Estimating population diversity with CatchAll
Estimating population diversity with CatchAll
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.
1045-1047
Bunge, John
6d2e583a-a816-4604-9e9a-8c41ebcbf12b
Woodard, Linda
9be4360a-9e85-463e-955f-3d4379c7abb4
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Foster, James
38d8d56f-0b7a-43c0-b61c-4b4275ee4351
Connolly, Sean
8620bff1-289d-4a9f-9e91-6ab1840ed13a
Allen, Heather
29ed0915-201e-4067-b415-2606f27d5b6c
15 March 2012
Bunge, John
6d2e583a-a816-4604-9e9a-8c41ebcbf12b
Woodard, Linda
9be4360a-9e85-463e-955f-3d4379c7abb4
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Foster, James
38d8d56f-0b7a-43c0-b61c-4b4275ee4351
Connolly, Sean
8620bff1-289d-4a9f-9e91-6ab1840ed13a
Allen, Heather
29ed0915-201e-4067-b415-2606f27d5b6c
Bunge, John, Woodard, Linda, Böhning, Dankmar, Foster, James, Connolly, Sean and Allen, Heather
(2012)
Estimating population diversity with CatchAll.
Bioinformatics, 28 (7), .
(doi:10.1093/bioinformatics/bts075).
(PMID:22333246)
Abstract
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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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
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Date deposited: 30 Apr 2012 09:47
Last modified: 15 Mar 2024 03:39
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Contributors
Author:
John Bunge
Author:
Linda Woodard
Author:
James Foster
Author:
Sean Connolly
Author:
Heather Allen
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