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![Annual Progress Report - Research Highlights 2011
National Resource for Network Biology
P41 RR031228-01
Contents:
● NRNB Study Published in PNAS: Correlated Genotypes in Friendship Networks
● NRNB Collaboration Producing Results: Synthetic Genetic Analysis of Budding Yeast
● NRNB Collaboration Connects Networks and Disease: Genetic Networks Underlying
DNA Damage
● Cytoscape 3.0: Development proceeding at Full Speed
● New NRNB Services, Training and Outreach: Year One
NRNB Study Published in PNAS: Correlated Genotypes in Friendship Networks (Fowler)
In their book CONNECTED, Nicholas Christakis and NRNB investigator, James Fowler,
argued that "social networks are in our nature." Then last year they published a paper showing
that genes influence our social network position -- how central we are, and how likely it is
that our friends know one another. In the NRNB study published in PNAS this year [1], we
examine another important social network process called "homophily" -- it's a word that literally
means "love of like" and it refers to the idea that we tend to make friends with people who
resemble us -- "birds of a feather flock together."
Humans are unusual as a species in that we form long-term, non-reproductive unions
with other members of our species. But why do we choose the friends we do? We hypothesize
that we not only choose friends who are socially similar, but who are biologically, actually even
genetically, similar to us.
In the NRNB study published in PNAS we find just that -- there are some gene variants
that we share in common with our friends and other gene variants that differ between friends
(opposites attract).
The results have a number of important implications:
• This is the first study to identify specific genes involved in these social network processes.
• This is a first step towards understanding the biology of "chemistry" -- that feeling you have
about a person that you will like or dislike them. We may choose our friends not just because of
the social features we consciously notice about them, but because of the biological features we
unconsciously notice. Some specific genotypes may be more compatible than others.
• What happens to us may depend not only on our own genes but also on the genes of our
friends. This has been shown already in hens, whose feathers change depending on the
genetic constitution of the hens that are caged near them. But something similar may happen in
humans. We each live in a sea of the genes of others. In fact, we are metagenomic.
• There can be feedback effects -- our genes not only influence us, but they bias our choice of
friends based their genes, which in turn has an additional effect on us. For example, the DRD2
gene variant we study has been associated with alcoholism, and if you have this gene variant,
your friends are likely to have it, too. So you are not only more susceptible to alcoholism
yourself, but you are likely to be surrounded by friends who are susceptible, too.](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-2-320.jpg)
![• Correlated genotypes means that it makes even more sense for us to treat outcomes like
alcohol abuse as social, group-level problems. And anything that spreads in networks --
from obesity to happiness to the flu -- may spread more easily in some parts of the human
population. There is a patchwork of localized susceptibility within networks, created by our
genes and the genes of those around us.
References
1. James H. Fowler, Jaime E. Settle, Nicholas A. Christakis. Correlated Genotypes in Friendship
Networks. PNAS 108 (5): 1993–1997 (1 February 2011). PMID: 21245293, PMC3033315.
NRNB Collaboration Producing Results: Synthetic Genetic Analysis of Budding Yeast
(Bader)
The Bader lab has been collaborating with the Boone and Andrews lab since 2001, including
analysis and visualization of the budding yeast genetic interaction network. Drs. Andrews and
Boone are working to complete the first complete genetic interaction network for a cell and to
decipher the general principles that govern this network. This reference map provides a model
for expanding genetic network analysis to higher organisms, and it will stimulate valuable
insights into gene function, drug target and mode-of-action analysis. The resulting complete
map of genetic interactions for budding yeast, with ~6000 genes, will contain 36 million
quantitative interaction pairs (18 million unique pairs). The most recent publication of roughly
20% of the complete map was in the top 30 most cited papers of 2.2 million in 2010 [1]. The
map currently is 75% complete and continues to be analyzed.
The fundamental principle underlying this work is that we need to discover the rules governing
how genes interact with one another in order to be able to predict which rare combinations of
gene mutations cause human disease or other significant phenotypes.
Their approach is paying off. In the last five months, NRNB investigator, Gary Bader, has
published three new papers with Drs. Boone and Andrews, extracting knowledge about protein
complexes [2], regions of protein disorder [3] and physiological fitness [4] from comparisons
of genetic interactions on a genome scale. All of these projects required Cytoscape, the open-
source network analysis and visualization engine promoted by NRNB investigators (see below).
References
1. Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JL,
Toufighi K, Mostafavi S, Prinz J, St Onge RP, VanderSluis B, Makhnevych T, Vizeacoumar FJ,
Alizadeh S, Bahr S, Brost RL, Chen Y, Cokol M, Deshpande R, Li Z, Lin ZY, Liang W, Marback
M, Paw J, San Luis BJ, Shuteriqi E, Tong AH, van Dyk N, Wallace IM, Whitney JA, Weirauch
MT, Zhong G, Zhu H, Houry WA, Brudno M, Ragibizadeh S, Papp B, Pál C, Roth FP, Giaever
G, Nislow C, Troyanskaya OG, Bussey H, Bader GD, Gingras AC, Morris QD, Kim PM, Kaiser
CA, Myers CL, Andrews BJ, Boone C. The genetic landscape of a cell. Science. 2010 Jan
22;327(5964):425-31. PubMed PMID: 20093466.
2. Michaut M, Baryshnikova A, Costanzo M, Myers CL, Andrews BJ, Boone C, Bader GD.
Protein complexes are central in the yeast genetic landscape. PLoS Comput Biol. 2011
Feb;7(2):e1001092. Epub 2011 Feb 24. PMID: 21390331; PMCID: PMC3044758.](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-3-320.jpg)
![3. Bellay J, Han S, Michaut M, Kim T, Costanzo M, Andrews BJ, Boone C, Bader GD, Myers
CL, Kim PM. Bringing order to protein disorder through comparative genomics and genetic
interactions. Genome Biol. 2011 Feb 16;12(2):R14. PMID: 21324131.
4. 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. PMID:
21076421.
NRNB Collaboration Connects Networks and Disease: Genetic Networks Underlying
DNA Damage (Ideker)
Although cellular behaviors are dynamic, the networks that govern these behaviors have been
mapped primarily as static snapshots. To explore network dynamics, the Ideker laboratory has
been collaborating with the laboratory of Nevan Krogan at UCSF to analyze interaction networks
as they are remodeled by different cellular stresses and stimuli. This year, they developed a
new approach called differential epistasis mapping (dE-MAP) which creates a genetic network
based on the changes in interaction strength observed between two static conditions. Using this
approach, they have mapped widespread changes in genetic interaction among yeast kinases,
phosphatases, and transcription factors as the cell responds to DNA damage [1]. Differential
interactions uncover many gene functions that go undetected in static conditions. In the
published study, they proved very effective at identifying DNA repair pathways, highlighting new
damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. Their
analysis also reveals that protein complexes are generally stable in response to perturbation,
but the functional relations between these complexes are substantially reorganized.
This proof-of-principle work suggests that differential networks chart a new type of
genetic landscape that will be invaluable for mapping many different cellular responses to
stimuli. We are now applying the dE-MAP procedure to examine the interaction dynamics
among yeast genes involved in cellular processes such as autophagy, aging, and the response
to chemotherapeutic compounds. This research is highly complimentary to the work of the
Bader, Boone and Andrews laboratories described above (see Synthetic Genetic Analysis of
Budding Yeast), which seeks to map the entire genetic network in yeast for a single condition.
References
1. Bandyopadhyay S, Mehta M, Kuo D, Sung MK, Chuang R, Jaehnig EJ, Bodenmiller B,
Licon K, Copeland W, Shales M, Fiedler D, Dutkowski J, Guénolé A, van Attikum H,
Shokat KM, Kolodner RD, Huh WK, Aebersold R, Keogh MC, Krogan NJ, Ideker T.
Rewiring of genetic networks in response to DNA damage. Science. 2010 Dec
3;330(6009):1385-9. Erratum in: Science. 2011 Jan 21;331(6015):284. PMID:
21127252; PMCID: PMC3006187.](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-4-320.jpg)
![Cytoscape 3.0: Proceeding at Full Speed
A recent New York Times article highlights the open source nature and plugin architecture of
Cytoscape as a model for modern day collaborative science [1]. Indeed, Cytoscape enables
a broad range of development projects and applied research that scale with support and
distribution. A primary goal of NRNB is to amplify and propagate the community development
model of Cytoscape. Cytoscape is a core research tool either used by or representing
the research effort of every project and collaboration engaged by the NRNB. As such, the
development and maintenance of Cytoscape receives a large amount of attention. Cytoscape
development is progressing along two fronts: we are continuing to maintain the existing 2.8
series of releases [2] and we are developing version 3.0 of Cytoscape which represents an
evolution of our architecture designed to modularize the core of Cytoscape, define a clear
and consistent API, and simplify the experience of developing and maintaining plugins for
Cytoscape.
The Cytoscape 3.0 development effort has resulted in the first developer milestone release of
3.0 at the end of January 2011. The purpose of this milestone was to present a functioning
application to the core Cytoscape development team so that they could begin porting plugins
and to use and critique the 3.0 API. The Bader Group ported a number of core plugins from 2.8,
including BioPAX and PathwayCommons and implemented session reading and writing. In early
March 2011 we held a small meeting of core developers at UC San Diego to discuss the design
of Cytoscape 3.0 and to plan the remaining development. We are currently on track to release
developer milestone 2 prior to the 2011 Cytoscape Symposium in May.
The primary goal of Cytoscape 3.0 is to achieve feature parity with Cytoscape 2.X, but
there will be new features included as well. We have begun initial development of a “Quick
Start” plugin designed to help novice users get their network and associated attribute data into
Cytoscape as quickly and easily as possible.
References
1. Markoff J. Digging Deeper, Seeing Farther: Supercomputers Alter Science. New York Times.
April 26, 2011. p. D1 (Science).
2. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data
integration and network visualization. Bioinformatics. 2011 Feb 1;27(3):431-2. Epub 2010 Dec
12. PMID: 21149340; PMCID: PMC3031041.
New NRNB Services, Training and Outreach: Year One
Less than a year since its inception, the National Resource for Network Biology (NRNB) is
establishing itself as an influential and effective resource. NRNB is setting the standard among
resources for software development and collaborative research, as well as training and support.
We provide a wide range of support services to the Cytoscape development and
research communities, including organizational, technical development, training and outreach.
In the first year, NRNB has made progress on nine new Technological Research and
Development projects that drive Cytoscape core and extension development into new territory.
These projects range from revealing network modules as biomarkers, to developing new
visualization tools, to inferring networks from data, to using social networks in the study of](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-5-320.jpg)
![identify modular structures within biological networks and to apply network modules to predict
disease outcomes. These developments are enabling what we call “network-based biomarkers”,
based on the concept that network modules are better markers of cell state than are individual
genes or proteins [1-5]. Indeed, many biological and clinical outcomes are based on modules
of several interacting proteins working in combination. In development, for instance, it is largely
combinatorial modules of transcription factors that give rise to the diversity of tissues. Protein
combinations are equally instrumental in the pathogenesis of human disease, for instance the
inappropriate fusion of Bcr and Abl that leads to chronic myelogenous leukemia or the abnormal
interactions acquired by the huntington protein in Huntington’s Disease.
