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<title>NRNB: Projects</title>
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<h3>Technology Research and Development</h3>
</div>
<a class="btn btn-primary pull-right"
href="presentations.html#nrnbannual">Annual Reports »</a>
<p>We are conducting an aggressive development program centered on
Technology and Research Development projects (TRDs) that will extend
the Cytoscape infrastructure to make it significantly more useful to
researchers.</p>
<ul class="nav nav-tabs" id="tabs">
<li class="active"><a href="#a-tab" data-toggle="tab">Project A</a></li>
<li><a href="#b-tab" data-toggle="tab">Project B</a></li>
<li><a href="#c-tab" data-toggle="tab">Project C</a></li>
<li><a href="#d-tab" data-toggle="tab">Project D</a></li>
</ul>
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<a class="btn btn-info btn-mini" href="project-tracking.html">Submit
Project Update</a>
<div class="tab-pane active" id="a-tab">
<h3>Project A: Identification of network-based biomarkers for
molecular classification</h3>
<h4>
Project Leaders: <a href="people.html#sander">Chris Sander</a>
(MSKCC) and <a href="people.html#trey">Trey Ideker</a> (UCSD)
</h4>
<div class="well">
<h4>A Gene Ontology Extracted from Molecular Networks (Trey Ideker, UCSD)</h4>
<p>Ontologies are of key importance to many domains of biological research.
The Gene Ontology (GO), in particular, has been instrumental in unifying
knowledge about biological processes, cellular components, and molecular
functions through a hierarchy of concepts and their interrelationships.
However, given only partial biological knowledge and inconsistency in how
this knowledge is curated, it has been difficult to construct, extend and
validate GO in an unbiased manner. We have recently showed that the existing
collection of high-throughput network maps, as are now becoming available,
can be analyzed to automatically assemble an ontology of gene function that
rivals manually curated efforts [1]. Our systematic computational approach
(Fig. 1) combines evidence from physical, genetic and transcriptional
networks to produce an ontology comprised of 4,123 biological concepts and
5,766 hierarchical concept relations (Fig. 2). Using a new ontology alignment
procedure (Fig. 1), we found that the network-based ontology captures the
majority of known cellular components and identifies approximately 600 new
cellular components and component relations – many of which we were able to
validate either experimentally or bioinformatically. By working closely with
the GO curators, we were able to incorporate selected new components and
relations into the Gene Ontology, thus providing proof-of-principle for how
to systematically update and revise the GO structure based on large-scale
data.
</p>
<p>The network-extracted ontology is a new resource for systems and synthetic
biology – i.e. a data-driven catalogue of cellular machinery, from genes, to
complexes, to pathways and higher-order processes. It provides a powerful
tool for performing multi-scale analysis of biological networks, including
automatically identifying, annotating and visualizing the complete
hierarchical structure. We also show how integrating the ontology with
additional high-throughput datasets leads to identification of new components
and processes altered in human disease. Based on our results, we suggest a
new role for ontologies in bioinformatics: rather than merely being used as
a gold-standard for performing functional enrichment, ontologies should serve
as evolvable models that are validated, revised, and expanded based on new
genomic data.</p>
<p>Moving forward, it will be interesting to see how the network-extracted
ontology can further be extended. For instance while NeXO represents a
rigorous approach to capture ontology terms and term relations, the ability
to systematically annotate the type of relation that occurs between terms
(e.g. “is a”, “part of”, “regulates”) poses a separate and very interesting
challenge. An in-depth investigation is needed to assess which network
properties are best at separating the different types of relations, and
whether there are additional data sets that might be brought to bear on this
problem. Similarly, while NeXO identifies the majority of known cellular
components, it will be interesting to further investigate what types of
network data could be used to increase the coverage of biological processes
and molecular functions. Finally, a key question is whether enough
high-quality data exist to build NeXO ontologies for other species,
particularly human, and, whether it is better to structure a common ontology
for all species, as has been done in GO, or to focus on individual
species-specific ontologies.</p>
<img src="images/IdekerF1.png" width="450"/><br/>
<b>Figure 1: Automated assembly and alignment of gene ontologies.</b> (A)
Probabilistic community detection within the input networks yields a binary
tree in which nodes correspond to ontology terms and links correspond to
parent-child term relations. Unsupported terms are replaced by multi-way
joins, and additional parent-child relations are added based on network data. The resulting ontology is aligned against
the Gene Ontology,
in a way that (B) prohibits non-unique mappings and ancestor-descendant
criss-crossing.
<br/>
<img src="images/IdekerF2.png" width="344"/><br/> <b>Figure 2:</b>
The NeXO ontology is shown as a tree, with nodes indicating terms and edges
indicating hierarchical relations between terms, i.e. that one term contains
another. Node sizes indicate the number of genes assigned to a term. Node
colors represent the degree of correspon-dence to a term in GO as determined
by ontology alignment, with high-level alignments labeled. Insets show the
hierarchy identified for the ribosome and actin cytoskeleton.
<p><b>References</b>
<ol>
<li>Dutkowski, J., Kramer, M., Surma, M.A., Balakrishnan, R., Cherry, J.M.,
Krogan, N.J., and Ideker, T., A gene ontology inferred from molecular
networks. <i>Nat Biotechnol</i>, 2013. 31(1): p. 38-45.
