGAHIT demonstrated the overall best assembly statistics with high N50 values with higher portion of longer contigs.
Conclusion:
These two assemblers also detected more diverse plasmids. Among the two, MEGAHIT showed more memory efficient assembly, therefore we suggest that the use of MEGAHIT for plasmid metagenome analysis may offer more diverse plasmids with less computer resource required. Here, we also summarized a fundamental plasmid metagenome work flow, especially for antibiotic resistance gene investigation.
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