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RICHEST--a web server for richness estimation in biological data.

Durden C, Dong Q - Bioinformation (2009)

Bottom Line: Richness is defined as the number of distinct species or classes in a sample or population.Although richness estimation is an important practice, it requires mathematical and computational methods that are challenging to understand and implement.Its user-friendly web interface allows users to analyze and compare their data conveniently over the web.

View Article: PubMed Central - PubMed

Affiliation: Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana, USA.

ABSTRACT

Unlabelled: Richness is defined as the number of distinct species or classes in a sample or population. Although richness estimation is an important practice, it requires mathematical and computational methods that are challenging to understand and implement. We have developed a web server, RICHness ESTimator (RICHEST), which implements three non-parametric statistical methods for richness estimation. Its user-friendly web interface allows users to analyze and compare their data conveniently over the web.

Availability: A web server hosting RICHEST is accessible at http://richest.cgb.indiana.edu/cgi-bin/index.cgi and the software is freely available for local installations.

No MeSH data available.


Screenshots of the RICHEST program outlining key features of the application. The RICHEST web interfaceconsists of three tabs, named Data, Tools, and Results that divide the analysis process into three stages as described below.Users are encouraged to follow the tutorial at the project web site to demonstrate the step‐by‐step usage of the program. (A)Loading data: The Data tab allows the user to load tab‐delimited data files. Users may also upload multiple datasets beforerunning any estimation procedures. (B) Selecting tools: The tools tab allows the user to select and run our integratedrichness estimation programs. The tab prompts the user to select which data to use as input, which method to use forestimation, and which options to use for that method. (C) Viewing results: RICHEST outputs a hyperlink to a tab‐delimitedtable and the corresponding graph representing the estimated sample richness curve which gives the richnessestimates as a function of the cumulative sample size.
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Figure 1: Screenshots of the RICHEST program outlining key features of the application. The RICHEST web interfaceconsists of three tabs, named Data, Tools, and Results that divide the analysis process into three stages as described below.Users are encouraged to follow the tutorial at the project web site to demonstrate the step‐by‐step usage of the program. (A)Loading data: The Data tab allows the user to load tab‐delimited data files. Users may also upload multiple datasets beforerunning any estimation procedures. (B) Selecting tools: The tools tab allows the user to select and run our integratedrichness estimation programs. The tab prompts the user to select which data to use as input, which method to use forestimation, and which options to use for that method. (C) Viewing results: RICHEST outputs a hyperlink to a tab‐delimitedtable and the corresponding graph representing the estimated sample richness curve which gives the richnessestimates as a function of the cumulative sample size.

Mentions: To bridge the gap between the published statistical methods and the biologists wishing to apply the methods to their data, we have implemented all three methods in RICHEST using the R program for statistical computing. For the Bayesian method, the estimates are achieved by optimizing Pitman's sampling formula with respect to two parameters of a Poisson‐Dirichlet distribution. In our implementation, we have taken advantage of the smoothness of the function of Pitman's sampling formula to apply the Nelder‐Mead optimization technique [6]. The implementation of the PNPML method is similar to that of ESTstat [2], except that it does not implement a penalty function to constrain the population richness parameter. Instead, a user can specify some estimate of the richness of the population and use the method to estimate the richness of subsequent samples. When such information is not scientifically available, one strategy for using the PNPML method is to set the population richness estimate parameter to the Chao lower bound, which is automatically calculated by RICHEST when the data is loaded. The PNPML method uses an expectation‐maximization algorithm [7] to estimate the maximum‐likelihood species abundance distribution based on the non‐parametric Poisson mixture model, and it uses this estimated distribution to generate sample richness estimates by rarefaction. Finally, the classic Good‐Toulmin estimator was implemented as described in [4]. The web interface of RICHEST (Figure 1) is implemented with the Perl programming language.


RICHEST--a web server for richness estimation in biological data.

Durden C, Dong Q - Bioinformation (2009)

Screenshots of the RICHEST program outlining key features of the application. The RICHEST web interfaceconsists of three tabs, named Data, Tools, and Results that divide the analysis process into three stages as described below.Users are encouraged to follow the tutorial at the project web site to demonstrate the step‐by‐step usage of the program. (A)Loading data: The Data tab allows the user to load tab‐delimited data files. Users may also upload multiple datasets beforerunning any estimation procedures. (B) Selecting tools: The tools tab allows the user to select and run our integratedrichness estimation programs. The tab prompts the user to select which data to use as input, which method to use forestimation, and which options to use for that method. (C) Viewing results: RICHEST outputs a hyperlink to a tab‐delimitedtable and the corresponding graph representing the estimated sample richness curve which gives the richnessestimates as a function of the cumulative sample size.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2655047&req=5

Figure 1: Screenshots of the RICHEST program outlining key features of the application. The RICHEST web interfaceconsists of three tabs, named Data, Tools, and Results that divide the analysis process into three stages as described below.Users are encouraged to follow the tutorial at the project web site to demonstrate the step‐by‐step usage of the program. (A)Loading data: The Data tab allows the user to load tab‐delimited data files. Users may also upload multiple datasets beforerunning any estimation procedures. (B) Selecting tools: The tools tab allows the user to select and run our integratedrichness estimation programs. The tab prompts the user to select which data to use as input, which method to use forestimation, and which options to use for that method. (C) Viewing results: RICHEST outputs a hyperlink to a tab‐delimitedtable and the corresponding graph representing the estimated sample richness curve which gives the richnessestimates as a function of the cumulative sample size.
Mentions: To bridge the gap between the published statistical methods and the biologists wishing to apply the methods to their data, we have implemented all three methods in RICHEST using the R program for statistical computing. For the Bayesian method, the estimates are achieved by optimizing Pitman's sampling formula with respect to two parameters of a Poisson‐Dirichlet distribution. In our implementation, we have taken advantage of the smoothness of the function of Pitman's sampling formula to apply the Nelder‐Mead optimization technique [6]. The implementation of the PNPML method is similar to that of ESTstat [2], except that it does not implement a penalty function to constrain the population richness parameter. Instead, a user can specify some estimate of the richness of the population and use the method to estimate the richness of subsequent samples. When such information is not scientifically available, one strategy for using the PNPML method is to set the population richness estimate parameter to the Chao lower bound, which is automatically calculated by RICHEST when the data is loaded. The PNPML method uses an expectation‐maximization algorithm [7] to estimate the maximum‐likelihood species abundance distribution based on the non‐parametric Poisson mixture model, and it uses this estimated distribution to generate sample richness estimates by rarefaction. Finally, the classic Good‐Toulmin estimator was implemented as described in [4]. The web interface of RICHEST (Figure 1) is implemented with the Perl programming language.

Bottom Line: Richness is defined as the number of distinct species or classes in a sample or population.Although richness estimation is an important practice, it requires mathematical and computational methods that are challenging to understand and implement.Its user-friendly web interface allows users to analyze and compare their data conveniently over the web.

View Article: PubMed Central - PubMed

Affiliation: Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana, USA.

ABSTRACT

Unlabelled: Richness is defined as the number of distinct species or classes in a sample or population. Although richness estimation is an important practice, it requires mathematical and computational methods that are challenging to understand and implement. We have developed a web server, RICHness ESTimator (RICHEST), which implements three non-parametric statistical methods for richness estimation. Its user-friendly web interface allows users to analyze and compare their data conveniently over the web.

Availability: A web server hosting RICHEST is accessible at http://richest.cgb.indiana.edu/cgi-bin/index.cgi and the software is freely available for local installations.

No MeSH data available.