</li>
</ol>
</p>
<h4>Network Analysis Tools for Cancer Genomics (Chris Sander, MSKCC)</h4>
<p>This project is focused on building network analysis tools for
interpreting high-throughput cancer genomic data sets to identify altered
disease networks and enable the identification of network-based biomarkers in
cancer. 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 to clinical, experimental and computational investigators
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.</p>
<p>During our first several years 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
CytoscapeWeb [1], as CytoscapeWeb 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 user groups. We transitioned our tools from prototype to
production mode, and have made our software available to the entire cancer
research community. Cancer researchers are now using these tools to perform
network analysis on up to 20 different cancer types, including TCGA-funded
projects, such as glioblastoma multiforme (GBM) [2] and serous ovarian cancer
[3].</p>
<p>The cBioPortal for Cancer Genomics code base has recently reached a stable
state where it is now being considered as a general framework to build our
other NRNB-related tools on. Our recently finalized drug-target data support
in the context of cBioPortal’s network analysis is one such example. During
the past year, we improved the network analysis capabilities of the
cBioPortal by providing query and visualization of aggregated drug data from
multiple resources. With this new feature, the portal currently contains
gene-centric drug-target information from the following resources: DrugBank
[5], KEGG Drug [6],
<a href="http://www.cancer.gov/cancertopics/druginfo/alphalist">NCI Cancer
Drugs</a>, and Rask-Andersen et al. [7]. Within the network analysis view,
drugs are hidden by default, but can be added to the network via the Genes &
Drugs menu on the right side of the screen. Users now have the option of
displaying FDA-approved drugs, cancer drugs defined by NCI Cancer Drugs, or
all drugs targeting the query genes. For example, when the user queries for
the gene EGFR in the portal, we not only show the network context of this
gene, but also provide information about the drugs targeting the product of
this gene: gefitinib and erlotinib are tyrosine kinase inhibitors that target
the catalytic domain of EGFR, and cetuximab and trastuzumab are monoclonal
antibodies that target the extracellular domain of EGFR and ERBB2,
respectively (Fig. 1) [8].</p>
<img src="images/SanderF1.png" width="459"/><br/>
<b>Figure 1: Improved Network tab: Network analysis of epidermal growth
factor receptor networks in serous ovarian cancer.</b> (A) Network view of
the EGFR and ERBB2 neighborhood in serous ovarian cancer (TCGA data set)
rendered by Cytoscape Web. EGFR and ERBB2 are query genes (thick border),
and nearest neighbor genes are color coded by their alteration frequency in
ovarian cancer. One can display drugs that target EGFR or ERBB2 (hexagons,
orange if FDA approved), as well as details about genomic alterations
and links to external resources (lower left panel, example MYC). (B) The
portal overlays multidimensional genomic data (copy number, mutation, and
mRNA expression) onto all nodes in the network. (C) Edges can represent
different interaction types (color-coded, such as “reacts with”). (D) Options
for filtering, cropping and searching the network of interest.
<br/><br/>
<p>Our new drug-target feature is now available as part of the open-access
cBioPortal and is helping cancer researchers in exploring the therapy options
within the network context of genes of interest. </p>
<p><b>Outreach Plans</b><br/>
Since its launch in mid-2010, the cBioPortal has been extensively used by
cancer researchers around the globe, particularly by The Cancer Genome Atlas
TCGA) network. The portal currently attracts more than 1,500 unique visitors
per week. In order to help researchers use cBioPortal in their studies, we
are actively communicating with various communities, such as the TCGA network
and publicizing the tool through different channels.</p>
<p>During the last year, we have adapted and are currently maintaining an e-mail
list for users who have questions regarding the use of the cBioPortal. This
e-mail list and the questions answered by our group are publicly available at
our <a href="http://groups.google.com/group/cbioportal/">Google Groups page</a>.
Furthermore we have recently completed a manuscript that explains the use cases
of cBioPortal and its network analysis feature in details in order to
encourage wider adaptation. We believe this publication (Science Signaling)
will help researchers interested in Cancer Research to use the portal in a
more efficient way.</p>
<p>We have also participated in the last year’s Google Summer of Code (GSoC)
Program for two separate projects under the NRNB organization. The first
project, a Cytoscape 3.0 Application to facilitate downloading cancer
genomics data through the cBioPortal Web API services, was successfully lead
by Dazhi Jiao under the advisement of two members from our group. This
Cytoscape 3.0 application allows users to download data from cBioPortal,
visualize it in the network context either in an overall or sample-specific
manner, and analyze it with the help of additional Cytoscape 3.0 applications
(see Figure 2). The source code for this project is freely available at our
<a href="http://bit.ly/cbioportal">Google Code project web site</a>. The
software implementation for this project is currently being finalized
and we are planning to distribute this application through Cytoscape’s App
Store interface in the next year.</p>
<img src="images/SanderF2.png" width="437"/><br/>
<b>Figure 2.</b> A screenshot of the Mondrian application, an open-source project
conducted as part of the Google Summer of Code 2012 project. The image shows
how genomics data, downloaded from the cBioPortal through this application,
is being overlaid onto the user’s network of interest. Once the data is
loaded from the cancer studies of interest through the cBioPortal’s Web Api,
users have the option to explore multi-dimensional cancer-related data within
Cytoscape framework in a fashion that is similar to cBioPortal’s network
analysis feature.
