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. 2011 Jul;39(Web Server issue):W406-11.
doi: 10.1093/nar/gkr200. Epub 2011 Apr 12.

The University of Minnesota Pathway Prediction System: multi-level prediction and visualization

Junfeng Gao  1 , Lynda B M Ellis, Lawrence P Wackett
Affiliations

The University of Minnesota Pathway Prediction System: multi-level prediction and visualization

Junfeng Gao et al. Nucleic Acids Res. 2011 Jul.

Abstract

The University of Minnesota Pathway Prediction System (UM-PPS, http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts microbial catabolism of organic compounds. Currently, its knowledge base contains 250 biotransformation rules and five types of metabolic logic entities. The original UM-PPS predicted up to two prediction levels at a time. Users had to choose a predicted product to continue the prediction. This approach provided a limited view of prediction results and heavily relied on manual intervention. The new UM-PPS produces a multi-level prediction within an acceptable time frame, and allows users to view prediction alternatives much more easily as a directed acyclic graph.

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Figures

Figure 1.
Figure 1.
UM-PPS pipeline system infrastructure (see text).
Figure 2.
Figure 2.
UM-PPS pipeline system flow (see text).
Figure 3.
Figure 3.
Flowchart showing UM-PPS multi-level prediction. (a) ‘Input query’ takes a query compound and converts it into a SMILES string. (b) ‘Compound validation’ checks the correctness of the string format and chemical structure. (c) ‘Query filter’ runs further checks on valid compounds, removing very low molecular weight compounds, predefined termination compounds and some types of compounds that should not be predicted by the current version of UM-PPS (3). (d) ‘Rule match’ identifies functional groups in a query compound that match rule targets. If there is a successful match, a virtual transformation will be applied to the target functional group. (e) ‘Product filter’ removes transformed products with fewer carbon atoms than a chosen cutoff value (default = 3). (f) ‘Likelihood match’ selects transformed products beyond a chosen aerobic likelihood value (either ‘aerobic’ or ‘all’). If ‘aerobic’ is chosen and there are no products, the UM-PPS will change to ‘all’ and retry the prediction. If there are still no products, the prediction process will stop and a ‘No rule’ message will be returned. At the end of a level, (g) ‘Product storage’ merges products from all prediction branches and removes duplicates. If the total number of products at a level does not reach the chosen breadth cutoff value and the current level does not reach the chosen depth cutoff value, (h) ‘Level iteration’ starts a new prediction branch for each transformed product and moves the prediction process into the next level. If either of these two cutoff values is reached, the UM-PPS will complete the prediction, and (i) ‘Results display’ displays all products and pathways in a DAG.
Figure 4.
Figure 4.
Three-level prediction results for benzene sulfinate (see text).
See this image and copyright information in PMC

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