As an example, we take the case study discussed in [34]. In this case study, a pathway is assembled from genes in the cell-cycle regulated MET cluster [30]. Results described in this tutorial have been obtained with KEGG RPAIR version 49.0.
The result of the mapping of the given genes to KEGG RPAIRS (reactant pairs, [18]) is displayed.
Since more than one reactant pair is associated to each gene, we end up with a group of reactant pair groups.
Note that each gene (except for Met5) is associated to one or more EC numbers, each of which has been mapped
to its corresponding reactions in KEGG, which have in turn be mapped to their corresponding reactant pairs.
You can now select how to deal with the groups. This is a sensitive choice that strongly affects the inferred pathway and which depends on your data. In general, if you keep the original groups, you assume implicitely that only a subset of the reactions associated to the given gene will be active in the pathway. If you think that all reactions associated to a gene might be active, choose "Treat each group member as a separate group" (the default treatment).
For the study case, we recommend you to keep the default.
Push GO. In a few minutes, the result page will be displayed.
This section assumes that you have installed the RSAT/NeAT command line tools.
Pathwayinference is a web application that calls the pathwayinference web service. You can use the Pathwayinference command line tool on the networks provided in the network repository (check the Pathwayinference Manual for this) to reproduce results obtained with the web application on command line. Note that the mapping of genes to reactions and group treatment can only be done via the web application.
Type the following command in one line:
java -Xmx800m graphtools.algorithms.Pathwayinference -g RPAIRGraph_allRPAIRs_undirected.txt -s 'RP00016#RP00182/RP00647/RP00561/RP00143#RP00960#RP04049/RP00096#RP00168# RP04532/RP00003/RP00446/RP00946#RP00857/RP04474/RP00050#RP04533' -f flat -b -y con -P -u -x 0.05
The resulting sub-network contains a large part of the pathway given in [34]. Note that the chosen algorithm (kWalks in combination with Takahashi & Matsuyama) may return one from a set of solutions, so your solution may deviate from the one described here. Despite of this disadvantage, Takahashi & Matsuyama in combination with kWalks is the default algorithm, because it performed best in our evaluation. If your result deviates from the one described below, repeat the inference with the algorithm "repetitive REA".
The pathway described in the study case unites the sulfur assimilation and methionine biosynthesis pathways. It consists of the following steps:
Sulfate 2.7.7.4 Adenylyl sulfate 2.7.1.25 3'phosphoadenylylsulfate 1.8.99.4 sulfite 1.8.1.2 sulfide (alias hydrogen sulfide) 4.2.99.10 Homocysteine 2.1.1.14 L-Methionine
The matching parts of the inferred pathway are:
RP00016 3'-Phosphoadenylyl sulfate RP00446 Adenylyl sulfate RP00960
and
RP00960 Sulfite RP00168 Hydrogen sulfide RP01406 L-Homocysteine RP00096
Seeds are printed in bold.
In addition, the inferred pathway contains a branch that leads
from 3'-Phosphoadenylylselenate to Adenylylselenate.
This branch mirrors sulfur incorporation, but instead of sulfur, selenium is incorporated.
The presence of both the selenium and sulfur incorporation pathways in the inferred sub-network
reflects the well-known fact that selenium might replace sulfur in metabolism.
This example demonstrated that given a set of differentially expressed genes from micro-array data and a metabolic network, it is possible to infer a metabolic pathway that might be affected by altered expression of the query genes.
Pathwayinference allows extraction of sub-networks from larger networks given a set of seed nodes. The web application is tailored to metabolic networks, but non-metabolic networks can be processed as well.
You provided insufficient or invalid parameters. Please check the pathwayinference manual page.
For the pre-loaded metabolic networks from KEGG and MetaCyc, each seed is mapped to data (e.g. compound/reaction identifiers, EC numbers) from these two databases. If the seeds do not map anything, they are considered to be invalid. At least two valid seed groups are needed to infer a network.
Make sure that your input network contains the node with the given identifier.
None of the seed node groups could be connected to any other seed node group. Each might belong to a separate component of the input network or mutual exclusion (in RPAIR networks) might prevent the connection of the seed groups.