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      Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible

      PLoS Computational Biology
      Public Library of Science (PLoS)

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          Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness

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            Small-sample estimation of negative binomial dispersion, with applications to SAGE data.

            We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.
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              Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex.

              We constructed error-correcting DNA barcodes that allow one run of a massively parallel pyrosequencer to process up to 1,544 samples simultaneously. Using these barcodes we processed bacterial 16S rRNA gene sequences representing microbial communities in 286 environmental samples, corrected 92% of sample assignment errors, and thus characterized nearly as many 16S rRNA genes as have been sequenced to date by Sanger sequencing.
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                Author and article information

                Journal
                10.1371/journal.pcbi.1003531
                http://creativecommons.org/licenses/by/4.0/

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