NRNB Annual Report 2012

Annual Progress Report - Research Progress 2012
National Resource for Network Biology
P41 GM103504 (RR031228)
05/01/2011 - 04/30/2012

The 2012 NRNB Network. On the left is a network representation of all NRNB personnel and
collaborators (blue circles), all TRD, DPB, Collaboration, and Service projects (orange
diamonds), and associated publications (green triangles). Node size is proportional to the
number of connections. Thick red borders indicate personnel and projects directly funded by the
NRNB P41 grant. On the right is a zoomed inset, inclusive of all NRNB-funded personnel
making up the vital core of the NRNB network. There are 315 nodes and 404 connections in the
network. NRNB funds 41 (13%) of these nodes, which make 217 (54%) of the connections. As a
Cytoscape network [1], we can interactively explore this representation with our External
Advisory Committee, offering dynamic views of our projects, collaborations and budgets. Also
see Appendix A for a full-page view of the entire network.

1. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: New features for data
integration and network visualization. Bioinformatics 27:431–432.

Summary

Continued advances in high-throughput experimental technologies release enormous amounts
of interaction data into the public domain. Analysis of these interactions – and the networks they
form – relies in large part on robust bioinformatics technology. The mission of the NRNB
(nrnb.org) is to develop and support a suite of bioinformatics tools that broadly enable the study
of network biology. In our second year as a resource, we have significantly advanced our goals
through basic research, collaboration, dissemination of software tools, and community support.
Here, we describe our progress in research, both basic and collaborative. This progress
includes algorithms for identification of network substructures (modules); use of network
modules for patient diagnostics; tools to enable new network analyses and visualizations; and
major new versions of our Cytoscape platform and plugin website.
Each progress report below specifies the associated personnel and FTEs funded by the
NRNB grant. In terms of our own research, NRNB enables a stable effort from each of the
resource member sites, ranging from 0.48 to 1.08 FTEs. Many of these TRD projects leverage
effort from other grants and funding mechanisms as well in order to maximize the return on
investment. Nevertheless, without NRNB support, these projects would be significantly
diminished, if not discontinued, and would lack the cohesion and synergy provided by a network
biology resource (see reports #1-7 below).
In terms of the services, training and dissemination, the impact of the NRNB resource is
clear. Specifically, the extra effort needed to drive our mailing list response rate from 64% to
93% is due to this resource (see Administrative Information report); the Open Tutorials system
for collecting, maintaining and serving tutorial materials; the administration of NRNB’s
participation in Google Summer of Code and our new NRNB Academy (see report #9 below);
the organization of annual Network Biology SIG and Cytoscape Retreat meetings; the new
Cytoscape App Store, which will catalyze Cytoscape user and developer communities (see
report #10 below). These efforts are maintained by the 0.5 FTE executive director and 0.3 FTE
communications coordinator roles defined and funded by NRNB.
And finally, NRNB has wide-ranging impact on biomedical research, both nationally and
internationally through its collaboration projects. NRNB member sites were collectively
maintaining an estimated two dozen collaborations prior to the formation of this Resource.
During the first year, we established close to 40. And now at the conclusion of our second year,
NRNB maintains almost 100 collaboration projects. These project range from the application of
Cytoscape as a research tool for network analysis and visualization, to the development of
Cytoscape plugins for custom data types and analyses, to the development and application of
other network and pathways tools and resources for network biology (see report #8 below). This
activity is a direct result of NRNB roles for executive director, communications coordinator and,
new this year, collaboration coordinator (0.5 FTE).
We’ve come a long way in just two years, and NRNB is still getting up-to-speed. With
continued support, we are committed to maintaining and growing these efforts as a Resource
for the network biology community.

Contents

I. Technology Research and Development: Progress and Applications
Within each TRD report, we have separated the description of development efforts from the applications
of each technology for our own groups and our DBPs. References and figures are provided for each
project and numbered independently.

1. Identification of Network Modules as Biomarkers (Ideker)
2. Network Analysis Tools for Cancer Genomics (Sander)
3. Network Analysis Methods for Inferring Causality in Networks (Sander)
4. Using Cytoscape for Social Network Research (Fowler, Pico)
5. Cytoscape 3.0 for the Visualization and Representation of Biological Networks
(Bader)
6. Visualizing Complex Networks as Ontology-Partitioned Mosaics (Pico)
7. The CYNI Modular Network Inference Framework (Schwikowski)

II. Collaboration and Service Projects: Progress
In addition to the direct impact of our TRD projects on our research, NRNB also impacts new science
through our many CSPs. A description for each CSP is provided in the bulk of the report. Here, we
summarize the efforts.

8. New Collaborations
9. Google Summer of Code and NRNB Academy

III. Progress on Supplemental Award, 2011-2013
We were awarded a two-year supplemental grant to work on the Cytoscape App Store. This is a progress
report on the first half of the first year.

10. The Cytoscape App Store (Pico)

Appendix A. The 2012 NRNB Network
A full-page view of this year’s network representation of NRNB.

I. Technology Research and Development: Progress and Applications
Within each TRD report, we have separated the description of development efforts from the applications
of each technology for our own groups and our DBPs. References and figures are provided for each
project and numbered independently.

1. Identification of Network Modules as Biomarkers (Ideker, 0.5 FTE: Mike Smoot,
Rintaro Saito, Kei Ono)
Biomarkers are typically thought of as individual genes or proteins. However, we and others
have demonstrated that biological pathways and protein interaction networks, which integrate
many individual proteins under a common function, can serve as powerful biomarkers and in
some cases are also more predictive [1-4]. Our ActiveModules method [1]is an unsupervised
approach that first projects molecular profiles (e.g. mRNA or methylation profiles) onto the
corresponding nodes in an existing protein interaction map. Subsequently, a network search is
performed to identify connected subnetworks (i.e. network modules) whose average node value
is higher or lower than expected by chance. The PinnacleZ method [2] is similar to
ActiveModules but supervised: each molecular profile is associated with a class label (i.e.
cancer subtype) and a network search is performed to identify network modules whose average
value is predictive of this sample class. Both PinnacleZ and ActiveModules are implemented as
plugins to Cytoscape. Several tools by others, such as the successful HotNet algorithm [5], have
been based on ideas introduced by the ActiveModules approach. The advantage of such
approaches over regular clustering and classification methods is that they associate the
molecular features with physical or functional structures, providing a wealth of hypotheses about
the pathway mechanisms underlying an observed set of molecular profiles. In some cases they
also provide more robust classification performance. Our projects have been pursuing
technological advances to better reveal network modular structure, define network logic
functions associated with disease outcomes, and extend existing network-biomarker
approaches to multiple types of molecular and phenotypic data.
While ActiveModules and PinnacleZ use simple summary functions such as ‘average’ or
‘median’ to summarize the activity of the genes within a module, these functions do not capture
the rich logical relationships known to occur within biological pathways. During the previous
reporting period we have developed an approach called Network Guided Forests (NGF) which
detects more complex logical relationships within modules such as AND, OR, A AND NOT B,
XOR and so on [6]. NGF integrates key ideas from decision trees and Random Forests [7] with
biological constraints induced by a protein-protein interaction network – the first use of protein
networks in ensemble learning. The result is that, rather than relying on a general measure of
module activity, NGF fits decision trees to each module directly from data thus capturing
potentially complex network activities. In this reporting period we have further developed the
method.
While many existing methods still use only one type of molecular feature (e.g. gene
expression levels or SNPs) and a single type of molecular interaction data (e.g. protein-protein
interactions), we anticipate that key improvements will come from integrating multiple layers of
molecular measurements, as well as different types of interaction networks. Extending previous
work by other groups (see e.g. [5]) we have developed a preliminary version of a new diffusion-
based method that is able to map disease-perturbed networks using combined evidence from
multiple heterogeneous data sources (Figure 1). Preliminary results suggest that network
modules supported by multiple data layers improve robustness and interpretability and provide
more complete models of the disease.

Figure 1. Map of network modules and associations integrating multiple data layers.
Large orange nodes are modules enriched for somatic mutations while large blue nodes are
modules of genes highly over-expressed in cancer (TCGA level 3 data, z > 100 compared to
control). Gene size is scaled according to the percentage of the cohort in which they are altered
relative to other genes in the module. Edges within a module represent protein interactions
while weighted edges between modules represent statistical associations between modules.
Insets in the top-left and top-right corner highlight representative modules for over-expression
and mutations, respectively.

Applications
Using NGF, we analyzed gene expression data gathered for diverse biological programs
including breast cancer metastasis [8,9] or mesenchymal transformation of brain tumors [10].
These case studies showed that, unlike the gene sets identified by regular Random Forests, the
network modules identified by NGF are highly enriched for known causal mechanisms of
disease (e.g. dominated by known oncogenes and tumor suppressors), and they have very
consistent performance across different sample cohorts.
In this reporting period we have performed multiple analysis of additional large datasets
including those collected by one of our DBPs, The Cancer Genome Atlas (TCGA) [11]. Through
this analysis we have identified and bioinformatically validated predictive modules found by NGF
to associate with the specific subtypes of glioblastoma. The most predictive module associated
with the mesenchymal subtype was strongly supported by independent transcriptional datasets.
On the basis of these findings, this module is now being validated experimentally. We also
published an abstract with another one of our DBPs on a subnetwork-based analysis of chronic
lymphocytic Leukemia, associating particular pathways with the progression of the disease [12].
Given a library of genes and network modules selected using various types of molecular
data, we can now investigate the relationships among these units such as the association
between a germline SNP and the output of a differentially-expressed network (i.e., an eQTL) or
the association between a pathway enriched for somatic cancer mutations and a clinical

phenotype such as survival. Together with our DBP, we have used this method to analyze The
Cancer Genome Atlas (TCGA) Ovarian Cancer data (somatic mutations and expression
profiles) using the HPRD protein interaction network. We identified modules enriched for genetic
mutations, as well as modules highly over-expressed in cancer compared to normal tissue. Next
we investigated all pairwise correlations between modules to reveal modular associations both
within and between the two data layers (Figure 1). Based on this preliminary analysis we
conclude that the existing data and our toolset will enable us to construct multi-level modular
maps of cancer that will significantly extend single-level network models provided by current
methods [13].

