Data Collection and Analysis for High
Throughput Quantitative Proteomics:
Current Status and Challenges
Ruedi Aebersold, Ph.D.
Institute for Systems Biology
Seattle, Washington
email: raebersold@systemsbiology.org
Proteomics:
The systematic (quantitative) analysis
of the proteins expressed in a cell at a time
Enumerate all the
components
of a proteome
Detect dynamic
changes in proteome
following external or
internal perturbations
Proteome as
database:
Proteomics as
Biol. or clin. assay:
Proteome analyzed
once
Proteome analyzed
multiple (infinite) times
Protein Identification Strategy
*
I
Protein
mixture
II
12
Peptides
14
Time (min)
16
1D, 2D, 3D peptide separation
*
200 400 600 80010001200
m/z
Q1
Q2
Collision Cell
Q3
Tandem mass spectrum
Correlative
sequence database
searching
III
200 400 600 800 10001200
200 400 600 800 10001200
m/z
Theoretical
m/z
Protein identification
Acquired
Accurate Quantitation Using Isotope Dilution
Sample 1
(Reference)
Incorporate
Stable Light
Isotope
Sample 2
Incorporate
Stable Heavy
Isotope
Combine Samples
Analyze by Mass Spectrometer
• h/l analytes are chemically identical  identical specific signal in MS
• Ratio of h/l signals indicates ratio of analytes
Isotope Coded Affinity Tags (ICAT)
Heavy reagent: d8-ICAT (X=deuterium)
Light reagent: d0-ICAT (X=hydrogen)
O
N
N
S
Biotin tag
O
X
N
X
X
X
O
X
O
O
X
Linker (heavy or light)
O
X
I
N
X
Thiol
reactive
Detection of Cys containing peptides and
accurate quantification using stable isotope dilution
Quantitative proteomics by isotope labeling-LC-MS/MS
Mixture 1
Optional fractionation
100
Light
0
isotopelabel
Heavy
550 560 570 580
m/z
100
NH2-EACDPLR-COOH
Combine and
proteolyze
Avidin affinity
enrichment
Mixture 2
0
200
400
600
800
m/z
Compatible with any separation/fractionation
method at protein/peptide level.
Quantitation and protein
identification
PROTEIN LABELING
Stable Isotope Labeling Strategies
Metabolic stable
isotope labeling
Isotope tagging
by chemical reaction
Label
Digest
Digest
DATA COLLECTION
Digest
Intensity
Intensity
Mass spectrometry
Intensity
DATA ANALYSIS
Stable isotope incorporation
via enzyme reaction
m/z
m/z
m/z
Quantitative Proteomics Technology

Protein identification: Automated peptide tandem
mass spectrometry of complex peptide mixtures

Protein quantification: Isotope dilution

Selective chemical reactions: reduction of sample
complexity; selective analyte isolation
Results
Identification of proteins in sample and quantitative profiles
Quantitative Proteomics Technology

Protein identification: Automated peptide tandem
mass spectrometry of complex peptide mixtures

Protein quantification: Isotope dilution

Selective chemical reactions: reduction of sample
complexity; selective analyte isolation
Results
Identification of proteins in sample and quantitative profiles
Current capacity: ~1000 proteins per day/instrument
Total yeast lysate: ~ 2000 proteins identified and quantified
Quantitative Proteomics Technology

Protein identification: Automated peptide tandem
mass spectrometry of complex peptide mixtures

Protein quantification: Isotope dilution

Selective chemical reactions: reduction of sample
complexity; selective analyte isolation
Results
Identification of proteins in sample and quantitative profiles
Current capacity: ~1000 proteins per day/instrument
Total yeast lysate: ~ 2000 proteins identified and quantified
In 1991, all the world’s labs combined had identified just
about 2000 genes
Current Limitations
(and Potential Solutions)
• The efficiency problem
