CyberinfraV1, Main, Exploration, bibRecord, 000508

Estimating population diversity with CatchAll

Identifieur interne : 000508 ( Main/Exploration ); précédent : 000507; suivant : 000509

Estimating population diversity with CatchAll

Auteurs : John Bunge ; Linda Woodard [États-Unis] ; Dankmar Böhning [Royaume-Uni] ; James A. Foster [États-Unis] ; Sean Connolly [États-Unis] ; Heather K. Allen [États-Unis]

Source :

Bioinformatics [ 1367-4803 ] ; 2012.

RBID : PMC:3315724

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.

Contact:jab18@cornell.edu

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315724

DOI: 10.1093/bioinformatics/bts075
PubMed: 22333246
PubMed Central: 3315724

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><p><bold>Motivation:</bold>
 The massive data produced by next-generation sequencing require advanced statistical tools. We address estimating the total diversity or <italic>species richness</italic>
 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.</p>
<p><bold>Results:</bold>
 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.</p>
<p><bold>Availability:</bold>
 Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at <ext-link ext-link-type="uri" xlink:href="www.northeastern.edu/catchall">www.northeastern.edu/catchall</ext-link>
.</p>
<p><bold>Contact:</bold>
<email>jab18@cornell.edu</email>
</p>
</div>
</front>
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<country name="Royaume-Uni"><noRegion><name sortKey="Bohning, Dankmar" sort="Bohning, Dankmar" uniqKey="Bohning D" first="Dankmar" last="Böhning">Dankmar Böhning</name>
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Estimating population diversity with CatchAll

Estimating population diversity with CatchAll

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Abstract

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