References
1. T. Ideker, O. Ozier, B. Schwikowski, A. F. Siegel, Discovering regulatory and signalling circuits in
molecular interaction networks. Bioinformatics 18 Suppl 1, S233 (2002).
2. H. Y. Chuang, E. Lee, Y. T. Liu, D. Lee, T. Ideker, Network-based classification of breast cancer
metastasis. Mol Syst Biol 3, 140 (2007).
3. E. Lee, H. Y. Chuang, J. W. Kim, T. Ideker, D. Lee, Inferring pathway activity toward precise disease
classification. PLoS Comput Biol 4, e1000217 (Nov, 2008).
4. I. W. Taylor et al., Dynamic modularity in protein interaction networks predicts breast cancer outcome.
Nat Biotechnol 27, 199 (Feb, 2009).
5. F. Vandin, E. Upfal, B. J. Raphael, Algorithms for detecting significantly mutated pathways in cancer. J
Comput Biol 18, 507 (Mar, 2011).
6. J. Dutkowski, T. Ideker, Protein networks as logic functions in development and cancer. PLoS Comput
Biol, (2011).
7. L. Breiman, Random forests. Machine Learning 45, 5 (2001).
8. Y. Wang et al., Gene-expression profiles to predict distant metastasis of lymph-node-negative primary
breast cancer. Lancet 365, 671 (Feb 19-25, 2005).
9. L. J. van 't Veer et al., Gene expression profiling predicts clinical outcome of breast cancer. Nature 415,
530 (Jan 31, 2002).
10. H. S. Phillips et al., Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern
of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157 (Mar, 2006).
11. R. G. Verhaak et al., Integrated genomic analysis identifies clinically relevant subtypes of
glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98 (Jan
19, 2010).
12. Chuang, Han-Yu, et al., Subnetwork-Based Analysis of Chronic Lymphocytic Leukemia Identifies
Pathways That Associate with Disease Progression, ASH Annual Meeting Abstracts 2011 118: 3564.
13. P. T. Spellman et al., Integrated genomic analyses of ovarian carcinoma. Nature 474, 609 (Jun 30,
2011).

2. Network Analysis Tools for Cancer Genomics (Sander, 0.65FTE: Ben Gross,
Ethan Cerami)
As described in our previous progress report, the first TRD project at MSKCC is focused on
building network analysis tools for interpreting high-throughput cancer genomic data sets. Our
primary focus is building user friendly, open source tools for visualizing and analyzing
multidimensional cancer genomic data sets (including copy number, mutation, and mRNA
expression) in the context of known biological pathways and interaction networks, and making
these tools broadly available within the cancer research community. Providing such tools to the
cancer research community is critical, as numerous large-scale projects, including the Cancer
Genome Atlas (TCGA) project and the International Cancer Genome Consortium (ICGC), are

profiling dozens of cancer types and subtypes. Identifying altered pathways and networks within
each of these cancer types remains a critical and open challenge.
During our first year of NRNB funding, we completed a prototype project for displaying
multi-dimensional cancer genomic data in the context of molecular interaction networks. We
chose to implement the prototype in Cytoscape Web [1], as Cytoscape Web does not require
any additional software installation or require Java Web Start. It therefore significantly lowers
the barriers for usage, particularly for biologists and clinical researchers – two of our main target
user groups.
In this progress report, we describe the transition of our tools from prototype to
production mode, and describe how we have now made our software available to the entire
cancer research community. Specifically, our NRNB-funded network tools are now available
within the cBio Cancer Genomics Portal, where it enables cancer researchers to perform
network analysis on up to 20 different cancer types, including TCGA-funded projects related to
our DBP, such as Glioblastoma Multiforme (GBM) [2] and serous ovarian cancer [3].
As general background, the cBio Cancer Genomics Portal (http://cbioportal.org) is an
open-access resource for interactively exploring multidimensional cancer genomics data sets. It
currently provides integrated access to cancer genomic data (including copy number, mutation,
mRNA and microRNA expression, methylation, and protein and phosphoprotein data) on more
than 5,000 tumor samples from 20 cancer studies. With a focus on usability and ease of use,
the cBio Portal specifically provides integrated access to multiple genomic data types, graphical
summaries of genomic alterations, survival analysis and predicted functional consequences of
somatic mutations. All features of the portal are available via a streamlined four-step web
interface, enabling researchers to interactively explore gene sets and pathways, and
dynamically broaden or limit the scope of their query. By integrating data on thousands of tumor
samples, and providing a simple, yet powerful and flexible interface, the cBio Portal enables
cancer researchers to translate genomic data into biological insights and clinical applications.
During the past year, we have added our NRNB-funded network analysis tools to the
cBio Portal (launched on November 14, 2011), and have made the functionality freely available
to the scientific community. The network functionality (Figure 1) is directly available via the main
cancer query interface, and the portal now automatically generates a cancer specific network of
interest, based on seed genes specified by the user. This network consists of pathways and
interactions from the Human Reference Protein Database (HPRD) [4], Reactome [5], NCI-
Nature [6], and the MSKCC Cancer Cell Map (http://cancer.cellmap.org), as derived from the
open source Pathway Commons Project [7].

Figure 1. Network visualization and analysis now available within the cBio Cancer
Genomics Portal (http://cbioportal.org). A. Network view of TP53 in TCGA Glioblastoma
Multiforme (GBM). Network of interest generated from the seed gene of TP53; MDM2 and
MDM4 are highlighted. B. The portal overlays multi-dimensional genomic data (copy number,
mutation, and mRNA expression) onto all nodes in the network. C. All edges are color-coded by
interaction types. Interaction types are derived from the BioPAX to Simple Interaction (SIF)
inference rules [7]. For example, In Same Component indicates that Genes A and B are
involved in the same biological component, such as a complex; State Change indicates that
Gene A causes a state change, such as a phosphorylation change within Gene B; Other is used
to indicate all other types of interactions, including protein-protein interactions derived from
HPRD. D. Options for filtering, cropping and searching the network of interest.

By default, the network of interest contains all neighbors of all seed genes specified by the user.
If more than 50 neighbor nodes exist in the network, all genes are ranked by the frequency of
genomic alteration within the specified cancer study, and less frequently altered genes are
automatically pruned from the network. By default, the portal also automatically overlays multi-
dimensional genomic data onto each node, highlighting the frequency of alteration by mutation
and copy number alteration (and optionally mRNA up/down regulation). This provides an
effective means of managing network complexity, while automatically highlighting those genes
most directly relevant to the cancer type in question. One can also download the full, non-
pruned network for more complete visualization and analysis.
In addition, users can filter the network by alteration frequency, highlight all neighbors of a
selected gene, hide specific nodes, crop to a selected set of nodes, or search the network by
gene symbol. These features enable cancer researchers to identify new cancer-specific genes
that go beyond the original set of seed genes, and provide an effective means for discovering
novel cancer genes and novel genomic alterations.
As originally outlined in our grant application, our goal is to eventually integrate cancer
genomic data, pathway data and drug target data. In the next year, we therefore intend to focus
on extending the network feature to include drug data and drug target information. We initially
plan to integrate drug data from DrugBank [8], but are also evaluating other sources, including:
ChEBI [9], NCBI PubChem [10], and PharmGKB [11].

Applications
See next section for summary of applications for this and the next TRD project.

References
1. Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-
based network browser. Bioinformatics 2010, 26(18):2347-2348.
2. TCGA: Comprehensive genomic characterization defines human glioblastoma genes and core
pathways. Nature 2008, 455(7216):1061--1068.
3. Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474(7353):609-615.
4. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D,
Raju R, Shafreen B, Venugopal A et al: Human Protein Reference Database--2009 update. Nucleic acids
research 2009, 37(Database issue):D767-772.
5. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob
H, Jassal B et al: Reactome knowledgebase of human biological pathways and processes. Nucleic acids
research 2009, 37(Database issue):D619-622.

6. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway
Interaction Database. Nucleic acids research 2009, 37(Database issue):D674-679.
7. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C:
Pathway Commons, a web resource for biological pathway data. Nucleic acids research, 39(Database
issue):D685-690.
8. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V et al: DrugBank
3.0: a comprehensive resource for 'omics' research on drugs. Nucleic acids research 2011, 39(Database
issue):D1035-1041.
9. de Matos P, Alcantara R, Dekker A, Ennis M, Hastings J, Haug K, Spiteri I, Turner S, Steinbeck C:
Chemical Entities of Biological Interest: an update. Nucleic acids research 2010, 38(Database
issue):D249-254.
10. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker
BA et al: PubChem's BioAssay Database. Nucleic acids research 2012, 40(Database issue):D400-412.
11. McDonagh EM, Whirl-Carrillo M, Garten Y, Altman RB, Klein TE: From pharmacogenomic knowledge
acquisition to clinical applications: the PharmGKB as a clinical pharmacogenomic biomarker resource.
Biomarkers in medicine 2011, 5(6):795-806.

3. Network Analysis Methods for Inferring Causality in Networks (Sander,
0.65FTE: Ben Gross, Ethan Cerami)
The goal of our second TRD project is to algorithmically infer causality within signaling networks
from specific perturbation-induced experiments. High-throughput screens conducted with
libraries of small molecules or inhibitory RNAs have the ability to identify compounds that induce
tumor suppressive responses in cancer cells [1]. While the effects of such perturbations can be
easily linked to transcriptional changes, identifying the causal mechanism is a main challenge.
In a collaboration with Somwar and colleagues [2], we used a computational approach to predict
the target of a small molecule inducing reduced growth in lung adenocarcinoma cell lines.
Interestingly, experimental follow up confirmed the prediction.
Building on this concept, we have started working on computational approaches to
reconstruct the causal signaling cascade inducing observed transcriptional changes within
perturbed cell lines. With NRNB funding, we have previously explored the use of an optimization
algorithm borrowed from statistical physics to connect altered genes in cancer into minimal
spanning networks. Now, we have begun to use the same approach to identify the minimal set
of interactions able to connect genes that are differentially expressed after a perturbation, with
candidate targets of the same perturbation (Figure 1).

Figure 1. Given a perturbation and an observed response, the proposed network analysis
algorithms that we are developing aim to identify the perturbat-ion target and the signaling
cascade inducing the observed transcriptional response.