• The validation problem
• The biological inference problem
Standard Method for Complex
Peptide Mixture Analysis
Cation Exchange
RP-HPLC
ESI-MS/MS
Proteome Analysis:
The Analytical Challenges
Yeast Proteome
• Expected number of ORFs: 6118
• Expected number of tryptic peptides:
~350,000
Synchronous Timepoint Samples
Compared to Reference Sample
Asynchronous
Reference
Sample
Timepoint Samples from
Yeast Cells Synchronously
Transiting the Cell Cycle
Data Summary
T0
T0
678
1648
T30
320
T60
342
T90
340
T120
319
•
•
•
•
•
T30
1095
1523
998
555
604
626
T60
1184
1055
1448
1006
571
587
T90
1112
1140
1051
1713
1243
684
T120
892
921
871
960
1229
1047
2735/6562 proteins quantified across all timepoints (42%)
696 proteins quantified in every experiment
1513 proteins quantified in at least one timepoint
34,400 peptides quantified on average per timepoint
>1 million mass spectra collected
Features: 2720
Pep3D: Xiao-jun Li et al. submitted
Features: 2720
CIDs: 1633
Features: 2720
CIDs: 1633
IDs: 363
ID/CID: 22%
ID/feature: 13%
Possible Solutions
• Better separation technology
• Selective peptide isolation
• Smart precursor ion selection
Number of peptides identified in each SCX fraction
Number of peptides identified in each FFE fraction
(aver age o ver lap: 52%)
(aver age o ver lap: 29%)
700
400
Num ber of p eptides
Num ber of p eptides
600
300
200
100
500
400
300
200
100
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
Nu m ber o f fr action
Nu m ber o f pep tides over lap ed w ith pr evious one fraction
Un ique pep tide in th e fr action
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
Nu m ber o f fr action
Nu m ber o f pep tides over lap ed w ith pr evious one fraction
Un ique pep tide in th e fr action
•Tryptic yeast digest separated by FFE-IEX or SAX
•30 fractions collected and analyzed by capLC-MS/MS
•Overlap: same peptide identified in adjacent fractions
Peptide overlap in SCX
2800
1400
2400
1200
Number of peptides
Number of peptides
Peptide overlap in FFE
2000
1600
1200
800
400
1000
800
600
400
200
0
0
1
6
11
16
21
26
1
Number of fractions one peptide distribute to
92%
6
11
16
21
Number of fractions one peptide distribute to
68%
26
Possible Solutions
• Better separation technology
• Selective peptide isolation
– Zhang H, et al. Curr. Op. Chem . Biol. (2004) 8: 6675
– Aebersold R Nature (2003) 422(6928):115-6.
• Smart precursor ion selection
– Griffin T et al. Anal Chem.( 2003) 75:867-74.
– Griffin et al. J Am Soc Mass Spectrom. (2001)
12:1238-46.
Summary: Efficiency Problem
• Only a (small) subset of peptides present is identified
• Current separation strategies do not have sufficient
resolving power
• MS/MS of every peptide in every experiment is a
bottleneck of current MS based proteomics
• LC-ESI MS/MS wastes a high fraction of MS/MS
cycles sequencing precursor ions that do not lead to
a positive identification
• Most positive identifications are not informative in
profiling experiments
• Smart precursor ion selection is required
Current Limitations
(and Potential Solutions)
• The efficiency problem
• The validation problem
• The biological inference problem
Protein Identification by MS/MS
protein
sample
protein
identifications
ABC D
ABC
peptide
mixture
peptide
identifications
MS/MS spectra
Protein Identification by MS/MS
Protein level
protein
sample
protein
identifications
ABC D
ABC
Peptide level
peptide
mixture
Database search
peptide
identifications Tools:
MS/MS spectrum
level
MS/MS spectra
-Sequest
-Mascott
-SpectrumMill
-Etc.