A fundamental unanswered question is how the proteins within each module contribute
to the overall module activity. Over the past year, we have performed a case study of the
modules underlying three representative biological programs related to tissue development,
breast cancer metastasis, or progression of brain cancer, respectively. To facilitate this study
we have developed a new bioinformatic method, called Network-Guided Forests (NGF), to
identify predictive modules together with logic functions which tie the activity of each module
to the activity of its component genes [6]. NGF integrates key ideas from Random Forests
(RF) [7] with biological constraints induced by a protein-protein interaction network— the first
use of protein networks in ensemble learning. The NGF framework learns a set of decision
trees (the “forest”) in which each tree maps to a connected component of the protein-protein
interaction network (Fig. 1). The decision tree specifies a function that determines the output
of the network component based on the activity of its genes. In turn, the collection of all tree
outputs is used to predict the cell type or disease state of the biological sample (the “class”).
By construction, decision trees detect genes that influence a phenotypic outcome both
individually and through multiway interactions with other genes. As in the standard Random
Forests algorithm, NGF uses a permutation-based procedure to assess the importance of each
gene on the classification accuracy of the forest. We also assess the importance of pairs of
genes in a tree — in our study these pairs are constrained by the network neighborhood. Genes
and gene pairs with significantly high importance scores are placed into clusters that capture
similar patterns of presence/absence across the forest of decision trees. Each cluster
aggregates genes that fall into the same network region and, in combination, have predictive
power over the sample class. Hence these clusters are termed “consensus decision modules”
(Fig. 1).
Use of NGF to analyze the three representative biological programs (early development,
breast cancer metastasis, and mesenchymal transformation of brain tumor) identifies network
modules which capture known causal mechanisms of development or disease. The modules
implement diverse logic functions using both coherent and opposing gene activities, in which
the module output depends on expression increases for some genes and concomitant
decreases for others. Notably, we found that in cancer progression the most predictive decision
functions can be linked to interactions between known oncogenes and tumor suppressors, such
that the combined activity of both types of genes determines the disease outcome.](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-15-320.jpg)
![DNA copy-number alterations, and mRNA expression profiles) in order to identify altered
sub-networks in cancer. We refer to these modules as “driver networks”, as they are likely
to contribute to tumorigenesis in multiple patients. Recently, NRNB investigators and others
have shown proof of principle that the use of network and pathway information can help us
understand the pronounced genetic heterogeneity seen in individual tumors of the same cancer
type [1] and that they can lead to more accurate and robust signatures for classifying disease
states [2-5]. To date, such methods have been explored in glioblastoma multiforme [6,7], as well
as pancreatic [8], lung [9], breast and colorectal cancer [1].
With NRNB funding, we have begun to explore the use of an optimization algorithm borrowed
from statistical physics to connect altered genes in cancer with minimal spanning networks.
Such networks can identify the set of interactions able to explain the pattern of correlated
alterations in cancer, i.e. the driver networks, from a human reference interaction network. The
algorithm we are using, which addresses the minimum Steiner tree problem, attempts to find
the shortest connection between altered genes in a specific cancer type. This network may be
constructed with direct connections between altered genes and/or with connections between
altered and unaltered genes within a human reference protein interaction network.
In general, this problem is classified as an NP-complete problem, meaning that there is
no efficient way of finding a solution. Additionally, once a network approaches an order of
magnitude of ~10 altered genes, the minimum Steiner tree can only be approximated. In order
to handle the much larger number of genes in the human protein interaction network, we are
using an algorithmic framework based on a distributed method called message passing. This
method has been shown to be successful in various applications, such as detecting protein
associations in cell signaling [10] and data clustering [11]. We are currently evaluating the use
of the Steiner tree algorithm by using a training dataset from glioblastoma and exploring the
range of improvement obtained by varying the interaction weights between genes in the human
reference network based on the mRNA expression profiles (Fig. 2).
This algorithmic research is being applied to analyze multiple cancer types derived from The
Cancer Genome Atlas (TCGA), prostate cancer genome data derived from the MSKCC Prostate
Cancer Genome Project (PCGP), and expression data from chronic lymphocytic leukemia (CLL)
patients at UCSD (Thomas Kipps, MD/PhD), all of which are being provided by active Driving
Biological Projects (DBPs).](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-17-320.jpg)
![3. Visualizing Cancer Genomic Data in the Context of Biological Networks (Sander: TRD
A)
This project focuses on visualizing cancer genomic data in the context of specific pathways and
networks. We have developed an initial prototype using Cytoscape Web [1], which is capable
of displaying networks derived from Pathway Commons [2], and overlaying these networks
with genomic data derived from the TCGA project. The prototype displays a fully interactive
network of the genes analyzed, plus details regarding individual genomic alterations (Figure
3). We are planning to transfer knowledge we have gained from this prototype and apply it to
our cBio Cancer Genomics Portal (http://cbioportal.org). The portal currently enables users to
visualize, analyze and download large-scale cancer genomic data sets, but is currently lacking
in network visualization. With Cytoscape Web, users will soon be able to enter a set of genes,
visualize those genes in a network context, and dynamically overlay genomic data onto the
networks of interest. This will provide a critical exploratory data analysis module to the portal,
enabling the wider research community to more easily visualize genomic data in the context of
biological pathways, and to develop and confirm hypotheses regarding cancer development and
progression.
Figure 3. Prototype of cancer network visualization, built with Cytoscape Web [1]. Left panel
shows a global network view of genes altered by somatic mutation or copy number alteration
in serous ovarian cancer (TCGA). Node size is proportional to frequency of alteration. Right
panel shows a local view of the BRCA/RB subnetwork, with genomic alterations displayed as a
compact OncoPrint. Experience gained from this prototype will be used to add a new network
visualization component to our cBio Cancer Genomics Portal (http://cbioportal.org).
References
1. Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD., Cytoscape Web: an
interactive web-based network browser. Bioinformatics. 2010 Sep 15;26(18):2347-8.
2. Cerami EG, Gross BE, Demir E, et al., Pathway Commons, a web resource for biological](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-19-320.jpg)
![pathway data. Nucleic Acids Res. 2011;39(Database issue):D685-D690. PMID:21071392
4. Recognizing Trend Motifs and Dynamics in Networks (Fowler: TRD B)
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 published in PNAS, we study six available genotypes from the
National Longitudinal Study of Adolescent Health to test for genetic similarity between friends
[1,2]. Maps of the friendship networks show clustering of genotypes, and, after we apply strict
controls for population stratification, the results show that one genotype is positively correlated
(homophily) and one genotype is negatively correlated (heterophily). A replication study on an
independent sample from the Framingham Heart Study verifies that DRD2 exhibits significant
homophily and that CYP2A6 exhibits significant heterophily. These novel results show that
homophily and heterophily obtain 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. This work continues to build off our original DBP for the “Role of Social
Networks in the Spread of Disease,” led by Nicholas Christakis.
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
5. General Layout Algorithms and Views for Hierarchical, Modular Networks (Bader: TRD
C)
Biologists frequently use networks to represent the structure and function of the cell, using
intuitive metaphors to reduce multiple levels of spatial and temporal relationships to a two-
dimensional image. At the same time, computational representations of the cell are more
abstract and tend to be less intuitive for biologists than human-made diagrams. We are
working to improve the biological relevance of computational visualizations of biological
networks in Cytoscape, in collaboration with investigators leading driving biological projects
and collaborative service projects. More intuitive biological network visualizations will speed
interpretation of large-scale data about cellular processes being generated by biologists.
We developed the Thematic Map plugin for Cytoscape, based on an earlier prototype
presented in our original NRNB grant application. This plugin ‘rolls-up’ node or edge attributes](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-20-320.jpg)
![into individual nodes, i.e. it transforms an input network of interactions among proteins into an
attribute network, in which node attributes are nodes and edges summarize all connections
between nodes with the corresponding attributes in the original network. This view can be used
in a number of biologically useful ways, such as summarizing the functional content of a large
protein-protein interaction network. We are currently testing this plugin for release in the second
half of 2011.
Figure 4. Thematic map based on node attributes.
We have also developed a second plugin, the Enrichment Map, in a similar spirit
to the Thematic Map plugin. Gene-set enrichment analysis is a useful technique to help
functionally characterize large gene lists, such as the results of gene expression experiments.
This technique finds functionally coherent gene-sets, such as pathways, that are statistically
over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the
number of genes in the list, thus simplifying interpretation. However, the increasing number and
redundancy of gene-sets used by many current enrichment analysis software works against
this ideal. To overcome gene-set redundancy and help in the interpretation of large gene
lists, we developed "Enrichment Map", a network-based visualization method for gene-set
enrichment results. Gene-sets are organized in a network, where each set is a node and edges
represent gene overlap between sets. Automated network layout groups related gene-sets into
network clusters, enabling the user to quickly identify the major enriched functional themes and
more easily interpret the enrichment results. Enrichment Map is a significant advance in the
interpretation of enrichment analysis. Any research project that generates a list of genes can
take advantage of this visualization framework. Enrichment Map is implemented as a freely
available and user friendly plug-in for the Cytoscape network visualization software (http://
baderlab.org/Software/EnrichmentMap/) [1].](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-21-320.jpg)
![Figure 5. Enrichment Map for estrogen-treated cells versus untreated cells
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.
6. Semantic Zooming and Information Layering (Bader: TRD C)
Our goal in this project is to develop methods to help researchers explore and interpret
large networks and their associated genome-scale data sets. As the volume, resolution and
complexity of biological data continue to increase, so do the challenges associated with
visualizing, analyzing and interpreting the data. Methods that we develop will help network
visualization scale, while still remaining interactive to support live exploration and hypothesis
testing.
We have developed an initial implementation of a new filtering API for Cytoscape 3,
which will enable us to develop the next-generation interactive filtering system for Cytoscape.
We have verified that using a BitSet implementation to handle filter set operations can support
large networks of up to 10 million nodes and edges. We are currently receiving feedback about
the new API and will implement it fully in Cytoscape 3.1 [1].
We are also making progress on this project by developing support for visualizing
detailed biological pathways in Cytoscape. We have recently implemented BioPAX Level 3
support in Cytoscape (BioPAX Level 3 reader, writer and visualizer) [2]. This enables import
of biological pathway information from various pathway databases, including Reactome [3],
WikiPathways [4] and Pathway Commons [5]. Future pathway visualization features that we
develop in Cytoscape will depend on this functionality.
We continue to closely collaborate with the Charlie Boone and Brenda Andrews labs](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-22-320.jpg)
![who lead our DBP: Synthetic genetic analysis of budding yeast (see DBP progress reports
below).