<p>Our second GSoC project was lead by the summer student, Istemi Bahceci
under the co-advisement of one member of our group in conjunction with our
NRNB-collaborator Ugur Dogrusoz at Bilkent University. The aim of this
project was to extend CytoscapeWeb to support the Systems Biology Graphical
Notation (SBGN) for more detailed biological pathway visualization. This
project was completed over the last summer and we are currently integrating
it into the cBioPortal’s to provide better network analysis options for users.</p>
<p><b>New Driving Biological Projects:</b> In the next year, we are
anticipating improving the network analysis feature in two ways: 1) detailed
visualization of the pathways and reactions in the network view; 2) inference
of indirect drug targets, for potentially interesting therapy options, by
using genomic alteration and drug-target data. Currently, interaction types
that are shown in the network analysis view are derived from the BioPAX to
SIF inference rules [4]. 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. This reduction from BioPAX to SIF was
necessary as the Cytoscape Web framework, by then, was not supporting
visualization of more complex elements, such as compartments. With the
technology being developed as part of the CSP-100 project (Gary Bader), it
recently become feasible to visualize biological networks in a more detailed
way, therefore enabling the use of Systems Biology Graphical Notation (SBGN)
for better representation of BioPAX. As part of our NRNB collaboration with
Ugur Dogrusoz (Bilkent University, Turkey), we are aiming to adopt
SBGN-complaint views to visualize multi-dimensional cancer genomics data with
the network context (see Figure 3). This project has recently been
implemented as a proof-of-concept prototype and is now being integrated into
cBioPortal. When complete, this new feature will allow better presentation of
proteomics data (e.g. Reverse Phase Protein Array data provided as part of
the TCGA network) by allowing users to optionally switch from
a gene-centric to protein-centric view.</p>
<img src="images/SanderF3.png" width="465"/> <br/> <b>Figure 3:
Proposed additions to the current simple network view.</b> On the left is the
Simple Interaction derived from BioPAX; on the right is an example
visualization of a BioPAX network obtained from Pathway Commons. The latter
is utilizing the new visualization capabilities, Systems Biology Graphical
Notation (SBGN), of the CytoscapeWeb project. The SBGN view provides a more
detailed representation of the pathway and also provides protein-centric view
with Proteomics data mapped to specific proteins or
phospho-proteins.
<p>In the next year, we are also planning to utilize genomic alteration and
pathway data to infer clinically relevant uses of drug-target data. For this,
we intend to use down- and up-stream relationships
between genes to suggest drugs of possible interest that can indirectly
target a particular genomic alteration event in cancer samples (see Figure 4).
One historical example for such cases is the use of AKT inhibitors in
patients who bear a homozygous PTEN deletion. Without the gene PTEN and its
product, Akt proteins, which are down-stream of PTEN, cannot be suppressed,
and therefore are found to be upregulated in cancer samples that have the
homozygous PTEN deletion. In the presence of an AKT inhibitor, this
up-regulation effect can be counteracted. Another similar example of this
concept is the use of CDK4/6 inhibitors when CDKN2A is either mutated or
homozygously deleted in cancer cells. Pathway resources, such as Pathway
Commons, already provide this type of relationships between genes;
and we plan to extract this information in a systematic way and combine it
with the drug-target data in order to infer such therapy options in an
automatic manner within the cBioPortal framework. This
method and the prototype are currently under development.</p>
<img src="images/SanderF4.png" width="201"/> <br/> <b>Figure 4:</b>
Conceptual framework for inferring novel and drug-based therapy options based
on specific genomic alteration with the use of pathway context -- e.g. use
of AKT inhibitors when PTEN is altered in the tumor.
<p><b>References</b>
<ol>
<li>Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape
Web: an interactive web-based network browser. <i>Bioinformatics</i>,
26(18):2347-2348.
</li>
<li>TCGA: Comprehensive genomic characterization defines human glioblastoma
genes and core pathways. <i>Nature</i> 2008, 455(7216):1061--1068.
</li>
<li>Integrated genomic analyses of ovarian carcinoma.<i>Nature </i>2011,
474(7353):609-615.
</li>
<li>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. <i>Nucleic acids research</i>, 39(Database issue):D685-690.
</li>
<li>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. <i>Nucleic acids research</i> 2011, 39(Database issue):D1035-1041.
</li>
<li>Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, & Kanehisa M (1999). KEGG:
Kyoto Encyclopedia of Genes and Genomes. <i>Nucleic acids research</i>, 27(1),
29–34
</li>
<li>Rask-Andersen M, Almen MS and Schioth HB: Trends in the exploitation of
novel drug targets. <i>Nature Drug Discovery</i> 2011; 10:579-590.
</li>
<li>Raymond E., Faivre S, Armand JP: Epidermal growth factor receptor
tyrosine kinase as a target for anticancer therapy. <i>Drugs</i> 2000;
60:41-42.
