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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    • "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™. "

    Italian Journal of Zoology 08/2015; DOI:10.1080/11250003.2015.1080312 · 0.79 Impact Factor
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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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    Frontiers in Microbiology 06/2014; 5:276. DOI:10.3389/fmicb.2014.00276 · 3.99 Impact Factor
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    • "Sequences from chloroplasts, mitochondria, Archaea and Eukaryotes were also removed. CatchAll was used to assess species richness [14]. "
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    ABSTRACT: The nasal microbiota of pigs has been poorly assessed but could play a role in carriage of important microorganisms such as methicillin-resistant Staphylococcus aureus (MRSA). The objectives of this study were to describe the nasal microbiota in slaughter age pigs, to evaluate the impact of farm management on the nasal microbiota and to provide a preliminary assessment of the influence of the microbiota on MRSA carriage. Nasal swabs were collected from five MRSA positive and eight MRSA negative pigs on one farm that used a liquid feeding system and routine tylosin treatment, and seven MRSA negative pigs from an antibiotic-free farm that used conventional feeding. A total of 946310 sequences passed all quality control filters. The number of sequences per sample ranged from 4307 to 165656 (mean 56092, SD 40007). CatchAll analysis of richness predicted a mean of 1749 OTUs (range 213-3736, SD 996). Overall, 6291 OTUs were identified, yet 5125 (81%) were identified less than 10 times and the 12 most abundant OTUs accounted for 80.7% of sequences. Proteobacteria predominated in all but two samples. Liquid-fed/tylosin-exposed pigs had significantly lower relative abundances of Verrucomicrobia (P = 0.004), Fibrobacteres (P = <0.0001) and sequences unclassified at the phylum level (P = 0.028). When comparing only liquid-fed pigs, MRSA carriers had significantly more Bacteroidetes (P = 0.037) than MRSA negative pigs. 124 genera were identified, with Moraxella accounting for 35.4% of sequences. In the Jaccard index tree, five of eight MRSA positive pigs clustered closely together, as did six of the seven conventionally-fed pigs. A significant difference was identified between conventional and liquid-fed pigs using parsimony test with the Jaccard (P < 0.001) but not the Yue&Clayton (P = 0.26) index. There were no significant differences between MRSA positive and negative pigs (P = 0.133 and 0.175). OTUs belonging to Firmicutes were the main indicators of MRSA negative pigs, including Lactobacillus and another Lactobacillaceae and Staphylococcus. Farm management can influence the nasal microbiota in pigs, but no impact of the microbiota on MRSA carriage was identified. Studies that further define the impact of management on the microbiota, and the impact of the microbiota on pathogen carriage are indicated.
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