Paper Info
Title
Estimating population diversity with CatchAll.
Abstract
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.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.Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at www.northeastern.edu/catchall.jab18@cornell.edu.
Year
DOI
Venue
2012
10.1093/bioinformatics/bts075
Bioinformatics
Keywords
Field
DocType
excel-based graphics program,computationally intensive statistical analysis,different diversity estimate,estimating population diversity,diversity estimate,statistical tool,catchall compute,total diversity,massive data,platform-independent program,available software,linear models,algorithms,computational biology
Data mining,Population,Linear model,Source code,Computer science,Outlier,Robustness (computer science),Software,Bioinformatics,Statistics,Mac OS,Executable
Journal
Volume
Issue
ISSN
28
7
1367-4811
Citations 
PageRank 
References 
2
0.47
3
Authors
6
Name
Order
Citations
PageRank
John Bunge182.89
Linda Woodard220.47
Dankmar Böhning35013.62
James A. Foster435361.38
Sean Connolly5304.81
Heather K Allen630.95