</li>
</ol>
</p>
<h4>Network Analysis Methods for Inferring Causality in Signaling Networks
(Sander, MSKCC)</h4>
<p>The goal of our second TRD project is to develop network analysis tools
that algorithmically infer causality within signaling networks and make these
tools available. 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 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.</p>
<p>Building on this concept, we have been working on computational approaches
to model causal signaling cascades inducing observed transcriptional changes
within perturbed cancer cell lines. We have been exploring the use of
optimization algorithms adapted from statistical physics 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. This initial approach relied 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. Our prediction was that the resulting
Steiner-tree could therefore contain both gene interactions able to explain
the observed transcriptional changes and the putative target of the
perturbation. Within this past year, we determined that this approach does
not work as well as expected, and are now in the process of exploring a new
algorithmic framework that combines Gaussian graphical models with maximum
entropy methods.</p>
<p><b>New Driving Biological Projects</b><br/>A new biological driver for
deriving causality networks is inferring causal relationships within data
types and between data types, such as copy number changes and cancer genomics.
For example, we would like to investigate the relationship between
mutations in the TP53 tumor repressor and the complex copy number profile in
ovarian cancer. Another example is the exploration of causal relationships
between gene mutations. For example, mutations in
the POLE gene lead to a characteristic spectrum of mutations in other proteins. We have preliminary
results and plan to develop a network analysis approach to identify causal relationships.
</p>
<p>We are also considering looking at interactions between microbial subpopulations, starting with
the gut microbiome, where a set of interacting bacterial populations change under fluctuating constraints
provided by the host and nutrient intake. Recent work has shown the precise composition and evolution
of this population is closely coupled to the state of health of the host. Certain deviations from
equilibrium present a significant risk of invasion by pathogenic bacteria, as seen with some cancer
patients receiving bone marrow transplantations [3]. A more detailed understanding of the relationships
between gut microbial subpopulations following such aggressive treatments in the host could inform
therapeutic development leading to improved outcomes.</p>
<p>
<p>
<b>Our Related Publications</b>
<ul>
<li>Gao J, et al, Integrative Analysis of Complex Cancer Genomics Profiles
using the cBioPortal. <i>Science Signaling Protocol</i> 2013 Apr 2;6(269):pl1.
</li>
<li>Molinelli E, Korkut A, Wang W, MIller M, Gauthier N, Jing X, Kaushik P,
et al. Perturbation Biology: inferring signaling networks in cellular systems.
<i>PLoS Comp Bio </i> 2013 Dec;9(12):e1003290.
</li>
<li>Cancer Genome Atlas Network. Comprehensive molecular portraits of human
breast tumours. <i>Nature</i>. 2012; 490(7418):61-70.
</li>
<li>Cerami E, et al, The cBio Cancer Genomics Portal: An open platform for
exploring multi-dimensional cancer genomics data. <i>Cancer Discovery</i>.
May 2012, 2:401.
</li>
<li>The Cancer Genome Atlas Network, Comprehensive Molecular Characterization
of Human Colon and Rectal Cancer. <i>Nature</i> 2012; 487(7407):330-337.
</li>
<li>The Cancer Genome Atlas Network, Comprehensive genomic characterization
of squamous cell lung cancers. <i>Nature</i> 2012; 489:519-525.
</li>
</ul>
</p>
<b>References</b>
<ol>
<li>Somwar R, Shum D, Djaballah H, Varmus H: Identification and preliminary
characterization of novel small molecules that inhibit growth of human lung
adenocarcinoma cells. <i>Journal of biomolecular screening </i>2009,
14(10):1176-1184.
</li>
<li>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. <i>Proceedings of the National
Academy of Sciences of the United States of America </i>2011, 108(39):16375-16380.
</li>
<li>Jenq RR, Ubeda C, Taur Y, Menezes CC, Khanin R, Dudakov JA, Liu C, et al.
Regulation of intestinal inflammation by microbiota following allogeneic bone
marrow transplantation. <i>J Exp Med.</i> 2012;209(5):903-911.
</li>
</ol>
</p>
</div>
</div>
<div class="tab-pane" id="b-tab">
<h3>Project B: Using Cytoscape for Social Network Research </h3>
<h4>
Project Leader: <a href="people.html#fowler">James Fowler</a>
(UCSD)
</h4>
<div class="well">
<!--Intro paragraph is from original grant. Consider revising at some point-->
<p>
<b>Introduction: <br/></b>Social networks are increasingly recognized as having an important
effect on a wide variety of human behaviors, affecting the spread
of health outcomes (AIDS, obesity, drinking and smoking behavior),
emotional states (depression, loneliness, and happiness), and
political outcomes (legislative activity, judicial decisions, and
terrorist events). However, the statistical tools used to study
these effects are drawn from a tradition that focuses on dyads, or
sets of two people who are socially connected. When we find an
association in behavior within dyads, we typically cannot
distinguish three possible explanations. 1) <i>Homophily:</i>
people might choose to be friends with others because they have
the same behavior. In other words, birds of a feather might flock
together. 2) <i>Context:</i> there might be variation in the
environment than causes both individuals in a dyad to exhibit the
same behavior. For example, poor neighborhoods might create
environments that promote less healthy behaviors, causing an
association between friends who live in the same neighborhood. 3)
<i>Influence:</i> one person in the dyad actually influences the
other in some way to cause their behavior to become more like
their own.