Our approach relies on an algorithm that solves the Steiner-tree problem. Given a set of
“terminal” nodes, the Steiner-tree is defined as the tree of minimum weight connecting these
terminals, allowing the inclusion of additional nodes. Differentially expressed genes after a
perturbation and/or candidate targets of the same perturbation can be used as terminals. The
resulting Steiner-tree can therefore contain both gene interactions able to explain the observed
transcriptional changes, and the putative target of the perturbation. This research remains a
work in progress, and we are continuing to explore new algorithmic frameworks.

Applications
Large-scale cancer genomics projects, such as the Cancer Genome Atlas (TCGA), and the
International Cancer Genome Consortium (ICGC), are providing an unprecedented and high-
resolution view of the molecular defects in dozens of cancer types [3]. A key open challenge is
to identify biological pathways that are frequently perturbed within tumor cells and lead to the
acquisition of tumorigenic properties, such as cell proliferation, angiogenesis or metastasis [4,
5]. A number of algorithmic methods have been identified for discovering altered networks and
pathways in cancer, including: Mutually Exclusive Modules in Cancer (MEMo) [6], PARADIGM
[7], and HotNet [8].
The network analysis tools we have built for our TRD enable researchers to interactively
explore perturbed pathways and networks in cancer. Unlike the algorithmic methods described
above, the tools we have developed are specifically designed to support exploratory data
analysis and hypothesis generation, and are designed for widespread use within the wider
cancer research community. By specifically adding network features to the cBio Cancer
Genomics Portal, we have also enabled network analysis on the full TCGA data set. In addition,
the portal has become a crucial tool within TCGA and is actively used by a large number of
TCGA disease working groups, including serous ovarian cancer, colorectal cancer, breast
cancer, and lung cancer (see collaborations).
To cite one concrete translation application, we used the network analysis features of
the portal to identify genomic alterations in the homologous recombination (HR) DNA repair
pathway in serous ovarian cancer. BRCA1 and BRCA2 are known to be involved in the HR
Pathway, but additional defects may also abrogate HR functionality, leading to potential
sensitivity to PARP inhibitors [9]. To identify potential HR defects in ovarian cancer, we used
BRCA1 and BRCA2 as seed nodes for the network view and explored the resulting altered
network of interest (Figure 2A). By this means, we quickly identified alterations in
C11orf30/EMSY (6% by amplification, 1.6% by mutation), a known interactor of BRCA2, and a
possible alternate means for abrogating HR functionality [9]. We also readily identified all altered
Fanconi Anemia genes (another family of genes involved in the HR pathway [9]), and identified
low frequency alterations in FANCA (altered in 3.5% of patients) and FANCE (2.8% of patients).
Combining these results with other genes known to be involved in the HR pathway, our DBP
(TCGA) was able to identify potential defects in the HR pathway in up to half of all patients,
providing a rationale for including such cases in clinical trials involving PARP inhibitors (Figure
2B) [10].

Figure 2: Extent of homologous recombination (HR) repair defects in serous ovarian
cancer. A. Network view of BRCA1/BRCA2 in TCGA serous ovarian cancer. BRCA1 and
BRCA2 are seed genes (indicated with thick border), and all other genes are automatically
identified as altered in ovarian cancer. Multidimensional genomic details are shown for FANCA,
FANC3 and C11orf30/EMSY. Darker red indicates increased frequency of alteration (defined by
mutation, copy number amplification or homozygous deletion) in ovarian cancer. B. Extent of
HR defects in TCGA Ovarian Samples. Reprinted from [10].

References
1. Somwar R, Shum D, Djaballah H, Varmus H: Identification and preliminary characterization of novel
small molecules that inhibit growth of human lung adenocarcinoma cells. Journal of biomolecular
screening 2009, 14(10):1176-1184.
2. Somwar R, Erdjument-Bromage H, Larsson E, Shum D, Lockwood WW, Yang G, Sander C, Ouerfelli
O, Tempst PJ, Djaballah H et al: Superoxide dismutase 1 (SOD1) is a target for a small molecule
identified in a screen for inhibitors of the growth of lung adenocarcinoma cell lines. Proceedings of the
National Academy of Sciences of the United States of America 2011, 108(39):16375-16380.
3. Stratton MR, Campbell PJ, Futreal PA: The cancer genome. Nature 2009, 458(7239):719--724.
4. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57--70.
5. Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell 2011, 144(5):646-674.
6. Ciriello G, Cerami E, Sander C, Schultz N: Mutual exclusivity analysis identifies oncogenic network
modules. Genome research 2012, 22(2):398-406.
7. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM: Inference of patient-
specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.
Bioinformatics 2010, 26(12):i237-245.

8. Vandin F, Upfal E, Raphael BJ: Algorithms for detecting significantly mutated pathways in cancer.
Journal of computational biology : a journal of computational molecular cell biology 2011, 18(3):507-522.
9. Turner N, Tutt A, Ashworth A: Hallmarks of 'BRCAness' in sporadic cancers. Nat Rev Cancer 2004,
4(10):814-819.
10. Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474(7353):609-615.

4. Using Cytoscape for Social Network Research (Fowler, 0.72FTE: Janusz
Dutkowski; Pico, 0.48FTE: Alex Pico, Alex Williams)
It is well known that humans tend to associate with other humans who have similar
characteristics, but it is unclear whether this tendency has consequences for the distribution of
genotypes in a population. Although geneticists have shown that populations tend to stratify
genetically, this process results from geographic sorting or assortative mating, and it is unknown
whether genotypes may be correlated as a consequence of non-reproductive associations or
other processes.
In this TRD project, we began with a study of social networks and genotypes from the
National Longitudinal Study of Adolescent Health [1,2] and a replication study on an
independent sample from the Framingham Heart Study. These studies showed that homophily
and heterophily occur on a genetic (indeed, an allelic) level, which has implications for the study
of population genetics and social behavior. In particular, the results suggest that association
tests should include friends' genes and that theories of evolution should take into account the
fact that humans might, in some sense, be "metagenomic" with respect to the humans around
them. The analytical methods developed for these studies were implemented in the R scripting
language, while the visualization methods were provided by a collection of disparate tools, none
of which were tailored for network visualization or for integration with R.
During this reporting period, we collaborated with the Pico group on developing new
technologies for network analysis and visualization that complement and many cases replace
prior methods. In particular, we developed the CyNetworkSignificance plugin, which can perform
the same analysis pipeline formerly executed in R and other chart and network visualization
tools, but all in a single tool, integrated with wide-ranging functionality through other plugins.
After loading a social network into Cytoscape together with genotypic or other data attributes,
you can launch CyNetworkSignificance and customize the following parameters. Select the data
attribute to use for correlation. Select the correlation method (e.g., Pearson). Choose the
number of randomized trials to compare against and randomization method (e.g., shuffle
nodes). The hit “Run” and the plugin will calculate correlation values for the original network and
each of the randomly generated networks for each Nth-degree represented in the network (e.g.,
from pairs of nodes directly connected, to pairs of nodes connected by N-degrees of
separation). These correlation values match the results of the existing R analysis. We will also
add a histogram visualization feature to the plugin before its official release (Fig 1.)

Figure 1. Social network of the Hadza hunter-gatherers of Tanzania. This analysis in
Cytoscape reproduces the results published earlier this year in Nature by Fowler et al., that
show a strong social network-dependence on the donation of public goods across and within
groups [3]. The histogram plot is a mock-up at this stage, but based on the correlation values
calculated by CyNetworkSignificance on the original and randomized networks.

For extended R analyses, we are leveraging a new community-contributed plugin called
RCytoscape, which allows us to send network data to Cytoscape from within R after completing
an analysis. The network and associated node and edge attributes are then available for
visualization and analysis within Cytoscape. The workflows enabled by these technologies will
support the types of analyses we are most interested in pursuing through our DBPs and
collaborations.
The NRNB grant has provided not only direct funding for my group, but also has created
a unique fluidity of ideas and effort across NRNB sites. This project, for example, would not
likely have been initiated (let alone completed) outside of this resource organization, where we
could immediately launch and execute the work in collaboration with the Pico group without
establishing a new subcontract. The success of this intra-NRNB collaboration serves as a
practical example of how our resource can work in new ways and will likely inspire future cross-
group activities.

Applications
We just recently completed the technical implementation of the new Cytoscape plugin and R
workflows. We have performed post-hoc analyses on prior datasets to confirm the reproduction
of results from the prior methods. Indeed, the tools work well and should streamline future
analyses. During the next reporting period we will apply the new technologies from this TRD to
our ongoing research, DBPs and Collaborations. Specifically, we will be following up on the
findings above with a genome-wide study of correlated genotypes with the goal of using

associations to learn more about the role of networks in recent human evolution. By correlating
these associations with measures of nucleotide diversity, we hope to show that the genotypes
under strongest friendship selection are also those under the strongest natural selection.
In the meantime, we continue to publish with and track the work of our DBPs, applying
social network analysis methods to the study of obesity and aspirin use and cardiovascular
events [4,5]

References
1. Fowler JH, Dawes CT, Christakis NA. Model of genetic variation in human social networks.
Proc Natl Acad Sci U S A. 2009 Feb 10;106(6):1720-4. Epub 2009 Jan 26. PMID: 19171900;
PMCID: PMC2644104.
2. Fowler JH, Settle JE, Christakis NA. Correlated genotypes in friendship networks. Proc Natl
Acad Sci U S A. 2011 Feb 1;108(5):1993-7. Epub 2011 Jan 18. PMID: 21245293, PMC3033315
3. Coren L. Apicella, Frank W. Marlowe, James H. Fowler and Nicholas A. Christakis. Social networks
and cooperation in hunter-gatherers. Nature, Vol. 481, Pg. 497-501.
4. Block JP, Christakis NA, O'Malley AJ, Subramanian SV. Proximity to food establishments and body
mass index in the Framingham Heart Study offspring cohort over 30 years. Am J Epidemiol. 2011 Nov
15;174(10):1108-14. Epub 2011 Sep 30.
5. Strully KW, Fowler JH, Murabito JM, Benjamin EJ, Levy D, Christakis NA.Aspirin use and
cardiovascular events in social networks. Soc Sci Med. 2012 Apr;74(7):1125-9. Epub 2012 Feb.