References
1. http://cytoscape.wodaklab.org/wiki/Outdated_Cytoscape_3.0/FilterAPI
2. Demir E et al. The BioPAX community standard for pathway data sharing. Nat Biotechnol.
2010 Sep;28(9):935-42. Epub 2010 Sep 9. PMID: 20829833
3. Matthews L, Gopinath G, Gillespie M, Caudy M, et al. Reactome knowledgebase of biological
pathways and processes. Nucleic Acids Res. 2008 Nov 3. PMID: 18981052
4. Pico AR, Kelder T, van Iersel MP, Hanspers K, et al. (2008) WikiPathways: Pathway Editing
for the People. PLoS Biol 6(7): doi:10.1371/journal.pbio.0060184
5. Cerami et al. Pathway Commons, a web resource for biological pathway data. Nucl. Acids
Res. (2010) doi: 10.1093/nar/gkq1039
7. Network Layout by Known Ontology Attributes (Conklin, Pico: TRD C)
Organizing network or pathway data into a diagram that effectively communicates information
about biological systems requires biological expertise and even a bit of artistry. A good diagram
might need to illustrate myriad interactions between genes, proteins and small molecules and
might also convey their spatial and temporal arrangement. One of the most biologically intuitive
ways to organize information about cellular systems is to place it in the context of a familiar
physical map of the cell, with the nucleus surrounded by cytosol, organelles and a plasma
membrane. Similarly, proteins known to be part of the same pathway should be placed close
together in the diagram. A good source of information about a protein’s cellular location and
biological process involvement is the Gene Ontology (GO) project [1], a collaborative effort
to standardize nomenclature for biological concepts and link these to genes and proteins
from many genomes. The GO project has developed three structured controlled vocabularies
(ontologies) that describe gene products in terms of their associated biological processes,
cellular components and molecular functions in a species-independent manner. Gene Ontology
provides much broader coverage of genomes for this type of information than is available from
any other source, such as traditional pathway models stored in pathway databases [2].
We developed a network layout plugin for Cytoscape, which utilizes Gene Ontology
(GO) annotations to help organize nodes in a biologically relevant way. The first version
of the GOLayout plugin is currently being tested and will be released in the second half of
2011. GOLayout first partitions a given network into subnetworks based on biological process
annotations, such as cell differentiation or cell cycle, provided by a pruned set of Gene Ontology
called “GO slim” (Fig. 6). Each subnetwork is laid out based on cellular component annotations
over a scalable template of a typical cell diagram. Finally, each node is colored based on a
discrete mapping to molecular function annotations, such that all kinases, for example, might be
colored green. The result is a biologically informative layout. This project is complementary to
the Thematic Map described in TRD 5 above. While that plugin generates a descriptive network
of attribute-based metanodes, the GOLayout plugin generates a series of subnetworks using
attributes to partition, layout and color given nodes. As both projects are under the umbrella of
the original TRD C proposal, we continue to coordinate on the development of these related](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-23-320.jpg)
![efforts. Key aspects of the design and implementation of the plugin were done as part of an
NRNB collaboration and service project with Allan Kuchinsky from Agilent Technologies, led by
Annette Adler (Visualizing Biological Networks with a Biologist’s Eye).
Figure 6. The result of using GOLayout to partition a massive “hairball” network into a series of
biological processes, each laid out into cellular compartments and colored by molecular function
according to Gene Ontology annotations.
The next version of GOLayout will include user-driven heuristics for highlighting
biologically interesting paths within the layout, as well as better ontology handling, i.e., for
navigating nested terms. Other key features planned for the next release include support for
importing/exporting/printing the layouts in multiple formats. This will allow for custom layout
templates, as well as unique visualization, analysis and sharing workflows.
References
1. The_Gene_Ontology_Consortium. Gene ontology: tool for the unification of biology. 25, 25-29
(2000).
2. Cary, M.P., Bader, G.D. & Sander, C. Pathway information for systems biology. FEBS Lett
579, 1815-20 (2005).
8. Mapping and Visualizing Complex Attributes (Conklin, Pico: TRD C)
An increasing number of experimental methods, such as scans for Single Nucleotide
Polymorphisms (SNPs) or exon microarrays, are generating data at sub-gene levels. It is
extremely useful to interpret this information in the context of biological networks and pathways
[1-4]. For this purpose, we are extending Cytoscape to enable network visualizations of data on
sub-gene structures, similar to how Cytoscape already allows visualization of gene expression
data on nodes that represent genes or proteins. The input to the system is a data set of sub-
gene or protein features, such as SNPs, exons or protein domains, and their associated data](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-24-320.jpg)
![governing how genes interact with one another in order to be able to predict which rare
combinations of gene mutations cause human disease or other significant phenotypes.
Andrews and Boone aim to discover the general principles of genetic interaction by mapping
the first complete genetic interaction network for a eukaryotic cell and directly testing the
conservation of these principles. They are taking a unique experimental approach to define
and dissect the rules of complex genetic networks. The strategy entails the use of combinatorial
genetic perturbations to systematically screen for genetic interactions. In particular, they
have established key infrastructure that enables the construction of all possible double gene
deletion mutant combinations in genetically tractable yeast model systems in an automated,
high throughput manner. Genetic interactions are subsequently scored by assessing extreme
phenotypes that result from the collapse of an essential cellular function. This information is
assembled into a network that reflects the genetic landscape of a cell.
During the reporting period NRNB investigator, Gary Bader, has collaborated with Drs.
Boone and Andrews on three new publications, extracting knowledge about protein complexes
[1], regions of protein disorder [2] and physiological fitness [3] from comparisons of genetic
interactions on a genome scale. Each of these published projects utilizes Cytoscape for network
analysis and visualization.
References
1. Michaut M, Baryshnikova A, Costanzo M, Myers CL, Andrews BJ, Boone C, Bader GD.
Protein complexes are central in the yeast genetic landscape. PLoS Comput Biol. 2011
Feb;7(2):e1001092. Epub 2011 Feb 24. PMID: 21390331; PMCID: PMC3044758.
2. Bellay J, Han S, Michaut M, Kim T, Costanzo M, Andrews BJ, Boone C, Bader GD, Myers
CL, Kim PM. Bringing order to protein disorder through comparative genomics and genetic
interactions. Genome Biol. 2011 Feb 16;12(2):R14. PMID: 21324131.
3. 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. PMID:
21076421.
2. New CSP: Dynamic Genetic Networks Underlying DNA Damage (Ideker, Krogan)
A very successful new CSP begun in the past year involves the laboratories of Trey Ideker
(representing the NRNB) and Nevan Krogan at UCSF. The goal of this project is to understand
the extent to which genetic and protein networks are remodeled by changes in conditions.
Indeed, although cellular behaviors are dynamic, the networks that govern these behaviors have
been mapped primarily as static snapshots.
To explore network dynamics, Ideker and Krogan are collaborating to generate
interaction networks as cells are exposed to different cellular stresses and stimuli. To analyze
the resulting network dynamics, the team has developed a new method we call differential
epistasis mapping (dE-MAP) which identifies “differential” interactions based on their changes in
interaction strength observed between two static conditions. Analyzing network data to identify
differential interactions is very similar to analyzing gene expression microarrays to identify](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-28-320.jpg)
![differential expression, or using ICAT or ITRAC mass spectrometry to identify differentially
expressed proteins or protein post-translational modifications. Two-color microarrays
revolutionized gene expression analysis because they permitted direct comparison of two
conditions and thus identification of differentially expressed genes. In the same way, we feel
that differential analysis will be key to extracting the major response pathways encoded by a
large biological network.
As proof-of-principle, we have recently used the dE-MAP approach to map widespread
changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as
the cell responds to DNA damage [1]. In the published study, analysis of differential interactions
proved very effective at identifying DNA repair pathways, highlighting new damage-dependent
roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. This analysis also
revealed that protein complexes are generally stable in response to perturbation, but the
functional relations between these complexes are substantially reorganized.
This proof-of-principle work suggests that differential networks chart a new type of
genetic landscape that will be invaluable for mapping many different cellular responses to
stimuli. We are now applying the dE-MAP procedure to examine the interaction dynamics
among yeast genes involved in cellular processes such as autophagy, aging, and the response
to chemotherapeutic compounds. This research is highly complimentary to the work of the
Bader, Boone and Andrews laboratories described above (see Synthetic Genetic Analysis of
Budding Yeast), which seeks to map the entire genetic network in yeast for a single condition.
This work is in continued collaboration with Nevan Krogan as well as with a cadre of other
investigators.
References
1. Bandyopadhyay S, Mehta M, Kuo D, Sung MK, Chuang R, Jaehnig EJ, Bodenmiller B,
Licon K, Copeland W, Shales M, Fiedler D, Dutkowski J, Guénolé A, van Attikum H, Shokat
KM, Kolodner RD, Huh WK, Aebersold R, Keogh MC, Krogan NJ, Ideker T. Rewiring of
genetic networks in response to DNA damage. Science. 2010 Dec 3;330(6009):1385-9. PMID:
21127252; PMCID: PMC3006187.
NRNB Software and Resources
1. Cytoscape Core
Cytoscape (http://cytoscape.org) is a core research tool either used by the majority of projects
and collaborations engaged by the NRNB. As such, the development and maintenance of
Cytoscape receives a large amount of attention. Cytoscape development is progressing along
two fronts. First, we are continuing to maintain the existing 2.8 series of releases. Second,
we are developing version 3.0 of Cytoscape which represents a significant evolution of our
architecture in order to modularize the core of Cytoscape, define a clear and consistent API,
and simplify the experience of developing and maintaining plugins for Cytoscape.
Cytoscape 2.8.0 was released in October of 2010 and a subsequent maintenance
version 2.8.1 was released in February of 2011. Version 2.8 introduces two powerful new
features that, when used together, can create rich visualizations [1]. These features are](https://image.slidesharecdn.com/nrnb-annualreport-2011-120725183523-phpapp01/85/NRNB-Annual-Report-2011-29-320.jpg)
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NRNB Annual Report 2011
- 1. National Resource for Network Biology Annual Report May 2011 NRNB
- 2. Annual Progress Report- Research Highlights 2011 National Resource for Network Biology P41 RR031228-01 Contents: ● NRNB Study Published in PNAS: Correlated Genotypes in Friendship Networks ● NRNB Collaboration Producing Results: Synthetic Genetic Analysis of Budding Yeast ● NRNB Collaboration Connects Networks and Disease: Genetic Networks Underlying DNA Damage ● Cytoscape 3.0: Development proceeding at Full Speed ● New NRNB Services, Training and Outreach: Year One NRNB Study Published in PNAS: Correlated Genotypes in Friendship Networks (Fowler) In their book CONNECTED, Nicholas Christakis and NRNB investigator, James Fowler, argued that "social networks are in our nature." Then last year they published a paper showing that genes influence our social network position -- how central we are, and how likely it is that our friends know one another. In the NRNB study published in PNAS this year [1], we examine another important social network process called "homophily" -- it's a word that literally means "love of like" and it refers to the idea that we tend to make friends with people who resemble us -- "birds of a feather flock together." Humans are unusual as a species in that we form long-term, non-reproductive unions with other members of our species. But why do we choose the friends we do? We hypothesize that we not only choose friends who are socially similar, but who are biologically, actually even genetically, similar to us. In the NRNB study published in PNAS we find just that -- there are some gene variants that we share in common with our friends and other gene variants that differ between friends (opposites attract). The results have a number of important implications: • This is the first study to identify specific genes involved in these social network processes. • This is a first step towards understanding the biology of "chemistry" -- that feeling you have about a person that you will like or dislike them. We may choose our friends not just because of the social features we consciously notice about them, but because of the biological features we unconsciously notice. Some specific genotypes may be more compatible than others. • What happens to us may depend not only on our own genes but also on the genes of our friends. This has been shown already in hens, whose feathers change depending on the genetic constitution of the hens that are caged near them. But something similar may happen in humans. We each live in a sea of the genes of others. In fact, we are metagenomic. • There can be feedback effects -- our genes not only influence us, but they bias our choice of friends based their genes, which in turn has an additional effect on us. For example, the DRD2 gene variant we study has been associated with alcoholism, and if you have this gene variant, your friends are likely to have it, too. So you are not only more susceptible to alcoholism yourself, but you are likely to be surrounded by friends who are susceptible, too.