</p>
<p>
In addition to the Network Correlation plugin developed in
collaboration with Alex Pico's group last year, we have now also
used Cytoscape to study the network of interactions in the Olfactory
system for a manuscript on “Friendship and Natural Selection”
. A theory paper predicting what we observe here was
published last year [1]. The target audience for this work is other
social network scholars and people interested in applying these
techniques to social network data. In our friendship and natural
selection paper we show that friends are more genetically related
than strangers, to the tune of about fourth cousins. So any project
that takes into account population structure might also consider
structure induced by friendship. And if there are gene
characteristics available in Cytoscape, we could apply the Network
Correlation plugin to see how far in gene-gene interaction networks
these characteristics tend to correlate.
</p>
<p>In terms of Cytoscape integration with our new work, it would be great
to have a database of natural selection scores available for each gene in
human studies, so scholars could easily visualize what parts of their network
are under recent natural selection. We have Pardis Sabeti’s Composite of
Multiple Signals scores for about 3 million SNPs. It would also be nice to
have easily available within Cytoscape tools for translating from SNPs to
genes. We will work with the Cytoscape team to make natural selection data
available and to implement methods for its visualization. This work might
relate to the visualization TRDs by Bader and Pico groups within NRNB.</p>
<p><b>Social Networks and Health</b><br/>
We originally proposed using trend motifs as a new statistical method to
investigate "Social Networks and Disease". We are no longer working on this
because we have come to believe there are other methods that are more
suitable. So we are now at Stage 1 in our work on "Social Networks and
Health", which has already led to a number of publications:
<ul>
<li>Strully KW, Fowler JH, Murabito J, Benjamin EJ, Levy D, Christakis NA.
Aspirin Use and Cardiovascular Events in Social Networks, <i>Social Science &
Medicine</i> 74 (7), 1125–1129 (March 2012)
</li>
<li>O'Malley J, Arbesman S, Steiger DM, Fowler JH, Christakis NA. Egocentric
Social Network Structure, Health, and Pro-Social Behaviors in a National Panel
Study of Americans, <i>PLoS ONE </i>7(5): e36250 (May 2012)
</li>
<li>Christakis NA, Fowler JH. Social Contagion Theory: Examining Dynamic
Social Networks and Human Behavior. <i>Statistics in Medicine</i> 32 (4): 556–577
(February 2013)
</li>
<li>Shakya HB, Christakis NA, Fowler JH. Parental Influence on Substance Use
in Adolescent Social Networks. <i>Archives of Pediatrics & Adolescent Medicine</i>
166 (12): 1132-1139 (December 2012)
</li>
<li>Rudolph AE, Crawford ND, Latkin C, Fowler JH, Fuller CM. Individual and
Neighborhood Correlates of Membership in Drug Using Networks with a Higher
Prevalence of HIV in New York City (2006-2009), <i>Annals of Epidemiology</i>,
forthcoming
</li>
</ul>
</p>
<p>
The target audience for this work includes scholars in public health.
There are also some non-health-related projects that will be precursors to
a new project in which we will match death records to the Facebook data to
ascertain social network correlates of health:
<ul>
<li>Jones JJ, Settle JE, Bond RM, Fariss CJ, Marlow C, Fowler JH. Inferring
Tie Strength from Online Directed Behavior. <i>PLoS ONE </i>8 (2): e52168 (February
2013)
</li>
<li>Jones JJ, Bond RM, Fariss CJ, Settle JE, Kramer ADI, Marlow C, Fowler JH.
Yahtzee: An Anonymized Group Level Matching Procedure. <i>PLoS ONE </i>8 (2): e55760
(February 2013)
</li>
<li>Bond RM, Fariss CJ, Jones JJ, Kramer ADI, Marlow C, Settle JE, Fowler JH.
A 61-Million-Person Experiment in Social Influence and Political Mobilization.
<i>Nature </i> 489: 295–298 (13 September 2012)
</li>
</ul>
</p>
<p>
This work might be ideally suited for a supplement grant. We would use the
Facebook and death data to predict longevity and health factors that
influence it (like MI). Next, we would develop and disseminate a Facebook
App that anyone can download that will give them health stats based on their
data. We could then use Cytoscape to show people their networks and the
health risks of their friends and friends’ friends.<br/>
<b>References</b>
<ol>
<li>Fu F, Nowak MA, Christakis NA, Fowler JH. The Evolution of Homophily.
<i>Scientific Reports</i> 2: 845 (13 November 2012)
</li>
</ol>
</p>
<!--Image from original grant-->
<img src="images/F9a.png" width="320" title="A"/><img
src="images/F9b.png" width="320" title="B"/><br/> <b>Figure:
Obesity in the Framingham Heart Study Social Network.</b> Panel (a)
shows a subcomponent of the whole FHS social network that contained
2,106 individuals in the year 2000. Node border indicates gender
(red=female, blue=male), node color indicates obesity
(yellow=BMI>30), node size is proportional to BMI, and arrow colors
indicate relationship (purple=friend or spouse, orange=family).