sort by search score
OUTPUT FROM SEARCH ALGORITHM
“correct”
incorrect
sort by search score
Threshold Model
threshold
SEQUEST:
Xcorr > 2.0
Cn > 0.1
MASCOT:
Score > 47
Difficulty Interpreting Protein
Identifications based on MS/MS
• Different search score thresholds used to
filter data
• Unknown and variable false positive error
rates
• No reliable measures of confidence
Statistical Model
entire dataset:
Spectrum
Peptide
Spectrum 1
Spectrum 2
Spectrum 3
…
Spectrum N
LGEYGH
FQSEEQ
FLYQE
…
EIQKKF
Score
4.5
3.4
1.3
…
2.2
best database
MS/MS
match search
spectrum
score
Statistical Model
entire dataset:
Spectrum
Spectrum 1
Spectrum 2
Spectrum 3
…
Spectrum N
Peptide
LGEYGH
FQSEEQ
FLYQE
…
EIQKKF
Score
4.5
3.4
1.3
…
2.2
1.0
0.97
0.01
0.3
incorrect --incorrect
p=0.5
correct
correct ---
probability
unsupervised learning
EM mixture model algorithm learns the most likely
distributions among correct and incorrect peptide
assignments given the observed data
Threshold Model:
Bad Discrimination and Inconsistency
SEQUEST thresholds
(from literature)
Sensitivity:
fraction of all
correct results
passing filter
Error Rate:
fraction of all
results passing
filter that are
incorrect
test data: A. Keller et al. OMICS 6(2), 207 (2002)
Ideal Spot
Discriminating Power of Peptide Prophet
SEQUEST thresholds
(from literature)
Sensitivity:
fraction of all
correct results
passing filter
Error Rate:
fraction of all
results passing
filter that are
incorrect
Ideal Spot
Improved discrimination:
more identifications (for the same error rate)
Keller at al. Anal. Chem. 2003
Protein Identification
>sp|P02754|LACB_BOVIN BETA-LACTOGLOBULIN PRECURSOR (BETA-LG)
(ALLERGEN BOS D 5) - Bos taurus (Bovine).
MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSA
PLRVYVEELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKLV
LDTDYKKYLLFCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHI
RLSFNPTQLEEQCHI
KPTPEGDLEILLQK : p = 0.83
LSFNPTQLEEQCHI : p = 0.65
LSFNPTQLEEQCHI : p = 0.76
TPEVDDEALEK : p = 0.96
TPEVDDEALEKFDK : p = 0.96
sp|P02754|LACB_BOVIN
Probability = ???
ProteinProphetTM software combines probabilities of peptides
assigned to MS/MS spectra to compute accurate probabilities
that corresponding proteins are present
Nesvizhskii et al Anal Chem. (2003)75:4646-58.
Issues for Protein Identification
• Many peptides are present in more than a single
database protein entry
ProteinProphet apportions such peptides among all
corresponding proteins to derive simplest list of proteins that
explain observed peptides
• Peptides corresponding to ‘single-hit’ proteins are less
likely to be correct than those corresponding to ‘multihit’ proteins
ProteinProphet learns by how much peptide probabilities
should be adjusted to reflect this protein grouping
information
Amplification of False Positive Error
Rate from Peptide to Protein Level
5
correct
(+)
+
Peptide 1
Peptide 2
+
+
+
Peptide 3
Peptide 4
Peptide 5
Peptide 6
Peptide 7
+
Peptide 8
Peptide 9
Peptide10
Peptide Level: 50% False
Positives
Prot A
Prot B
Prot
Prot
Prot
Prot
Prot
in the sample
(enriched for
‘multi-hit’ proteins)
not in the
sample
(enriched for
‘single hits’)
Protein Level: 71% False
Positives
Serum Protein Identifications from
Large-scale (~375 run) Experiment
Data Filter
# ids
# non-single hits
# single-hits
Publ. Threshold model#1 2257
359
1898
Publ. Threshold model #2 2742
441
2301
ProteinProphet, p 0.5
713
(predicted error rate: 7%)
511
202
Reference: H. Zhang et al., in prep
Consistency of Manual Validation of SEQUEST Search Results
Manual Authenticators
Search
Results
Correct Validation
Incorrect Validation
Validation Withheld
Tasks for a proteomic analysis pipeline
mzXML
Suitable
input
Peptide
assignment
Data Analysis Pipeline
Peptide
