Abstract
BackgroundHigh-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches. Here, we introduce the analytical and statistical framework of sequence similarity networks, increasingly used in evolutionary studies and graph theory, into the field of ecology to analyze novel pyrosequenced V4 small subunit rDNA (SSU-rDNA) sequence data sets in the context of previous studies, including SSU-rDNA Sanger sequence data from cultured ciliates and from previous environmental diversity inventories.ResultsOur broadly applicable protocol quantified the progress in the description of genetic diversity of ciliates by environmental SSU-rDNA surveys, detected a fundamental historical bias in the tendency to recover already known groups in these surveys, and revealed substantial amounts of hidden microbial diversity. Moreover, network measures demonstrated that ciliates are not globally dispersed, but are structured by habitat and geographical location at intermediate geographical scale, as observed for bacteria, plants, and animals.ConclusionsCurrently available ‘universal’ primers used for local in-depth sequencing surveys provide little hope to exhaust the significantly higher ciliate (and most likely microbial) diversity than previously thought. Network analyses such as presented in this study offer a promising way to guide the design of novel primers and to further explore this vast and structured microbial diversity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-015-0125-5) contains supplementary material, which is available to authorized users.
Highlights
High-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches
The nodes represent the objects to be compared, each pair being linked by an edge if there is significant similarity between the two corresponding nodes (here a minimum % identity (%ID), E-value, length, and alignment cover spanning over the two sequences)
Thresholded sequence similarity networks effectively provide a first structure of the data by partitioning it, since the continuity and discontinuity of resemblances between sequences generally produces distinct subgraphs, called connected components (Figure 1A)
Summary
High-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches. Forster et al BMC Biology (2015) 13:16 rapidly progressed far beyond our ability to best collect and analyze the data This situation encouraged developments to justify which DNA region should be targeted for sequencing and which primer-pairs should be used, for example, in foraminifera [22], ciliates [17,23,24,25], dinoflagellates [26], fungi [27], bacteria [28] and archaea [29]. Computational steps are still a major bottleneck in molecular-based environmental studies These shortcomings have hampered the identification of novel taxa, of distribution patterns, and their biological and ecological causes
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