Created using Pajek.131 Panel (b) shows the estimated difference
between the probability that an alter is obese given that an ego is
obese in the observed FHS network and an identical network (with
topology preserved) in which the same number of obese individuals
is randomly distributed. Alter distance refers to closest social
distance between the alter and ego (alter = distance 1, alter's
alter = distance 2, etc.). Error bars show 95% confidence intervals
based on 1,000 simulations. The waves correspond to survey waves
spread out between 1971 and 2003.
</div>
</div>
<div class="tab-pane" id="c-tab">
<h3>Project C: Visualization and Representation of Biological
Networks</h3>
<h4>
Project Leaders: <a href="people.html#bader">Gary Bader</a> (U
Toronto) and <a href="people.html#alex">Alex Pico</a>
(Gladstone/UCSF)
</h4>
<div class="well">
<h4>Cytoscape 3.0 and CytoscapeWeb for the Visualization and Representation
of Biological Networks (Bader, U Toronto) </h4>
<p><b>Technologies developed with NRNB funds:</b><br/>
Our goal is to develop new technologies for visualization and representation
of biological networks.
Our grant aims are:
<ul>
<li><b>Aim 1.</b> Simplifying network views by hierarchically organizing
networks and their modules.
</li>
<li><b>Aim 2.</b> Showing only the information needed across multiple levels
of detail (semantic zooming)
and data sources (information layering)
</li>
<li><b>Aim 3.</b> Simultaneously viewing network attributes collected from
many biological experiments.
</li>
</ul>
<p>Our major activity over the past year has continued 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 working on releasing Cytoscape 3.0 and making sure all
of the application programming interfaces (APIs) present in that system will
support our aims. Cytoscape 3.0 has now been released and we are focusing on
developing new features. Over the past year, we implemented support for multiple
renderers so app authors can contribute their own visualizations. We helped
integrate the Metanodes plugin into Cytoscape 3 as CyGroups in collaboration
with Scooter Morris at UCSF, which enables hierarchical views. We developed
technology to integrate OpenGL into Cytoscape, which required changes to the
code building process. This will support faster new visualizations in the future
that take advantage of current graphics card functionality. We also mentored a
GSoC project for visualizing dynamic networks (e.g. networks that vary over time).
</p>
<p>Excitingly, we have also ported over the CytoscapeWeb visualization tool that
we originally developed for the GeneMANIA project to use open web standards
such as HTML5. This software is now available at
github.com/cytoscape/cytoscape.js. This involved testing various HTML5 canvas
and WebGL based libraries for rendering networks within the browser, such as
Three.js, KineticJS, EaselJS, performing performance testing using WebGL directly
for drawing networks using HTML5 technology, performing performance testing using
HTML5 canvas directly and finally implementing a complete HTML5 canvas based
rendered that is fully compatible with touch devices, like the Apple iPad.
This technology will improve accessibility of Cytoscape visualizations by
supporting any computing device that runs a modern web browser (many of which
can not run the Cytoscape Java application). As more groups develop web-based
network visualization and analysis functionality, we expect extensive innovation
in user interfaces and visualization techniques to occur on this platform. We
are also continuing to lay the groundwork for representation and visualization
of detailed biological pathway information in Cytoscape 3. We have completed
the following activities in this area:
<ul>
<li>Developed a new CyPath2 Cytoscape 3.0 app, which can load biological
pathway data from the Pathway Commons web service in BioPAX level 3 format.
</li>
<li>Updated BioPAX and Pathway Commons core plugins/apps for Cy2 and Cy3</li>
</ul>
</p>
<p>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 our API designs are robust. We expect this work will pay off in
2013 now that we have finally released Cytoscape 3 and have started working
on novel visualization features in earnest.</p>
<p>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 and by others (the two papers describing the method
garnered over 90 citations since 2010). We have recently established a
collaboration with Jill Mesirov’s group at the Broad Institute, MIT to integrate
Enrichment Map into their popular Gene Set Enrichment Analysis (GSEA) software.
This work, funded by a separate NIH R01 grant, is now complete and will be
released in the coming months.</p>
<p>Over the past year, we have developed strong ties with a new driving
biological project – the
<a href="http://oicr.on.ca/oicr-programs-and-platforms/innovation-programs/cancer-stem-cells">
Cancer Stem Cell program</a> at the Ontario Institute for Cancer Research (OICR)
in Toronto, led by John Dick, the discoverer of cancer stem cells. Fifteen labs
participate in this program, most of which run genomics experiments and are very
interested in pathway analysis to help interpret their results. As such, they
have funded two full time research associates in my lab, Veronique Voisin who
does pathway analysis using Enrichment Map and Shaheena Bashir, who is a
biostatistician who processes raw genomics data to prepare for pathway analysis.
This has led to the four published projects in the past year.</p>
<p>
<b>Our Related Publications</b>
<ul>
<li>Lechman ER, Gentner B, van Galen P, Giustacchini A, Saini M, Boccalatte
FE, Hiramatsu H, Restuccia U, Bachi A, Voisin V, Bader GD, Dick JE, Naldini
L. Attenuation of miR-126 activity expands HSC in vivo without exhaustion.
<i>Cell Stem Cell</i>. 2012 Dec 7;11(6):799-811.
