Rethinking microbial diversity analysis in the high throughput sequencing era

Abstract

The analysis of amplified and sequenced 16S rRNA genes has become the most important single approach for microbial diversity studies. The new sequencing technologies allow for sequencing thousands of reads in a single run and a cost-effective option is split into a single run across many samples. However for this type of investigation the key question that needs to be answered is how many samples can be sequenced without biasing the results due to lack of sequence representativeness? In this work we demonstrated that the level of sequencing effort used for analyzing soil microbial communities biases the results and determines the most effective type of analysis for small and large datasets. Many simulations were performed with four independent pyrosequencing-generated 16S rRNA gene libraries from different environments. The analysis performed here illustrates the lack of resolution of OTU-based approaches for datasets with low sequence coverage. This analysis should be performed with at least 90% of sequence coverage. Diversity index values increase with sample size making normalization of the number of sequences in all samples crucial. An important finding of this study was the advantage of phylogenetic approaches for examining microbial communities with low sequence coverage. However, if the environments being compared were closely related, a deeper sequencing would be necessary to detect the variation in the microbial composition.

Network topology reveals high connectance levels and few key microbial genera within soils

“…This could explain why a large amount of taxons found in different land uses did not present interactions with other member of the community; (v) the network analysis is considered an OTU-based approach since it relies on detection of correlation between taxonomic unities. According to Lemos et al (2011), in order to apply such an approach, a large sampling intensity (coverage ≥ 90%) is needed to get reliable results. Datasets with low number of sequences are likely to present a low sequence coverage that in turn will make it more unlikely to found OTUs correlation; (vi) finally, another drawback related to microbial network construction is the faulty prediction of a relationship between two taxa since interspecies interactions might be affected by third-party organisms in prokaryotic ecosystems (Haruta et al, 2009).…”

Equine Stomachs Harbor an Abundant and Diverse Mucosal Microbiota

“…These pairwise distances served as input to DOTUR (51) for clustering the sequences into operational taxonomic units (OTUs) of defined sequence similarity ranging from 0% to 20% dissimilarity. From the literature, we can expect that 0% and even 3% dissimilarity in sequences generated from pyrosequencing (based upon rarefaction) will provide dramatic overestimation of the phylotypes present in a sample (32,50). At 5% dissimilarity (roughly genus-level classification), we expect to obtain a more accurate estimation of comparative diversity present across the samples.…”

The impact of physico-chemical water quality parameters on bacterial diversity in the Vaal River, South Africa

“…This method opened up new avenues of research on the diversity of microorganisms present in complex aquatic environments. Currently, metagenomic analysis of microbial ecology, such as highthroughput sequencing (HTS), has been the focus of several environmental studies such as soil, (Lemos et al, 2011), freshwater lakes (Marshall et al, 2008) and deep sea microbiota (Sogin et al, 2006). Metagenomic analysis provides extensive information on community structure and composition (Kakirde et al, 2010).…”