An Improved, Bias-Reduced Probabilistic Functional
Gene Network of Baker’s Yeast, Saccharomyces
cerevisiae
Insuk Lee1, Zhihua Li1, Edward M. Marcotte1,2*
1 Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, United States ofAmerica, 2 Department of Chemistry and Biochemistry, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, UnitedStates of America

Background. Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneousfunctional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of
millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic
interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses
regarding specific gene functions and associations. Methodology/Principal Findings. We report a significantly improved
version (v. 2) of a probabilistic functional gene network [1] of the baker’s yeast, Saccharomyces cerevisiae. We describe our
optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network trainingreference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic
interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships.
Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal
subunit biogenesis. Conclusions/Significance. YeastNet v. 2, constructed using these optimizations together with additionaldata, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among
5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.
Citation: Lee I, Li Z, Marcotte EM (2007) An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker’s Yeast, Saccharomyces
cerevisiae. PLoS ONE 2(10): e988. doi:10.1371/journal.pone.0000988

Probabilistic functional gene networks represent a class of gene
network models that attempt to solve these problems, allowing
integrative network models to be built from heterogeneous
genomics data (e.g., [1,3,8,10–13]). One key idea of such networkmodels is the reinterpretation of genomics data as providing
evidence for ‘‘functional coupling’’ between genes [1]. This nonmechanistic, but nonetheless useful, high level notion of geneassociation enables the integration of many different types of data,
capturing diverse types of associations (e.g., direct physicalinteractions, regulatory interactions, membership in the same
physical protein complex, etc.) precisely because the definition ofgene association is inclusive. Such associations can be discovered
using Bayesian statistical methods which allow robust evaluations
to be made of functional associations between genes ina supervised learning framework, such as by measuring known

INTRODUCTION
Gene networks provide a simple basis for organizing thousands ofcellular components and their associations with each other, as well
as for generating testable hypotheses about the components andthe system as a whole. A number of research efforts havedemonstrated that heterogeneous functional genomics andproteomics data can be integrated into gene (or protein) networks(e.g., [1–12]), thus organizing and relating highly complex data
sets, as well as simplifying the prediction of new gene functions andassociations on basis of the network connections. In such network
integration approaches, relationships between genes are detectedby various experimental or computational methods, and then
combined in a bottom-up fashion in order to build a networkmodel. As high-throughput biological experiments advance, we
expect corresponding gains in network models derived from these
data. Such improvements, however, are often tempered by thealready extreme and growing complexity of the biological data.
There are three major problems in integrating diverse genomics
data into network models. First, the genomics data areheterogeneous in their sensitivity and specificity for relationships
between genes. For example, experimental methods such as mass
spectrometry preferentially observe abundant proteins, while
comparative genomics methods apply only to evolutionarily
conserved genes. Increasing the sensitivity of detection usually
carries a cost of increasing false positive identifications. Thus, thesystematic bias for each method should be understood andconsidered during data integration. Second, genomics data setsvary widely in their utility for reconstructing gene networks. Thus,
we need robust benchmarking methods that can evaluate eachdata set and allow comparison of their relative merits. Third, data
sets are often correlated, complicating integration. However, thecorrelation can be difficult to measure because of both data
incompleteness (a common problem) and sampling biases.
PLoS ONE | www.plosone.org

Academic Editor: Andrea Califano, Columbia University, United States of America
Received June 15, 2007; Accepted September 10, 2007; Published October 3,
2007
Copyright: ß 2007 Lee et al. This is an open-access article distributed under theterms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original authorand source are credited.
Funding: This work was supported by grants from the N.S.F. (IIS-0325116, EIA0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard
Fellowship (EMM). These agencies were not involved in the design and conduct of
the study, in the collection, analysis, and interpretation of the data, or in the
preparation, review, or approval of the manuscript.
Competing Interests: The authors have declared that no competing interests
exist.
* To whom correspondence should be addressed. E-mail: marcotte@icmb.
utexas.edu

1

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

pathways and cellular systems for their recapitulation by the data
sets being analyzed. We previously reported such a probabilistic
genome-wide gene network for yeast genes (dubbed YeastNet v.1)
[1].
Here, we present optimized methods that improve ourprobabilistic functional gene network models. Table 1 summarizes the major improvements. In particular, optimization of
three major areas is highlighted, illustrating their effects on
network quality. First, we reduced functional bias toward thedominant gold standard reference annotation during training.
For example, most yeast gene functional annotation sets show
biases towards genes of ‘‘protein biosynthesis’’ or ‘‘ribosomal
proteins’’ [14,15]. This bias inflates scores in a manner that does
not generalize for other functions. Second, we apply a simple
probability model for calculating confidence in protein physicalinteraction and genetic interaction data sets. We find thehypergeometric probability of an interaction occurring at
random chance provides an excellent error confidence modelfor the interactions and simplifies their integration. Third, we
introduce two thresholds that significantly improve the derivationof functional linkages from DNA microarray experiments. The
combination of these improvements with additional data results
in a markedly improved overall yeast gene network, spanning
95% of the validated yeast protein-coding genes. We demonstrate the network topology is predictive of essential genes, andapply the network to predict, then experimentally confirm, thefunction of the yeast gene PUF6 in 60S ribosomal subunit
biogenesis.