</li>
<li>Labbé RM, Irimia M, Currie KW, Lin A, Zhu SJ, Brown DD, Ross EJ, Voisin V,
Bader GD, Blencowe BJ, Pearson BJ. A comparative transcriptomic analysis
reveals conserved features of stem cell pluripotency in planarians and
mammals.<i> Stem Cells</i>. 2012 Aug;30(8):1734-45.
</li>
<li>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. <i>Proc Natl Acad Sci U S A.</i>
2012 Apr 10;109(15):5832-7.
</li>
<li>Bozhena Jhas, Shrivani Sriskanthadevan, Marko Skrtic, Mahadeo A. Sukhai,
Veronique Voisin, Yulia Jitkova, Marcela Gronda, Rose Hurren, Rob C. Laister,
Gary D. Bader, Mark D. Minden and Aaron D. Schimmer, Metabolic adaptation to
chronic inhibition of mitochondrial protein synthesis in acute myeloid
leukemia cells.<i>PLOS ONE</i> . 2013;8(3):e58367
</li>
</ul>
</p>
<h4>Analyzing Complex Networks Using Ontologies and Cytoscape 3.0 (Alex Pico,
Gladstone/UCSF)</h4>
<p>To make use of the vast wealth of data and knowledge to elucidate the
function of biological networks is one of the biggest challenges in
bioinformatics. The development of high-throughput technology has given rise
to an enormous increase of data on biomolecular expression and interactions,
which results in protein interaction networks, gene regulatory networks,
signaling networks and metabolic networks. One approach to understanding
networks relies on ontologies, such as Gene Ontology, and enrichment analysis.
Common approaches, such as BiNGO [1], regard a network as a list of genes,
performing gene-level annotation and enrichment methods. However, it is
apparent that the same list of genes with different interactions may perform
different functions. Thus, the functional analysis of networks should take
into consideration network topology.</p>
<p>Following up on last year's work on the Mosaic app for the partitioning
and visualization of complex networks using Gene Ontology [2], we focused on
the development of the Network Ontology Analysis (NOA) app for Cytoscape.
The NOA app implements the NOA algorithm [3] for network-based enrichment
analysis, which extends Gene Ontology annotations to network links, or edges.
First, NOA assigns ontology terms to interactions based on the
known annotations of connected genes via optimizing two novel indexes
‘Coverage’ and ‘Diversity’. Then, NOA generates two alternative reference
sets to statistically rank the enriched functional terms for a given
biological network. NOA was shown to be more efficient not only for dynamic
regulatory networks but also for rewired protein interaction networks.
However, there are several shortcomings with the initial implementation:
no graphical interface or visualization of results; limited support for
species, gene names and ontology types; no batch mode for large-scale
computation; and no integration or interoperability with other network
tools. To overcome these shortcomings, we have reimplemented the NOA
algorithm as a Cytoscape app and added interfaces, extensible ontology and
identifier support, new visualizations, a batch mode, and interoperability
with other Cytoscape apps, such as CyThesaurus and Mosaic. The NOA
app facilitates the annotation and analysis of one or more networks in
Cytoscape according to user-defined parameters. In addition to tables, the
NOA plugin also presents results in the form of heatmaps and overview
networks in Cytoscape, which can be exported for publication figures. A
manuscript on this work is currently in review.
</p>
<p><b>Application</b>
As a proof-of-principle example, we prepared a file containing all the human
pathways curated at WikiPathways and translated Ensembl identifiers for batch
mode analysis by the NOA plugin. We ran edge-based analysis to make use of
the interaction content in the pathways, rather than just treating them like
disconnected gene sets. Each pathway was analyzed against the entire
collection as reference and no correction was applied. As expected, curated
pathways of known function were significantly annotated with relevant GO
terms (Fig. 1). Users could add novel pathways or uncharacterized interaction
networks to this batch analysis to assess functional annotation of edges in
the context of known biology.</p>
<img src="images/PicoF1.png" width="242"/><br/>
<b>Figure 1: </b>A heatmap of GO-annotated human pathways based on batch mode NOA plugin
analysis. Axes are labeled in inset, highlighting apoptosis-related pathways
and GO terms.
<p><b>Cytoscape 3.0:</b><br/>
Adding Dr. Scooter Morris to the Gladstone subcontract this year, we not only
gained a valuable Roving Engineer for training and outreach, but also a key
contributor to Cytoscape 3.0 design and development. During this reporting
period, Dr. Morris, worked on porting key plugins to 3.0, that other apps
depend on as well as many core features, including context menus, Tunables,
and custom graphics. As part of this work, he met with Bader lab members at
the University of Toronto to work on improving the user interface for the
VizMapper and extending Metanodes. He also improved the standardization of
the Cytoscape context menus and the esthetics of Cytoscape dialogs by
enhancing the Tunables interface. The focus of the Cytoscape 3 effort during
the later half of this reporting period has been the 3.0.1 release.