Prophet
Protein
Prophet
Validation
Protein
assignment
Interpretation
SBEAMS
Cytoscape
COMET
ProbID
Quantitation
ASAPRatio
Data Analysis Summary:
• Processing of data collected from different
platforms, samples, experiments, operators
requires transparent methods to score data
• Publication and relational database analysis
require consistently scored data
• Tools assigning probability based scores are
essential
• Openly accessible, transparent (OS) tools
bring in new talent and lead to community
improved tools
Nesvizhskii and Aebersold (2004) Drug Discov Today. 9:173-81
http://www.proteomecenter.org/software.php
Current Limitations
(and Potential Solutions)
• The efficiency problem
• The validation problem
• The biological inference problem
Mock-treated
IFN-treated
C12
ICAT label
C13
C12/C13
HPLC-MS/MS
Wei Yan et al
Name
DNAH11: dynein, axonemal, heavy polypeptide 11
Cellular
pathway
moto protein
complex
UBE2L6: ubiquitin-conjugating enzyme E2L 6
ubiquitination and protein degradation
0.57
DNAH11:
dynein, axonemal,
heavy with
polypeptide
11
IFIT1: interferon-induced
protein
tetratricopeptide
repeats 1
moto
protein
unknown
andcomplex
ESIs
0.94
0.48
9999
9999
-1
-1
UBE2L6: ubiquitin-conjugating
enzyme
E2L 6
GPR111:
G protein-coupled receptor
111
ubiquitination
and receptor
protein degradation
G-protein
coupled
and G-protein signaling
0.57
0.63
9999
21.270
-1
4.741
IFIT1:
protein
with tetratricopeptide
repeats 1
PASK interferon-induced
PAS domain containing
serine/threonine
kinase
unknown
and ESIs
signaling pathway
0.48
0.49
9999
12.006
-1
1.024
GPR111: adhesion
G protein-coupled
receptor
111 1
ADRM1:
regulating
molecule
G-protein molecule
coupled receptor
and G-protein
signaling
adhesion
and extracellular
matrix
protein
0.63
0.79
21.270
9.508
4.741
1.043
PASK
PAS domain containing
serine/threonine
kinase
CSA_PPIasePEPTIDYL
PROLYL
CIS TRANS ISOMERASE
signaling
pathway
chaperone
and protein folding
0.49
1
12.006
8.104
1.024
1.070
ADRM1:
adhesion regulating molecule
1
AHCY:
S-adenosylhomocysteine
hydrolase
adhesion molecule
andmetabolism
extracellular matrix protein
one-carbon
compound
0.79
0.93
9.508
6.279
1.043
0.936
CSA_PPIasePEPTIDYL
PROLYL
TRANS
ISOMERASE
IFIT4: interferon-induced
proteinCIS
with
tetratricopeptide
repeats 4
chaperone
and protein folding
unknown
1
1
8.104
6.230
1.070
0.794
AHCY: S-adenosylhomocysteine
hydrolase
FLJ32915:
hypothetical protein FLJ32915
S100
IFIT4:
protein protein
with tetratricopeptide
4
GNB1:interferon-induced
guanine nucleotide binding
(G protein), betarepeats
polypeptide
1
P100
P3
one-carbon compound metabolism
unknown
Probability
0.94
Sum
unknown
G-protein coupled receptor and G-protein signaling
ASAPRatio
9999
Mean
9999
Unique ID
ASAPRatio
-1
Std.-1
0.93
0.73
6.279
6.054
0.936
4.883
1
1
6.230
5.845
0.794
0.133
FLJ32915:
hypothetical
protein FLJ32915
G1P2:
interferon,
alpha-inducible
protein (clone IFI-15K)
unknown
cytoskeletion
and intracellular transport
0.73
0.98
6.054
4.858
4.883
0.661
GNB1:
guanine nucleotide
binding
protein
(G (large
protein),
beta polypeptide
MTP:
microsomal
triglyceride
transfer
protein
polypeptide,
88kDa)1
G-protein
coupled
and G-protein signaling
lipid
and fatty
acid receptor
metabolism
1
0.97
5.845
4.748
0.133
0.751
G1P2: interferon,
alpha-inducible
protein (clone IFI-15K)
PLCD1:
phospholipase
C, delta 1
cytoskeletion
and intracellular
transport
signaling
pathway;
lipid metabolism
0.98
0.69
4.858
4.569
0.661
0.116
P  0.9
523
270
671
1464
P  0.4
590
330
748
1668
MTP: CD7
microsomal
transfer protein (large polypeptide, 88kDa)
CD7:
antigentriglyceride
(p41)
1113
0.97
0.57
4.748
4.523
0.751
2.204
0.69
1(1)(1
)
0.57
4.569
4.164(2.741)(2.
2)
4.523
0.116
1.284(0.195)(0.39
4)
2.204
1(1)(1
1
)
4.164(2.741)(2.
