Downlad source code
The Perl programs and modules for the KGML-based model reconstruction and the SBML(template)-based model reconstruction can be downloaded.
Download KGML-based reconstruction. This offers further options to improve the pathway reconstruction based on KEGG database information.
Download SBML-based reconstruction. This enables comparative genome analysis depending on already existing draft SBML templates.
- After you have downloaded the tar file, you can extract the compressed file using "tar xfv filename.tar".
- The extracted directory contains a configuration file. You have to adapt this file and download additional data.
For instructions use "perldoc CarmenConfig.pm" respectively "perldoc CarmenConfigTemplate.pm" or open the source code of a configuration file.
- Adapt the configuration file and start "perl KGML_reconstruction.pl" or "perl SBML_reconstruction.pl". In the following, the program options are listed.
SBML_reconstruction.pl to reconstruct metabolic pathways based on an SBML template model.
perl SBML_reconstruction.pl -s <CellDesigner SBML file> -o <Output> (-l <List of ortholougous genes> or -h <List of highlighted genes> or -b <'Genbank file 1 for a BLAST run' 'Genbank file 2 for a BLAST run>' or -c <List of core genome genes>) [-a <Additional reactions based on Genbank file>] [-w <BLAST work folder>] [-e <Evalue cutoff>]
KGML_reconstruction.pl to reconstruct metabolic pathways based on KGML files of the KEGG database.
perl KGML_reconstruction.pl -g <Genbank file> -o <Output> -n <Number of columns> -k <KEGG maps> [-m <metabolite abbreviation>] [-c <Cofactor integration>] [-e <EC number joining>]
The CARMEN software can easily be connected to genome annotation systems to gain information about coding sequences and their features of an unpublished genome. Software developers have to adapt the module for genome annotation data input. Required data are annotated gene names and the EC numbers of their gene products. The adaptivity of CARMEN is a huge advantage for analysing new sequenced genomes to get a rapid overview of main metabolic features.