5. Cytoscape 3.0 for the Visualization and Representation of Biological Networks
(Bader, 1.0FTE: Christian Lopes, Jason Montojo)
Our major activity over the past year has been to ensure that Cytoscape 3.0 supports the
advanced visualization and representation features that we proposed in the NRNB grant, both in
system design and performance. This has required major effort porting visualization features
from Cytoscape 2.8 and developing new visualization features in Cytoscape 3.0 to test the
design of the new Cytoscape 3 application programming interfaces (APIs). For instance, we
worked with the Ideker software development team to port Cytoscape 2 graph layout algorithms
to Cytoscape 3. We also developed a full featured 3D graph visualization and layout system to
test that Cytoscape can handle multiple types of visualization systems at the same time
(http://wiki.cytoscape.org/Cytoscape_3/3D_Renderer). This resulted in a substantially improved
design for support of multiple simultaneous visualization engines in Cytoscape 3. Finally, we
worked in collaboration with the i-Vis Information Visualization Research Group of Bilkent
University to develop a compound node model for Cytoscape Web, which is a necessary feature
for pathway visualization on the web and full compatibility with the Cytoscape 3 network model.
We are also laying the groundwork for representation and visualization of detailed
biological pathway information in Cytoscape 3. We have completed the following activities in this
area.
● Tested and updated the design of the core Cytoscape 3 model to ensure hierarchical
network models can be stored, queried, saved and loaded. This is the foundation for
many advanced visualization features that we proposed in the grant, such as
hierarchical views necessary for biological pathway visualization.
● Developed a prototype of a new app that uses the latest Cytoscape 3 API and Pathway
Commons web services and client API, which provides search, access, and analysis of
biological pathway information from the BioPAX Level 3 data warehouse (warehouse
development funded by the Pathway Commons project). Also, we ensured that biological
pathway information in the standard BioPAX format can be seamlessly mapped to the
Cytoscape 3 network model.

Ensuring Cytoscape 3 will enable our stated aims has required tremendous effort, in that we
have need to implement a number of prototype features to test that the API design is robust.
This work will pay off in 2012-2013 as we finally release Cytoscape 3 and start working on novel
visualization features in earnest.

Applications
While Cytoscape 3 work is still in the active development phase and we anticipate many
applications next year and beyond, we continue to maintain our highly successful Enrichment
Map visualization plugin for Cytoscape 2.8, responding to frequent requests by users for new
features. This visualization tool is heavily used in all of our collaborations with local biology
groups (see Collaboration and Service Projects) and by others (the papers describing the
method garnered almost 40 citations since 2010 [1]). In the following year, we plan to port this
system to Cytoscape 3.0 and to integrate it with popular pathway enrichment analysis software,
such as the Gene Set Enrichment Analysis (GSEA) software from Jill Mesirov’s group at the
Broad Institute, MIT. We also continue to publish with and follow the work of our DBPs, whom
have had a very productive year applying Cytoscape and network analysis approaches to the
study of the yeast interactome, genetic interactions and metabolism [2-5].

References
1. Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for
gene-set enrichment visualization and interpretation. PLoS One. 2010 Nov 15;5(11):e13984. PMID:
21085593; PMCID: PMC2981572.
2. Baryshnikova A, Costanzo M, Kim Y, Ding H, Koh J, Toufighi K, Youn JY, Ou J,San Luis BJ,
Bandyopadhyay S, Hibbs M, Hess D, Gingras AC, Bader GD, Troyanskaya OG, Brown GW, Andrews B,
Boone C, Myers CL. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.
Nat Methods. 2010 Dec;7(12):1017-24. Epub 2010 Nov 14
3. Bellay J, Atluri G, Sing TL, Toufighi K, Costanzo M, Ribeiro PS, Pandey G,Baller J, VanderSluis B,
Michaut M, Han S, Kim P, Brown GW, Andrews BJ, Boone C, Kumar V, Myers CL. Putting genetic
interactions in context through a global modular decomposition. Genome Res. 2011 Aug;21(8):1375-87.
Epub 2011 Jun 29
4. Magtanong L, Ho CH, Barker SL, Jiao W, Baryshnikova A, Bahr S, Smith AM,Heisler LE, Choy JS,
Kuzmin E, Andrusiak K, Kobylianski A, Li Z, Costanzo M,Basrai MA, Giaever G, Nislow C, Andrews B,
Boone C. Dosage suppression genetic interaction networks enhance functional wiring diagrams of the
cell. Nat Biotechnol. 2011 May 15;29(6):505-11. doi: 10.1038/nbt.1855.
5. Szappanos B, Kovács K, Szamecz B, Honti F, Costanzo M, Baryshnikova A,Gelius-Dietrich G, Lercher
MJ, Jelasity M, Myers CL, Andrews BJ, Boone C, Oliver SG, Pál C, Papp B. An integrated approach to
characterize genetic interaction networks in yeast metabolism. Nat Genet. 2011 May 29;43(7):656-62.
doi:10.1038/ng.846.

6. Visualizing Complex Networks as Ontology-Partitioned Mosaics (Pico,
0.48FTE: Alex Pico, Kristina Hanspers)
Increasing throughput and quality of molecular measurements in the domains of genomics,
proteomics and metabolomics continues to fuel the understanding of biological processes.
Collected per molecule, the scope of these data extends to physical, genetic and biochemical
interactions that in turn comprise extensive networks. One challenge faced by these tools is how
to make sense of such networks, which are often represented as massive “hairballs.” Many
network analysis algorithms filter or partition networks based on topological features, optionally
weighted by orthogonal node or edge data [1,2]. Another approach is to mathematically model
networks and rely on their statistical properties to make associations with other networks,

phenotypes and drug effects, sidestepping the issue of making sense of the network itself
altogether [3]. Acknowledging that there is still great value in engaging the minds of researchers
in exploratory data analysis at the level of networks, we have produced a Cytoscape plugin
called Mosaic [4] to support interactive network annotation and visualization that includes
partitioning, layout and coloring based on biologically-relevant ontologies (Fig 1). The ultimate
effect of Mosaic is to present slices of a given network in the visual language of biological
pathways, which are familiar to any biologist and ideal frameworks for integrating knowledge.

Figure 1. Mosaic control panel, context menu and tiled result windows. The control panel
shows both the color mapping legend and subnetwork display. Context menus for listed
subnetworks allow the user to partition deeper within a given ontology branch.

While Mosaic can run using practically any annotation, the primary usage relies on
ontology-based annotations, especially Gene Ontology. GO provides a controlled vocabulary of
terms describing key characteristics of gene products (i.e., process, location, and function).
Mosaic manages all identifier mapping and ontology annotation functions via integrated
databases and CyCommand access to CyThesaurus. The program then proceeds to partition,
layout and color the provided network. All subnetworks are listed hierarchically, including
subnetworks that fall outside defined thresholds for display. Selecting a subnetwork in the
control panel will bring it into focus in the tiled window view. Additional functions can be
accessed by right-clicking on the name of a particular subnetwork in the control panel. In
particular, "partition this network to one further level" allows users to interactively partition a
huge network to deep levels of GO efficiently without generating hundreds of other subnetworks
from parallel branches.

Applications
This visualization approach is ideal for many types of ontology-based overrepresentation
analyses. As such, we are now working on an ensemble of plugins to handle the complete
pipeline from annotation to analysis to visualization. This is in collaboration with two new CSPs
established during this reporting period. Through these collaborations and others we will publish

a series of reports on the applications of Mosaic and our integrated ontology analysis tools in
Cytoscape during the next reporting period.

References
1. Bader, G.D. and Hogue, C.W. (2003) An automated method for finding molecular complexes in large
protein interaction networks, BMC Bioinformatics, 4, 2.
2. Royer, L., et al. (2008) Unraveling protein networks with power graph analysis, PLoS Comput Biol, 4,
e1000108.
3. Machado, D., et al. (2011) Modeling formalisms in Systems Biology, AMB Express, 1, 45.
4. Zhang C, Hanspers K, Kuchinsky A, Salomonis N, Xu D, Pico AR. Mosaic: Making Biological Sense of
Complex Networks. Bioinformatics, 2012. (accepted with minor revisions)

7. The CYNI Modular Network Inference Framework (Schwikowski, 1.08FTE: Frank
Rugheimer, Oriol Guitart)
Our goal during this period was the definition, implementation, and testing of workflows for
network induction for use in biological application projects and Cytoscape DBPs and CSPs. As
the other TRD projects, this project, too, requires a combination of domain expertise (research-
grade expertise in the area of network induction), which has been available to us for one year at
the time of this writing (Frank Rügheimer, who had been involved in the DBP) and software
engineering capability, which we found difficult to muster until recently. We therefore proceeded
to first develop and implement a CYNI prototype in C, and apply it in the context of our DBP, to
transcriptome data from the soil bacterium Bacillus subtilis. In a second step (starting March 1,
2012), a professional computer engineer with more than five years of experience in industry and
academia (Oriol Guitart-Pla) has begun to integrate these software components into the
Cytoscape 3 framework. Proceeding in this order had the added advantage that CYNI can now
be implemented against a stable Cytoscape 3 core. As the prototype was implemented using an
object-oriented design, its translation into Java is straightforward.

Definition of the CYNI software components
The Figure below outlines the CYNI software architecture and current implementation state. The
core of the ‘astre Extended prototype’ is a network inference toolbox that provides a data model
and functionality for computing association measures, which are an essential component of
network inference algorithms, from data. This prototype was combined with an external text
parser library (distributed under LGPL) and expanded into a functional command-line tool in C.
In combination with the prototype implementation of a higher-level path-based network induction
approach (scoreKO) and supporting command line scripts for preprocessing a complete
processing pipeline is provided. The pipeline was developed within the DBP, which allowed to
evolve design and its implementation in its application context, and helped guide the integration
of software features towards relevant requirements of that application.

Figure 1. Current view of CYNI architecture and implementation.

astre Network inference toolbox
In our prototype toolbox, Cytoscape node attribute tables are represented via feature vectors.
Each feature vector represents a case that is described as a joint instantiation over an attribute
set (e.g. time series for RNA expression levels for a given gene). Simple node association
measures, such as correlation, are computed directly for pairs of feature vectors. Beyond that,
additional support functionality for contingency tables, discretization and ranking, enables the
implementation of more advanced measures that draw on robust statistics and information
theory.

