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https://doi.org/10.1186/1471-2105-11-152
Title: | Alignment and clustering of phylogenetic markers - implications for microbial diversity studies | Authors: | White, J.R Navlakha, S Nagarajan, N Ghodsi, M Kingsford, C Pop, M |
Keywords: | Algorithm parameters Bacterial community Critical assessment High-throughput sequencing Microbial diversity Operational taxonomic units Semi-supervised Clustering Taxonomic composition Clustering algorithms RNA Bacteria (microorganisms) Otus bacterial DNA bacterial RNA RNA 16S article cluster analysis genetic marker genetic variability genetics phylogeny sequence alignment Cluster Analysis DNA, Bacterial Genetic Markers Genetic Variation Phylogeny RNA, Bacterial RNA, Ribosomal, 16S Sequence Alignment |
Issue Date: | 2010 | Citation: | White, J.R, Navlakha, S, Nagarajan, N, Ghodsi, M, Kingsford, C, Pop, M (2010). Alignment and clustering of phylogenetic markers - implications for microbial diversity studies. BMC Bioinformatics 11 : 152. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-11-152 | Rights: | Attribution 4.0 International | Abstract: | Background: Molecular studies of microbial diversity have provided many insights into the bacterial communities inhabiting the human body and the environment. A common first step in such studies is a survey of conserved marker genes (primarily 16S rRNA) to characterize the taxonomic composition and diversity of these communities. To date, however, there exists significant variability in analysis methods employed in these studies.Results: Here we provide a critical assessment of current analysis methodologies that cluster sequences into operational taxonomic units (OTUs) and demonstrate that small changes in algorithm parameters can lead to significantly varying results. Our analysis provides strong evidence that the species-level diversity estimates produced using common OTU methodologies are inflated due to overly stringent parameter choices. We further describe an example of how semi-supervised clustering can produce OTUs that are more robust to changes in algorithm parameters.Conclusions: Our results highlight the need for systematic and open evaluation of data analysis methodologies, especially as targeted 16S rRNA diversity studies are increasingly relying on high-throughput sequencing technologies. All data and results from our study are available through the JGI FAMeS website http://fames.jgi-psf.org/. © 2010 White et al; licensee BioMed Central Ltd. | Source Title: | BMC Bioinformatics | URI: | https://scholarbank.nus.edu.sg/handle/10635/181675 | ISSN: | 14712105 | DOI: | 10.1186/1471-2105-11-152 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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