- 3. • Correlated genotypesmeans that it makes even more sense for us to treat outcomes like alcohol abuse as social, group-level problems. And anything that spreads in networks -- from obesity to happiness to the flu -- may spread more easily in some parts of the human population. There is a patchwork of localized susceptibility within networks, created by our genes and the genes of those around us. References 1. James H. Fowler, Jaime E. Settle, Nicholas A. Christakis. Correlated Genotypes in Friendship Networks. PNAS 108 (5): 1993–1997 (1 February 2011). PMID: 21245293, PMC3033315. NRNB Collaboration Producing Results: Synthetic Genetic Analysis of Budding Yeast (Bader) The Bader lab has been collaborating with the Boone and Andrews lab since 2001, including analysis and visualization of the budding yeast genetic interaction network. Drs. Andrews and Boone are working to complete the first complete genetic interaction network for a cell and to decipher the general principles that govern this network. This reference map provides a model for expanding genetic network analysis to higher organisms, and it will stimulate valuable insights into gene function, drug target and mode-of-action analysis. The resulting complete map of genetic interactions for budding yeast, with ~6000 genes, will contain 36 million quantitative interaction pairs (18 million unique pairs). The most recent publication of roughly 20% of the complete map was in the top 30 most cited papers of 2.2 million in 2010 [1]. The map currently is 75% complete and continues to be analyzed. The fundamental principle underlying this work is that we need to discover the rules governing how genes interact with one another in order to be able to predict which rare combinations of gene mutations cause human disease or other significant phenotypes. Their approach is paying off. In the last five months, NRNB investigator, Gary Bader, has published three new papers with Drs. Boone and Andrews, extracting knowledge about protein complexes [2], regions of protein disorder [3] and physiological fitness [4] from comparisons of genetic interactions on a genome scale. All of these projects required Cytoscape, the open- source network analysis and visualization engine promoted by NRNB investigators (see below). References 1. Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JL, Toufighi K, Mostafavi S, Prinz J, St Onge RP, VanderSluis B, Makhnevych T, Vizeacoumar FJ, Alizadeh S, Bahr S, Brost RL, Chen Y, Cokol M, Deshpande R, Li Z, Lin ZY, Liang W, Marback M, Paw J, San Luis BJ, Shuteriqi E, Tong AH, van Dyk N, Wallace IM, Whitney JA, Weirauch MT, Zhong G, Zhu H, Houry WA, Brudno M, Ragibizadeh S, Papp B, Pál C, Roth FP, Giaever G, Nislow C, Troyanskaya OG, Bussey H, Bader GD, Gingras AC, Morris QD, Kim PM, Kaiser CA, Myers CL, Andrews BJ, Boone C. The genetic landscape of a cell. Science. 2010 Jan 22;327(5964):425-31. PubMed PMID: 20093466. 2. Michaut M, Baryshnikova A, Costanzo M, Myers CL, Andrews BJ, Boone C, Bader GD. Protein complexes are central in the yeast genetic landscape. PLoS Comput Biol. 2011 Feb;7(2):e1001092. Epub 2011 Feb 24. PMID: 21390331; PMCID: PMC3044758.
- 4. 3. Bellay J,Han S, Michaut M, Kim T, Costanzo M, Andrews BJ, Boone C, Bader GD, Myers CL, Kim PM. Bringing order to protein disorder through comparative genomics and genetic interactions. Genome Biol. 2011 Feb 16;12(2):R14. PMID: 21324131. 4. 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. PMID: 21076421. NRNB Collaboration Connects Networks and Disease: Genetic Networks Underlying DNA Damage (Ideker) Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. To explore network dynamics, the Ideker laboratory has been collaborating with the laboratory of Nevan Krogan at UCSF to analyze interaction networks as they are remodeled by different cellular stresses and stimuli. This year, they developed a new approach called differential epistasis mapping (dE-MAP) which creates a genetic network based on the changes in interaction strength observed between two static conditions. Using this approach, they have mapped widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage [1]. Differential interactions uncover many gene functions that go undetected in static conditions. In the published study, they proved very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. Their analysis also reveals that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. This proof-of-principle work suggests that differential networks chart a new type of genetic landscape that will be invaluable for mapping many different cellular responses to stimuli. We are now applying the dE-MAP procedure to examine the interaction dynamics among yeast genes involved in cellular processes such as autophagy, aging, and the response to chemotherapeutic compounds. This research is highly complimentary to the work of the Bader, Boone and Andrews laboratories described above (see Synthetic Genetic Analysis of Budding Yeast), which seeks to map the entire genetic network in yeast for a single condition. References 1. Bandyopadhyay S, Mehta M, Kuo D, Sung MK, Chuang R, Jaehnig EJ, Bodenmiller B, Licon K, Copeland W, Shales M, Fiedler D, Dutkowski J, Guénolé A, van Attikum H, Shokat KM, Kolodner RD, Huh WK, Aebersold R, Keogh MC, Krogan NJ, Ideker T. Rewiring of genetic networks in response to DNA damage. Science. 2010 Dec 3;330(6009):1385-9. Erratum in: Science. 2011 Jan 21;331(6015):284. PMID: 21127252; PMCID: PMC3006187.
- 5. Cytoscape 3.0: Proceedingat Full Speed A recent New York Times article highlights the open source nature and plugin architecture of Cytoscape as a model for modern day collaborative science [1]. Indeed, Cytoscape enables a broad range of development projects and applied research that scale with support and distribution. A primary goal of NRNB is to amplify and propagate the community development model of Cytoscape. Cytoscape is a core research tool either used by or representing the research effort of every project and collaboration engaged by the NRNB. As such, the development and maintenance of Cytoscape receives a large amount of attention. Cytoscape development is progressing along two fronts: we are continuing to maintain the existing 2.8 series of releases [2] and we are developing version 3.0 of Cytoscape which represents an evolution of our architecture designed to modularize the core of Cytoscape, define a clear and consistent API, and simplify the experience of developing and maintaining plugins for Cytoscape. The Cytoscape 3.0 development effort has resulted in the first developer milestone release of 3.0 at the end of January 2011. The purpose of this milestone was to present a functioning application to the core Cytoscape development team so that they could begin porting plugins and to use and critique the 3.0 API. The Bader Group ported a number of core plugins from 2.8, including BioPAX and PathwayCommons and implemented session reading and writing. In early March 2011 we held a small meeting of core developers at UC San Diego to discuss the design of Cytoscape 3.0 and to plan the remaining development. We are currently on track to release developer milestone 2 prior to the 2011 Cytoscape Symposium in May. The primary goal of Cytoscape 3.0 is to achieve feature parity with Cytoscape 2.X, but there will be new features included as well. We have begun initial development of a “Quick Start” plugin designed to help novice users get their network and associated attribute data into Cytoscape as quickly and easily as possible. References 1. Markoff J. Digging Deeper, Seeing Farther: Supercomputers Alter Science. New York Times. April 26, 2011. p. D1 (Science). 2. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011 Feb 1;27(3):431-2. Epub 2010 Dec 12. PMID: 21149340; PMCID: PMC3031041. New NRNB Services, Training and Outreach: Year One Less than a year since its inception, the National Resource for Network Biology (NRNB) is establishing itself as an influential and effective resource. NRNB is setting the standard among resources for software development and collaborative research, as well as training and support. We provide a wide range of support services to the Cytoscape development and research communities, including organizational, technical development, training and outreach. In the first year, NRNB has made progress on nine new Technological Research and Development projects that drive Cytoscape core and extension development into new territory. These projects range from revealing network modules as biomarkers, to developing new visualization tools, to inferring networks from data, to using social networks in the study of
- 6. disease. Each projectis driven by a number of biological and biomedical applications with human health implications. In addition to our own projects, we actively seek and support collaborations around Cytoscape development and application. In the first year, we have established a total of 33 collaborations involving NRNB investigators. With such rapid early growth, we anticipate this number will double over the next two years. NRNB is responsible for measuring and increasing the impact of Cytoscape on the research community. We track news, publications, events and collaborations related to Cytoscape development and usage. Our policy is to report these measures at the NRNB website, the annual meeting of our External Advisory Committee (EAC) and in our annual report to the National Center for Research Resources. These objective metrics make it possible to identify a variety of areas for improvement. For example, we recently performed an extensive analysis of help desk activity for the Cytoscape user community. For many users, the help desk is the primary access point to NRNB support. Based on our analysis, we found that only ~60% of messages were being responded to. To raise this percentage to an effective 100% (not every post is a question), we have developed a three-pronged approach: (1) Identify particularly weak months during which greater vigilance is needed, (2) Transform the most common issues into technical solutions, e.g., effective user messages, and (3) Implement a weekly alert for unanswered threads which are then discussed at weekly conference calls among NRNB software developers. This approach should improve the user experience by increasing responsiveness and enhancing the usability of the software tool. The most popular service NRNB provides is training. We launched a new tutorial management system called Open Tutorials, which is being used by researchers, developers and presenters. The link to training receives more clicks than anything else on our website. There is a clear demand for tutorials, which we are only just beginning to meet. Through the material resources at Open Tutorials and our organizing efforts to seek out and support training events, we anticipate this will be a growing and effective NRNB function. Finally, in terms of outreach, NRNB serves a number of roles. We organize and run the annual Google Summer of Code event for Cytoscape and related network biology tools. For the summer of 2011, we will have ten students writing code for NRNB. The students and mentors are paid by Google for this work, totaling $55,000 for 12 weeks. The new NRNB website is also a major form of outreach. Through the website, we gather programmers, collaborators and researchers looking for training materials. To help direct traffic to the NRNB site and related software sites (e.g., Cytoscape download page), we make use of a free, non-profit account for Google AdWords through the Cytoscape Consortium. We are directing >1,300 clicks a month to NRNB tools and resources. This is worth just over $1,000 a month, which we are getting free-of-charge. Note: approximately half of the traffic is for WikiPathways, which is not yet officially a NRNB resource. We have a spending limit of $329 per day through this program, so we will continue to identify new ad words and relevant resources to promote. NRNB is also helping to organize this year’s annual Cytoscape Retreat and Symposium, http://cytoscape.org/ CytoscapeRetreat2011. In addition to developer meetings, the retreat will include user and new developer tutorials, a Plugin Expo, and special symposium. The symposium will be presented in conjunction with the San Diego Center for Systems Biology as the sixth annual Systems to Synthesis Symposium on May 20th at the Salk Institute. It has been an exciting first year ramping up NRNB research and services. While we
- 7. have met earlysuccess in facilitating collaborations, training and outreach, we have also identified many areas for improvement. We are confident that NRNB will become an essential resource for network biology research and its application to human health.