Effect of a functionally biased reference set inlearning a gene network from functional genomics
data
The derivation of a probabilistic functional network from functional
genomics and proteomics data using the log-likelihood strategy is an
example of a supervised learning approach, distinguishing positive
functional associations from negative associations on the basis of theperformance of training associations in the data sets under analysis.
The learning efficiency, however, is contingent upon the quality of
the reference training sets, although the algorithms we employ arechosen for their robustness to false examples in the references.
Learning efficiency also correlates with the extent of reference
examples, as we cannot learn effectively using only a few examples. A
third important characteristic of reference sets affecting supervised
learning is the systematic bias among examples. In agreement withprevious observations of yeast gene annotation [15,16], we foundthat this last issue in particular was important for reconstructing
a functional yeast gene network.
The most comprehensive and reliable functional annotation
currently available for yeast is the Gene Ontology [17] annotation
set. More than 70% of validated yeast protein-encoding genes areannotated by at least one of over 1,000 Gene Ontology ‘‘biologicalprocess’’ terms with support derived from reliable small-scale experimental evidence. Therefore, yeast Gene Ontology ‘‘biologicalprocess’’ annotation meets the first two requirements of a good reference set for efficient learning. However, the frequency distribution ofannotation terms is heavily biased toward the single term ‘‘protein
biosynthesis’’ (GO:0006412). This term alone is responsible for.27% of the total reference gene pairs (Figure 1A). We observed
a similar bias in another widely used annotation set, The Kyoto
Encyclopedia of Genes and Genomes (KEGG) [18] (data not shown).
There are many possible reasons for such biased annotation,
ranging from bias in scientific interest—yeast has historically beena major model for studying many core cellular processes including
eukaryotic protein biosynthesis—to bias in technological feasibility—it is generally easier to study highly expressed proteins such as
ribosomal proteins—to intrinsic bias in the cellular system

RESULTS AND DISCUSSION.............................................................................

We incorporated three major improvements to the yeast
probabilistic gene network, beyond inclusion of additional data
sets: the reduction of bias in the reference training set, theintroduction of probabilistic scores for physical and genetic
interactions, and the introduction of filters to remove false-positive
linkages from analysis of mRNA co-expression. We first discusseach of these improvements in turn, before demonstrating theoverall quality of the network.

Table 1. A summary of major improvements to YeastNet version 2.
..................................................................................................................................................
YeastNet v1 (Science 2004)

YeastNet v2 (This study)

34,000 linkages among 4,681 genes

102,803 linkages among 5,483 genes

Trained by KEGG pathway annotation

Trained by Gene Ontology biological process annotation

Training set includes linkages among biased term ‘‘Ribosome (KEGG:03010)’’Training set excludes linkages among biased term ‘‘Protein biosynthesis(GO:0006412)’’
Two new genome-wide complex mapping studies (Gavin et al. 2006, Krogan et al.
2006) were incorporated
Probabilistic error model used to score functional linkages inferred from proteinprotein interaction data
Functional linkages inferred from ‘‘Gene neighbors’’ method were added

Genome-context approaches (Phylogenetic profiling, Rosetta Stone proteins)with 57 genomes

Genome-context approaches (Phylogenetic profiling, Rosetta Stone proteins, Geneneighbors) with 149 genomes
Optimized methods inferring co-expression linkages including exclusion of gene pairs
with potential cross-hybridization of cDNA and using two threshold parameters

Integration by weighted-sum with exponentially decaying weights forsecondary evidence, optimized by one free parameter (D) determining decay
rate

Integration by weighted-sum with linearly decaying weights for secondary evidence,
optimized by two free parameters—D determining decay rate and T determining
threshold of likelihood scoresOver-fitting was tested by independent annotation sets.

Over-fitting was tested by 0.632 bootstrapping

Additional functional linkages were inferred from network context(ContextNet)

Network context linkages did not improve network model, and were therefore
omitteddoi:10.1371/journal.pone.0000988.t001

PLoS ONE | www.plosone.org

2

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

B
3.5

0.30

3.0

Protein biosynthesis (GO:0006412)

0.25
0.06
0.05

Log likelihood score

Fraction of total reference genes (gene pairs)by each functional term

A

Cell wall organization and biosynthesis(GO:0007047)
Protein amino acid phosphorylation(GO:0006468)
Ubiquitin-dependent protein
catabolism (GO:0006511)

0.04
0.03
0.02

Heat-shock treatment

2.5

Unbiased benchmarking(No "protein_biosynthesis")
Biased benchmarking

2.0
1.5
1.0
0.5
0.0

0.01

-0.5
0.70

0.00
0

10

20

30

1000

0.75

0.80

0.85

0.90

0.95

1.00

Pearson correlation coefficient

Gene Ontology biological process terms (1067 terms)

Figure 1. The effect of functionally biased Gene Ontology annotation on network training. (A) Frequency histograms of the usage of 1,067 Gene
Ontology ‘‘biological process’’ annotations, ranked by the number of genes annotated with each term (black bars) and by the number of reference
linkages derived using that term (white bars). Functional annotation is highly biased towards genes with the term ‘‘protein biosynthesis’’. Thisfunctional bias becomes more severe in the reference linkages, given the combinatorial increase after linking all genes sharing a given term. As
a result, linkages among protein biosynthesis genes compose .27% of total reference linkages. By contrast, the second most frequent term accounts
for ,5% of total reference linkages. (B) The likelihood of functional association between genes on the basis of the co-expression of their mRNAs
across DNA microarray experiments (here, following heat-shock [19]) is significantly affected by the dominant reference term ‘‘protein biosynthesis’’.
For example, for the 1,000 most strongly co-expressed gene pairs, the likelihood of functional association between co-expressed genes is ,30 foldhigher than random chance (LLS,3.4) (empty circles), but drops to ,6 fold (LLS,1.8) after masking the term ‘‘protein biosynthesis’’ in the referenceset (filled circles). Thus, the high likelihood score from the biased reference set cannot be generalized to other functions. The black and red lines
indicate sigmoid curve fits to the unbiased and biased reference analyses, respectively.
doi:10.1371/journal.pone.0000988.g001