This is primarily a "bug-fix" release that follows on the heels of the
initial release of Cytoscape 3, and is intended to be the "solid" release we
want to advocate for new users. In addition to several general fixes,
Dr. Morris' work has addressed several issues with graphical annotations and
groups that are important for wider use. We also began the design for an API
to export the functionality of graphical annotations to other apps, in
particular apps such as the GPML App, which will use these features to render
pathway diagrams imported from the WikiPathways resource, which is another
NRNB-supported service.</p>
<p>
<b>References</b>
<ol>
<li>Maere, S., Heymans, K. and Kuiper, M. (2005) BiNGO: a Cytoscape plugin to
assess overrepresentation of gene ontology categories in biological networks,
<i>Bioinformatics</i>, 21, 3448-3449.
</li>
<li>Zhang, C., et al. (2012) Mosaic: making biological sense of complex
networks, <i>Bioinformatics</i>, 28, 1943-1944.
</li>
<li>Wang, J., et al. (2009) Disease-aging network reveals significant roles
of aging genes in connecting genetic diseases, <i>PLoS Comput Biol</i>, 5,
e1000521.
</li>
</ol>
</p>
</div>
</div>
<div class="tab-pane" id="d-tab">
<h3>Project D: Predictive Modeling and Network Inference</h3>
<h4>
Project Leaders: <a href="people.html#benno">Benno Schwikowski</a>
(Institut Pasteur)
</h4>
<div class="well">
<h4>The CYNI Modular Network Inference Framework</h4>
<p>The objectives of this TRD work are to (1) Create 'fill-in-the-algorithm'
infrastructure for network inference and (2) Make methods accessible to
biologists within the Cytoscape framework. Our approach is to create a
software infrastructure (CYNI) for network inference algorithms (Fig 1).
<!--Figure is the original from grant-->
<img src="images/F20.png" width="700"/><br/> <b>Figure:</b>
The CYNI framework extends Cytoscape to support an iterative
computational-experimental cycle for network-based modeling.
</p>
<p>During this reporting period, we made progress on the following fronts:
<ul>
<li>Working CYNI App</li>
<li>Documented API</li>
<li>User and CYNI App writer documentation</li>
<li>Additional CYNI functionality: Discretization and Imputation</li>
<li>Implemented and documented downloadable “Hello World examples”</li>
<li>CYNI App presented at two French network biology meetings</li>
<li>CYNI App and Comprehensive documentation publicly available</li>
</ul>
The project is now <a href="http://www.proteomics.fr/Sysbio/CyniProject">
live</a> and is also available at the Cytoscape App Store. Data imputation
and discretization techniques are provided along with several known inference
algorithms to make this tool fully operational for any kind inference
requirement. While data imputation and discretization techniques allow you
modify Cytoscape tables, network inference algorithms produce a new network
after applying the chosen technique.
</p>
<p>Cyni Toolbox is not only an application to perform a result, but it also
provides a framework to other app developers to facilitate the implementation
of other algorithms. The goal of Cyni framework is to provide standardized,
extensible and configurable default solutions for all components different
from the core algorithms, such as GUI, parameter handling, configuration of
distance/similarity measures. Thereby, developers can focus on their core
expertise, instead of trying to spend significant effort constructing
software components foreign to their expertise. Once, you are familiar with
the principles of Cytoscape Apps, you will notice that there is a new concept
to develop apps, which is to develop an app that depends on another API app.
This new option brings a new variety of possibilities but at the same time
requires app developers to be more rigorous on their developments. Cyni
provides two sets of elements grouped in two types called Algorithms and
Metrics, but also allows other app developers to create new Algorithms and
metrics through the Cyni framework. To help other app developers to create
any of these two types using the Cyni framework, we offer tutorials to show
how a new algorithm or metric can be easily developed through Cyni.
</p>
<p>CYNI was presented at the French NETBIO network inference community
(Paris, Nov. '12) and at the Prospectom workshop (Grenoble, Dec. '12).
e received this feedback:
<br/>"I was very happy to hear that Cytoscape is going inferential :).
And that members of our community are interested in collaborating with
your to make their tools available in the form of network inference Apps!"
<br/>
We also helped implement the first "non-native" CYNI plug-in, which tested
and evolved our API, UI elements and documentation. This was published this
year as a joint Bioinformatics Application Note [1]. There are other future
CYNI Apps in the works from the d'Alche-Buc lab [2]. Overall, we aim to drive
the evolution from network inference to iterative network modeling.</p>
<p>
<b>References</b>
<ol>
<li>Céline Brouard, Florence d'Alché-Buc, Marie Szafranski: Semi-supervised
Penalized Output Kernel Regression for Link Prediction. <i>ICML</i>
2011: 593-600
</li>
<li>Quach, M., Brunel, N. and d'Alche Buc, F., (2007), Estimating parameters
and hidden variables in non-linear state-space models based on ODEs for
biological networks inference, <i>Bioinformatics</i>, 23, 23, pp. 3209–3216.
</li>
</ol>
</p>
<p>
<b>Cytoscape Europe 2013</b><br/>
Our group is also in the early stages of planning a European Cytoscape
Workshops and Symposium in 2013. We will report on this in the next APR,
but the planning has already begun during this reporting period.
The audience will include new and existing users and app developers,
as well as researchers interested in network biology. This field and
Cytoscape interest in particular are quite active in Europe, so we anticipate
a large response.
</p>
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