Estimating population diversity with CatchAll
Bunge, John, Woodard, Linda, Böhning, Dankmar, Foster, James, Connolly, Sean and Allen, Heather (2012) Estimating population diversity with CatchAll. Bioinfomatics, 28, (7), 1045-1047. (doi:10.1093/bioinformatics/bts075). (PMID:22333246).
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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.
|Digital Object Identifier (DOI):||doi:10.1093/bioinformatics/bts075|
|Subjects:||H Social Sciences > HA Statistics
Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
|Divisions:||Faculty of Medicine > Primary Care and Population Sciences
Faculty of Social and Human Sciences > Mathematical Sciences > Statistics
Faculty of Social and Human Sciences > Southampton Statistical Sciences Research Institute
|Date Deposited:||30 Apr 2012 09:47|
|Last Modified:||31 Mar 2016 14:26|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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