Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities

Abstract

Summary Next generation sequencing has dramatically changed the landscape of microbial ecology, large-scale and in-depth diversity studies being now widely accessible. However, determining the accuracy of taxonomic and quantitative inferences and comparing results obtained with different approaches are complicated by incongruence of experimental and computational data types and also by lack of knowledge of the true ecological diversity. Here we used highly diverse bacterial and archaeal synthetic communities assembled from pure genomic DNAs to compare inferences from metagenomic and SSU rRNA amplicon sequencing. Both Illumina and 454 metagenomic data outperformed amplicon sequencing in quantifying the community composition, but the outcome was dependent on analysis parameters and platform. New approaches in processing and classifying amplicons can reconstruct the taxonomic composition of the community with high reproducibility within primer sets, but all tested primers sets lead to significant taxon-specific biases. Controlled synthetic communities assembled to broadly mimic the phylogenetic richness in target environments can provide important validation for fine-tuning experimental and computational parameters used to characterize natural communities.

Hydrocarbon-degrading bacteria enriched by the Deepwater Horizon oil spill identified by cultivation and DNA-SIP

“…Though low cell abundance for any particular microbial group in situ does not always correlate with inactivity, it is unlikely that Alcanivorax had contributed a significant role to the fate of the oil. The absence of Alcanivorax in pyrosequencing libraries constructed by other authors from Gulf spill water column samples may reflect differences in the parameters used for analysing sequence data (for example, distance threshold setting; type of bioinformatics software package) and the type of sequencing platform they used (Shakya et al, 2013).…”

Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes

“…Both shotgun and amplicon data show Actinobacteria, Proteobacteria, and Acidobacteria as the three most abundant phyla, as is expected for soil (43). Since shotgun data are more accurate at estimating community structure than other methods, we accepted the shotgun data as the reference (9,12). The 926F/ 1392R (V6-V8) primer set is biased against Verrucomicrobia (0.3% in amplicon data versus 5.8% in shotgun data by RDP database) in bulk soil sample SB1 (Fig.…”

“…Metarna (rna_hmm3.py) (the package was not assigned a version number; the most recent version update, 21 October 2011, was used) was run with flags "-k euk,bac,arc -e 0.00001" (16), and metaxa (metaxa_x) version 2.0.2 was run with flags "-allow_single_domain 1e-5,0 -N 1 -E 1e-5." A bulk soil (SB1), a rhizosphere soil (M1), and a synthetic community sample (12) were used as test data. We aligned the HMMER hits from the E value cutoff of 10 using the multiple-sequence aligner align.seqs in mothur (version 1.33.3) (32).…”

“…We used shotgun data of a synthetic community comprised of 64 species, which were sequenced by the paired-end 100-bp method on an Illumina HiSeq 2000 (12). For testing the effect of target region size on clustering, we picked V4 with starting position 577 in Escherichia coli.…”

“…Hence, while amplicon sequencing can provide far deeper coverage of SSU rRNA genes with the same amount of sequencing, shotgun sequencing may provide a more accurate characterization of microbial diversity, including functional diversity (12). In particular, shotgun sequencing may provide an improved means to detect divergent sequences not recovered by standard SSU rRNA gene primers, such as those of Verrucomicrobia, as well as eukaryotic members of the community (8,(12)(13)(14). Note that both approaches remain prone to sequencing error and bias from environmental DNA extraction (9).…”