BIOINFORMATICS APPLICATIONS NOTE
Data and text mining

Vol. 30 no. 21 2014, pages 3139–3141
doi:10.1093/bioinformatics/btu501

Advance Access publication July 28, 2014

NetPathMiner: R/Bioconductor package for network path mining
through gene expression
Ahmed Mohamed1,*, Timothy Hancock1,2, Canh Hao Nguyen1 and Hiroshi Mamitsuka1
1

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan and 2Department ofComputing and Information Systems, The University of Melbourne, Victoria, Australia

Associate Editor: Jonathan Wren

ABSTRACT

Received on April 14, 2014; revised on June 30, 2014; accepted on
July 17, 2014

1 INTRODUCTION
Mining subnetworks from genome-scale biological networks is
an important step in biological data analysis because as their sizeand complexity increase, manual analysis becomes infeasible.
Various methods for subnetwork mining from experimental
data are available, where output subnetworks are expressed as
sets of nodes (Vert and Kanehisa, 2003), frequent subgraphs(Chechik et al., 2008) or linear paths (Hancock et al., 2010).
We focus on linear paths, which are particularly useful by carrying an intuitive meaning, as in metabolic reaction paths and signaling cascades.
Currently, software for mining paths from networks is hampered by two main challenges: (i) The lack of a universal parsing
tool for different pathway types and database file formats. In
fact, pathways can be diverse, such as metabolic or signaling,representing chemical conversions or protein interactions,respectively. Also, pathway information in databases can beaccessed through different file formats, with each format representing pathway structure and annotation differently. (ii) The
lack of effective visualizations for (a) different network typesand (b) up to thousands of paths.
Currently available tools are either specific to particular pathway types or file formats. An earlier approach, PathRanker*To whom correspondence should be addressed.

(Hancock et al., 2010), is limited to mining paths from metabolic
networks constructed from KGML files, providing static visualization on one network representation only. Tools like Pathview(Luo and Brouwer, 2013) and rBiopaxParser (Kramer et al.,2013) also construct networks from only one file format.
We present NetPathMiner, an R package, to overcome thetwo main challenges. Figure 1 describes the process flow implemented in our package (see next section and Supplementary
Materials for details). NetPathMiner implements a flexible
module-based process flow for network path mining and visualization, which can be fully integrated with user-customized functions. NetPathMiner enables fast processing for all KGML,
SBML and BioPAX formats into genome-scale metabolic or signaling networks (See Supplementary Figures S1 and S2 in detail).
NetPathMiner also provides path highlighting over three different representations and interactive visualizations, making theanalysis of even thousands of output paths possible.

2

MAIN FEATURES

2.1

Pathway file processing (Step 1 in Figure 1)

Table 1 summarizes key differences among file formats supported byNetPathMiner. To alleviate these differences, NetPathMiner processes
files into either one of the two outputs, regardless of input formats: (i)Metabolic networks, in metabolic representation, as a bipartite graphwith metabolites and reactions parts, and edges representing production
or consumption of metabolites. (ii) Signaling networks, in gene representation where vertices and edges represent genes and their interactions.
NetPathMiner can merge multiple files into a single network.

Network objects and annotations. We chose igraph (Csardi andNepusz, 2006) to represent all constructed network objects in R to efficiently handle large graphs commonly encountered in biology and toallow NetPathMiner to integrate with other network analysis tools.
NetPathMiner stores extracted annotations according to MIRIAM
guidelines and implements an attribute fetcher using BridgeDb web service (van Iersel et al., 2010) to convert between different annotations.

2.2

Network representations (Step 2)

For metabolic representation, NetPathMiner generates two other representations: (i) Reaction representation, where edges represent successivereactions, (ii) Gene representation, where vertices represent genes andedges represent participation in successive reactions. Multiple representations allow investigation of both chemical and genetic components ofmetabolic activity.

