Species Identification and Profiling of Complex Microbial Communities Using Shotgun Illumina Sequencing of 16S rRNA Amplicon Sequences
| Autor: | Niranjan Nagarajan, Swee Hoe Ong, Andreas Wilm, Louie Low, Martin L. Hibberd, Christophe Lay, Vinutha Uppoor Kukkillaya, Eliza Xin Pei Ho |
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| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Adult
Sequence analysis Applied Microbiology In silico Science Computational biology Biology Microbiology DNA sequencing Microbial Ecology Genome Analysis Tools RNA Ribosomal 16S Humans Quantitative Biology - Genomics Genome Sequencing Phylogeny Illumina dye sequencing Genetics Genomics (q-bio.GN) Sequence Assembly Tools Multidisciplinary Bacteria Shotgun sequencing Computational Biology High-Throughput Nucleotide Sequencing Genomics Amplicon Ribosomal RNA Gastrointestinal Tract Metagenomics Child Preschool FOS: Biological sciences Metagenome Medicine Sequence Analysis Research Article |
| Zdroj: | PLoS ONE, Vol 8, Iss 4, p e60811 (2013) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | The high throughput and cost-effectiveness afforded by short-read sequencing technologies, in principle, enable researchers to perform 16S rRNA profiling of complex microbial communities at unprecedented depth and resolution. Existing Illumina sequencing protocols are, however, limited by the fraction of the 16S rRNA gene that is interrogated and therefore limit the resolution and quality of the profiling. To address this, we present the design of a novel protocol for shotgun Illumina sequencing of the bacterial 16S rRNA gene, optimized to capture more than 90% of sequences in the Greengenes database and with nearly twice the resolution of existing protocols. Using several in silico and experimental datasets, we demonstrate that despite the presence of multiple variable and conserved regions, the resulting shotgun sequences can be used to accurately quantify the diversity of complex microbial communities. The reconstruction of a significant fraction of the 16S rRNA gene also enabled high precision (>90%) in species-level identification thereby opening up potential application of this approach for clinical microbial characterization. 17 pages, 2 tables, 2 figures, supplementary material |
| Databáze: | OpenAIRE |
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