NRNB EAC Meeting 2012

National Resource for
Network Biology

External Advisory Council (EAC) Meeting
Wednesday December 12, 2012

Overview Technology Software Collabs Outreach Plans

NRNB Overview
• An NIGMS Biomedical Technology Resource Center focused
on Network Biology
• Research and develop new bioinformatic technologies to
enable the use of biological networks (molecular, genetic, and
social) among NIH-funded investigators
• Collaborate directly with biological and clinical users to push
the envelope of network analysis capabilities
• Distribute open-source network technologies to the greater
scientific community
• Stimulate a portfolio of educational and outreach
opportunities surrounding network biology


NRNB Investigators
Trey Ideker, PhD
Principal Investigator, NRNB Gary Bader, PhD
Departments of Medicine and Bioengineering Assistant Professor, Terrence Donnelly Centre
University of California, San Diego for Cellular & Biomolecular Research
Dr. Ideker uses genome-scale measurements to University of Toronto
construct network models of DNA damage Dr. Bader works on biological network analysis
response and cancer. He was the 2009 recipient and pathway information resources.
of the Overton Prize from the International
Society for Computational Biology.

James Fowler, PhD
Alex Pico, PhD Associate Professor, CalIT2 Center for Wireless &
Executive Director, NRNB Population Health Systems and Political Science
Gladstone Institute of Cardiovascular Disease University of California, San Diego
Staff Research Scientist
Dr. Fowler’s research concerns social networks,
University of California, San Francisco
behavioral economics, evolutionary game theory,
Dr. Pico develops software tools and resources and genopolitics (the study of the genetic basis of
that help analyze, visualize and explore political behavior). His research on social networks
biomedical data in the context of these networks has been featured in Time’s Year in Medicine.

Chris Sander, PhD
Chair, Computational Biology Center, Benno Schwikowski, PhD
Tri-Institutional Professor Chef du Laboratoire/Group Leader
Memorial Sloan-Kettering Cancer Center Pasteur Institute
Dr. Sander’s research focuses on Computational Dr. Schwikowski’s expertise lies in
and Systems Biology of molecules, pathways, and combinatorial algorithms for Computational
processes. and Systems Biology.

The National Resource for Network Biology:
Integrating genomes & networks to understand health & disease
NIH NCRR / NIGMS P41 GM103504

Draft Network Assembly

Patient genotype
Genome sequencing
Phenotype
Disease diagnosis
Response to therapy/drug
Side effects
Developmental outcome
1) How to assemble and visualize
Rate of aging, etc.
Gene expression & network models of the cell?
other large scale
molecular state
measurements 2) How to use networks in healthcare?

NRNB statistics ‘at-a-glance’
Year 3 of 5 funded years, $700,000 per year direct + $175,000 supplement
4 technology projects with matched driving biology
20 NRNB-supported publications since start of funding
Cytoscape used in ~250 pubs / year and has supported ~1400 NIH grants
96 collaborations with NIH-funded investigators (60 new this year)
27 training events over 7 countries, 32 academic courses by NRNB staff
16 Google Summer of Code Students = $80,000 invested by Google
Representative Publications:
Dutkowski et al. A gene ontology inferred from molecular networks. Nature Biotech. (2013) [cover]
Rügheimer et al. Applications of Intelligent Data Analysis for the Discovery of Gene Regulatory
Networks. Computational Intelligence in Intelligent Data Analysis (2013)
R Saito et al. A travel guide to Cytoscape plugins. Nature Methods (2012)
Cerami et al. The cBio Cancer Genomics Portal. Cancer Discovery (2012)
Morris, S. et al. ClusterMaker: multi-algorithm clustering for Cytoscape. BMC Bioinformatics (2011)
Ideker, T. et al. Boosting signal-to-noise in complex biology: prior knowledge is power. Cell (2011)
Dutkowski et al. Protein networks as logic functions in development and cancer. PLoS Comp Bio (2011).
Fowler et al. Correlated genotypes in friendship networks. PNAS (2011)
Bandyopadhyay and Ideker. Integrated Systems Biology [Poster], Nature Genetics (2010)

Canadian Institutes
of Health
Research, 23 NICHD, 22
Others, 127
NIDA, 25
Wellcome Trust, 29
NINDS, 39
NIGMS, 354

NIMH, 47

NLM, 54

NHGRI, 58

NIEHS, 62 NCI, 205

NIDDK, 88

NHLBI, 88 NCRR, 110

NIAID, 103

Use of Cytoscape by grantees. We have identified >1,400
grants acknowledged in publications citing a Cytoscape
paper (Shannon P et al. 2003; Cline MS et al. 2007; Smoot
ME et al. 2011). One pie slice represents the number of
grants from a particular agency. NIH institutes are
represented by standard abbreviations.


