Abstract
This report summarizes a meeting held in Boulder, CO USA (19–20 October 2012) on fungal community analyses using ultra-high-throughput sequencing of the internal transcribed spacer (ITS) region of the nuclear ribosomal RNA (rRNA) genes. The meeting was organized as a two-day workshop, with the primary goal of supporting collaboration among researchers for improving fungal ITS sequence resources and developing recommendations for standard ITS primers for the research community.
Highlights
This report summarizes a meeting held in Boulder, CO USA (19–20 October 2012) on fungal community analyses using ultra-high-throughput sequencing of the internal transcribed spacer (ITS) region of the nuclear ribosomal RNA genes
For Bacteria and Archaea, where the ribosomal small subunit (SSU/16S) gene is the primary marker in environmental sequencing, efforts have been made to improve the quality of the public reference sequence datasets, including GreenGenes [7] and RDP [8]
The same is true for more general SSU/large subunit (LSU) ribosomal RNA (rRNA) gene sequence databases, such as SILVA, which includes all three domains of life [9]
Summary
Sequencing-based techniques have allowed characterization of microbial communities from environmental samples without relying on cultivation. For Bacteria and Archaea, where the ribosomal small subunit (SSU/16S) gene is the primary marker in environmental sequencing, efforts have been made to improve the quality of the public reference sequence datasets, including GreenGenes [7] and RDP [8]. An even more important problem is that of misidentified sequences (environmental sequences included) currently in public databases These can lead to erroneous placement of unknowns, even if treebased approaches are used. Identifying these errors and re-annotating in an automated fashion is a critical challenge, especially for extremely large datasets where manual phylogenetic analysis is not feasible due to the presence of millions of sequence reads that correspond to “unknown” operational taxonomic units (OTUs). Clean reference databases as well as automated phylogenetic assignment and analysis methods are critical needs
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