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Estimating population diversity with CatchAll

Record type: Article

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.
Availability: Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at www.northeastern.edu/catchall.

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Citation

Bunge, John, Woodard, Linda, Böhning, Dankmar, Foster, James, Connolly, Sean and Allen, Heather (2012) Estimating population diversity with CatchAll Bioinformatics, 28, (7), pp. 1045-1047. (doi:10.1093/bioinformatics/bts075). (PMID:22333246).

More information

e-pub ahead of print date: 13 February 2012
Published date: 15 March 2012
Organisations: Statistics, Statistical Sciences Research Institute, Primary Care & Population Sciences

Identifiers

Local EPrints ID: 337593
URI: http://eprints.soton.ac.uk/id/eprint/337593
ISSN: 1367-4803
PURE UUID: 9a57bc40-eeb0-4d82-89d9-3035f9d1d47e

Catalogue record

Date deposited: 30 Apr 2012 09:47
Last modified: 18 Jul 2017 06:02

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Contributors

Author: John Bunge
Author: Linda Woodard
Author: James Foster
Author: Sean Connolly
Author: Heather Allen

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