Literature DB >> 21121044

Comparing bacterial communities inferred from 16S rRNA gene sequencing and shotgun metagenomics.

Neethu Shah1, Haixu Tang, Thomas G Doak, Yuzhen Ye.   

Abstract

16S rRNA gene sequencing has been widely used for probing the species structure of a variety of environmental bacterial communities. Alternatively, 16S rRNA gene fragments can be retrieved from shotgun metagenomic sequences (metagenomes) and used for species profiling. Both approaches have their limitations-16S rRNA sequencing may be biased because of unequal amplification of species' 16S rRNA genes, whereas shotgun metagenomic sequencing may not be deep enough to detect the 16S rRNA genes of rare species in a community. However, previous studies showed that these two approaches give largely similar species profiles for a few bacterial communities. To investigate this problem in greater detail, we conducted a systematic comparison of these two approaches. We developed PHYLOSHOP, a pipeline that predicts 16S rRNA gene fragments in metagenomes, reports the taxonomic assignment of these fragments, and visualizes their taxonomy distribution. Using PHYLOSHOP, we analyzed 33 metagenomic datasets of human-associated bacterial communities, and compared the bacterial community structures derived from these metagenomic datasets with the community structure derived from 16S rRNA gene sequencing (71 datasets). Based on several statistical tests (including a statistical test proposed here that takes into consideration differences in sample size), we observed that these two approaches give significantly different community structures for nearly all the bacterial communities collected from different locations on and in human body, and that these differences cannot be be explained by differences in sample size and are likely to be attributed by experimental method.

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Year:  2011        PMID: 21121044     DOI: 10.1142/9789814335058_0018

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  64 in total

1.  Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes.

Authors:  Jiarong Guo; James R Cole; Qingpeng Zhang; C Titus Brown; James M Tiedje
Journal:  Appl Environ Microbiol       Date:  2015-10-16       Impact factor: 4.792

2.  Metaproteomics analysis reveals the adaptation process for the chicken gut microbiota.

Authors:  Yue Tang; Anthony Underwood; Adriana Gielbert; Martin J Woodward; Liljana Petrovska
Journal:  Appl Environ Microbiol       Date:  2013-11-08       Impact factor: 4.792

3.  Development of a New Application for Comprehensive Viability Analysis Based on Microbiome Analysis by Next-Generation Sequencing: Insights into Staphylococcal Carriage in Human Nasal Cavities.

Authors:  Yu Jie Lu; Takashi Sasaki; Kyoko Kuwahara-Arai; Yuki Uehara; Keiichi Hiramatsu
Journal:  Appl Environ Microbiol       Date:  2018-05-17       Impact factor: 4.792

4.  Saliva microbiomes distinguish caries-active from healthy human populations.

Authors:  Fang Yang; Xiaowei Zeng; Kang Ning; Kuan-Liang Liu; Chien-Chi Lo; Wei Wang; Jie Chen; Dongmei Wang; Ranran Huang; Xingzhi Chang; Patrick S Chain; Gary Xie; Junqi Ling; Jian Xu
Journal:  ISME J       Date:  2011-06-30       Impact factor: 10.302

Review 5.  The Microbiome and Chronic Rhinosinusitis.

Authors:  Do-Yeon Cho; Ryan C Hunter; Vijay R Ramakrishnan
Journal:  Immunol Allergy Clin North Am       Date:  2020-01-16       Impact factor: 3.479

6.  A novel normalization and differential abundance test framework for microbiome data.

Authors:  Yuanjing Ma; Yuan Luo; Hongmei Jiang
Journal:  Bioinformatics       Date:  2020-07-01       Impact factor: 6.937

7.  Comparison of two bioinformatics tools used to characterize the microbial diversity and predictive functional attributes of microbial mats from Lake Obersee, Antarctica.

Authors:  Hyunmin Koo; Joseph A Hakim; Casey D Morrow; Peter G Eipers; Alfonso Davila; Dale T Andersen; Asim K Bej
Journal:  J Microbiol Methods       Date:  2017-06-24       Impact factor: 2.363

Review 8.  The Lung Microbiome, Immunity, and the Pathogenesis of Chronic Lung Disease.

Authors:  David N O'Dwyer; Robert P Dickson; Bethany B Moore
Journal:  J Immunol       Date:  2016-06-15       Impact factor: 5.422

9.  An exploration of smokeless tobacco product nucleic acids: a combined metagenome and metatranscriptome analysis.

Authors:  R E Tyx; A J Rivera; L M Keong; S B Stanfill
Journal:  Appl Microbiol Biotechnol       Date:  2019-12-09       Impact factor: 4.813

10.  Prospective study of oral microbiome and colorectal cancer risk in low-income and African American populations.

Authors:  Yaohua Yang; Qiuyin Cai; Xiao-Ou Shu; Mark D Steinwandel; William J Blot; Wei Zheng; Jirong Long
Journal:  Int J Cancer       Date:  2018-12-11       Impact factor: 7.396

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