of such interactions among proteins is an invaluable source forlearning about protein functions and pathways. Among manytechniques of mapping protein physical interaction, yeast two
hybrid assays and affinity purification followed by mass spectrometry have proved to be the most popular for their scalability. Two
major genome-scale yeast two hybrid screens reported more than
4,000 binary interactions [20,21]. While these interactions passed
minimum quality criteria, we might not expect all to be equally
informative for inferring functional associations. The original
confidence measures—dividing interactions into a more reproducible ‘‘core’’ set and less reproducible ‘‘non-core’’ set [20]—iscoarse-grained and may often miss functionally informativeinteractions.
Mass-spectrometry-derived interaction data, usually provided asa list of baits of affinity purification and their identified preys, iseven more complicated for inferring binary physical or functional
associations. Two different models of inferring binary interactions
from the lists of identifications have been widely used—the spoke
and matrix models [22]. The spoke model allows pair-wise
relationships only between baits and preys in the same complexes,
whereas the matrix model includes additional relationships
inferred by pairing preys in the same complexes. These
interpretative models exhibit different trade-offs between completeness and accuracy—the spoke model achieves high accuracy
at the cost of incompleteness, whereas the matrix model provides
a more complete model but relatively low accuracy due to pairing
all prey proteins from a given bait with each other.
A probabilistic model of protein-protein interactions should
bypass the limitations of these coarse descriptive models, whileproviding higher resolution scoring important for data integration.
We found that calculating the hypergeometric probability of theprotein interactions occurring at random chance in a given data
set generates a very well-behaved ranking of interaction accuracy

themselves—core molecular machines such as the ribosome
legitimately incorporate more genes than many other cellular
systems. We suspect that such bias is inevitable; nonetheless, weneed to minimize its adverse effects for network reconstruction.
We examined the consequences of this bias by ‘‘masking’’ thisdominant term in the annotation reference set, thereby removing
all reference gene pairs linked via this term, and then testing data
sets for their performance on the full and masked reference sets.
For example, mRNA co-expression relationships between yeast
genes across various heat-shock treatments [19] appear to strongly
predict functional associations when benchmarked using the full,biased reference set (Figure 1B, open circles). However, thatstrong relationship largely disappears after masking only the single
reference term ‘‘protein biosynthesis’’ (Figure 1B, closed circles).
This observation clearly indicates that the strong functional
associations derived from co-expression over these particulararrays are limited largely to protein biosynthesis genes. Thus,
assigning a high likelihood score for gene pairs that co-express
highly but are not in protein biosynthesis would be misleading.
Examination of the frequency distribution of reference set genepairs (Figure 1A) shows that the next most dominant term (‘‘Cell
wall organization and biosynthesis’’, GO:0007047) accounts for,5% of reference pairs, with contributions from remaining terms
decaying fairly smoothly. We therefore removed only thedominant ‘‘protein biosynthesis’’ term before reconstructing theprobabilistic yeast gene network.

Probabilistic inference of gene functional
associations from physical protein-protein
interactions and genetic interactions
Because of the generally strong correlation between protein
physical or genetic interactions and functional associations, a map
PLoS ONE | www.plosone.org

3

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

A 50

in recall-precision analyses (Figure 2A&B). Note that thisapproach does not require training—instead, confidence is based
only upon observations in the experiment under analysis andreflects the specificity with which a particular protein pairinteracts, down-weighting promiscuous interactors and rewarding
well-observed specific interactions. This scoring scheme outperforms the spoke model and attaches confidence values to eachinteraction in the matrix model, thereby separating high and low
confidence matrix model interactions (Figure 2A&B). The
hypergeometric score appears to work equally effectively for yeast
two-hybrid and mass spectrometry interactions.
Interestingly, we also observed the hypergeometric probabilitybased confidence scores to effectively rank genetic interactions
according to their utility for functional inferences (Figure 2C).
Considering the likely low false positive rate of genetic interactions,this ranking probably does not reflect differences in the quality ofinteractions. Instead, it likely reflects the specificity of the genetic
interactions, as each gene can participate in a varying number ofgenetic interactions and span a wide range of biological pathways[23]. For example, the yeast Hsp90 chaperone HSP82 isconsidered to be a genetic capacitor genetically interacting withmany (289) genes from diverse cellular processes, and may
generically buffer phenotypic variation [24]. Since HSP82 isa global modulator, the genetic interactions in which it participates
only allow for weak function inferences. Therefore, the appropriate interpretation for the low precision observed for interactions
involving HSP82 is probably not that of false positives, but rather
promiscuous interactions. Genetic interactions have been previously classified into within-pathway and between-pathway
interactions [23,25]; the hypergeometric probability model
appears to rank within-pathway interactions above betweenpathway interactions, thereby increasing the utility of genetic
interactions for inferring functional associations between genes.

Precision (%)

Ito-core

30
20
10

Ito-full

0
0.0

0.1

0.2

60

Precision (%)

0.3

0.4

Recall of linkages (%)

B 70

Gavinprobabilistic

50
40
Gavin-spoke

30
20
10

Gavin-matrix

0
0

2

4

6

8

10

Recall of linkages (%)

C 70
Precision (%)

60

Optimized method of inferring functional links byco-expression analysis
For inferring functional linkages from DNA microarray evidence,
we employ the divide-test-integrate approach [1] for discoveringfunctionally informative cases of mRNA co-expression. Thismethod is in contrast to simply concatenating the results of all
DNA microarray experiments to create a single, monolithic
expression vector for each gene, then measuring correlation
between these vectors. A co-expression network derived in thismanner indeed shows a robust correlation between the extent ofexpression correlation and the degree of functional association, inpart because of its high dimensionality. However, it generally
works as a useful model only for limited groups of genes, such as
consistently co-expressed housekeeping genes. The problem facing
this method is that context-specific co-expression patterns evident
in only a subset of experiments are overwhelmed by stochastic or
uncorrelated expression changes across the remaining experiments. For example, consider the case of combining several
experiments designed to detect expression dynamics during heatshock with a large number of unrelated experiments. Linkage
between genes that respond coordinately under heat shock but notin the remaining experiments are unlikely to be detected in ananalysis of the monolithic expression vectors. In contrast, linkages
among these genes might be detected by the divide-test-integrate
approach, in which each group of biologically coherent experiments is analyzed separately for co-expression linkages, followed
by integration of linkages across the sets of experiments. However,its actual practice often entails increased false positive coexpression linkages because of lower dimension expression vectors
PLoS ONE | www.plosone.org