Supported discretization/ranking mechanisms to-date:
● Standard ranking
● Fractional ranking
● Quantile-based binning

Supported association measures to-date:
(values marked with * use contingency tables)
● Pearson correlation coefficient (numerical vectors only)
● Spearman rho rank correlation (ordinal scale or better)
● d2* (sum of element-wise squared deviation of contingency table from expected
distribution under independence) (any type)
● Mutual information* (also Shannon information gain) (any type)
● Shannon information gain ratio* (any type)
● Kendall tau rank correlation* (ordinal scale or better)

The astre Network inference toolbox can be used either interactively or in batch mode. At
startup the program reads an attribute value table that contains data to be used for computing
interaction measures. In interactive mode the program will then continuously process queries for
edge association measures and write output as is becomes available. This on-demand
computation allows highly efficient heuristic search strategies. Alternatively, a predefined list of
queries can be processed in batch mode. By restricting the selection of queries, it is possible to
enforce structure constraints on the induced network.

astre also implements unit tests for critical data structures and the majority of
implemented measures and discretization methods. As the unit tests can mostly be translated
into Java in a straightforward way, they provide a defense against regression errors during the
code refinement and optimization phase of CYNI development. For the same purpose, we
conducted profiling runs and optimized a number of the core algorithms (initially planned for
year 3).
Converter scripts are provided to re-import the externally calculated results into
Cytoscape for visualization and optional further processing.

Sample workflow (compute association measures):
1. Load table data (e.g. expression matrix) into CLI tool and select suitable association
measure
2. Generate queries and pass them to CLI tool to obtain association values or edges
3. Integrate association values into higher level network induction strategies
Implementation of the scoreKO approach
In addition to simple co-expression networks, we implemented a prototype higher-level network
induction component, which we developed in the context of a large integrated EU-funded
research project. This prototype generates networks based on plausible chains of gene
regulatory interactions that connect a selection of source nodes to targets nodes in the network
(manuscript in preparation).

Figure 2. Illustration of prototype network induction component. From left to right: Network
based on initial node association measures; Selected source nodes {A,B,C}; Selected target
node {I}; Reduced network consisting of all interaction occurring on (near-) optimal interaction
chains.

Feature export from CYNI to other modules
Some CYNI elements share functionality with other Cytoscape plugins. In particular the
symmetric association measures implemented (all but mutual information and mutual
information gain) provide natural notions of similarity and can be used in tasks such as
hierarchical clustering. The same holds true for symmetric versions of the information gain ratio,
that can be produced e.g., by averaging the value obtained by for both possible link directions.[1]
An interesting option, which we consider, is an interface to register, group and access
implementations of similarity and distance measures as a useful approach to foster reuse and to
prevent redundancy between Cytoscape plugins. We are currently in contact with other
Cytoscape developers (e.g., of the ClusterMaker plug-in) to present a draft proposal for such an
interface to the Cytoscape community. The export of discretization and ranking features could
be organized in a similar way.

Current Activities, translation of astre into the Cytoscape 3 framework
The arrival of a software engineer (Oriol Guithart) on March 1, 2012, marked the start of the
CYNI implementation and integration of astre into Cytoscape. astre data structures and
algorithms can largely be translated without modifications into Java/the Cytoscape framework.

In parallel, we continue to increase test coverage of the implemented algorithms and
evaluate the addition/modification of features based on experiences in ongoing application
projects.

Applications
In our collaboration with the lab of Jan Maarten van Dijl (Groningen, Netherlands), this workflow
was applied to a network (418 nodes; 174,306 edges) to explore the unknown chains of
regulatory interactions between the central carbon metabolism and the competence subsystem
of Bacillus subtillis. The approach identifies hypothetical regulatory chains from expression data,
perturbation sites in the known regulatory network segment and a marker gene associated with
the so-called competence phenotype. Suggested knockout targets were selected from
candidate pathways identified by our network induction prototype. Currently, a subset of the
proposed genes are evaluated in knock-out experiments to validate or their reject their
involvement in the putative regulatory cascade, and to collect additional pertinent transcriptome
data that may be fed back into our analysis.

II. Collaboration and Service Projects: Progress (1.3FTE: Alex Pico,
Rintaro Saito, Kristina Hanspers)
In addition to the direct impact of our TRD projects on our research, NRNB also has an effect on new
science through our many CSPs. A description for each CSP is provided in the bulk of the report. Here,
we summarize the efforts.

8. New Collaborations
During our second year, we established a formal collaboration processing system for NRNB.
Each of the 5 NRNB sites has a designated Collaboration Contact who is responsible for
managing collaboration and service requests. They can start by directing potential collaborators
to the main NRNB website at nrnb.org, where they will find numerous hooks into our
collaboration system. Clicking on ‘Collaborate’ for example, leads to a simple web-based form,
which is automatically logged in our Collaboration Tracker spreadsheet and email notifications
are sent to the contact. Entries are assessed per the availability and interest of each group. If
accepted, they are marked for entry into our annual reporting system. If not accepted, they are
marked as rejected but still recorded for reporting purposes. Numerous potential collaborators
also independently find the collaboration hooks on our website, such as the mentoring programs
which bring in the largest numbers and some of the most diverse and productive collaborations
(see below).
At the end of year-one, we had established close to 40 collaborations. During the course
of our second year, we took on another 60, totaling 97 collaborations in all! These range from
the application of Cytoscape as a research tool for network analysis and visualization, to the
development of Cytoscape plugins for custom data types and analyses, to the development and
application of other network and pathways tools and resources for network biology.

Applications of Cytoscape
In this category, we are enabling a wide range of medical research applications [1-3] including
the study of Frontal Temporal Dementia, Alzheimer’s disease, Diabetes, Anorexia nervosa,
Glaucoma, Heart disease, Leukemia, Brain tumors, Autism, Prostate cancer, Breast cancer,
Endometrial cancer, Colorectal cancer, Lung cancer, and Malaria. Through NRNB
collaborations, Cytoscape is also being applied to study of the mechanisms [3,4] underlying
inflammation, stem cell differentiation, B-cell differentiation, ciliogenesis, cell-cell
communication, oxidative stress response, DNA repair, cancer stem cells, and wound healing,
as well as general interactome, proteomics and metabolomics research [5,6].

Development of Cytoscape Plugins/Apps
It is a testament to the extensible model of Cytoscape and our outreach efforts to provide
training and documentation to developers, that we get an equal number of collaboration
requests for developing new Cytoscape features, which in turn can be applied to not only our
immediate collaborators’ research, but more broadly to the Cytoscape user community. This is a
very gratifying virtuous cycle that NRNB is specifically enabling and amplifying. In this category,
we have established collaborations to develop plugins and apps [7,8] to connect with public
databases to access and load interactions and annotations, to provide new types of data
visualizations, to perform ontology analysis, graph analysis, partitioning, quantitative modeling,
and to handle new data types such as next-gen sequencing data and variant data. We also
have collaborations to develop interoperability between Cytoscape and 3D molecular
visualization tools, and integrated workbenches, such as the Cancer Gene Encyclopedia and
the cBio Cancer Genomics Portal.

Development and Application of Other NRNB Tools and Resources
In this final category of collaborations, we are beginning to extend beyond the immediate reach
and scope of Cytoscape to identify complementary tools and resources that contribute
significantly to network biology. NRNB allocates time and resources to promote and engage
these other efforts, such as by making NRNB-funded network tools available within cBio, by
coordinating the curation of biofuel pathways at WikiPathways, by adding network analysis
functionality to Broad’s IGV (Integrative Genomics Viewer), and by promoting the use of
BaSysBio (Bacillus Systems Biology) [9-11].