3.963
2)
1.284(0.195)(0.39
0.659
4)
0.62
3.815
0.058
1
0.98
0.62
0.99
3.963
3.684
3.815
3.533
0.659
0.224
0.058
1.659
NUDT2: nudix
(nucleoside diphosphate
linked
moiety X)-type motif 2
ACACA:
acetyl-Coenzyme
A carboxylase
alpha
translation
and
ribosomalnucleotide
protein; anti-viral
response
nucleobase,
nucleoside,
and nucleic
acid
metabolism
unknown and ESIs
chaperone and protein folding
nucleobase, nucleoside, nucleotide and nucleic acid
metabolism
lipid
and fatty acit metabolism
0.98
1
3.684
3.351
0.224
0.259
CABC1:
chaperone,
ABC1 activity of bc1 complex like (S. pombe)
KNS2: kinesin
2 60/70kDa
chaperone
and
protein
folding transport
cytoskeletion
and
intracellular
0.99
1
3.533
3.140
1.659
0.335
ACACA: acetyl-Coenzyme
A carboxylase alpha
LOC151636:
rhysin 2
lipid and fatty and
acit intracellular
metabolism transport?
cytoskeletion
1
1
3.351
2.975
0.259
0.231
KNS2:
kinesin
2 60/70kDa
M96: likely
ortholog
of mouse metal response element binding transcription
factor 2
LOC151636: rhysin 2
ETFA: electron-transfer-flavoprotein, alpha polypeptide (glutaric aciduria II)
M96: likely ortholog of mouse metal response element binding transcription
factorN-myc
2
NMI:
(and STAT) interactor
cytoskeletion and intracellular transport
transcription
cytoskeletion and intracellular transport?
electron transfer
1
0.98
1
0.45
3.140
2.923
2.975
2.890
0.335
0.390
0.231
0.484
transcription
signaling
pathway; transcription; apoptosis
0.98
0.57
2.923
2.875
0.390
0.138
ETFA:
alphaprotein
polypeptide (glutaric aciduria II)
GSA7: electron-transfer-flavoprotein,
ubiquitin activating enzyme E1-like
electron
transfer
ubiquitination
and protein degradation
0.45
0.98
2.890
2.844
0.484
0.663
NMI:
N-mychypothetical
(and STAT)protein
interactor
MGC3207:
MGC3207
signaling
pathway;
transcription;
translation
and ribosomal
proteinapoptosis
0.57
0.61
2.875
0.499
0.138
0.071
GSA7:
ubiquitin activating enzyme E1-like protein
SPK: symplekin
ubiquitination
and protein degradation
unknown
0.98
1
2.844
0.496
0.663
0.029
KRT10: keratin 10 (epidermolytic hyperkeratosis; keratosis palmaris et
plantaris)
cytoskeletion and intracellular transport
0.97
0.495
0.055
0.98
0.484
0.008
1
0.452
0.165
0.98
0.455
0.138
0.82
0.434
0.224
1
0.426
0.014
0.98
0.416
0.081
0.95
0.391
0.074
1
0.383
0.165
RNA splicing and processing
0.96
0.378
0.154
23 IFN-repressed proteins (0.5-fold)
PLCD1: phospholipase C, delta 1
EEF1A protein [Fragment]
CD7: CD7 antigen (p41)
PRKR protein kinase, interferon-inducible double stranded RNA
dependent
EEF1A protein [Fragment]
KIAA1276: KIAA1276 protein
PRKR protein kinase, interferon-inducible double stranded RNA
dependent
NUDT2: nudix (nucleoside diphosphate linked moiety X)-type motif 2
KIAA1276: KIAA1276 protein
CABC1: chaperone, ABC1 activity of bc1 complex like (S. pombe)
SARDH: sarcosine dehydrogenase
TRA1: tumor rejection antigen (gp96) 1
lipid and response
fatty acid metabolism
immune
signaling pathway; lipid metabolism
translation and ribosomal protein; GTP binding
immune response
translation and ribosomal protein; anti-viral response
translation and ribosomal protein; GTP binding
unknown and ESIs
54 IFN-induced proteins (2-fold)
electron transfer
chaperone and protein folding
GPS1: G protein pathway suppressor 1
G-protein coupled receptor and G-protein signaling
15 previously reported