Itoprobabilistic

40

Genetic interactionsprobabilistic

50
40
30
20
10

Genetic interactions-binary

0
0

1

2

3

4

5

Recall of linkages (%)
Figure 2. Assigning confidence scores to physical or genetic interactions. Performance of the hypergeometric probabilistic score is shown forgene functional associations inferred from (A) protein-protein physicalinteractions measured by the high-throughput yeast two hybrid (Y2H)
screen of Ito et al. [20], (B) affinity-purified complexes identified by mass
spectrometry by Gavin et al. [52], and (C) genetic interactions [43,74].
Performance with the probability score is measured cumulatively for eachsuccessive bin of 200 interactions (A–C, red filled triangles), ranked byprobability score. Recall and precision are calculated using the reference
linkages derived from Gene Ontology ‘‘biological process’’ annotation
masking the term ‘‘protein biosynthesis’’. The Y2H core model describedin [20] (A, filled circle) is more precise than the complete data set (A, open
circle), but with reduced recall. Similarly, two different ways of inferring
binary linkages from mass spectrometry-derived protein complexes[22]—the spoke (B, filled circle) and matrix models (B, open circle)—show differing trade-offs between precision and recall. The set of binary
genetic interactions (C, open circle) shows very low precision forfunctional inferences, although the false positive rate of genetic
interactions is generally perceived to be low; in contrast, the hypergeometric probability identifies a functionally informative subset of linkages.
In general, the hypergeometric probability scores provide an excellent
ranking of interactions in each of the data sets consistent with thelinkages’ functional informativeness.
doi:10.1371/journal.pone.0000988.g002

4

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

as shown in Figure 3C on the independent MIPS proteinfunctional linkage reference set (excluding the term ‘‘protein
synthesis’’).

..................................................................................

and the correspondingly increased probability of observing suchcorrelations at random.
For robust as well as sensitive co-expression linkage detection,
we introduced two new parameters to filter false positive coexpression linkages. These filters operate by removing genes fromthe co-expression analysis that fail to show a minimum ratio ofexpression change (R) in a minimum number of microarrayexperiments (M), thereby eliminating the genes most likely to be
unresponsive in the array set being analyzed. We optimized the
choice of these two parameters for each set of array experiments
by maximizing the area under a recall-precision curve(Table 2).
Beyond filtering genes, we also removed entire data sets thatproved uninformative for reconstructing a functional network: We
measured the relationship between the degree of co-expression
between two genes, measured as the Pearson correlation coefficient (PCC) of their expression levels across the arrays underconsideration, and the likelihood of their functional association,measured by the log likelihood of belonging to the same pathway(LLS, see Methods) between the genes in each successive bin of1000 gene pairs ranked in descending order by PCC. Across 18
total sets of DNA microarrays from SMD [26], containing 581
individual array experiments, we found 14 sets showed a significant
relationship (e.g., Cell cycle; Figure 3A) and 4 sets showed no
relationship (e.g., Oxidative stress with Menadione; Figure 3A),
as listed in Tables 2 and 3. Alternate measures of expression
correlation (the non-parametric Spearman rank coefficient andmutual information measures) failed to improve performance over
PCC. Filtering the unresponsive genes as described above further
improved the relationships, as shown for an example in
Figure 3B. In order to ensure representation of housekeeping
genes, the 14 informative array sets were also concatenated intomonolithic expression vectors spanning 500 experiments andanalyzed for co-expression linkages as above. The benefits of thedivide-test-integrate method are illustrated in the improved
precision for any given coverage of genes or reference linkages,

Assessment of YeastNet version 2 as a predictivemodel
In total, ten types of functional genomics, proteomics, andcomparative genomics data sets are integrated into the network(Table 4), as described in the Methods section (see the pseudocode for an overview of the procedure). Approximately 1,800,000
individual experimental observations were integrated into the
network model, optimizing a total of ,155 free parameters inorder to construct the network. Using a permissive scoring
threshold corresponding to the log likelihood score (LLS.0.916)
of non-core genome-wide Y2H screens [20], YeastNet v. 2
contains a total of 102,803 linkages covering 5,483 yeast proteins(covering .95 % of validated yeast proteome).
The integrated model, along with the various sets of linkages
derived from individual data sets, was assessed on an independent
test set of gene functional linkages derived from the MIPS proteinfunction annotation set, calculating recall and precision of the
MIPS reference linkages (Figure 4). For this purpose, we measurerecall of genes, rather than gene pairs, in order to assess thegenerality of predictions across the entire genome. The integrated
network shows high gene coverage and high precision across theentire network. As expected, the integrated network surpasses any
individual data set for precision at a given coverage; the complete
network covers .95% of the protein-coding genes in the yeast
genome with .60% precision on the inferred linkages (i.e., at least
6 out of 10 predicted linkages are true).
We compared the overall performance of YeastNet version 2 tothat of YeastNet version 1 by recall-precision analysis on the
independent test sets. We previously defined a confident subnetwork by taking only the top 34,000 functional linkages(covering 4,681 yeast proteins) [1] and used that for detailed
biological interpretation. We therefore selected the top 34,000
linkages of both versions of YeastNet in order to perform a faircomparison. This subset of YeastNet v. 2 covers 4,649 yeast
proteins (.80% of the validated yeast proteome). In tests of the
MIPS functional linkage reference set that included linkages
derived using the functional category ‘‘protein synthesis’’, theprecision of the two networks is comparable, while coverage—forboth genes and reference linkages—is significantly improved forthe new network model (Figure 5A). Superiority of the new
network becomes more obvious when we mask the reference
linkages derived from the term ‘‘protein synthesis’’. Precision of
the new network is minimally influenced by masking of this single
term. In contrast, YeastNet v. 1 shows a noticeable drop in
precision, indicating a bias towards protein synthesis-related
functions. We observe the same trend using another independentfunctional linkage reference set derived from KOG functional
categories (Figure 5B), with the precision of YeastNet v. 2changing only minimally with removal of the KOG reference term‘‘protein synthesis’’, while precision of YeastNet v. 1 drops below
v. 2. Roughly 17% of total KOG reference linkages are derived
from the annotation term ‘‘protein synthesis’’, while only 4.3% of
total MIPS linkages are, accounting for the larger effect seen on
the KOG benchmark. (The effect is also accentuated by the factthat MIPS annotates 3,752 yeast genes, whereas KOG annotates
only 3,022.) Therefore, we conclude that the new network,
YeastNet v. 2, is a significantly improved gene functional network,
both showing higher accuracy and coverage, as well as better
generalization to a more diverse set of cellular systems.

