Literature DB >> 31187446

Comparison of de-novo assembly tools for plasmid metagenome analysis.

Sachin Kumar Gupta1, Shahbaz Raza1, Tatsuya Unno2,3.   

Abstract

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.

Entities:  

Keywords:  IDBA-UD; MEGAHIT; Plasmid metagenome; SOAPdenovo2; SPAdes; Velvet

Mesh:

Year:  2019        PMID: 31187446     DOI: 10.1007/s13258-019-00839-1

Source DB:  PubMed          Journal:  Genes Genomics        ISSN: 1976-9571            Impact factor:   1.839


  20 in total

1.  KEGG: kyoto encyclopedia of genes and genomes.

Authors:  M Kanehisa; S Goto
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth.

Authors:  Yu Peng; Henry C M Leung; S M Yiu; Francis Y L Chin
Journal:  Bioinformatics       Date:  2012-04-11       Impact factor: 6.937

Review 3.  The metagenomics of soil.

Authors:  Rolf Daniel
Journal:  Nat Rev Microbiol       Date:  2005-06       Impact factor: 60.633

4.  Velvet: algorithms for de novo short read assembly using de Bruijn graphs.

Authors:  Daniel R Zerbino; Ewan Birney
Journal:  Genome Res       Date:  2008-03-18       Impact factor: 9.043

5.  Prodigal: prokaryotic gene recognition and translation initiation site identification.

Authors:  Doug Hyatt; Gwo-Liang Chen; Philip F Locascio; Miriam L Land; Frank W Larimer; Loren J Hauser
Journal:  BMC Bioinformatics       Date:  2010-03-08       Impact factor: 3.169

Review 6.  BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences.

Authors:  Minoru Kanehisa; Yoko Sato; Kanae Morishima
Journal:  J Mol Biol       Date:  2015-11-14       Impact factor: 5.469

7.  CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database.

Authors:  Baofeng Jia; Amogelang R Raphenya; Brian Alcock; Nicholas Waglechner; Peiyao Guo; Kara K Tsang; Briony A Lago; Biren M Dave; Sheldon Pereira; Arjun N Sharma; Sachin Doshi; Mélanie Courtot; Raymond Lo; Laura E Williams; Jonathan G Frye; Tariq Elsayegh; Daim Sardar; Erin L Westman; Andrew C Pawlowski; Timothy A Johnson; Fiona S L Brinkman; Gerard D Wright; Andrew G McArthur
Journal:  Nucleic Acids Res       Date:  2016-10-26       Impact factor: 16.971

8.  Assembling metagenomes, one community at a time.

Authors:  Andries Johannes van der Walt; Marc Warwick van Goethem; Jean-Baptiste Ramond; Thulani Peter Makhalanyane; Oleg Reva; Don Arthur Cowan
Journal:  BMC Genomics       Date:  2017-07-10       Impact factor: 3.969

9.  Fast and accurate long-read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2010-01-15       Impact factor: 6.937

10.  PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures.

Authors:  Pawel S Krawczyk; Leszek Lipinski; Andrzej Dziembowski
Journal:  Nucleic Acids Res       Date:  2018-04-06       Impact factor: 16.971

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