NRNB Funding Allocation
1000
Supp-Crowdsourcing
900 Collaboration
800 Outreach
Admin-Pico
700
Admin-Ideker
600 Admin-Asst.
$1000s

500 Core Comp/Software
TRD-A-Ideker
400
TRD-A-Sander
300 TRD-B-Fowler
200 TRD-C-Pico
TRD-C-Bader
100
TRD-D-Schwikowski
0 Co-I Salaries
Year 1 Year 2

Response to previous EAC critiques
• Clearly articulate the link between funding and progress
See previous chart; all described work is directly funded by NRNB
• Focus not only on network visualization but on network-based
analysis and decision making
Exciting progress has been made for using networks as
diagnostic biomarkers under TRD A
• Encourage increased citation of Cytoscape
Refactored Cytoscape.org to encourage citation; perform
systematic literature tracking
• Better track Cytoscape plugins and leverage developers
Developed new Appstore (apps.cytoscape.org)
• Track use of Cytoscape in the classroom
Identified 32 courses using Cytoscape in 2011 - 2012


Agenda
• Progress on Technology Research & Dev.
• Release of Cytoscape 3.0
• Review of Collaboration and Service Projects
• Progress on Training and Outreach
• Plans for NRNB moving forward
• Generate discussion and seek advice on all
aspects


Pico (UCSF)

Networks (in various states of curation) Circuits Coupled w/ Decision Tree Logic
(or somatic mutation)
Patient Expression

TRD A:
Protein networks as
biomarkers of disease
Ideker group

Janusz Dutkowski et al
PLoS Comp Bio 2011

TRD A: Network Biomarkers (Ideker); DBP 2 (Kipps)
Publication: Chuang et al. Blood (2012) [cover article]

TRD A: cBio Cancer Genomics Portal (Chris Sander / MSKCC)
http://www.cbioportal.org

• Make Cancer Genomics data available to a
broad audience:
– “Wet lab” biologists involved in functional
studies.
– Computational biologists
• Allow quick access to small data slices (few
genes, subset of samples)
• Facilitate explorative data analysis / hypothesis
generation and testing

Cerami et al. 2012, Cancer Discovery


Cerami et al 2010, Nucl Acids Res


Features for the network view
Targeted-drugs Suggesting new targeted-drugs
Direct targets (Released) indirect targets (work in progress)
Targeted-drug
Pathway data
data

Tumor data

Infer and suggest novel,
drug-based therapy options

Sources: DrugBank, KEGG Drugs, Cancer.gov

e.g. AKT inhibitor for PTEN deletion


TRD B: Using Cytoscape for Social Network Research

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

TRD C: Network Visualization and Representation (Pico / Bader)
Visualizing Complex Networks as Ontology-Partitioned Mosaics

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


• Annotation
• Gene set analysis
• Edge analysis
• Module analysis


TRD C: Network visualization and
representation (Ideker)

Introducing NeXO

A Network
eXtracted Ontology

Janusz Dutkowski et al.
Nature Biotechnology (2013)


Assembly and alignment of a
network-extracted gene ontology


TRD D: Inference of Transcriptional Networks
(Schwikowski group)
• Create „fill-in-the-algorithm‟ infrastructure for inference of gene regulatory networks
• Make methods accessible to biologists within the Cytoscape framework
Approach: Create a software infrastructure (CYNI) for network inference algorithms


Since the last EAC meeting – Summary
-C design prototype
-Hired engineer Oriol Guitart-Pla (02/15/12)
-Working CYNI App
-Documented API
-User and CYNI App writer documentation
-Additional CYNI functionality: Discretization and Imputation
-Implemented and documented downloadable “Hello World
examples”
-CYNI App presented at two French network biology meetings
-CYNI App and Comprehensive documentation publicly
available

TRD Outreach Ideas


Cytoscape 3.0 Progress
(Core NRNB Software)
Major update of technology behind Cytoscape
Creates a fully modular architecture and API
Cytoscape 2.x would not support the features
needed for the current vision of network
analysis, e.g. scripting, multiple and 3D
renderers, crowd-sourcing, interoperability with
web and other tools


- Cytoscape 2.x: Maintenance Releases
- 2.8.2: Bug fixes and Improved memory allocation

- 2.8.3: Bug fixes and better trackpad support for Mac
platforms

- Cytoscape 3
- 3.0.0 M5: June 2012: For developers

- 3.0.0-Beta1: 10/9/2012: First Release for Users

- 3.0.0 Final: This Week

UBE2L3
RRM1

EDIL3 NFKB1
CTBP1

UBE2N
POLR2A

NME1

OTUB1
JAK1

PARK2
KPNA1

DNMT1
LAT
NF1 XRCC6 VCP
EXO1

DNAH8
ENSG00000137337
H2AFX
GPR3
SMAD4
HSD17B1P1

FANCD2
BLM

STAT1 UBE2D3
WNT2B WWP2 FANCG
CDC25C
ECHDC1 ID4
ACACA
PSAP PARP2 CHGB
RCHY1
BRCC3 FBXL7 UIMC1
HERC2 ORC2L
C17orf70 C11orf30
TOX3 CASP3
BUB1 9606.ENSP00000358154
XRCC3
ELK4 BIRC5
RNASEL

MAP2K1
GSTP1 RNF2

MED17 TRRAP RARB CCNA2 CREBBP
EP400 MSH2
NEK2
UBA1
ECT2
PGR
GTF2F1 NEK10 YRDC
GMNN
ORC3L CDK13 DNAJA3
FBXW4 PLK3
HRAS ANTXR1
CDK1

