Dataset Information

Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes.

ABSTRACT: Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias against Verrucomicrobia in a commonly used V6-V8 primer set, as well as discovering likely bias against Actinobacteria and for Verrucomicrobia in a commonly used V4 primer set. This pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure. The pipeline can scale to handle large amounts of soil metagenomic data (5 Gb memory and 5 central processing unit hours to process 38 Gb [1 lane] of trimmed Illumina HiSeq2500 data) and is freely available at https://github.com/dib-lab/SSUsearch under a BSD license.

SUBMITTER: Guo J

PROVIDER: S-EPMC4702641 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes.

Guo Jiarong J Cole James R JR Zhang Qingpeng Q Brown C Titus CT Tiedje James M JM

Applied and environmental microbiology 20151016 1

Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsu ...[more]

PMID: 26475107

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Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Project description:The dynamics of individual microbial populations and their gene functions in agricultural soils, especially after major activities such as nitrogen (N) fertilization, remain elusive but are important for a better understanding of nutrient cycling. Here, we analyzed 20 short-read metagenomes collected at four time points during 1 year from two depths (0 to 5 and 20 to 30 cm) in two Midwestern agricultural sites representing contrasting soil textures (sandy versus silty loam) with similar cropping histories. Although the microbial community taxonomic and functional compositions differed between the two locations and depths, they were more stable within a depth/site throughout the year than communities in natural aquatic ecosystems. For example, among the 69 population genomes assembled from the metagenomes, 75% showed a less than 2-fold change in abundance between any two sampling points. Interestingly, six deep-branching Thaumarchaeota and three complete ammonia oxidizer (comammox) Nitrospira populations increased up to 5-fold in abundance upon the addition of N fertilizer. These results indicated that indigenous archaeal ammonia oxidizers may respond faster (are more copiotrophic) to N fertilization than previously thought. None of 29 recovered putative denitrifier genomes encoded the complete denitrification pathway, suggesting that denitrification is carried out by a collection of different populations. Altogether, our study identified novel microbial populations and genes responding to seasonal and human-induced perturbations in agricultural soils that should facilitate future monitoring efforts and N-related studies.IMPORTANCE Even though the impact of agricultural management on the microbial community structure has been recognized, an understanding of the dynamics of individual microbial populations and what functions each population carries are limited. Yet, this information is important for a better understanding of nutrient cycling, with potentially important implications for preserving nitrogen in soils and sustainability. Here, we show that reconstructed metagenome-assembled genomes (MAGs) are relatively stable in their abundance and functional gene content year round, and seasonal nitrogen fertilization has selected for novel Thaumarchaeota and comammox Nitrospira nitrifiers that are potentially less oligotrophic than their marine counterparts previously studied.

Dataset Information

Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes.

Publications

Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes.

Similar Datasets

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