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Summary: NetPathMiner is a general framework for mining, fromgenome-scale networks, paths that are related to specific experimental conditions. NetPathMiner interfaces with various input formats
including KGML, SBML and BioPAX files and allows for manipulationof networks in three different forms: metabolic, reaction and generepresentations. NetPathMiner ranks the obtained paths and applies
Markov model-based clustering and classification methods to theranked paths for easy interpretation. NetPathMiner also provides
static and interactive visualizations of networks and paths to aid
manual investigation.
Availability: The package is available through Bioconductor and from
Github at http://github.com/ahmohamed/NetPathMiner
Contact: mohamed@kuicr.kyoto-u.ac.jp
Supplementary information: Supplementary data are available at
Bioinformatics online.

A.Mohamed et al.

Table 1. Differences between major pathway file formats
Features

KGML SBML

BioPAX

Number of pathways
per file
Are metabolic reactions
distinct?
Transport reactions
Reaction kinetics
Cellular location
MIRIAM annotations
Databases

OneOneOne or moreYes

NoYes

No
No
NoNo
KEGG

Yesa
Yes
Yes
Yes
Reactome,Biomodels,Recon X

Yes
No
Yes
Yes
PID, Reactome,BioCyc, Biocarta,
WikiPathways

Fig. 2. NetPathMiner path visualization. Top 100 paths on Reactome’s
carbohydrate metabolism network, grouped into three clusters (red, blue,green). (a–c) Metabolic, reaction and gene representations. (d) Paths(rows) and their reactions (columns). (e) Probability that a top path belongs to a cluster

making manual investigation easier. Figure 2a-c show a visualization example of different graph representations using the output of the last step,
allowing users to examine metabolic regulation at different biologicalsystem levels. Visualization function matches vertices across all input representations and plots them using the same layout. To make visualizationof a huge number of paths clearer, NetPathMiner assigns the same color
to all paths in each obtained cluster, and assigns the same color to vertices
within the same cellular compartment (not shown). Figure 2d and e show
vertices in each path as well as probability of each path belonging toclusters. NetPathMiner also supports interactive visualization in
Cytoscape by either exporting networks in GML format or using
RCytoscape (Shannon et al., 2013), which allows thorough investigationof vertex annotations and full customization of network colors and layout.

aTransport reactions are detected indirectly from reaction description.

3CONCLUSION

Given a network in reaction or gene representations, edge weights can becomputed by Pearson correlation, or a user-defined function, of expression profiles of adjacent genes. If gene expression is measured for different categories (control/treatment), edge weights can be computed for eachcategory separately. From a weighted network, NetPathMiner ranks
node sequences by two methods: (i) ‘shortest path’ (Hancock et al.,2010) returning a list of k-maximum weighted paths, (ii) ‘P-value’(Hancock et al., 2012) returning the paths of which the sum of edge
weights is significantly higher than that of random paths. Path ranking
can be used independently or as a part of the entire process.

We present NetPathMiner, an easy-to-use R package for network path mining. NetPathMiner constructs genome-scale networks from KGML, SBML and BioPAX files, overcoming thelimitations of current tools. NetPathMiner summarizes output
paths using machine learning methods and provides different
visualizations, easing manual investigations. All modules inNetPathMiner can be used independently, particularly the pathway file processing module, and replaced with user-customizedmodules. Future developments include supporting other network
representations, such as rule-based modeling formats, as inputs
and outputs for broader integration.

2.4

Funding: This work is partially supported by KAKENHI(2430054) of MEXT, Japan.

2.3

Weighting networks and path ranking (Steps 3, 4)

Clustering and classification of paths (Step 5)

NetPathMiner provides machine learning methods to summarize output
paths. Using Markov mixture models, paths can be clustered or classified
according to their structure or association with a response label, respectively. Both methods are adopted from PathRanker (Hancock et al., 2010).

2.5Visualization (Step 6)

NetPathMiner provides both static and interactive visualizations of ranked
paths using annotation information and machine learning techniques,

3140

Conflict of Interest: none declared.

REFERENCES
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Fig. 1. NetPathMiner process flow chart

NetPathMiner

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