References
1. Liu JC, Voisin V, Bader GD, Deng T, Pusztai L, Symmans WF, Esteva FJ, Egan SE,Zacksenhaus E.
Seventeen-gene signature from enriched Her2/Neu mammary tumor-initiating cells predicts clinical
outcome for human HER2+:ERα- breast cancer. Proc Natl Acad Sci U S A. 2012 Apr 10;109(15):5832-7.
Epub 2012 Mar 28.
2. Zhang L, Lim SL, Du H, Zhang M, Kozak I, Hannum G, Wang X, Ouyang H, Hughes G,Zhao L, Zhu X,
Lee C, Su Z, Zhou X, Shaw R, Geum D, Wei X, Zhu J, Ideker T, Oka C, Wang N, Yang Z, Shaw PX,
Zhang K. High temperature requirement factor A1(HTRA1) gene regulates angiogenesis through
transforming growth factor-β family member growth differentiation factor 6. J Biol Chem. 2012 Jan
6;287(2):1520-6.Epub 2011 Nov 2.
3. Dutkowski J, Ideker T. Protein networks as logic functions in development and cancer. PLoS Comput
Biol. 2011 Sep;7(9):e1002180. Epub 2011 Sep 29
4. Atwood A, DeConde R, Wang SS, Mockler TC, Sabir JS, Ideker T, Kay SA.Cell-autonomous circadian
clock of hepatocytes drives rhythms in transcription and polyamine synthesis. Proc Natl Acad Sci U S A.
2011 Nov 8;108(45):18560-5.Epub 2011 Oct 31
5. Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol. 2010 Nov
10;26:721-44. Review
6. Diezmann S, Michaut M, Shapiro RS, Bader GD, Cowen LE. Mapping the Hsp90 Genetic Interaction
Network in Candida albicans Reveals Environmental Contingency and Rewired Circuitry. PLoS Genet.
2012 Mar;8(3):e1002562. Epub 2012 Mar 15.
7. Aranda B, Blankenburg H, Kerrien S, Brinkman FS, Ceol A, Chautard E, Dana JM, De Las Rivas J,
Dumousseau M, Galeota E, Gaulton A, Goll J, Hancock RE, Isserlin R, Jimenez RC, Kerssemakers J,
Khadake J, Lynn DJ, Michaut M, O'Kelly G, Ono K,Orchard S, Prieto C, Razick S, Rigina O, Salwinski L,
Simonovic M, Velankar S,Winter A, Wu G, Bader GD, Cesareni G, Donaldson IM, Eisenberg D, Kleywegt
GJ,Overington J, Ricard-Blum S, Tyers M, Albrecht M, Hermjakob H. PSICQUIC and PSISCORE:
accessing and scoring molecular interactions. Nat Methods. 2011 Jun 29;8(7):528-9. doi:
10.1038/nmeth.1637
8. Morris JH, Apeltsin L, Newman AM, Baumbach J, Wittkop T, Su G, Bader GD,Ferrin TE. clusterMaker:
a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 2011 Nov 9;12:436.
9. Buescher JM, Liebermeister W, Jules M, Uhr M, Muntel J, Botella E, Hessling B,Kleijn RJ, Le Chat L,
Lecointe F, Mäder U, Nicolas P, Piersma S, Rügheimer F,Becher D, Bessieres P, Bidnenko E, Denham
EL, Dervyn E, Devine KM, Doherty G,Drulhe S, Felicori L, Fogg MJ, Goelzer A, Hansen A, Harwood CR,
Hecker M, Hubner S, Hultschig C, Jarmer H, Klipp E, Leduc A, Lewis P, Molina F, Noirot P, Peres
S,Pigeonneau N, Pohl S, Rasmussen S, Rinn B, Schaffer M, Schnidder J, Schwikowski B, Van Dijl JM,
Veiga P, Walsh S, Wilkinson AJ, Stelling J, Aymerich S, Sauer U. Global network reorganization during
dynamic adaptations of Bacillus subtilis metabolism. Science. 2012 Mar 2;335(6072):1099-103.
10. Nicolas P, Mäder U, Dervyn E, Rochat T, Leduc A, Pigeonneau N, Bidnenko E,Marchadier E,
Hoebeke M, Aymerich S, Becher D, Bisicchia P, Botella E, Delumeau O, Doherty G, Denham EL, Fogg
MJ, Fromion V, Goelzer A, Hansen A, Härtig E,Harwood CR, Homuth G, Jarmer H, Jules M, Klipp E, Le
Chat L, Lecointe F, Lewis P,Liebermeister W, March A, Mars RA, Nannapaneni P, Noone D, Pohl S, Rinn
B,Rügheimer F, Sappa PK, Samson F, Schaffer M, Schwikowski B, Steil L, Stülke J,Wiegert T, Devine
KM, Wilkinson AJ, van Dijl JM, Hecker M, Völker U, Bessières P,Noirot P. Condition-dependent
transcriptome reveals high-level regulatory architecture in Bacillus subtilis. Science. 2012 Mar
2;335(6072):1103-6.

11. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT, Pico AR. WikiPathways:
building research communities on biological pathways. Nucleic Acids Res. 2012 Jan;40 (Database
issue):D1301-7. Epub 2011 Nov 16.

9. Google Summer of Code and NRNB Academy
In addition to the outreach effort described above, we also leverage a Google-sponsored
program called Google Summer of Code (GSoC) to attract new developers for Cytoscape core,
plugins/apps, WikiPathways, PathVisio and other tools we deem relevant to the NRNB mission.
This year is the sixth year that Dr. Pico has coordinated the collective GSoC effort involving
Cytoscape; this is the second year we’ve participated under the new banner of “NRNB”.
Through the GSoC program we not only recruit new developers, but we are also significantly
promoting NRNB as an open source-friendly organization, putting us in an exclusive list of ~175
organizations selected from around the world by Google to participate. Dr. Pico attends the
annual GSoC Mentors Summit with other NRNB mentors to further engage the open source
development community. In terms of collaborations, GSoC brings in new potential collaborators
who want to participate as mentors in addition to the 40-60 student applicants. This year we
coordinated 36 mentors (10 with NRNB funding), thus leveraging the effort of 26 additional
developers from the open source communities surrounding NRNB-related tools. And through
the GSoC program we received over 60 student applications this year. From these we’ve
selected 16 students to mentor on Cytoscape and NRNB-related projects. The projects range
from core Cytoscape 3.0, to Cytoscape 3.0 apps, to GeneMANIA and MedSavant, to PathVisio
and WikiPathways, to the cBio Cancer Genomics Portal, but the majority of the projects are
Cytoscape 3.0 related. Google is paying $5,000 per student, making their investment $80,000 in
NRNB for 3 months of work. That’s what I call leveraging the community!
Inspired by this very successful model for recruiting new code contributors, we designed
and launched NRNB Academy in January of this year. The idea behind NRNB Academy is very
similar to GSoC, except it’s not restricted to students, it’s not affiliated with Google, and it’s
100% volunteer. Our experience has been that the major draw to our projects in the past has
been the opportunity to get direct mentorship in developing Cytoscape and our other tools. The
students and external mentors are eager to contribute time and effort when they know it will be
guided and effectively amplified by the interaction with NRNB, thus dramatically increasing the
odds for a productive output. In the first three months, we have already received 9 applications,
started 4 new projects, and recruited 3 new mentors. We anticipate continued growth of this
program as word spreads. One of the principal goals of NRNB is to promote and enhance the
development community around Cytoscape. The new NRNB Academy program gives us one
more way to reach out to the community and realize this potential. Based on our experience so
far, this program is effective in launching new developers and in establishing new collaborations
with long-term potential.

III. Progress on Supplemental Award, 11/2011-07/2013
We were awarded a two-year supplemental grant to work on the Cytoscape App Store. This is a progress
report on the first half of the first year.

10. The Cytoscape App Store (Pico, 1.0FTE: Samad Lotia)
The Cytoscape App Store will offer a whole new way for researchers to search, install and
develop custom apps for Cytoscape. Much of the Cytoscape App Store content will be created
by its users: ratings, comments, tags and the submission of new apps. Dynamic web sites like
the Cytoscape App Store often make use of a web framework to manage frequent changes.
First, the web site puts all of its content in a database, because databases make it easy and fast
to get the content back later. The web site code retrieves the content from the database. It then
processes the content and sends the user HTML, image, CSS, and JavaScript files, which are
shown in the user's web browser. At each step the web framework is involved in the web site's
code.
The Cytoscape App Store uses the Django web framework, which is written in Python
making it concise, versatile, and familiar. As a popular framework in the web development
community, Django also has many online forums with experienced developers willing to answer
technical questions. Django developers also have made a variety of software extensions that
provide additional functionality relevant to our App Store plans. Beyond the web framework, we
are using the MySQL database due to its ubiquity in web development. We make extensive use
of the jQuery library in JavaScript, a programming language that adds interactivity to web
pages. We also pervasively use the Twitter Bootstrap CSS library to provide a consistent and
professional-quality look to the web site.
Together, these technologies enable a rich set of features (Figure 1). Everything from
keyword search with auto-completion and dynamic navigation through tag lists and tag clouds,
to the display of interactive app buttons with icons, brief descriptions and ratings. Clicking on an
app button takes you to the corresponding app page where you’ll find a full description of the
app along with screenshots, version and author information, links to source websites and
tutorials, and a comment section for reviews, questions and bug reports. We are currently
implementing a “one-click install” feature on each app page that will allow users to install apps
from the website to any instance of Cytoscape 3.0+ that they have running. The submission of
new Cytoscape apps is also handled directly by the App Store. Simply sign in (you can use an
existing Google account), click “submit a new app”, upload your .jar file, then interactively edit
the app page as it will appear to other users.

Figure 1. Screenshots of Cytoscape App Store. The top screenshot is of the main page,
showing navigation tools on the left and two columns of app buttons (with icons, names and
brief descriptions). The first app, MetaNetter, is moused-over and expands to show ratings,
number of download and tags. The bottom screenshot show the app page for MetaNetter with
screenshots, full description, version details and the “one-click install” option.

This project will completely replace the existing Cytoscape plugins web page in the next
month or two when we roll out the 2.x version of the site. Then, in conjunction with the public

release of Cytoscape 3.0, we will update the site with the 3.x-specific features like “one-click
install”.
One of the main goals of NRNB is to actively engage developers and researchers.
Ultimately, we can provide better tools and resources by facilitating participation by the greater
community and not discounting the sum of thousands of small contributions. This model is
extensible beyond the Cytoscape project and could support software-as-a-service distribution.
As NRNB broadens its scope in future years, this app-centric, community-based model can be
cloned for other tool and resource projects.

Applications
Presently, the community is limited in how it can contribute to improve and build upon
Cytoscape. Recent developments in crowdsourcing technology and social structures and
processes have enabled public software projects to engage vastly more users. These advances
promise to take Cytoscape community support to the next level. Just as Cytoscape’s open
source extensible software architecture has enabled a rich community of app developers to
flourish, crowdsourcing technology will enable users to contribute to software testing,
documentation updates, app creation, data set curation, workflow sharing and more.
The crowdsourcing infrastructure we are proposing will not only reach out to users and
developers of apps, but also to external data sources (e.g., Sage Commons, Pathway
Commons) and other data-centric research tools (Taverna, Genome Space) through web
service and format standards tailored for the web. Advances in web technologies and
broadband connections are allowing more data and computation to migrate to the “cloud” while
user-friendly data mining and analysis tools are enabling more researchers to access these
resources. Online representations of Cytoscape apps will become hubs for groups of
researchers to connect to data resources, analytical methods and relevant results.

Appendix A. The 2012 NRNB Network

A network representation of all NRNB personnel and collaborators (blue circles), all TRD, DPB,
Collaboration, and Service projects (orange diamonds), and associated publications (green
triangles). Node size is proportional to the number of connections. Thick red borders indicate
personnel and projects directly funded by the NRNB P41 grant. There are 315 nodes and 404
connections in the network. NRNB funds 41 (13%) of these nodes, which make 217 (54%) of
the connections.

