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Applications of species accumulation curves in large-scale biological data analysis

Quantitative Biology 2015年第3期3卷 135-144页

作者： Chao Deng Timothy Daley Andrew Smith Molecular and Computational Biology University of Southern California

The species accumulation curve,or collector's curve,of a population gives the expected number of observed species or distinct classes as a function of sampling *** accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional *** applications have focused on ecological populations but emerging large-scale applications,for example in DNA sequencing,are orders of magnitude larger and present new *** developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing *** method uses rational function approximations to a classical nonparametric empirical Bayes estimator due to Good and Toulmin [Biometrika,1956,43,45–63].Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data *** include estimating microbial species richness,immune repertoire size,and k-mer diversity for genome assembly *** show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be *** also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.

关键词： species accumulation curve accumulation region rational function approximation immune repertoire microbiome diversity species richness

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Applications of species accumulation curves in arge-scale biological data analysis

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Frontiers of Electrical and Electronic Engineering in China 2015年第3期10卷 135-144页

The species accumulation curve, or collector＇s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non- parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45~63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and R-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.

关键词： species accumulation curve accumulation region rational function approximation immune repertoire microbiome diversity species richness

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