SRRM2: serine/arginine repetitive matrix 2
RNA splicing and processing
KIAA0007: KIAA0007 protein
unknown
FACL4: fatty-acid-Coenzyme A ligase, long-chain 4
lipid and fatty acid metabolism
39 novel
FXR2: fragile X mental retardation, autosomal homolog 2
RNA binding and ribosomal association
TUBA6: tubulin alpha 6
cytoskeletion and intracellular transport; GTP binding
CPSF4: cleavage and polyadenylation specific factor 4, 30kDa
1272
MAPRE1: microtubule-associated protein, RP/EB family, member 1
cytoskeletion and intracellular transport
0.98
0.339
0.016
OAT: ornithine aminotransferase (gyrate atrophy)
amino acid and peptide metabolism
0.98
0.331
0.018
PPGB: protective protein for beta-galactosidase (galactosialidosis)
chaperone and protein folding; protein protection
1
0.323
0.084
WNT9A: wingless-type MMTV integration site family, member 9A
signaling pathway
0.99
0.316
0.091
FASN: fatty acid synthase
lipid and fatty acid metabolism
0.99
0.304
0.100
Ig lambda chain C regions
immune response
0.98
0.265
0.110
G2AN: alpha glucosidase II alpha subunit
carbohydrate metabolism
1
0.198
0.033
Hypothetical protein FLJ21140
unknown
0.71
0.043
0.064
KRT6: keratin 6
cytoskeletion and intracellular transport
1
0.003
0.008
MIG-6: Gene 33/Mig-6
signaling pathway
0.99
0.000
-1.250
HIC1: hypermethylated in cancer 1
transcription suppression
0.94
0.000
-1.250
Lots of data -what does it mean?
Interferon (IFN) Pathway
2.215 ± 0.079
IFN / Mock
PKR
2’,5’-OAS
3.963 ± 0.659
2.460 ± 0.076
Mx
2.359 ± 0.149
ADAR
1.398 ± 0.118
IRFs
Not identified
MHC
Katze et al (2002) 2: 675
-2-microglobulin
(MHC I)
2.768 ± 0.583
IFI-30 (MHC II)
2.219 ± 0.183
GO Analysis of Interferon regulated proteins
GO level
Physiological process
3
Cell growth and/or maintenance Death
Metabolism
Response to
external stimulus Response
to stress
Pathogenesis
4
Cell organization
Cell growth
Cell death
Transport
Cytoplasm
Nuclear
organization organization
Catabolism Nitrogen metabolism
5
DNA metabolism
Defense
response
6
Amino acid
metabolism
Fatty acid
metabolism
Immune
response
7
8
9
10
11
12
Cell growth and/or maintenance
Metabolism
Cellular defense response
Islands of intense knowledge in ocean of unknown
Hormone
responses
Cell
motility
Energy
metabolism
Transcription
Charting the path between landmarks
Hormone
responses
Cell
mobility
Energy
metabolism
Unassigned observations
Transcription
Walking down the interaction map
A
B
F
G
C
E
H
D
I
First round of TAP-tagging:
Identification of IGBP1 and TIP41 interactors
TCP1
CCT2
CCT3
CCT4
CCT5
CCT6A
CCT7
CCT8
CCT complex
PPP2CA
IGBP1
PPP2CB
PPP4C
TIP41
Catalytic subunits
PP2A-type
phosphatases
PPP6C
PPP4R2*
PPP6R1*
PPP6R2A*
Uncharacterized
proteins
Anne-Claude Gingras
Human phosphatase-interaction network:
Segregation into functional modules
Centrosome; Meiosis
Exit from mitosis; Actin cytoskeleton
G1  S transition
PP4
C
PP6
C
PP2
C
PP2
B
PP2A a
Acknowledgements
Separation strategies
Hookeun Lee
Eugene Yi
Mingliang Yi
Abundance dependent MS/MS
Tim Griffin
Chris Lock (Sciex)
Software development and statistical models
Eric Deutsch
Xiao-Jun Li
Jimmy Eng
Alex Nesvizhskii
Andy Keller
Benno Schwikowski
Patrick Pedrioli
Ning Zhang
Inference of biological function
Wei Yan
Anne-Claude Gingras
Cytoscape project (www.cytoscape.org)
Funding:
NIH (NCI, NCRR, NIDA, NHBLI), Merck, ABI