Annual Progress Report - Research Highlights 2012
P41 GM103504 (RR031228)
05/01/2011 - 04/30/2012

Contents
● NRNB Supports Development of cBio Cancer Genomics Portal
● Cytoscape 3.0 and the Cytoscape App Store in 2012
● NRNB Academy Is Now Accepting Applications

NRNB Supports Development of cBio Cancer Genomics Portal
The National Resource for Network Biology is proud to support the cBio Cancer Genomics
Portal (www.cbioportal.org), which has become a major resource for cancer genomics research
both within the TCGA and within the broader cancer research community. Since the launch of
the network analysis features in November 2011, the Portal has had 6,306 unique visitors, and
has served up over 275,000 page views. The cBio Portal was also recently highlighted in The
Scientist, as “a user-friendly site for working with data from TCGA and other data sets” [1]. The
article points out the easy-to-use and valuable network and pathway visualization capabilities:

Just enter your gene—say, Trim2—in the gray field and click Submit. After you
select the tumor type and click View Cancer Study Details, you can review the
network of known gene interactions and pathways involving the gene under the
Network tab. You can mouse over a gene, represented as a node, to see a color-
coded wheel summarizing its mutation, expression, and copy number status.

Bringing network perspectives to critical data sets is a shared goal of the cBio project and
NRNB.

1. Storrs C: Combing the Cancer Genome. The Scientist 2012, Mar.

Cytoscape 3.0 and the Cytoscape App Store in 2012
A primary goal of NRNB is to amplify and propagate the community development model of
Cytoscape. Cytoscape is a core research tool that is used and/or developed by almost every
project and collaboration engaged by the NRNB. We are developing version 3.0 of Cytoscape,
which represents a marked evolution of our architecture designed to modularize the core of
Cytoscape, define a clear and consistent API, and simplify the experience of customizing
Cytoscape. The 4th milestone release and the first beta release of the API will be available at
the end of May 2012. The beta API release is the point at which we expect external developers
to be able to comfortably port their plugins without having to make significant changes before
the final 3.0 release. Some of new features included in 3.0 include a quick-start welcome
screen that provides simple mechanisms for loading networks and attributes, a simplified user
interface, and many small improvements such as edge bundling layout.
The Cytoscape App Store will open with the release of Cytoscape 3.0 and offer a whole
new way for researchers to search, install and develop custom extensions to Cytoscape. As

extensions are ported from older versions or developed anew for 3.0, they will be rebranded as
apps to acknowledge the shift in the underlying technology and in our focus on these
customizations as the primary drivers for Cytoscape’s success and its future relevance and
impact. The Cytoscape App Store will manage the submission of new apps, generating a suite
of unique content and functions around each app to support community reviews, ratings,
comments, as well as “one-click install” and a variety of navigational tools.
In conjunction with the Cytoscape App Store, the 3.0 of Cytoscape release will further
accelerate the recognition, adoption and customization of the Cytoscape platform by the
network biology research community.

NRNB Academy Is Now Accepting Applications
Taking on a new approach to outreach and training, we launched NRNB Academy in January,
2012. NRNB Academy offers software developers from around the world the opportunity to work
with our open source development team on network biology related tools and resources. The
program provides a framework for training with a list of starter projects and a host of mentors to
be paired with new developers. It is completely volunteer-based and offers participants flexible
project terms. The main goals of the NRNB Academy are:

○ To promote development of scientific tools for network biology
○ To offer participants practical open source dev experience
○ To produce useful tools and resources for the research community

More information about potential projects and the application process is available at
nrnb.org/academy. In the first three months, we received 9 applications, started 4 new projects,
and recruited 3 new mentors for our Google Summer of Code effort. We anticipate continued
growth of this program as word spreads. One of the principal goals of NRNB is to promote and
enhance the development community around Cytoscape. The new NRNB Academy program
gives us one more way to reach out to the community and realize this potential. Based on our
experience so far, this program is not only effective in launching new developers, but also in
establishing new collaborations with long-term potential.

Annual Progress Report - Administrative Information 2012
P41 GM103504 (RR031228)
05/01/2011 - 04/30/2012

Administrative Structure
During the first year, we defined the administrative structure of the resource, including some
unique new roles within the organization. The roles of Principal Investigator (PI), Co-PI, External
Advisory Committee (EAC), Resource Administrator and Chief Software Architect were defined
as in the original grant. We defined a new role of Executive Director (ED) to oversee some of
the new resource functions that NRNB provides, including Training & Outreach,
Communications and Infrastructure. The ED (Alex Pico, Gladstone Institutes) is responsible for
coordinating these efforts as well as conducting all of the necessary tracking and due diligence
for the annual reporting to NIH. During the second year, we defined the new role of
Collaboration Coordinator to screen and process collaboration requests to our resource. This
has been a vital role in supporting the 60+ new collaborations in year two. Finally, we were very
pleased to have all seven invited members promptly agree to join and attend our first EAC
meeting last summer, including Dr. Stephen Friend as chair of the committee.
Budget changes between years 1 and 2 were minimal, with a few exceptions. In Figure
1A, you will notice an increase overall due mainly to annual cost-of-living raises for personnel in
each of the 3 budget categories: PIs, TRDs and Staff. The one main exception is the new staff
position for Collaboration Coordinator created in year 2 (Fig 1A, red, circled).
A B

Figure 1. Budget graphs. Area charts showing the distribution of funds for years 1 and 2 (x-
axis) per category (A) and per group (B). Y-axis is in units of $1,000s of US dollars. Each stripe
corresponds to an individual with a specific role in NRNB, totaling just over 7 FTEs. Note that
groups are sorted by degree of change, which is critical in this style of visualization to minimize
misperception of change when slopes are actually parallel.

In panel B of figure 1, you will notice slight increases from raises, except where countered by a
decrease in FTE (e.g., Fowler). More significant increases Conklin and Ideker budgets are due
to increased TRD support for the Conklin group (which needed correction after new ED and
Communications Coordinator staff roles were defined and not originally budgeted for) and to the
new role of Collaboration Coordinator in the Ideker group (same as in panel A).
As the basis for the graphs above, here are itemized tables of FTEs and funding for both
years 1 and 2 (Table 1).

FTEs $1,000s
Roles and Groups Year 1 Year 2 Year 1 Year 2
Collaboration Coord. 0.00 0.50 0 50
Resource Admin. 1.00 0.56 52 38
Chief Architect 0.40 0.40 47 51
TRD-Ideker 0.50 0.50 40 45
PI-Ideker 0.30 0.30 74 78
Communications Coord. 0.30 0.30 29 29
Executive Director 0.50 0.50 56 56
TRD-Conklin 0.20 0.48 21 39
PI-Conklin 0.02 0.02 5 5
TRD-Sander 0.65 0.65 90 97
PI-Sander 0.02 0.02 5 5
TRD-Bader 1.00 1.00 90 93
PI-Bader 0.10 0.10 0 0
TRD-Schwikowski 1.00 1.08 81 83
PI-Schwikowski 0.08 0.08 0 0
TRD-Fowler 1.00 0.72 58 54
PI-Fowler 0.10 0.10 21 26
SUBTOTAL 7.17 7.32 669 750

Supplement-Ideker 0.00 0.40 0 45
Supplement-Conklin 0.00 1.00 0 85
Supplement-Bader 0.00 0.40 0 45
SUBTOTAL 0.00 1.80 0 175
GRAND TOTAL 7.17 9.12 669 925

Table 1. NRNB effort and budget. Annual budgeting of FTEs and $1,000s, itemized by roles
and groups. Subtotals are provided for the main grant and supplemental funding (bold).

Allocation of Resource Access
Beyond the active distribution and support of Cytoscape, which is covered in later sections,
NRNB resource allocation can be categorized in the following way:

1. On-site training events: NRNB staff have participated in 20 training events during the
reporting period, up from just 7 last year. These events include tutorials, workshops and
courses.
2. Requests for collaboration and mentorship: This year we ramped up our
responsiveness to requests for collaboration by designation Collaboration Czars at each
NRNB site and funding a Collaboration Coordinator position to oversee the processing of

collaboration requests. With a 277% increase in established collaborations (from 35 to
97), we are confident our new strategies are working. Many of these collaborations are
coming through our participation in Google Summer of Code (GSoC) and our own NRNB
Academy efforts (see #3). All told, we rejected 43 requests during this same time period;
39 of these were students through GSoC.
3. Google Summer of Code and NRNB Academy: In addition to receiving requests from
potential students through these programs, we also receive requests from a number of
groups to join our organization as mentors. This brings new technology and ideas to our
effort. GSoC has been our most successful outreach program by far. It’s responsible for
25% of all our NRNB collaborations (24 out of 97). And by the website traffic report
below (Fig. 2), you can also see that it is the most active time period for use of
NRNB.org online resources, getting NRNB broad exposure in the open source
community. Building on the success of this model, we launch NRNB Academy in
January of this year. Our Academy follows the same approach as GSoC, organizing
around available mentors, ideas and interested students. However, we are not restricted
to supporting university students in our program as it is independent of GSoC and 100%
volunteer based. The Research Progress and Highlights provide more details.
4. Requests for training material support: We receive requests for tutorial materials
throughout the year from inside and outside the Cytoscape core development team. Our
homegrown Open Tutorials system makes it easy to accommodate all such requests.
Open Tutorials is an easy-to-use wiki system that provides content formatted to be used
as online sessions, slide shows and printed handouts. This year we are seeing more
content from more contributors, in addition to a steady rise in visitors (see details in the
Training section below).
5. Providing software community support: Our goal is to develop a generic template of
services based on the support we provide the Cytoscape community of users and
developers. So far we have extended support to two additional software projects,
internal to NRNB PI sites: WikiPathways and cBio Cancer Genomics Portal. These
proven resources complement Cytoscape and help demonstrate the broader scope of
the NRNB mission. We are providing distribution links, showcases, tutorial support, news
and event tracking, and GSoC and NRNB Academy participation to these projects.

Awards and Honors
None

Dissemination
We averaged just over 23,000 visits per month (304,000 total visits) to the Cytoscape website
during this reporting period (8% increase over last period). An additional 28,000 visits were
made to Open Tutorials and another 17,000 visits were logged at the NRNB website during the
reporting period (350% and 120% increases over last period, respectively). The front page of
the NRNB website now includes a video presentation introducing NRNB. A new Showcase page
displays graphical highlights of common workflows involving NRNB tools. The Training page is
regularly updated with information on current training events and also includes a full listing of
courses relevant to NRNB tools. But based on the analytics report, it is clear that the dominant
activity on the site relates to our outreach and collaboration through Google Summer of Code
(Fig 2).

Figure 2. A plot of daily visits since the launch of the NRNB website, December 2012 - April
2012. Notice the dramatic spikes in activity during the GSoC application weeks at the end of
March and beginning of April.

