Virtual Diegest (ISPaR) · T-RFLP Analysis (APLAUS+) · T-RFLP Analysis (PAT+) · P.S.P.A. · E.R.P.A.
October 1, 2014
  MiCA: About MiCA
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MiCA website was developed by students and faculty of the Initiative for Bioinformatics and Evolutionary Studies (IBEST) at the University of Idaho. This research was funded by the P&G company.

This web site and the tools you find here were developed by students and faculty of the Initiative for Bioinformatics and Evolutionary Studies (IBEST) at the University of Idaho who are members of the Departments of Computer Science and Biological Sciences. The research was funded by the P&G Company.

The aim of this research was to develop a suite of web-based tools that would enable researchers to perform analyses of microbial community structure based on terminal-restriction fragment length polymorphisms (T-RFLP). MiCA enables researchers to perform the following tasks:

(a). in silico PCR amplification and restriction of 16S rRNA gene sequences found in public database;

(b). automatic retrieval of data and archival storage in a relational database;

(c). comparison of multiple T-RFLP profiles obtained from a single sample using different primer-enzyme combinations;

(d). statistical analysis of T-RFLP data and clustering of samples based on similarities and differences.

MiCA currently operates on a server computer with 1.0GB of RAM and four Intel Xeon 2.8GHz processor running Linux. The web server runs on Apache, which has been fully configured and optimized for better performance and security. We have devleoped several PHP scripts to facilitate the virtual digest. The primary database contains a large number of 16S rRNA genes retrieved from the Michigan State University RDP II database. We have gathered the most commonly used primers and restriction enzymes in the database. There are 19 forward and 21 reverse primers, and 53 restriction enzymes. A digest can incorporate at most three restriction enzymes. The output is available in CSV (Common Separated Values) and plain-text formats. CSV file format allow data to be easily retrieved into spreadsheet or database programs. Majority of spreadsheet applications, such as Linux Gnumeric or Microsoft Excel, can read and write CSV files. Most web browsers will invoke an appropriate application if one has been installed. Users may also save the file first then convert it to a desired format.

We hope you will find MiCA useful. Please any comments, suggestions or questions.

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If you use the data generated by MiCA in a publication, please cite:
Shyu, C., Soule, T., Bent, S.J., Foster, J.A., Forney, L.J. (2007) MiCA: A Web-Based Tool for the Analysis of Microbial Communities Based on Terminal-Restriction Fragment Length Polymorphisms of 16S and 18S rRNA Genes. Journal of Microbial Ecology 53:562-570.

All rights reserved. Copyright ©, University of Idaho, 2007.
Last updated on June 06 2011 14:00:21.