Table 2. SMD data sets used for co-expression links (total 500
experiments)......................................................................
SMD category

N

R

M

Cell cycle

83

1.4

8

Diamide treatment

8

1.6

6

Diauxic shift

19

1.4

13

DNA damage response in mec mutant

19

1.8

4

DNA damage response in WT

19

1.4

7

DTT treatment

15

1.3

10

Heat shock treatment

31

1.8

16

Nitrogen limitation

9

1.6

7

Nutrition limitation (Leu, Ura, Phosphate, Sulfate)

100

1.0

4

Osmotic shock (hyper, hypo)

26

1.2

13

Oxidative stress with HP (H2O2)

40

1.5

14

Measuring the number of mRNA-associated ribosome

42

1.3

29

RNA decay measurement

58

1.9

36

YPD stationary culture

31

1.1

20

N: the number of experiments in the set
R: minimum absolute value of log base 2 ratio of expression between treatedexperiment and control
M: minimum number of microarray experiments that exceed the R thresholddoi:10.1371/journal.pone.0000988.t002

PLoS ONE | www.plosone.org

5

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

Ar[77] (open circles) shows a noisy relationship between co-expression
and the likelihood of functional association, especially for gene pairs
with the highest Pearson correlation coefficients. However, by introducing the two threshold parameters, the relationship improves(filled circles), in particular decreasing variance considerably andimproving the corresponding regression model. (C) The divide-testintegrate strategy [1] for inferring linkages, shown here calculated
across all 500 microarray experiments (empty triangles) considerably
outperforms analysis of the expression vectors constructed byconcatenating the 500 experiments (filled circles). Precision is measured
using reference linkages derived from MIPS functional annotation,masking the term ‘‘protein synthesis’’, and recall is calculated for either
reference linkages or total yeast genes (inset).
doi:10.1371/journal.pone.0000988.g003

3
Oxidative stress with Menadione
Cell cycle

Log likelihood score

2
1
0
-1
-2
0.5

0.6

0.7

0.8

0.9

Another aspect of the predictive quality of a gene network
relates to an observed correlation between a gene’s tendency to be
essential [27] and its centrality in a network, measured as thenumber of interactions in which the gene participates. This
correlation was initially observed for the yeast physical proteinprotein interaction network [28]. Consistent with the original
observation, the high quality physical protein-protein interactions
derived from small-scale experiments (here, collected from
bioGRID [29] and DIP [30]) show a strong correlation between
degree centrality and lethality (Spearman rank correlation (rs) =0.94; Figure 6A). YeastNet v. 1 also showed a strong correlation—slightly worse in quality than the physical interactions butcovering a higher proportion of the experimentally identified
essential genes (increasing from 85% coverage in the proteinprotein interaction network to 91% by YeastNet v. 1; Figure 6B).
One explanation for this slightly lower correlation is that linkages
in YeastNet v. 1 are enriched among genes of protein biosynthesis,
especially ribosomal proteins, because of the biased reference set.
This trend would lower the correlation, as only ,18% of yeast
ribosomal genes (defined by GO cellular component annotation)
are essential, similar to the general background proportions ofessential genes [27]. Consistent with this notion, we found that
YeastNet v. 2 shows a higher correlation between degree centrality
and lethality (Spearman rank correlation (rs) = 0.95) while
covering nearly all (99%) of the experimentally identified essential
yeast genes (Figure 6C). This indicates that the optimized
learning methods, considering functional bias, produce a moreglobally predictive network model.

1.0

Pearson correlation coefficientB
3.0

Diauxic shift - Before filtration
Diauxic shift - After filtration

2.5

Log likelihood score

2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
0.80

0.85

0.90

0.95

1.00

Pearson correlation coefficient

Precision by MIPS functional linkage (%)

C
80

80

concatenate
divide-integrate

70

70

60
60

50

Experimental validation of the top ribosome
biogenesis prediction, PUF6

40

50

0

20
40
60
Recall of genes (%)

80

In addition to the above computational validation, we also
experimentally validated predictions arising from the new gene
network. Using the new network, we predicted new genes to be
involved in the process of ribosomal biogenesis, which is

40
0.0

0.2

0.4

0.6

0.8

1.0

1.2

.....................................

Recall of MIPS functional linkages (%)

Figure 3. Optimizing the inference of linkages from mRNA coexpression. (A) Examples of a functionally informative DNA microarraydata set and a non-informative one. Each set is illustrated as a scatterplot showing the log likelihood of functional association for eachsuccessive bin of 1,000 gene pairs (circles) ranked by decreasing
Pearson correlation coefficient between expression vectors derived
from that array set. The set of microarray data measuring oxidative
stress responses following Menadione treatment [75] (filled circles) does
not show a significant relationship between co-expression and thelikelihood of functional association. In contrast, the set of cell cycle timecourse experiments [76] (open circles) shows a strong relationship. The
effect of filtering genes using the parameters M and R is illustrated in(B). A data set of genes changing expression during the diauxic shift

PLoS ONE | www.plosone.org

6

Table 3. SMD data sets tested but rejected (total 81
experiments)......................................................................
SMD category

N

Calcium treatment

24

Oxidative stress with Menadione

30

Salt treatment

18

Zinc treatment

9

N: the number of experiments in the set
doi:10.1371/journal.pone.0000988.t003

October 2007 | Issue 10 | e988

................................................................