- 8. Annual Progress Report- Administrative Information 2011 National Resource for Network Biology P41 RR031228-01 Administrative Structure Within the first few weeks of establishing the NRNB, we finalized the administrative structure of the resource, including defining and filling some unique new roles within the organization (Fig. 1). 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 Institute / UCSF) is responsible for coordinating these efforts as well as conducting all of the necessary tracking and due diligence for the annual reporting to NIH. The Technology Research and Development (TRD) projects and leads have not changed from their original description other than to factor them into proper subprojects per BTRC reporting conventions. Each TRD, for example, is diversifying over time into between 1 - 4 discretely defined subprojects. Finally, we were very pleased to have all seven invited members promptly agree to join our EAC, including Dr. Stephen Friend as chair of the committee. Figure 1. Resource Administration Structure. Blue boxes down the center define the
- 9. core leadership positionsfor NRNB. Purple boxes at right define resource roles under the coordination of the Executive Director. The red boxes along the bottom describe the main TRD projects currently defining the resource’s research direction, as well as its driving biological projects and collaborations. 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 has organized 8 training events (in 7 cities in 4 countries), with an additional 4 events already planned for 2011. These events include tutorials, workshops and courses. 2. Funding requests: This year we had a request to fund a Cytoscape training event for the Medical Library Association’s annual meeting in Minnesota. We denied this request as it is outside the scope of our resource to fund external meetings that do not involve NRNB staff. However, we have adapted this request into a new idea of establishing a travel scholarship for external, distributed Cytoscape tutors to attend the annual Cytoscape Retreat. We will be presenting this idea as a proposal to our EAC during their annual meeting next month. If approved, we may see more requests of this type in the future. 3. Requests for training material support: We receive requests for tutorial materials throughout the year from inside and outside the Cytoscape core development team. We have implemented a new Open Tutorials system which makes it easy to approve all such requests. For example, we recently directed colleagues from the University of Michigan to existing tutorials already formatted to be used as online sessions, slide shows and printed handouts. 4. Joining our Google Summer of Code effort: We have received requests from a number of groups to join our NRNB umbrella organization for the Google Summer of Code. Such requests translate into significant administrative support services that we can provide for these groups. As long as a group is working on open source software relating to our core network biology projects, our policy is to be very open to these requests. We have the additional requirement that each group be able to demonstrate that their personnel can commit sufficient time as mentors. These policies have been developed over the past four years of successfully participating in the Google-sponsored program. This year we accepted (or vouched for) a total of nine groups in addition to our core Cytoscape team, representing the following software projects: Vanted, Reactome, Cytoscape Web, GenMAPP-CS, PathVisio, WikiPathways, Savant Genome Browser, and Systems Biology projects by the Theoretical Biophysics group at Humboldt University Berlin. 5. Providing software community support: Our software “menu of services” is rapidly growing. Our goal is to develop a generic template of services based on the support we provide the Cytoscape community of users and developers. We will be seeking EAC advice on the scope and depth of such services at the EAC meeting this May, 2011. We
- 10. anticipate adding oneor several new groups to the list of NRNB-supported tools and resources over the next year. Awards and Honors None Dissemination We are averaging just over 10,000 visits (~60,000 page views) to the Cytoscape website per month. An additional 3,000 visits per month were logged at the new NRNB website, which went live in December 2010. Our new tutorial management system, Open Tutorials, has received over 1,000 visitors in the past month and is being used by researchers, developers and presenters. 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 a dramatic uptick in Cytoscape downloads in the first quarter of 2011, representing a doubling in download activity over the past year (Fig. 2). Figure 2. Chart of Cytoscape software downloads per month over the past 10 months. The NRNB website has a dedicated Tools page, which provides links to Cytoscape for download. We also offer a Training page, which displays upcoming training events and training materials in multiple formats. The Training page is the most popular page on the site, indicating the need and demand for the training services NRNB is providing. 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 Nineteenth Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2011) which will be held jointly with the Tenth Annual European Conference on Computational Biology (ECCB) in Vienna, Austria, July 17 - July 19, 2011. 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 first annual NetBio Special Interest Group (SIG) meeting dedicated to network biology tools, resources and research applications. NRNB tools are also represented in the research literature through our development and
- 11. research publications. NumerousCytoscape plugin articles and research articles using Cytoscape are published annually: 235 in the past year alone (HighWire search). We are currently drafting a position paper that will describe NRNB to the research community to increase awareness of our new resource. 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 it most ardent evangelists, presenting new functionality at their home institutions and through conferences and publications. Our open source commitment also allows us to participate in programs such as the Google Summer of Code, where Google sponsors 9-10 students to write code for us each summer. Patents, Licenses, Inventions, and Copyrights None. We are committed to an Open-Source dissemination policy. Training and Outreach Annual Cytoscape Retreat We are actively planning this year’s annual Cytoscape Retreat and Symposium, hosted by the National Resource for Network Biology (NRNB) in collaboration with the San Diego Center for Systems Biology (SDCSB). In addition to developer meetings, the retreat will include user and new developer tutorials, a Plugin Expo, and a special symposium. The symposium will be presented in conjunction with the SDCSB as the sixth annual Systems to Synthesis Symposium on May 20th at the Salk Institute (http://cytoscape.org/CytoscapeRetreat2011/). Workshops For the reporting period, NRNB has organized a total of 8 training events (in 7 cities in 4 countries), with an additional 4 events planned for the remainder of 2011. These events include tutorials, workshops and courses. For the same period, NRNB investigators and staff have given 7 invited lectures. For 2011, several conferences are planned, including the annual Cytoscape Retreat and Symposium, which will take place on May 18-21 in San Diego, CA. Helpdesk A major means of support for NRNB tools is through dedicated helpdesk and discussion mailing lists. The NRNB has begun monitoring the activity of these lists for the Cytoscape community
- 12. as an ongoingmetric for the effectiveness of our support. As a starting baseline, this first year saw 723 messages and a response rate of 61%. A fraction of the messages are informational posts that do not require a response, so we do not expect our response rate to hit 100%. Nevertheless, we have identified an opportunity for substantial improvement. From an analysis of our mailing list patterns, we have identified three approaches for improving response rates and disseminating information to users: ● Monthly response rates will be collected to identify months with lower than average response rates. A targeted strategy can then be employed to increase the response rate during these months. ● The most common discussion topics and questions will be identified, in order to improve the dissemination of critical information to users. In addition to FAQ topics, we will use this information to create innovative context-specific solutions tailored to each question. For example, users often ask about the syntax for increased memory allocation for Cytoscape. This information could be communicated in an error message any time Cytoscape experiences a memory-related failure, before the user even formulates the question. ● We are automating the analysis of helpdesk activity so that weekly alerts can be sent to NRNB staff whenever an email goes unanswered. This will allow us to maximize our response rate and to quickly address gaps in our collective attention. 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). Google AdWords We were awarded a non-profit account in the Google AdWords program. We are directing >1,300 clicks a month to NRNB tools and resources via AdWords. We are running 7 campaign groups consisting of over 500 key words and phrases. These activities are worth just over $1,000 a month, which we are getting free-of-charge. Note: approx half of the traffic is for WikiPathways, which is not yet officially a NRNB resource. 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 We were accepted as a mentoring organization in the 2011 GSoC program. Google allocated 10 student “slots” to us, which we have filled with qualified and enthusiastic summer students. The students will write open source code for NRNB-related projects during the summer. This is equivalent to $55,000 paid out as student and mentor stipends.
- 13. Annual Progress Report- Advisory Committee 2011 National Resource for Network Biology P41 RR031228-01 In our first year (8 months), we have assembled an External Advisory Committee (EAC) and scheduled the first EAC meeting for May 19th, 2011. We were very pleased to have all seven invited members promptly agree to join our EAC, including Dr. Stephen Friend as chair of the committee. 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 theIMAGER Graphics, Visualization and HCI research group. ● Nicholas Schork, Ph.D. is Director of Biostatistics and Bioinformatics at theScripps 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. ● Annette Adler is the Section Manager for the Computational Biology and Informatics within Agilent Labs. A full report of our first EAC meeting will be provided in next year’s progress report. The agenda includes discussion of major NRNB projects, including Cytoscape 3.0, and our collaboration, service and outreach efforts. In addition to asking for feedback on our progress so far, we will prepare a set of specific proposals to engage the EAC in our most complex decisions. Finally, we will also set milestones for our second year as a resource.
