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Yip et al.
The tYNA Platform for Comparative Interactomics: A Web Tool for Managing, Comparing and Mining Multiple Networks
Yip et al.
Application
Note
The tYNA Platform for Comparative Interactomics: A Web Tool for Managing, Comparing and Mining Multiple Networks Kevin Y. Yip1, Haiyuan
Yu2, Philip M. 1Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA 2Department of Cancer Biology, Dana-Farber Cancer Institute, 1 Jimmy Fund Way, SM854, Boston, MA 02115, USA
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*abstract
Biological processes involve complex
networks of interactions between molecules. Various large-scale experiments
and curation efforts have led to preliminary versions of complete cellular
networks for a number of organisms. To grapple with these networks,
we present developed
tYNA, a Web system for managing, comparing and mining multiple networks,
both directed and undirected. tYNA efficiently implements methods that
have proven useful in network analysis, including locating
identifying defective cliques, finding small network motifs
(such as feed-forward loops), calculating global statistics (such as
the clustering coefficient and eccentricity), and identifying hubs and
bottlenecks. It also allows one to manage a large number of private
and public networks using a flexible tagging system, to filter them
based on a variety of criteria, and to visualize them through an interactive
graphical interface. A number of commonly used biologically
datasets have been pre-loaded into tYNA, standardized and grouped into
different categories.
Availability: The tYNA system can be accessed at http://networks.gersteinlab.org/tyna. The source code, JavaDoc API, and WSDL can also be downloaded from the web site. tYNA can also be accessed from the Cytoscape software using a plugin.
Supplementary Information: Additional figures and tables can be found at http://networks.gersteinlab.org/tyna/supp.
In the era
of systems biology, the focus on understanding complex organisms is
shifting from studying individual genes and proteins towards the relationships
between them (Sharan and Ideker, 2006). These relationships are usually
expressed in terms of various kinds of biological networks, including
protein-protein interactions, gene regulation, and metabolic pathways.
Recent developments of large-scale experiments such as mass spectrometry
and array-based techniques (Lee et al., 2002, Gavin et al.,
2006, Krogan et al., 2006) have generated rough descriptions
of the complete networks in the cells. Many studies have reported interesting
biological findings from these networks, including the relationships
between various statistical properties of a gene and its function and
essentiality, and the elucidation of controls at the molecular level
based on network motifs (Lee et al., 2002, Milo et al.,
2002, Shen-Orr et al., 2002, Yu
et al., 2003, Han et al., 2004, Yu et al.,
2004).
These
studies require heavy computations on multiple networks. We have developed
a Web system, tYNA (TopNet-like Yale Network Analyzer), to
provide researchers with a set of tools to carry out such computations
with great ease. The system provides five main types of functionality:
(1) Network management: storing, retrieving and categorizing networks,
as well as performing format conversion. A comprehensive set of
widely used network datasets is preloaded, put into standard form, and
categorized with a set of tags. (2) Network visualization: displaying
networks in an interactive graphical interface, with
interactive controls for zooming, panning, coloring and exporting networks
(Fig. 1). (3) Network comparison and manipulation:
various kinds of filtering
based on network statistics and node names, and multiple network
operations such as taking the union and intersection of networks.
(4) Network analysis: computing various statistics for the whole network
and subsets, and finding motifs and defective cliques. (5) Network Mining:
predicting one network based on the information in another.
Fig. 1.The intersection of two yeast-two-hybrid datasets (Ito et al., 2000, Uetz et al., 2000) with all nodes having no edges in the intersection filtered by a statistics filter. The nodes are colored according to their degrees. Also shown in the figure are the various statistics of the resulting network.
Our system
shares some elements with a number ofsome other
recently developed network analysis and visualization
systems, includingsuch as BioGrid1,
Cytoscape (Shannon et al., 2003), JUNG21, N-Browse32, and Osprey43, but also offers some unique
additional features such as defective clique finding.
Besides, being a Web-based system, tYNA also has some unique advantages:
Table 1 summarizes some major differences between the systems. A more detailed description is given on the supplementary web site.
Table 1.A brief comparison of several existing systems
Cytoscape | JUNG | N-Browse | Osprey | tYNA | |
Main purpose | Visualization | Library | Visual. | Visual. | Analysis |
System | Standalone | Standalone | Web | Standalone | Web |
Link from external resources | Indirect: Java Web Start | No | Direct | No | Direct |
Stat. calculation | Plugin | Built-in | No | No | Built-in |
Motif finding | Plugin | No | No | No | Built-in |
tYNA
provides a simple view with some basic features, and an advanced view
for more complex analyses. One may start with the simple view,
and switch to the advanced view later after getting familiar with the
interface.
