BMC Sys. Biol.
Multivariate Data Analysis for Genomics and Systems Biology
NEWS: version 0.60 now available, with Factor Analysis, improved NMF algorithm, and a corrected version of GA-DFA
ometer is software for multivariate analysis of functional genomics and systems biology data (it can be used for other types of data, too). Ometer is driven by a plain command line interface, which allows it to be called directly from scripts. If you prefer to analyse your data with nicer interfaces, then we suggest TIGR's MeV. Ometer is focused on efficient calculation of numerical results, which are written to tab-delimited files. Files are produced for visualization with gnuplot and graphviz, as well as reports formated in html.
Current analyses provided:
ometer is available for Linux, various UNIX variants, OS X, and MS Windows.
Downloadversion 0.60 for windows (1.92 Mb)
To install the windows version: download the executable file an save it in any folder that is in your path (eg C:\WINDOWS). If you are not able to do so, then put it in a new folder and change your path to include that folder (right-click on My Computer, select Properties, Advanced, and click on Environment Variables, then add it to the PATH variable) .
version 0.60 executable binary for Linux on Pentium4 (4.23 Mb)
To install only on a Intel Pentium. Put this executable file on your path and change its the permissions to executable (chmod a+x ometer). Note that this will not run on other processors (eg on AMD). If you need to install and run on other processors, you should build ometer from source code.
ometer-0.60.tar.gz (source code 300.22 Kb)
To install, first download this file and untar it. Then run ./configure && make && make install, this will put ometer in /usr/local/bin. If you need to install ometer in other location, do instead ./configure --prefix=yourpath && make && make install. This source code is good for Linux, OS X and other Unix-based systems. For Windows, a special Visual C++ project file is needed (to appear later).
CREDITSOmeter was written by Pedro Mendes Biochemical Networks Modeling Group at the Virginia Bioinformatics Institute, Virginia Tech. The partial correlation algorithm was developed by Alberto de la Fuente.