Yeast Functional Gene Network

Table 4. Ten genomics data types incorporated into YeastNet version 2..................................................................................................................................................
Linkage set

Raw data sourcesNE

Co-citation

29,135 PubMed abstracts for S. cerevisiae

3,605

29,483Co-expression

500 S. cerevisiae microarray experiments from Stanford Microarray Database [26]2,923

31,543

Gene Neighbor

BLAST hits for 133 completely sequenced archaeal and bacterial genomes

1,098

4,961

Genetic Interaction

MIPS genetic interactions and large-scale synthetic lethal screening [74]3,556

12,538Affinity purified complex mapping by mass
spectrometry

Three large-scale mass spectrometry analyses of affinity purified complexes [52–54]

3,368

31,931Phylogenetic Profile

BLAST hits for 117 completely sequenced bacterial genomes

351

1,050

Rosetta Stone proteins

BLAST hits for 149 completely sequenced genomes

801

856

Literature curation

Protein interactions supported by small scale experiments collected by manual literature
curation, and deposited into BioGRID [29] and DIP [30]3,390

11,728Inferred interaction from protein tertiary Structure

Prediction of physical interaction based on protein tertiary structure data [72]

1,092

3,405

High-throughput yeast 2 hybrid

Five large-scale yeast 2 hybrid screens [20,21,55–57]

1,792

2,055

N : Total number of genes incorporated into the integrated YeastNet version 2
E : Total number of linkages incorporated into the integrated YeastNet version 2
doi:10.1371/journal.pone.0000988.t004

Precision by MIPS functional linkages (%)

a fundamental process critical for cells and widely conserved acrosseukaryotes. New ribosomal biogenesis genes were inferred by
identifying close network neighbors to the known ribosomal
biogenesis genes. Specifically, we generated a seed set of known
ribosome biogenesis genes based on their Gene Ontology biologicalprocess annotation (n = 238 yeast genes annotated by the terms‘‘ribosome assembly’’, ‘‘rRNA’’, or ‘‘35S’’), then prioritized their
network neighbors by the sum of their LLS scores to genes of theseed set. This list of genes was filtered to remove known ribosomal
proteins. Table 5 lists the top 5 predictions. Two of the top 5 genes,
CIC1 and ESF2 have been verified in the literature [31,32] but had
not yet been included in the ribosome biogenesis annotation set we
employed, and thus can be considered true predictions alreadyverified by published studies. Moreover, these predictions are also

supported by multiple lines of evidence including inferred functional
linkages based on high-throughput data (e.g., co-expression andmass spectrometry analysis; Table 5). All five genes are known to belocalized to the nucleolus [33], strongly supporting a possible role inribosome biogenesis.
We selected the top-ranked prediction, PUF6, for experimental
validation. PUF6 encodes an RNA-binding protein previously
known to be involved in mating-type determination via its
translational repression of ASH1 mRNA prior to ASH1 mRNA
localization to the bud tip [34]. While previous computational
evidence associates PUF6 with ribosomal biogenesis [35], there is
not yet direct experimental support for its involvement. We
therefore experimentally tested PUF6 for its participation inribosomal biogenesis.

100
Co-citation
Co-expression
Gene Neighbors
Genetic Interaction
Mass Spec
Phylogenetic Profile
Rosetta Stone proteins
Tertiary StructureHigh-throughput
yeast 2 hybrid
Literature curation
Integrated Yeastnet v2

90
80
70
60
50
40
30
0

10 20 30 40 50 60 70 80 90 100
Recall of genes (%)

Figure 4. Summary of benchmarking for the YeastNet v. 2 integrated functional gene network, along with the functional linkage sets derived
from the ten individual types of data. Precision and recall of yeast genes are calculated using the unbiased MIPS functional linkage reference set, as
in Figure 3C. Gene pairs in each set were ranked by LLS scores, then cumulative precision and recall were calculated for each successive bin of 1,000
gene pairs (each symbol indicates a bin). The confidence measures for each individual data type can be seen to rank the gene pairs effectively.
YeastNet v. 2 shows high overall performance, with the integration of the heterogeneous genomics data improving both reliability and completenessof the overall model.
doi:10.1371/journal.pone.0000988.g004

PLoS ONE | www.plosone.org

7

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

Figure 5. YeastNet v. 2 outperforms YeastNet v. 1 [1], as measured by precision and recall of either genes or reference linkages on independent
reference sets. (A) shows performance on the MIPS functional linkage reference set, with or without masking the term ‘‘protein synthesis’’, while (B)shows performance on reference linkages derived from KOG functional categories. For both reference sets, we observe significantly improved recall
by YeastNet v. 2 for both yeast genes and reference linkages. The effect of the annotation ‘‘protein synthesis’’ is revealed by a significant drop of
precision in YeastNet v. 1 but not v. 2 after masking the term during benchmarking. The higher fraction of linkages derived from ‘‘protein synthesis’’in KOG (,17% of total linkages) than MIPS (,4% of total MIPS linkages) explains the apparently higher precision of v. 1 than v. 2 when including theterm in (B), resulting in a correspondingly larger drop in precision of v. 1 when the term is masked. All analyses in (A) and (B) are for the 34,000 mostconfident linkages of each network.
doi:10.1371/journal.pone.0000988.g005

