Supervised classification of microbiota mitigates mislabeling errors.
Knights, Dan; Kuczynski, Justin; Koren, Omry; Ley, Ruth E.; Field, Dawn; Knight, Rob; DeSantis, Todd Z.; Kelley, Scott T.. 2011 Supervised classification of microbiota mitigates mislabeling errors. ISME Journal, 5. 570-573. 10.1038/ismej.2010.148Full text not available from this repository.
The exponential growth of DNA sequencing technologies and concomitant advances in bioinformatics methods are revolutionizing our understanding of diverse microbial communities (Riesenfeld et al., 2004; Tyson et al., 2004; Hugenholtz and Tyson, 2008; Tringe and Hugenholtz, 2008; Caporaso et al., 2010). Large-scale microbial metagenomics studies have particularly exciting applications in the arena of human health, laying the foundation for the Human Microbiome Project (HMP). In the context of the HMP and related efforts, care has been taken to understand the impact of amplification biases or sequencing errors. However, far less attention has been paid to the impact of errors in metadata on biological interpretations and the mitigation of such errors. During processing and pooling of hundreds of samples, some mislabeling is likely. Figure 1 illustrates a real world example: several 16S rRNA amplifications of bacterial community DNA samples collected along a time series were accidentally mislabeled (late switched to early) (Koenig et al., 2010). Automated detection of such errors will be important as datasets become increasingly large and complex.
|Programmes:||CEH Topics & Objectives 2009 onwards > Biodiversity > BD Topic 1 - Observations, Patterns, and Predictions for Biodiversity|
|NORA Subject Terms:||Data and Information|
|Date made live:||31 Mar 2011 11:35|
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