Estimating population diversity with CatchAll

Department of Statistical Science, Cornell University, Ithaca, NY, 14853, USA.
Bioinformatics (Impact Factor: 4.98). 02/2012; DOI: 10.1093/bioinformatics/bts075
Source: PubMed


Motivation: The massive data produced by next-generation sequencing require advanced statistical tools. We address estimating the total diversity or species richness in a population. To date, only relatively simple methods have been implemented in available software. There is a need for software employing modern, computationally intensive statistical analyses including error, goodness-of-fit and robustness assessments. Results: We present CatchAll, a fast, easy-to-use, platform-independent program that computes maximum likelihood estimates for finite-mixture models, weighted linear regression-based analyses and coverage-based non-parametric methods, along with outlier diagnostics. Given sample 'frequency count' data, CatchAll computes 12 different diversity estimates and applies a model-selection algorithm. CatchAll also derives discounted diversity estimates to adjust for possibly uncertain low-frequency counts. It is accompanied by an Excel-based graphics program.

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Available from: James Arthur Foster
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    • "CatchAll fits four parametric models via maximum likelihood and five non-parametric richness estimates to the data. The best model is then chosen as the model that shows the best fit to the data, i.e., that has both low standard error (SE) of the estimated total number of species in the community and low values of the goodness-of-fit (GOF) statistic on the observed data (Hong et al. 2006; Bunge et al. 2012; also see the CatchAll manual for further details at Figure 1. Sampling sites A, on the Miage Glacier and B, on Belvedere Glacier, from Google Earth™. "
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    • "To visualize changes in community structure, the Bray-Curtis dissimilarity statistic (OTU data) and Pearson's correlations (vectors of environmental variables) were calculated and plotted by nonmetric multidimensional scaling (NMDS) in PAST (Hammer et al., 2001). Community metrics such as diversity (Shannon index, inverse Simpson index), evenness (Heips index), and richness [best parametric model in CatchAll (Bunge, 2011; Bunge et al., 2012)] were calculated in mothur based on the OTU data. Multiple t-tests were performed on community metrics in GraphPad Prism v. 6.02 (La Jolla, CA). "
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    • ". Such mixtures are implemented in the CatchAll program (Bunge et al., 2012). "
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