First, our current functional genomics knowledge is severely
biased. This bias leads to biased learning unless appropriately
taken into account, as the effect of reference linkages from thedominant GO term ‘‘protein biosynthesis’’ is quite strong(Figures 1 and 5). Second, physical protein interaction andgenetic interaction data can be assigned scores that allow, on a perinteraction basis, for fine-grained, continuous valued confidence
measures. The score that we employ, based on the hypergeometricprobability, is simple and robust, and works across a variety ofdifferent experimental techniques, and would therefore even be
appropriate as a final confidence score directly out of large-scale
experimental assays (e.g., as in [41]). Introduction of this scoresignificantly improves the performance of these data in deriving
the probabilistic gene network. Third, introducing two additionalparameters into the analysis of mRNA co-expression linkages
significantly decreases the number of false positive linkages while
simultaneously decreasing the variance in the quality of thederived linkages (Figure 3B). Incorporation of each of these
optimizations into YeastNet v. 2 significantly improves the qualityof the model, improving precision and recall on independent test
sets and increasing generality of the model for more diverse
cellular systems. We expect that the protocol we present forcalculating the network is general and could be applied to otherorganisms essentially directly as described.
We describe applications of the gene network for functional
prediction (prediction of ribosomal biogenesis genes) and prediction of essential genes. In order to perform similar analyses of
YeastNet v. 2, we have established a web site (http://www.
yeastnet.org) where the network can be downloaded in full. We
anticipate posting future updates of the network to this site as new
data sets become available.

We might expect yeast strains defective in ribosomal biogenesis
to show a slow growth phenotype; we tested a puf6D deletion strain[36] and indeed observed significant growth retardation comparedto the wild-type strain when cultured at 20˚ C (Figure 7A). We
then analyzed the polysome profile of the deletion strain in order
to assess defects in ribosome processing consistent with a biogenesis
defect. We observed an abnormal decrease in the ratio of 60S/40S
ribosomal subunits and detected the presence of halfmers in the
puf6D deletion strain (Figure 7B). Such halfmers—shoulders on
the 80S and polysome peaks—arise from mRNA bound by an
extra 40S subunit stalled at the AUG initiation codon and arecharacteristic of 60S subunit biogenesis defects and blockage oftranslation initiation at the 60S subunit joining stage (e.g., [37–39]). Both the decrease in 60S subunit abundance relative to 40S
and the presence of halfmers indicate a probable role of PUF6 in
60S ribosomal subunit biogenesis. We further tested theparticipation of PUF6 in 60S biogenesis by performing Western
blot analysis on an epitope-tagged version of the Puf6 protein [40].
We observed the epitope-tagged Puf6 protein to co-sediment ina sucrose gradient with the 60S ribosome in a fashion similar to
the known 60S ribosome biogenesis factor Nmd3p [38](Figure 7C). Therefore, the top network prediction for proteins
most likely to participate in ribosome biogenesis could be
experimentally confirmed. In all, 3 of the top 5 predictions could
be directly confirmed, with the remaining 2 highly likely given
their nucleolar localization.

Conclusions
In this study, we present several optimizations that significantly
improve the predictive power of a probabilistic functional gene
network of yeast. There are three major aspects worth noting.
PLoS ONE | www.plosone.org

8

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

MATERIALS AND METHODS
Saccharomyces cerevisiae gene set

P(lethality) (log scale)A

1

Reference and benchmark sets

0.1

In order to benchmark the assigned functional linkages in thisstudy, three different reference sets were used. As a major
reference set for benchmarking, we used the Gene Ontology (GO)annotation, downloaded from the Saccharomyces cerevisiae Genome
Database (SGD) [17] on March 2005. The GO schema lists threehierarchies of function describing ‘‘biological process’’ (i.e.,pathways and systems), ‘‘molecular function’’ (i.e., biochemical
activities), and ‘‘cellular component’’ (i.e., subcellular localization).
For training the network, we used the Saccharomyces cerevisiae GO‘‘biological process’’ annotation, which contains up to 14 different
levels of information under the term ‘‘biological process’’ within
the hierarchy. We used terms belonging to levels 2 through 10. We
also excluded the term ‘‘protein biosynthesis’’ because it annotates
so many genes as to significantly bias the benchmarking. To
construct the reference set of linkages, we considered all gene pairs
as functionally linked if they shared annotation from this set of GO
terms. These pairs comprised our positive reference set for training
network models. Negative examples were constructed as pairs ofannotated genes not sharing any annotation terms, i.e., all otherlinks among this annotated set of genes.
Specifically, 66,174 positive reference pairs were employed,
representing all gene pairs sharing any GO biological process
terms between levels 2–10 (except for the biased term ‘‘protein
biosynthesis’’). These pairs are provided on the supporting web site(http://www.yeastnet.org). All other pairs of these genes were
implicitly defined as the negative reference pairs. For example, thegenes NOP1 and SIK1 represent a positive example, sharing the
GO terms ‘rRNA modification’, ‘35S primary transcript processing’, ‘processing of 20S pre-rRNA’. The genes BUD5 (‘bud site
selection’, ‘pseudohyphal growth’, ‘small GTPase mediated signaltransduction’) and NOG1 (‘ribosome-nucleus export’) are annotated, but do not share terms, and represent a negative example.
We also employed two independent functional linkage reference
sets for testing functional linkages. One was derived from theMunich Information Center for Protein Sequences (MIPS) [43]protein function annotation. We used the 11 major categories
from the top level MIPS functional category annotation. The
second reference set was derived from the clusters of orthologous
group (COG) annotation [44], which is based on reconstructing
homologous groups of proteins in such a manner as toconsiderably enrich for orthologous proteins within each group,with the functions of genes assigned within 23 broad categories(such as ‘‘Transcription’’ and ‘‘Signal Transduction Mechanisms’’)
based on the well-annotated proteins with each COG. We use theversion of COG that includes multicellular eukaryotic genomes(named eukaryotic orthologous groups, or KOG) [45]. Positive
and negative linkage sets were constructed from each of thesereference sets as for the GO set.

rs = 0.94
coverage = 0.85
0.01
0.001

0.01

0.1

1

Degree centrality (log scale)B
P(lethality) (log scale)

YeastNet version 2 is based on the verified 5,794 protein encoding
open reading frames (ORFs) of the yeast genome downloaded
from Saccharomyces cerevisiae Genome Database (SGD) [42] on
March 2005. All linkages and calculations of genome coverage arebased on this gene set.