A key statistic in terms of dissemination is number of software downloads. Currently, the primary
software offered and supported by NRNB is Cytoscape and its suite of plugins. We have seen
consistent activity over the past 12 months averaging close to 5,000 downloads per month for
the Cytoscape distribution (Fig. 3).

Figure 3. Chart of Cytoscape software downloads per month over the past 12 months.

We are sustaining the increase in downloads that we experienced last year, and see this period
as the “calm before the storm.” With the anticipation for the Cytoscape 3.0 release and the
exciting plans around the new Cytoscape App Store, these numbers are sure to take on a new
growth curve before the next report.
We also make researchers aware of our tools and services through the many
conferences our representatives attend. For example, the NRNB will have a major presence at
the Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2012),
which will be held in Long Beach, California. ISMB has become the largest conference on
computational biology worldwide. This year over 1500 attendees are expected. As part of this
meeting, we are organizing the second annual Network Biology Special Interest Group (NetBio
SIG) meeting dedicated to network biology tools, resources and research applications. NRNB
tools are also represented in the research literature through our development and research
publications. Numerous Cytoscape plugin articles and research articles using Cytoscape are
published annually: 309 during this report period alone (HighWire search). We have a review
article currently under revision that covers all submitted Cytoscape plugins. We will follow that

up with a paper introducing Cytoscape 3.0 and another introducing the Cytoscape App Store,
both scheduled for release in 2012.
Finally, most visibility for our software arguably comes from our consistent dedication to
an “open source” policy. Our open-source license allows us to easily disseminate our software
code through public repositories (Sourceforge, code.google, self-hosted servers) and participate
in social networks in support of code development (Ohloh). We take very seriously our active
participation and cultivation of an open development community. This should not be taken for
granted. Many academic software projects suffer from relatively short cycles of commitment
from graduate students and postdocs progressing through their careers. The open source
model offers a means to develop software inclusively and sustainably. We have worked hard to
build, develop and maintain this community. The benefits are a sustained project that continues
to grow and to stay relevant. It also instills confidence in potential contributors as well as users
that their work will be acknowledged and that the product will persist and remain free and open.
It is through the software development community that Cytoscape maintains its most ardent
evangelists, presenting new functionality at their home institutions and through conferences and
publications.

Patents, Licenses, Inventions, and Copyrights
None. We are committed to an Open-Source dissemination policy.

Training and Outreach
Annual Cytoscape Retreat
We are just beginning to plan this year’s annual Cytoscape Retreat and Symposium, hosted by
the National Resource for Network Biology (NRNB) at the Gladstone Institutes on the UCSF
Mission Bay campus in San Francisco. In addition to developer meetings, the retreat will include
user and new developer tutorials, a Plugin Expo, and a special symposium. This year we will be
able to shift the bulk of development discussion to Cytoscape 3.0 core and apps, including
assessment of our new App Store web site and services.

Workshops
For the reporting period, NRNB has participated a total of 20 training events in 7 countries.
These events include tutorials, workshops and courses. Cytoscape is taught in many classroom
and workshop settings. We try to track all of these on our website and Event Tracker. We’ve
identified 32 courses offered in the 2011-2012 calendar year! And these are just the ones
affiliated with NRNB staff.

Open Tutorials
Our tutorial management system, Open Tutorials, is still the main source for tutorial materials for
the Cytoscape project, and is being used both internally by presenters, and by researchers and
developers. We have seen a steady increase in visits to Open Tutorials over the last year, with
an average of 2,700 visits per month for the last three months. The increase in traffic can partly
be explained by the addition of 12 new editors in the last year, contributing to several new
tutorials. Most of the development was focused on a set of 4 developer tutorials for Cytoscape
3.0, which will be critical for continued momentum on Cytoscape 3.0 development. Overall,
Open Tutorials has allowed NRNB to reach our goal of providing tutorial support to a broad and
diverse community.

Helpdesk
A major means of support for NRNB tools is through dedicated helpdesk and discussion mailing
lists. We began monitoring the activity of these lists last year for the Cytoscape community as

an ongoing metric for the effectiveness of our support. Since the previous report, we have
implemented several strategies for improving user communication and support. We are now
using an automated method for analyzing mailing list activity, which has resulted in an increase
in overall thread response rate from 64% (420/656) to 93% (583/628). Though the number of
topic threads remained about the same (-4%, from 656 to 628), the overall number of actual
messages on the mailing lists has increased 14%, from 1653 to 1877, during this reporting
period, reflecting primarily the increase in response rate as well as an overall increase in
interactive discussion. It is also worth pointing out that 25% (469/1877) of messages are
authored by NRNB staff. Periodic decreases in response rate are now easily identified and
remedied. Specifically, unanswered messages are now identified on a weekly basis and
assigned to specific staff members. Based on the analysis of mailing list topics, we have tailored
FAQ topics for maximized support impact.

Social Media
We have initiated a social media effort for Cytoscape through a number of different tools
(http://www.cytoscape.org/community.html). For example, a Twitter account is used for quick
announcements (http://twitter.com/cytoscape) and YouTube is utilized for video tutorials
(http://www.youtube.com/results?search_query=cytoscape). During this reporting period we
started a Tumblr site to capture published figures using Cytoscape. Pairs of figures are posted
on a weekly basis on the front page of cytoscape.org based on this Tumblr feed.

Google AdWords
We were awarded a non-profit account in the Google AdWords program. We are directing
>2,000 clicks a month to NRNB tools and resources via AdWords. We are running 7 campaign
groups consisting of over 700 key words and phrases. These activities are worth over $1,600 a
month, which we are getting free-of-charge. We have a spending limit of $329 per day through
this program, a potential value of $120,000 per year, so we will continue to identify new ads and
relevant resources.

Google Summer of Code and NRNB Academy
In addition to the outreach effort described above, we also leverage a Google-sponsored
program called Google Summer of Code to attract new developers. This year we coordinated 36
mentors, leveraging the effort of developers from open source communities surrounding NRNB-
related tools. And through the GSoC program we received over 60 student applications this
year. From these we’ve selected 16 students to mentor on Cytoscape and NRNB-related
projects. Google is paying $5,000 per student, making their investment $80,000 in NRNB for 3
months of work.
Inspired by this very successful model for recruiting new code contributors, we designed
and launched NRNB Academy in January of this year. The idea behind NRNB Academy is very
similar to GSoC, except it’s not restricted to students, it’s not affiliated with Google, and it’s
100% volunteer. We have already received 9 applications, started 4 new projects, and recruited
3 new mentors. We anticipate continued growth of this program as word spreads.

Annual Progress Report - Advisory Committee 2012
P41 GM103504 (RR031228)
05/01/2011 - 04/30/2012

At the conclusion of our first year, we scheduled the first External Advisory Committee (EAC),
which took place May 19th, 2011. We were very pleased to have all seven invited members
promptly agree to join our EAC and attend the first meeting. Dr. Stephen Friend serves as chair
of the committee. Following the list of committee members below are the summary statements
provided by the EAC.

Committee Members:
● Stephen Friend, M.D, Ph.D. is President, Co-Founder and Director of Sage Bionetworks. He
was previously Senior Vice President and Franchise Head for Oncology Research at Merck &
Co., Inc.
● David Hill, Ph.D. is Associate Director of the Center for Cancer Systems Biology at the
Dana-Farber Cancer Institute where he is also co-leader of the Pathogen Host Interactomes
group.
● Tamara Munzner, Ph.D. is Associate Professor in the Department of Computer Science at
the University of British Columbia and is a member of the IMAGER Graphics, Visualization
and HCI research group.
● Nicholas Schork, Ph.D. is Director of Biostatistics and Bioinformatics at the Scripps
Translational Science Institute and Professor in the department of Molecular and
Experimental Medicine at the Scripps Research Institute.
● Gustavo Stolovitzky, Ph.D. is Manager of the Functional Genomics and Systems Biology
group at the IBM Computational Biology Center. He is a Fellow of the American Physical
Society, a Fellow of the New York Academy of Sciences, and an adjunct Associate Professor
at Columbia University.
● Marian Walhout, Ph.D. is Associate Professor at the University of Massachusetts Medical
School in the program of Program in Gene Function and Expression.
● Steve Laderman, Ph.D. is the Director of the Molecular Tools Lab at Agilent Technologies,
Inc.

Summary Statements From the First External Advisory Committee
May 19, 2011
San Diego, CA

TRDs and DBPs

David Hill
DFCI/Harvard

The NRNB Technology Research and Development Projects

Each of the TRDs is successfully using existing Cytoscape tools as well as developing new
features to address important questions in network biology, and an intriguing application of
Cytoscape by the TRDs is in the social networking arena. The current efforts of all the TRDs
emphasizes the fact that Cytoscape has become the premier software for data visualization as
the TRDs are each using different features of Cytoscape for their projects. The ability to
integrate diverse data sets is key to Cytoscape maintaining a pre-eminent position, and several
of the TRDs have made effective use of dataset integration. While network visualization has
been the hallmark of Cytoscape, visualization alone is insufficient for decision-making, and
visualization can lead to erroneous conclusions/decisions without readily available statistical
analysis (including randomizations) and background annotation to support nodes in the
networks. For next year, it will be helpful to see a comparison of all of the various tools applied
to any one TRD project in order to show how meaningful results can be obtained using judicious
application of the correct set of tools and justify continued development of new tools. As a way
to demonstrate how Cytoscape provides “value added”, it would be useful to know that results
obtained using the full spectrum of Cytoscape features are at least comparable to those
obtained using standard statistical packages first. Basically, how is Cytoscape poised to move
from being an effective and efficient visualization tool to a more robust decision-making tool that
is superior to or more efficient than existing systems such as MatLab?

We are willing to serve as an alpha or beta test site for data integration and novel visualizations
as well as testing plug-ins for statistical analysis coupled to visualizations.

Cytoscape 3.0 progress

Gustavo Stolovitzky
IBM Computational Biology Center

Progress in Cytoscape 3.0
There was a discussion on the issue of backwards compatibility. There is a strong pressure
from users to have every feature of Cytoscape backwards compatible. However, many of the

NRNB Annual Report 2012

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