Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities

Abstract Background Quantification and identification of microbial genomes based on next-generation sequencing data is a challenging problem in metagenomics. Although current methods have mostly focused on analyzing bacteria whose genomes have been sequenced, such analyses are, however, complicated...

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Main Authors: Quang Tran, Diem-Trang Pham, Vinhthuy Phan
Format: Article
Language:English
Published: BMC 2017-12-01
Series:BMC Bioinformatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12859-017-1901-8
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spelling doaj-95061a98e1ac45159cbe68b0819ef9f92020-11-24T21:44:29ZengBMCBMC Bioinformatics1471-21052017-12-0118S1415516110.1186/s12859-017-1901-8Using 16S rRNA gene as marker to detect unknown bacteria in microbial communitiesQuang Tran0Diem-Trang Pham1Vinhthuy Phan2Department of Computer Science, University of MemphisDepartment of Computer Science, University of MemphisDepartment of Computer Science, University of MemphisAbstract Background Quantification and identification of microbial genomes based on next-generation sequencing data is a challenging problem in metagenomics. Although current methods have mostly focused on analyzing bacteria whose genomes have been sequenced, such analyses are, however, complicated by the presence of unknown bacteria or bacteria whose genomes have not been sequence. Results We propose a method for detecting unknown bacteria in environmental samples. Our approach is unique in its utilization of short reads only from 16S rRNA genes, not from entire genomes. We show that short reads from 16S rRNA genes retain sufficient information for detecting unknown bacteria in oral microbial communities. Conclusion In our experimentation with bacterial genomes from the Human Oral Microbiome Database, we found that this method made accurate and robust predictions at different read coverages and percentages of unknown bacteria. Advantages of this approach include not only a reduction in experimental and computational costs but also a potentially high accuracy across environmental samples due to the strong conservation of the 16S rRNA gene.http://link.springer.com/article/10.1186/s12859-017-1901-8MetagenomicsBacteria detectionNGS analysis
collection DOAJ
language English
format Article
sources DOAJ
author Quang Tran
Diem-Trang Pham
Vinhthuy Phan
spellingShingle Quang Tran
Diem-Trang Pham
Vinhthuy Phan
Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities
BMC Bioinformatics
Metagenomics
Bacteria detection
NGS analysis
author_facet Quang Tran
Diem-Trang Pham
Vinhthuy Phan
author_sort Quang Tran
title Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities
title_short Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities
title_full Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities
title_fullStr Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities
title_full_unstemmed Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities
title_sort using 16s rrna gene as marker to detect unknown bacteria in microbial communities
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2017-12-01
description Abstract Background Quantification and identification of microbial genomes based on next-generation sequencing data is a challenging problem in metagenomics. Although current methods have mostly focused on analyzing bacteria whose genomes have been sequenced, such analyses are, however, complicated by the presence of unknown bacteria or bacteria whose genomes have not been sequence. Results We propose a method for detecting unknown bacteria in environmental samples. Our approach is unique in its utilization of short reads only from 16S rRNA genes, not from entire genomes. We show that short reads from 16S rRNA genes retain sufficient information for detecting unknown bacteria in oral microbial communities. Conclusion In our experimentation with bacterial genomes from the Human Oral Microbiome Database, we found that this method made accurate and robust predictions at different read coverages and percentages of unknown bacteria. Advantages of this approach include not only a reduction in experimental and computational costs but also a potentially high accuracy across environmental samples due to the strong conservation of the 16S rRNA gene.
topic Metagenomics
Bacteria detection
NGS analysis
url http://link.springer.com/article/10.1186/s12859-017-1901-8
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