- 14. Annual Progress Report- Research Progress 2011 National Resource for Network Biology P41 RR031228-01 Recent progress in high-throughput experimental technologies has released enormous amounts of interaction data into the public domain. Analysis of these interactions— and the networks they form— relies in large part on robust bioinformatic technology. The mission of the NRNB is to develop and support a suite of bioinformatic tools that broadly enable Network Biology for the NIH-funded public. In this first year of our 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 fundamentally new network visualizations; and a major new version of our Cytoscape network analysis platform. Contents: ● NRNB Technology Research and Development Projects 1. Network-Guided Forests Identify Network Modules as Biomarkers (Ideker) 2. Identifying Altered Networks in Cancer (Sander) 3. Visualizing Cancer Genomic Data in the Context of Biological Networks (Sander) 4. Recognizing Trend Motifs and Dynamics in Networks (Fowler) 5. General Layout Algorithms and Views for Hierarchical, Modular Networks (Bader) 6. Semantic Zooming and Information Layering (Bader) 7. Network Layout by Known Ontology Attributes (Conklin, Pico) 8. Mapping and Visualizing Complex Attributes (Conklin, Pico) 9. The CYNI Modular Network Induction Framework (Schwikowski) ● NRNB Research Driving Biological Projects and Collaborations 1. Continuing DBP: Synthetic Genetic Analysis of Budding Yeast 2. New CSP: Genetic Networks Underlying DNA Damage ● NRNB Software and Resources 1. Cytoscape Core 2. SDSC Triton Resource 3. Open Tutorials 4. New NRNB Website NRNB Technology Research and Development Projects In the original grant proposal, we detailed four Technology Research and Development (TRD) projects. These projects have specialized and diversified into the nine TRD projects listed below. We anticipate further diversification and thus are shifting away from the limiting, original notation. To help translate, we include the labels TRD A - TRD D below for each project. 1. Network-Guided Forests Identify Network Modules as Biomarkers (Ideker: TRD A) Over the past year, the NRNB has been pursuing a number of bioinformatic advances to better
- 15. identify modular structureswithin biological networks and to apply network modules to predict disease outcomes. These developments are enabling what we call “network-based biomarkers”, based on the concept that network modules are better markers of cell state than are individual genes or proteins [1-5]. Indeed, many biological and clinical outcomes are based on modules of several interacting proteins working in combination. In development, for instance, it is largely combinatorial modules of transcription factors that give rise to the diversity of tissues. Protein combinations are equally instrumental in the pathogenesis of human disease, for instance the inappropriate fusion of Bcr and Abl that leads to chronic myelogenous leukemia or the abnormal interactions acquired by the huntington protein in Huntington’s Disease. A fundamental unanswered question is how the proteins within each module contribute to the overall module activity. Over the past year, we have performed a case study of the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. To facilitate this study we have developed a new bioinformatic method, called Network-Guided Forests (NGF), to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes [6]. NGF integrates key ideas from Random Forests (RF) [7] with biological constraints induced by a protein-protein interaction network— the first use of protein networks in ensemble learning. The NGF framework learns a set of decision trees (the “forest”) in which each tree maps to a connected component of the protein-protein interaction network (Fig. 1). The decision tree specifies a function that determines the output of the network component based on the activity of its genes. In turn, the collection of all tree outputs is used to predict the cell type or disease state of the biological sample (the “class”). By construction, decision trees detect genes that influence a phenotypic outcome both individually and through multiway interactions with other genes. As in the standard Random Forests algorithm, NGF uses a permutation-based procedure to assess the importance of each gene on the classification accuracy of the forest. We also assess the importance of pairs of genes in a tree — in our study these pairs are constrained by the network neighborhood. Genes and gene pairs with significantly high importance scores are placed into clusters that capture similar patterns of presence/absence across the forest of decision trees. Each cluster aggregates genes that fall into the same network region and, in combination, have predictive power over the sample class. Hence these clusters are termed “consensus decision modules” (Fig. 1). Use of NGF to analyze the three representative biological programs (early development, breast cancer metastasis, and mesenchymal transformation of brain tumor) identifies network modules which capture known causal mechanisms of development or disease. The modules implement diverse logic functions using both coherent and opposing gene activities, in which the module output depends on expression increases for some genes and concomitant decreases for others. Notably, we found that in cancer progression the most predictive decision functions can be linked to interactions between known oncogenes and tumor suppressors, such that the combined activity of both types of genes determines the disease outcome.
- 16. Figure 1. Networkdecision modules underlying embryonic origin, breast cancer metastasis and mesenchymal transformation of brain tumors. Expression profiles for each of these three case studies are combined with a network of protein-protein interactions among human transcription factors. Network-guided forests are used to identify key network modules that are most important for correct sample classification (representative modules are shown for each study). Grey edges indicate physical protein-protein interactions, blue edges indicate interactions that occur in the same decision trees and are most important for classification. Node color indicates gene importance as indicated by a permutation test. Each module is assigned a decision tree that specifies the output of the module based on the activity of its genes. References 1. Segal, E., et al., Module networks: identifying regulatory modules and their condition- specific regulators from gene expression data. Nat Genet, 2003. 34(2): p. 166-76. 2. Chuang, H.Y., et al., Network-based classification of breast cancer metastasis. Mol Syst Biol, 2007. 3: p. 140. 3. Muller, F.J., et al., Regulatory networks define phenotypic classes of human stem cell lines. Nature, 2008. 455(7211): p. 401-5. 4. Ravasi, T., et al., An atlas of combinatorial transcriptional regulation in mouse and man. Cell, 2010. 140(5): p. 744-52. 5. Ulitsky, I., et al., DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS One, 2010. 5(10): p. e13367. 6. Dutkowski, J. and Ideker, T. Protein networks as logic functions in development and cancer. PLoS Comp. Bio., In second round of review. 7. Breiman, L., Random forests. Machine Learning, 2001. 45(1): p. 5-32. 2. Identifying Altered Networks in Cancer (Sander: TRD A) As another project involving network-based biomarkers, we have been developing tools to vertically integrate multidimensional genomic profiling data (including sequence mutations,
- 17. DNA copy-number alterations,and mRNA expression profiles) in order to identify altered sub-networks in cancer. We refer to these modules as “driver networks”, as they are likely to contribute to tumorigenesis in multiple patients. Recently, NRNB investigators and others have shown proof of principle that the use of network and pathway information can help us understand the pronounced genetic heterogeneity seen in individual tumors of the same cancer type [1] and that they can lead to more accurate and robust signatures for classifying disease states [2-5]. To date, such methods have been explored in glioblastoma multiforme [6,7], as well as pancreatic [8], lung [9], breast and colorectal cancer [1]. With NRNB funding, we have begun to explore the use of an optimization algorithm borrowed from statistical physics to connect altered genes in cancer with minimal spanning networks. Such networks can identify the set of interactions able to explain the pattern of correlated alterations in cancer, i.e. the driver networks, from a human reference interaction network. The algorithm we are using, which addresses the minimum Steiner tree problem, attempts to find the shortest connection between altered genes in a specific cancer type. This network may be constructed with direct connections between altered genes and/or with connections between altered and unaltered genes within a human reference protein interaction network. In general, this problem is classified as an NP-complete problem, meaning that there is no efficient way of finding a solution. Additionally, once a network approaches an order of magnitude of ~10 altered genes, the minimum Steiner tree can only be approximated. In order to handle the much larger number of genes in the human protein interaction network, we are using an algorithmic framework based on a distributed method called message passing. This method has been shown to be successful in various applications, such as detecting protein associations in cell signaling [10] and data clustering [11]. We are currently evaluating the use of the Steiner tree algorithm by using a training dataset from glioblastoma and exploring the range of improvement obtained by varying the interaction weights between genes in the human reference network based on the mRNA expression profiles (Fig. 2). This algorithmic research is being applied to analyze multiple cancer types derived from The Cancer Genome Atlas (TCGA), prostate cancer genome data derived from the MSKCC Prostate Cancer Genome Project (PCGP), and expression data from chronic lymphocytic leukemia (CLL) patients at UCSD (Thomas Kipps, MD/PhD), all of which are being provided by active Driving Biological Projects (DBPs).
- 18. Figure 2. Applicationof the Steiner tree algorithm to glioblastoma mulitforme (GBM). Blue nodes represent genes altered by somatic mutation or copy number alteration. Pink nodes represent Steiner tree “linker” nodes that minimally connect altered nodes. Canonical pathways, including PI3K, P53 and RB signaling are outlined. References 1. Lin, J. et al. A multidimensional analysis of genes mutated in breast and colorectal cancers. Genome Research 17, 1304-18 (2007). 2. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol Syst Biol 3, 140 (2007). 3. Efroni, S., Schaefer, C.F. & Buetow, K.H. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS ONE 2, e425 (2007). 4. Tuck, D.P., Kluger, H.M. & Kluger, Y. Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinformatics 7, 236 (2006). 5. Ideker, T. & Sharan, R. Protein networks in disease. Genome Research 18, 644-52 (2008). 6. TCGA. Comprehensive genomic characterization defines novel cancer genes and core pathways in human glioblastomas 43 (2008). 7. Parsons, W.D. et al. An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science, 13 (2008). 8. Jones, S. et al. Core Signaling Pathways in Human Pancreatic Cancers Revealed by Global Genomic Analyses. Science (2008). 9. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069-75 (2008). 10. Bailly-Bechet M, Borgs C, Braunstein A, et al. Finding undetected protein associations in cell signaling by belief propagation. Proc Natl Acad Sci U S A. 2011;108(2):882-887. 11. M. Bailly-Bechet, S. Bradde, A. Braunstein, A. Flaxman, L. Foini, R. Zecchina. Clustering with shallow trees. J Stat Mech. 2009;P12010.
- 19. 3. Visualizing CancerGenomic Data in the Context of Biological Networks (Sander: TRD A) This project focuses on visualizing cancer genomic data in the context of specific pathways and networks. We have developed an initial prototype using Cytoscape Web [1], which is capable of displaying networks derived from Pathway Commons [2], and overlaying these networks with genomic data derived from the TCGA project. The prototype displays a fully interactive network of the genes analyzed, plus details regarding individual genomic alterations (Figure 3). We are planning to transfer knowledge we have gained from this prototype and apply it to our cBio Cancer Genomics Portal (http://cbioportal.org). The portal currently enables users to visualize, analyze and download large-scale cancer genomic data sets, but is currently lacking in network visualization. With Cytoscape Web, users will soon be able to enter a set of genes, visualize those genes in a network context, and dynamically overlay genomic data onto the networks of interest. This will provide a critical exploratory data analysis module to the portal, enabling the wider research community to more easily visualize genomic data in the context of biological pathways, and to develop and confirm hypotheses regarding cancer development and progression. Figure 3. Prototype of cancer network visualization, built with Cytoscape Web [1]. Left panel shows a global network view of genes altered by somatic mutation or copy number alteration in serous ovarian cancer (TCGA). Node size is proportional to frequency of alteration. Right panel shows a local view of the BRCA/RB subnetwork, with genomic alterations displayed as a compact OncoPrint. Experience gained from this prototype will be used to add a new network visualization component to our cBio Cancer Genomics Portal (http://cbioportal.org). References 1. Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD., Cytoscape Web: an interactive web-based network browser. Bioinformatics. 2010 Sep 15;26(18):2347-8. 2. Cerami EG, Gross BE, Demir E, et al., Pathway Commons, a web resource for biological
- 20. pathway data. NucleicAcids Res. 2011;39(Database issue):D685-D690. PMID:21071392 4. Recognizing Trend Motifs and Dynamics in Networks (Fowler: TRD B) 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 published in PNAS, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends [1,2]. Maps of the friendship networks show clustering of genotypes, and, after we apply strict controls for population stratification, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study on an independent sample from the Framingham Heart Study verifies that DRD2 exhibits significant homophily and that CYP2A6 exhibits significant heterophily. These novel results show that homophily and heterophily obtain 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. This work continues to build off our original DBP for the “Role of Social Networks in the Spread of Disease,” led by Nicholas Christakis. 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 5. General Layout Algorithms and Views for Hierarchical, Modular Networks (Bader: TRD C) Biologists frequently use networks to represent the structure and function of the cell, using intuitive metaphors to reduce multiple levels of spatial and temporal relationships to a two- dimensional image. At the same time, computational representations of the cell are more abstract and tend to be less intuitive for biologists than human-made diagrams. We are working to improve the biological relevance of computational visualizations of biological networks in Cytoscape, in collaboration with investigators leading driving biological projects and collaborative service projects. More intuitive biological network visualizations will speed interpretation of large-scale data about cellular processes being generated by biologists. We developed the Thematic Map plugin for Cytoscape, based on an earlier prototype presented in our original NRNB grant application. This plugin ‘rolls-up’ node or edge attributes
- 21. into individual nodes,i.e. it transforms an input network of interactions among proteins into an attribute network, in which node attributes are nodes and edges summarize all connections between nodes with the corresponding attributes in the original network. This view can be used in a number of biologically useful ways, such as summarizing the functional content of a large protein-protein interaction network. We are currently testing this plugin for release in the second half of 2011. Figure 4. Thematic map based on node attributes. We have also developed a second plugin, the Enrichment Map, in a similar spirit to the Thematic Map plugin. Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http:// baderlab.org/Software/EnrichmentMap/) [1].
