CloVR-ITS: Automated internal transcribed spacer amplicon sequence analysis pipeline for the characterization of fungal microbiota

Abstract

BackgroundBesides the development of comprehensive tools for high-throughput 16S ribosomal RNA amplicon sequence analysis, there exists a growing need for protocols emphasizing alternative phylogenetic markers such as those representing eukaryotic organisms.ResultsHere we introduce CloVR-ITS, an automated pipeline for comparative analysis of internal transcribed spacer (ITS) pyrosequences amplified from metagenomic DNA isolates and representing fungal species. This pipeline performs a variety of steps similar to those commonly used for 16S rRNA amplicon sequence analysis, including preprocessing for quality, chimera detection, clustering of sequences into operational taxonomic units (OTUs), taxonomic assignment (at class, order, family, genus, and species levels) and statistical analysis of sample groups of interest based on user-provided information. Using ITS amplicon pyrosequencing data from a previous human gastric fluid study, we demonstrate the utility of CloVR-ITS for fungal microbiota analysis and provide runtime and cost examples, including analysis of extremely large datasets on the cloud. We show that the largest fractions of reads from the stomach fluid samples were assigned to Dothideomycetes, Saccharomycetes, Agaricomycetes and Sordariomycetes but that all samples were dominated by sequences that could not be taxonomically classified. Representatives of the Candida genus were identified in all samples, most notably C. quercitrusa, while sequence reads assigned to the Aspergillus genus were only identified in a subset of samples. CloVR-ITS is made available as a pre-installed, automated, and portable software pipeline for cloud-friendly execution as part of the CloVR virtual machine package (http://clovr.org).ConclusionThe CloVR-ITS pipeline provides fungal microbiota analysis that can be complementary to bacterial 16S rRNA and total metagenome sequence analysis allowing for more comprehensive studies of environmental and host-associated microbial communities.

Immune status, antibiotic medication and pH are associated with changes in the stomach fluid microbiota

“…Samples contained between 19 and 81 genus-level OTUs. On average, 77.5% of ITS reads could not be taxonomically assigned, even at the phylum level, most likely due to incomplete representation of human-associated fungi in available ITS reference sequence collections (White et al, 2013). Candida was one of the only two genera found in all samples, the other being Phialemonium.…”

“…ITS amplicon sequence data were processed using the automated CloVR-ITS pipeline (White et al, 2013) (http://www.nature.com/protocolexchange/ protocols/2597). Briefly, sequence were binned, trimmed and filtered with QIIME (Caporaso et al, 2010) using similar criteria as applied for the 16S rRNA sequence analysis.…”

“…Briefly, sequence were binned, trimmed and filtered with QIIME (Caporaso et al, 2010) using similar criteria as applied for the 16S rRNA sequence analysis. Sequences were clustered at 99% similarity with UCLUST (Edgar, 2010), chimeric clusters removed with UCHIME (mean: 0.9% of ITS reads per sample) and non-chimeric clusters (OTUs) taxonomically assigned based on BLASTN searches against an ITS reference database (White et al, 2013).…”

“…See Table 1 for additional details. a subset of nine randomly chosen samples, sequenced and processed using a similar methodology as for the 16S rRNA-based microbiota analysis (White et al, 2013) (see Supplementary Table 6). After sequence binning, trimming and filtering between 5766 and 19 589 reads were obtained per sample and assigned to taxonomic lineages (Table 2).…”

Insights into study design and statistical analyses in translational microbiome studies

“…Reads are classified into taxonomic categories using bioinformatic algorithms that operate in a sequential manner to (I) trim bases from the edges of the sequence reads that have been flagged as low-quality or less informative by the sequencing platform; (II) stitch paired reads together into one contiguous read (contigs), if the target DNA of interest was sequenced in two segments (also called paired-end sequencing), or construct several contigs if shotgun sequencing was performed; (III) remove sequencing artefacts such as chimeras, which consist of merged sequences from two distinct organisms inadvertently spliced together at the PCR stage; (IV) in the case of 16S profiling, filter out non-16S DNA by comparison with reference databases; and (V) assign taxonomic identities to the sequences in a probabilistic manner by comparing the sequences (or genecontent in case of shotgun sequencing) to those present in reference databases. Several such sequential bioinformatics workflows or pipelines for sequence processing and taxonomic assignment are available for 16S profiling (66)(67)(68)(69)(70) and metagenomic sequence analysis (71)(72)(73)(74)(75)(76). While there are pros and cons to using each of these existing workflows, there are also large-scale evaluations and comparative studies that discuss the strengths and limitations of each of these pipelines under various conditions (77)(78)(79)(80)).…”