Login
is optional. However, some features such as the management of private
networks are for registered users only.
The first step of analysis is to upload networks. tYNA accepts various
file formats, including the SIF format of Cytoscape, and it can
handle both directed and undirected networks, as well as simple and
multigraph networks. One may also enter additional attributes
to organize the networks into groups, such as network type (e.g. protein-protein
interaction), organism (e.g. yeast) and experimental method (e.g. yeast-two-hybrid)
(Fig. S1). Furthermore, tYNA allows users to analyze subsets
of the networks (e.g., active parts in a dynamic network (Han et
al., 2004, Luscombe et al., 2004)) by using category
files.. For that purpose, category files grouping the nodes
into different categories can be uploaded.
After uploading
a network, one may view its statistics and visualize it graphically
by loading it into a workspace. A workspace is a working area for a
single network (Fig. 1). There are several workspaces, which allow
one to work on multiple networks at the same time.
Various statistics are computed for each node, such as the
degree, clustering coefficient, eccentricity and betweenness (Yu
et al., 2004). The average, minimum, maximum and standard deviation
of each statistic in the network and each category-defined sub-network
are displayed, as well as the number of nodes, edges, and connected
components. Networks are visualized in Scalable Vector Graphics
(SVG) using the aiSee package5package4, which facilitates an interactive
interface: one may change the appearance of the network such as
zoom level and coloring in real time (Fig. S2, S3).
After
loading a network into a workspace, one may perform filtering and motif
finding. Filtering allows one to retain a portion of the network,
based on a statistics cutoff (e.g. the 5% of nodes with the highest
out-degrees) or a regular expression relating to
node names. They It
will easily allow one to identify the hubs and bottlenecks in a graph.
Motif finding identifies various regular patterns in the network, including
chains, cycles, feed-forward loops and complete two-layers. They generalize
the motifs discussed in previous studies (Lee et al., 2002, Milo
et al., 2002, Shen-Orr et al., 2002). Currently all
occurrences of a specified motif pattern are returned. We will study
the feasibility of returning only statistically over-represented motifs
in future work. tYNA also identifies defective cliques (Yu
et al., 2006) that suggest potential missing edges in a network
(Fig. S4, S5).
Multiple-network operations allow one to select multiple networks, perform some operations on each of them, and merge them into a single network. For example, the intersection of multiple high-throughput protein-protein interaction networks offers a high-confidence set of potential interactions. The relationships between different kinds of networks, such as gene regulation and co-expression, can also be studied (Fig. S6).
The edge overlap
feature allows the comparison of the edges in two networks. It can test,
using some prediction functions, how well one network predicts another.
Some prediction functions are predefined, such as identity, sibling
and couple (Table S1S2).
Finally, one may save a working network into the database, or send it to another workspace (as a temporary backup). Likewise, one may download a working network in various network and graphics formats, including SIF, bitmap, postscript and PDF (Fig. S3, S4).
All source codes were written in Java using standard J2EE architecture. A detailed JavaDoc API is available for users who want to use the classes in their own codes. The tYNA database can also be accessed through standard SOAP-based web services, and we have developed a plugin (available on the tYNA website) to interface tYNA with the network visualization system Cytoscape (Fig. S7).
As biological
network analysis is a vigorous research area, new statistics, motifs,
and mining algorithms are expected to emerge continuously. tYNA was
thus designed in a modular fashion so that new features can be readily
added. Being a Web system, the new features are immediately made accessible
to users without requiring them to perform software updates.
We plan on connecting tYNA with dataset management systems such as YeastHub (Cheung et al., 2005), and Bind (Alfarano et al., 2005), DIP (Xenarios et al., 2002), MINT (Zanzoni et al., 2002), Reactome (Joshi-Tope et al., 2005) and annotation databases to provide a unified platform for performing complex analyses. We think that the combination of the analysis features provided by tYNA and the advanced visualization facilities of Cytoscape can prove particularly powerful. We also plan on interfacing tYNA with analysis and visualization tools such as bioPIXIE (Myers et al., 2005) and Pajek (Batagelj and Mrvar, 2003), which would allow researchers to combine the distinct features of each tool.
References
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*To whom correspondence should be addressed.
1http://jung.sourceforge.net/
2http://nematoda.bio.nyu.edu:8080/NBrowse/N-Browse.jsp?last=false
3http://biodata.mshri.on.ca/osprey/
4http://www.aisee.com/
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