- 22. Figure 5. EnrichmentMap for estrogen-treated cells versus untreated cells 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. 6. Semantic Zooming and Information Layering (Bader: TRD C) Our goal in this project is to develop methods to help researchers explore and interpret large networks and their associated genome-scale data sets. As the volume, resolution and complexity of biological data continue to increase, so do the challenges associated with visualizing, analyzing and interpreting the data. Methods that we develop will help network visualization scale, while still remaining interactive to support live exploration and hypothesis testing. We have developed an initial implementation of a new filtering API for Cytoscape 3, which will enable us to develop the next-generation interactive filtering system for Cytoscape. We have verified that using a BitSet implementation to handle filter set operations can support large networks of up to 10 million nodes and edges. We are currently receiving feedback about the new API and will implement it fully in Cytoscape 3.1 [1]. We are also making progress on this project by developing support for visualizing detailed biological pathways in Cytoscape. We have recently implemented BioPAX Level 3 support in Cytoscape (BioPAX Level 3 reader, writer and visualizer) [2]. This enables import of biological pathway information from various pathway databases, including Reactome [3], WikiPathways [4] and Pathway Commons [5]. Future pathway visualization features that we develop in Cytoscape will depend on this functionality. We continue to closely collaborate with the Charlie Boone and Brenda Andrews labs
- 23. who lead ourDBP: Synthetic genetic analysis of budding yeast (see DBP progress reports below). References 1. http://cytoscape.wodaklab.org/wiki/Outdated_Cytoscape_3.0/FilterAPI 2. Demir E et al. The BioPAX community standard for pathway data sharing. Nat Biotechnol. 2010 Sep;28(9):935-42. Epub 2010 Sep 9. PMID: 20829833 3. Matthews L, Gopinath G, Gillespie M, Caudy M, et al. Reactome knowledgebase of biological pathways and processes. Nucleic Acids Res. 2008 Nov 3. PMID: 18981052 4. Pico AR, Kelder T, van Iersel MP, Hanspers K, et al. (2008) WikiPathways: Pathway Editing for the People. PLoS Biol 6(7): doi:10.1371/journal.pbio.0060184 5. Cerami et al. Pathway Commons, a web resource for biological pathway data. Nucl. Acids Res. (2010) doi: 10.1093/nar/gkq1039 7. Network Layout by Known Ontology Attributes (Conklin, Pico: TRD C) Organizing network or pathway data into a diagram that effectively communicates information about biological systems requires biological expertise and even a bit of artistry. A good diagram might need to illustrate myriad interactions between genes, proteins and small molecules and might also convey their spatial and temporal arrangement. One of the most biologically intuitive ways to organize information about cellular systems is to place it in the context of a familiar physical map of the cell, with the nucleus surrounded by cytosol, organelles and a plasma membrane. Similarly, proteins known to be part of the same pathway should be placed close together in the diagram. A good source of information about a protein’s cellular location and biological process involvement is the Gene Ontology (GO) project [1], a collaborative effort to standardize nomenclature for biological concepts and link these to genes and proteins from many genomes. The GO project has developed three structured controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. Gene Ontology provides much broader coverage of genomes for this type of information than is available from any other source, such as traditional pathway models stored in pathway databases [2]. We developed a network layout plugin for Cytoscape, which utilizes Gene Ontology (GO) annotations to help organize nodes in a biologically relevant way. The first version of the GOLayout plugin is currently being tested and will be released in the second half of 2011. GOLayout first partitions a given network into subnetworks based on biological process annotations, such as cell differentiation or cell cycle, provided by a pruned set of Gene Ontology called “GO slim” (Fig. 6). Each subnetwork is laid out based on cellular component annotations over a scalable template of a typical cell diagram. Finally, each node is colored based on a discrete mapping to molecular function annotations, such that all kinases, for example, might be colored green. The result is a biologically informative layout. This project is complementary to the Thematic Map described in TRD 5 above. While that plugin generates a descriptive network of attribute-based metanodes, the GOLayout plugin generates a series of subnetworks using attributes to partition, layout and color given nodes. As both projects are under the umbrella of the original TRD C proposal, we continue to coordinate on the development of these related
- 24. efforts. Key aspectsof the design and implementation of the plugin were done as part of an NRNB collaboration and service project with Allan Kuchinsky from Agilent Technologies, led by Annette Adler (Visualizing Biological Networks with a Biologist’s Eye). Figure 6. The result of using GOLayout to partition a massive “hairball” network into a series of biological processes, each laid out into cellular compartments and colored by molecular function according to Gene Ontology annotations. The next version of GOLayout will include user-driven heuristics for highlighting biologically interesting paths within the layout, as well as better ontology handling, i.e., for navigating nested terms. Other key features planned for the next release include support for importing/exporting/printing the layouts in multiple formats. This will allow for custom layout templates, as well as unique visualization, analysis and sharing workflows. References 1. The_Gene_Ontology_Consortium. Gene ontology: tool for the unification of biology. 25, 25-29 (2000). 2. Cary, M.P., Bader, G.D. & Sander, C. Pathway information for systems biology. FEBS Lett 579, 1815-20 (2005). 8. Mapping and Visualizing Complex Attributes (Conklin, Pico: TRD C) An increasing number of experimental methods, such as scans for Single Nucleotide Polymorphisms (SNPs) or exon microarrays, are generating data at sub-gene levels. It is extremely useful to interpret this information in the context of biological networks and pathways [1-4]. For this purpose, we are extending Cytoscape to enable network visualizations of data on sub-gene structures, similar to how Cytoscape already allows visualization of gene expression data on nodes that represent genes or proteins. The input to the system is a data set of sub- gene or protein features, such as SNPs, exons or protein domains, and their associated data
- 25. (e.g. population frequency,expression level or domain type). The parent node (gene) color may then be based on the expression values of the exon, or could be based on a gene expression experiment, to allow comparison between exon expression and gene expression. We have made progress toward supporting the mapping of attributes across these various levels of abstraction with our ongoing work on entity grouping concepts and representations in Cytoscape. The initial benefits of this work are expressed in new metanode features, supporting the mapping of member node attributes up to the parent node using basic functions (average, sum, minimum, maximum, median). Next, we plan to add weighted average, threshold and modal functions. This mapping infrastructure for metanodes is critical to all downstream visualization work with sub-gene and supra-gene level features and entities. Proteins and genes in biological networks are associated with an increasing amount of data from multiple experiments, such as gene expression measured across a time series or across normal and disease states. Ideally, this multi-dimensional information could be visualized in the context of networks, but this is not possible with the current version of Cytoscape. We are extending the Cytoscape visual mapping system to support multiple node attributes at the same time using new types of visual attributes. Our primary DBP (Alternative splicing in embryonic stem cells, Mercola/Burnham Institute), for instance, requires this ability to view time series gene expression experiments. Through a new collaboration and service project (Visualizing Multiple Attributes, Morris/ UCSF), we coordinated on the design and implementation of the new nodeCharts plugin. This plugin provides an interface for drawing pie, line, bar charts, and histograms onto nodes using either attribute data or arbitrary data values (Fig. 7). Figure 7. The sample network galFiltered with nodes painted with a pie graph representing the significance of the expression difference for each experimental condition as expressed in the attributes "gal1RGsig (red), gal4RGsig (yellow), and gal80Rsig (green)". This work is just the beginning of our larger aim of supporting information layering and complex attribute visualization. There are other visual styles to add to nodeCharts, including radar, concentric, grid, and so on. Furthermore, the current nodeCharts plugins provides only programmatic support through the CyCommands interface to the Cytoscape core application. We plan to implement control panels that utilize nodeCharts to present a user interface to support complex mapping decisions. Finally, it will be important to connect the mapping work to the visualization work and to consider the unique cases of visualizing aggregate information from sub-gene features to network nodes or from network nodes to metanodes. References 1. Mourich, D.V. & Iversen, P.L. Splicing in the immune system: potential targets for therapeutic
- 26. intervention by antisense-mediatedalternative splicing. Curr Opin Mol Ther 11, 124-32 (2009). 2. Venables, J.P. et al. Cancer-associated regulation of alternative splicing. Nat Struct Mol Biol (2009). 3. Chang, J.S. et al. Pathway analysis of single-nucleotide polymorphisms potentially associated with glioblastoma multiforme susceptibility using random forests. Cancer Epidemiol Biomarkers Prev 17, 1368-73 (2008). 4. Hoffman, A.E. et al. Clock-cancer connection in non-Hodgkin's lymphoma: a genetic association study and pathway analysis of the circadian gene cryptochrome 2. Cancer Res 69, 3605-13 (2009). 9. The CYNI Modular Network Induction Framework (Schwikowski: TRD D) In spite of steady progress in the development of methods that automatically learn network structure from data, these methods have not yet found broader use in the biological literature. The CYNI project aims to provide an easy-to-use interface for network inference algorithms making data-driven analysis of biological problems (including clustering and classification tasks, hypothesis generation from data, and support for experiment design) amenable to users of the Cytoscape software platform. It will also provide method developers with supporting functionality and technical infrastructure that makes it straightforward to distribute software to a wide community. For tool users, the unified interface will permit easy access to a large number state-of-the-art methods allowing for the rapid adaptation of existing data-processing workflows to new biological problems or the integration of novel tools in direct comparison with extant methods. We will use reference implementations of tools that demonstrate the new interface to method developers, and provide examples of their use in biological application projects. Classification, clustering and network induction provide conceptually homogeneous approaches with a wide range of practical applications. A large number of variations exist, for instance with respect to the choice of particular algorithms, the distance/similarity measures and the standardization of input data. For optimal results, these choices must be made in compliance with desired properties of the results and are thus application-dependent. To retain the flexibility and extensibility required for a widely applicable framework, we are developing CYNI with a modular approach that allows functionality to be shared between tasks and allows tailoring of application-specific workflows from predefined building blocks. Specifically, the network induction consist of three stages: 1. An edge assessment using an information-theoretic measure 2. A pathway aggregation step 3. A component for experiment selection These stages can be supplemented by an optional pre-processing step. Moreover, the pathway aggregation step can be configured to harmonize with various edge scoring measures (the default setting being adapted to a general-purpose method that does not put restrictions on the interpretation of the edge weights). We have applied this design to a network induction and experiment design methodology for de-novo identification of pathways from large-scale data within the BaSysBio project, which