FAM175A JUN
FGF11

CDK8 SMARCB1 KRT14 HSPA8
FANCB FBXL3 GATA3

New Features for 3
DDB1
TSPAN17
ARIH1 CDC25A CDC45 PAX6
ELAC2 APLP2 CDKN1A
EIF4G1 TOP3A
PEG3
PTEN
CHEK2
KPNA3 PPP1CA

1 TUBB2A POU2F1
HIC1

CHGA CTCFL PML
FLI1 MAP3K4
NUP153
DCAF11 EZH2
RNF31
PIK3CA
2
ERCC2
EGFR SMARCE1
RPA1
GTF2E1 AKAP10
NUSAP1
SMARCC2 FANCA
MED21

RAD9A
CDKN1B HMMR

WRN
MLH1 MRE11A
ETS1
AP2B1
ESR1
TRIM28 UBE2I

SMC1A XRCC5 RBL1
TSPAN9 NMI RNF144B

SHFM1 ERCC1
RNF8

HSD17B1
SUGT1

•Edge Bundling
WT1 UBC
ELK1 RBMXL2

UBB
AURKA JUND CNR1

CDK4 MELK
PPP1R3A

•Welcome Screen
SUMO2

ERCC4

BRCA1
LMO7
FBXO4 MDM2
PLK1 NBR1

•New Data Import Clients
PMS1
IGF1R

TOPBP1 TUBA4A UBA52
FBXO11

TAF2G

•Network Annotations
PPT1
CSTF1 SMARCA4

RNF168
JUNB TERF2
PPM1D DHFR

•Network Collections
BATF
FBXL5 PALB2
CDK16

FBXO25 SMARCC1
SMARCD2 NCOA3

FAM175B BRCA1

•Enhanced Search
MED24
STK11 MED13
PIAS1 WHSC2 CREB1

SMAD3 E2F4 RBBP7

CHD8
ERBB2 BRE HDAC2
WWP1 TP53 LMO4

•Cleaner API
MAD2L1 FANCC
KIAA1182
EP300
RAD17
BRAP CDK2
HYRC
FANCE NUFIP1
PIAS4
DCUN1D1 HIST1H4A
PMS2 RNF53
BARD1

•Experimental Headless Mode
BRCC1
POLL RBBP8
FANCL CLSPN
FGFR2
RFC1 BRCA1
CHTF18
AT2A

NBR2 MYC
FANCF
MAP4K4 SMAD1
TFDP2
GTF2H4
FBXO24 MAP3K3
DCLRE1C DDB2 BRIP1
RAD54B

RBL2

RBBP4
BAP1
RNF20

RAD51 DPPA3
AR
MPP3 CHEK1 NFYA
FBXO9
BUB1B
ATF1 RNF11 ZCCHC8
C5orf4
PSMC3IP
RPL31 TRAF7 RNF53

PRC1
RELA SCG2
NR1H2
SMURF2
MCM4 SUMO1
UBE3A TADA3 CCNB1
TUBGCP4
RND2
IFI27 INSC
RAD51L3
USF2
RAD51C HOXA5 Brca1
TP53BP1
MKI67 COBRA1 TUBG1

FANCI LIG3
SPO11 CDH1 RAD50
TSC1
C19orf40 FBXO21 PCNA
CDKN2A FHIT MNAT1

ZNF350
MED23
STAT3
KIF11 CYP17A1 SP1
C19orf62
ERCC3
GADD45A
H2AFY DMC1 FBXO7
RASSF1 CSTF2
SRC SIRT1
RFC2 KPNA6 FHL2
ARNT ATRIP DLK1
RAD52 PARP1 EREG
XRCC2 ASPM
DPH1 BMI1 DHX9
ENSG00000204435 IFI16
AKT1 XPA POU4F1 WDR48
RPA2 CDKL4 MZT1

MSH6

CTCF
30
TIPARP
HSD17B2
PIK3R1
GLI1 XRCC4
CCNE1 OPCML


Cytoscape 3.0 Advantages:

Feature Beneficiary
Welcome screen New users (for solicitousness),
existing users (for convenience)
Import network Experienced users (for ease of use)
Edge bend visual property Paper and presentation writers
Edge bundling Users of high degree networks
Network annotations Users of hierarchical networks
Enhanced search Users of highly populated networks
Show All in Table Browser Users with highly annotated networks
Multiple network management All users

Disadvantages:
• Unproven stability (i.e., likely numerous bugs)
• Slow startup (4 times longer than v2.8)
• Fewer plugins (though the missing plugins are the less popular ones)
• Larger memory footprint leaves less room (especially on WinXP) for networks 31

TRD Outreach Ideas

32

NRNB EAC Meeting 2012

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (14)

Ähnlich wie NRNB EAC Meeting 2012

Ähnlich wie NRNB EAC Meeting 2012 (20)

Mehr von Alexander Pico

Mehr von Alexander Pico (16)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

NRNB EAC Meeting 2012

Hinweis der Redaktion