Download

Complete Databases

To download complete copies of all of the plant metabolic pathway databases hosted by the PMN, including AraCyc, PlantCyc, CornCyc, etc., please fill out and submit the license agreement form.

Please note that this license is freely available to everyone, including commercial users.

The complete download, together with the Pathway Tools software, will allow a user to install and have a local copy of AraCyc/PlantCyc, just like the one visible from the PMN web site. In addition, all of the BioCyc format flat files and a complete biopax.owl file are available as part of this download. You can get more details about the format of all the file types from BioCyc.


 Tab-delimited text files 

  • Pathways ("nnn"cyc_pathways.yyyy.mm.dd)
  • Compounds ("nnn"cyc_compounds.yyyy.mm.dd)
    (Note:This file ONLY contains compounds used in the "nnn"cyc_pathways)

 BLAST Sets
The current and archived BLAST sets provided by the PMN can also be downloaded without registration:


SAVI Validation Sets
The PMN has also generated several lists of pathways that are used to help expedite the validation process when generating new single-species databases. These Semi-Automated Validation and Integration (SAVI) lists are updated during the preparation of each new PMN release.

Current and archived SAVI pathway lists are available to download:

The original NPP and UPP SAVI lists were reviewed by members of the PMN Editorial Board. The remainder of the lists are still awaiting external validation.


Additional feedback from users is very welcome.


E2P2 enzyme function annotation software

Ensemble-based Enzyme Prediction Program (E2P2) predicts metabolic enzymes in a sequenced genome.


SAVI pathway validation software

Semi-Automated Validation Infrastructure (SAVI) processes predicted metabolic pathways using pathway meta data such as taxonomic distribution and key reactions and makes decisions about which pathways to keep, remove, or subject to manual validation. More info


PlantClusterFinder

A pipeline to predict metabolic gene clusters from plant genomes