- 27. aims to elucidateregulatory networks in the gram-positive bacterium Bacillus subtilis. The approach links an observed phenotype to an external perturbation and is currently implemented as a series of stand-alone programs. Output is generated in several formats, including the .sif format read by Cytoscape. Following the implementation of the Cyni-plugin interface, we are planning to integrate the developed algorithms as reference plugin implementations for demonstrating the network induction interface. We have applied the approach to a transcriptome time series measurement of cells following a nutrient change, in which a surprising consequence (bacterial competence) was induced. Our computational approach induced a network between regulatory pathway candidates involving a total of only 26 genes from expression data, from an initial selection of more that 400 genes. Many of the inferred edges coincide with known regulatory interactions. Newly indicated putative pathways are now being tested experimentally. This network induction problem is in many ways similar to the one posed in our DBP (Agents that Boost Innate Antimicrobial Defenses, Sansonetti/Institut Pasteur). We expect to be able to apply the pathway aggregation and experiment selection modules with the data generated in that project. Problem-specific interaction measures will be developed in close collaboration with the Sansonetti group. NRNB Research Driving Biological Projects and Collaborations During the first year, our research projects have remained coupled with the DBPs and CSPs originally presented in the grant proposal. You will find explicit references in many of the descriptions above and each is registered as a subproject, which will be tracked and updated annually. In addition, we have picked up many new collaborations this year (37 in total). These collaborations involve both the application and technical development of NRNB tools and resources. We recognize that collaborations best showcase the actual utility of our Resource and drive the direction and purpose of many of our research projects. In this progress report, we highlight two examples: one new and one continuing from the original grant (these are also Research Highlights). 1. Continuing DBP: Synthetic Genetic Analysis of Budding Yeast (Bader, Boone, Andrews) Since 2001, the Bader lab has been collaborating with the Boone and Andrews laboratories on the analysis and visualization of the budding yeast genetic interaction network. Cytoscape is in heavy use in the Boone and Andrews labs for this purpose. Accordingly, the Boone and Andrews labs provide a strong scientific driver for Bader lab network visualization and software projects (TRD 5, above). Drs. Andrews and Boone are working to complete the first complete genetic interaction network for a cell and to decipher the general principles that govern these networks. This reference map provides a model for expanding genetic network analysis to higher organisms, and it will stimulate valuable insights into gene function, drug target and mode-of-action analysis. The resulting complete map of genetic interactions for budding yeast, with ~6000 genes, will contain 36 million quantitative interaction pairs (18 million unique pairs). The fundamental principle underlying this DBP is that we need to discover the rules
- 28. governing how genesinteract with one another in order to be able to predict which rare combinations of gene mutations cause human disease or other significant phenotypes. Andrews and Boone aim to discover the general principles of genetic interaction by mapping the first complete genetic interaction network for a eukaryotic cell and directly testing the conservation of these principles. They are taking a unique experimental approach to define and dissect the rules of complex genetic networks. The strategy entails the use of combinatorial genetic perturbations to systematically screen for genetic interactions. In particular, they have established key infrastructure that enables the construction of all possible double gene deletion mutant combinations in genetically tractable yeast model systems in an automated, high throughput manner. Genetic interactions are subsequently scored by assessing extreme phenotypes that result from the collapse of an essential cellular function. This information is assembled into a network that reflects the genetic landscape of a cell. During the reporting period NRNB investigator, Gary Bader, has collaborated with Drs. Boone and Andrews on three new publications, extracting knowledge about protein complexes [1], regions of protein disorder [2] and physiological fitness [3] from comparisons of genetic interactions on a genome scale. Each of these published projects utilizes Cytoscape for network analysis and visualization. References 1. Michaut M, Baryshnikova A, Costanzo M, Myers CL, Andrews BJ, Boone C, Bader GD. Protein complexes are central in the yeast genetic landscape. PLoS Comput Biol. 2011 Feb;7(2):e1001092. Epub 2011 Feb 24. PMID: 21390331; PMCID: PMC3044758. 2. Bellay J, Han S, Michaut M, Kim T, Costanzo M, Andrews BJ, Boone C, Bader GD, Myers CL, Kim PM. Bringing order to protein disorder through comparative genomics and genetic interactions. Genome Biol. 2011 Feb 16;12(2):R14. PMID: 21324131. 3. 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. PMID: 21076421. 2. New CSP: Dynamic Genetic Networks Underlying DNA Damage (Ideker, Krogan) A very successful new CSP begun in the past year involves the laboratories of Trey Ideker (representing the NRNB) and Nevan Krogan at UCSF. The goal of this project is to understand the extent to which genetic and protein networks are remodeled by changes in conditions. Indeed, although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. To explore network dynamics, Ideker and Krogan are collaborating to generate interaction networks as cells are exposed to different cellular stresses and stimuli. To analyze the resulting network dynamics, the team has developed a new method we call differential epistasis mapping (dE-MAP) which identifies “differential” interactions based on their changes in interaction strength observed between two static conditions. Analyzing network data to identify differential interactions is very similar to analyzing gene expression microarrays to identify
- 29. differential expression, orusing ICAT or ITRAC mass spectrometry to identify differentially expressed proteins or protein post-translational modifications. Two-color microarrays revolutionized gene expression analysis because they permitted direct comparison of two conditions and thus identification of differentially expressed genes. In the same way, we feel that differential analysis will be key to extracting the major response pathways encoded by a large biological network. As proof-of-principle, we have recently used the dE-MAP approach to map widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage [1]. In the published study, analysis of differential interactions proved very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. This analysis also revealed that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. This proof-of-principle work suggests that differential networks chart a new type of genetic landscape that will be invaluable for mapping many different cellular responses to stimuli. We are now applying the dE-MAP procedure to examine the interaction dynamics among yeast genes involved in cellular processes such as autophagy, aging, and the response to chemotherapeutic compounds. This research is highly complimentary to the work of the Bader, Boone and Andrews laboratories described above (see Synthetic Genetic Analysis of Budding Yeast), which seeks to map the entire genetic network in yeast for a single condition. This work is in continued collaboration with Nevan Krogan as well as with a cadre of other investigators. References 1. Bandyopadhyay S, Mehta M, Kuo D, Sung MK, Chuang R, Jaehnig EJ, Bodenmiller B, Licon K, Copeland W, Shales M, Fiedler D, Dutkowski J, Guénolé A, van Attikum H, Shokat KM, Kolodner RD, Huh WK, Aebersold R, Keogh MC, Krogan NJ, Ideker T. Rewiring of genetic networks in response to DNA damage. Science. 2010 Dec 3;330(6009):1385-9. PMID: 21127252; PMCID: PMC3006187. NRNB Software and Resources 1. Cytoscape Core Cytoscape (http://cytoscape.org) is a core research tool either used by the majority of projects and collaborations engaged by the NRNB. As such, the development and maintenance of Cytoscape receives a large amount of attention. Cytoscape development is progressing along two fronts. First, we are continuing to maintain the existing 2.8 series of releases. Second, we are developing version 3.0 of Cytoscape which represents a significant evolution of our architecture in order to modularize the core of Cytoscape, define a clear and consistent API, and simplify the experience of developing and maintaining plugins for Cytoscape. Cytoscape 2.8.0 was released in October of 2010 and a subsequent maintenance version 2.8.1 was released in February of 2011. Version 2.8 introduces two powerful new features that, when used together, can create rich visualizations [1]. These features are
- 30. custom node graphicsand attribute equations. Custom node graphics allow Cytoscape end users to map arbitrary graphical images onto nodes in a Cytoscape network using the existing VizMapper interface. Attribute equations provide Excel-like functionality to the Cytoscape attribute browser. We provide a variety of functions that allow normal Cytoscape attributes (numbers, strings, lists) to be manipulated in common ways within Cytoscape. The purpose of attribute equations is not to supplant the use of R or Excel for data analysis, but rather to provide a convenient means for users to manipulate data within Cytoscape. Combining custom node graphics with attribute equations permits the generation of rich graphics. For example, given a Cytoscape node attribute linking each node to a corresponding identifier in the Protein Data Bank (PDB), one is able to write an equation that concatenates the identifier string together with other text to form a complete URL pointing to an image of the 3D structure provided on the PDB website. It is then possible to map this URL to a node for which the URL is interpreted as an image resulting in the 3D structure of the specified protein being displayed on the node image in the network view. In conjunction with Cytoscape 2.8, we have also begun developing the next generation of Cytoscape, version 3.0. The Cytoscape 3.0 development effort has resulted in the first developer milestone release of 3.0 at the end of January 2011. The purpose of this milestone was to present a functioning application to the core Cytoscape development team so that they could begin porting plugins and providing feedback on the 3.0 Application Programmer Interface (API). The Bader Group has ported a number of core plugins from 2.8, including BioPAX and PathwayCommons, and they have implemented session reading and writing. In early March 2011 we held a small meeting of core developers at UC San Diego to discuss the design of Cytoscape 3.0 and to plan the remaining development efforts that are required. We are currently on track to release developer milestone 2 prior to the 2011 Cytoscape retreat in May. Although the primary goal of Cytoscape 3.0 is to have feature parity with Cytoscape 2.X, there will be new features included as well. We have begun initial development on a “Quick Start” plugin designed to help novice users get their attribute and network data into Cytoscape was quickly and easily as possible. References 1. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011 Feb 1;27(3):431-2. Epub 2010 Dec 12. PMID: 21149340; PMCID: PMC3031041. 2. SDSC Triton Resource In the short time that we have been using the Triton Resource, our users have used over 100,000 hours of CPU time for NRNB projects. We have lined up approximately 500,000 additional hours to be used in the next year. 3. Open Tutorials We have developed a unique tutorial management system that caters to developers (with wiki tools for creating and updating content), presenters (with prepared slideshows and handouts), and students (with up-to-date online content). Open Tutorials (http://opentutorials.cgl.ucsf.edu) is now the primary source of tutorial material for the Cytoscape project. We recently created
- 31. a new Cytoscapetutorial for "Basic Expression Analysis" that uses publicly available human experimental data. This tutorial, like the original tutorial for yeast, represents one of the most common use cases of Cytoscape for biologists. The site has received over 1,000 visitors in the past month, including visits by biologists, clinicians, developers and presenters. Moving forward, this scalable tutorial management solution will allow NRNB to provide tutorial support services to a broad community. 4. New NRNB Website The new NRNB website (http://www.nrnb.org) went live in late 2010 within a month of our award announcement. The website is the main representation of the NRNB resource for collaborators and researchers. The site includes information about available tools, resources, workshops and training opportunities. There are easy-to-use web forms for requesting services, starting a collaboration, and organizing a training event. We also use these forms for tracking internal activity throughout the year. Overall, the website is relatively dynamic with continuously updated events, news and community interactions. Over the past 5 months, we have registered 34 events, 15 news items, 19 internal project updates, and 37 collaborations. During the last month, traffic analytics show that we averaged close to 100 visitors a day. Interestingly, half of this traffic is coming from our participation in the Google Summer of Code program (see Outreach section in Research Highlights).
- 32. !"#$"%&'()"*+,#"-!").+)/%0'1.2*3,)$%,#"'45%3,6'7)"3,6'8)990*:;'19*<*"6'="*> ? !M?@@NO?PPQRNP =@AB'1(ACA@6'!D( NS-?O-PN?N NM-ON-PN?? EF1GA@H1=B'I7'JK517I@F1K'HKF'(1ALI @*3#T"+*'U#"'K.%0>3)36'G)VT%0)V%,)#.6'%.9'1.,*$"%,)#.'#U'F*,W#"<3'4@KG1F: !DH'PXSN'4@*2Y'NZ-NS: !"#$%&'()%*
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