1

0.1
rs = 0.87
coverage = 0.91
0.01
0.001

0.01

0.1

1

Degree centrality (log scale)P(lethality) (log scale)

C

1

0.1
rs = 0.95
coverage = 0.99
0.01
0.001

0.01

0.1

1

Degree centrality (log scale)Figure 6. YeastNet v. 2 shows improved correlation between genecentrality and lethality. Each plot presents the correlation (for a given
network) between network centrality, calculated as the number ofinteractions per gene normalized by the maximum observed value,
versus the essentiality of the genes, calculated as the fraction of
essential yeast genes [27] for each successive bin (open circle) of 100
genes ranked by decreasing degree centrality. (A) shows the trend fora high quality protein-protein physical interaction network derived from
DIP [30] and bioGRID [29], (B) shows the trend for YeastNet version 1(34,000 most confident linkages only), and (C) shows the trend for
YeastNet version 2. For both functional networks, degree centrality isweighted by the interaction LLS scores (i.e., calculated as the sum of LLS
scores for a gene, divided by the maximum sum of LLS scores observedin the network). The degree of correlation is measured as the Spearman
rank correlation coefficient (rs).
doi:10.1371/journal.pone.0000988.g006

PLoS ONE | www.plosone.org

Benchmarking and integrating heterogeneousfunctional genomics data
Different types of genomics data sets differ considerably in theirutility for inferring functional linkages. We standardized the9

October 2007 | Issue 10 | e988

........................................

Yeast Functional Gene Network

Table 5. Top five predictions of new ribosomal biogenesis genes..................................................................................................................................................
Rank

Gene

Evidence

GO description

1

PUF6

*

CX, MS, LC

nucleus, nucleolus, regulation of transcription, mating-type specific

2

CIC1*

CX, MS, LC

proteasome complex (sensu Eukaryota), nucleolus, protein catabolism

3

KRE33

MS, CX, LC

nucleolus

4

ESF2*

CX, MS, LC, GT

nucleolus, cytoplasm

5

BFR2

CX, MS, LC, YH

nucleolus, ER to Golgi transport*

Experimentally validated by this study and others.
CX: co-expression, GT: genetic interaction, LC: literature curation, MS: mass spectrometry complex analysis, YH: genome-scale yeast two hybrid
doi:10.1371/journal.pone.0000988.t005

pairs in the data set being analyzed that were found in the positiveor negative reference sets, respectively. P(I) and P(,I) were
calculated as the overall frequencies of positive reference pairs(annotated gene pairs sharing annotation) and negative reference
pairs (annotated gene pairs not sharing annotations). Thus, an LLS
score of zero indicates that the data is no more informative thanrandom expectation for discovering functional linkages; increasingly positive LLS scores indicate increasing information in thedata set for discovering functional linkages.
To avoid overtraining, we employed 0.632 bootstrapping[46,47] for all LLS calculations. 0.632 bootstrapping has beenshown to provide a robust estimate of classifier accuracy, outperforming cross-validation [48], especially for very small data sets
(e.g., see [49]), and is thus appropriate even for more poorly
annotated genomes. Unlike cross-validation, which uses sampling

contributions from heterogeneous genomic data sets by scoring
using the log likelihood score (LLS) scheme previously described in[1].
In this scheme, the score for each data set (or subset; e.g., a set ofgene pairs co-expressed to a certain extent) is calculated asP(IjD)=P(*IjD)
LLS~ ln,
P(I)=P(*I)
where P(I|D) and P(,I|D) are the probabilities for gene pairs
linked by the given data (D) to share (I) or not share (,I)
functional annotation, respectively, and P(I) and P(,I) represent
the prior probabilities of sharing/not sharing functional annotation, respectively. For estimating the conditional probabilities
P(I|D) and P(,I|D), we calculated the fraction of annotated gene

Figure 7. Experimental validation of the participation of PUF6 in 60S ribosomal subunit biogenesis. (A) A puf6D deletion strain [27] showsa marked conditional growth defect compared to wild-type (WT) cells when grown at 20˚ C, accompanied by (B) a decrease in the ratio of 60S/40S
ribosomal subunits and the formation of halfmer polysomes, as measured by monitoring absorbance at 254 nm of clarified yeast cell lysate separated
on a 7 to 47 % sucrose density gradient. Additional evidence for the participation of PUF6 in 60S biogenesis can be seen (C) in the co-sedimentationof the TAP-tagged Puf6 protein [40] with the 60S ribosomal subunits, as measured by Western blotting of lysates separated by sucrose density
gradient. TDH1 encodes a cytoplasmic protein with no known association with ribosomal subunits, serving as a negative control. NMD3 encodesa known 60S biogenesis factor [38], serving as positive control. Puf6-TAP shows a similar sedimentation profile as Nmd3-TAP, supporting the role of
PUF6 in 60S ribosomal subunit biogenesis.
doi:10.1371/journal.pone.0000988.g007

PLoS ONE | www.plosone.org

10

October 2007 | Issue 10 | e988

Yeast Functional Gene Networkof new data sets also has the effect of increasing the quantity of
secondary evidence. Thus we empirically observe that as
secondary lines of evidence become more available and informative, the linear dependency model performs better.

without replacement for constructing test and training data sets,
0.632 bootstrapping employs sampling with replacement, constructing the training set from data sampled with replacement andthe test set from the remaining data that weren’t sampled. Eachlinkage has a probability of 1-1/n of not being sampled, resulting
in ,63.2% of the data in the training set and ,36.8% in the test
set [50]. The overall LLS is the weighted average of results on thetwo sets, equal to 0.632*LLStest + (1-0.632)*LLStrain.
For data sets in which each gene pair is associated witha continuous score (e.g., correlation coefficient, mutual information, etc.), we calculated LLS scores for bins containing equal
numbers of gene pairs. Those LLS scores and their corresponding
data scores (the mean data scores for a bin) were used to calculate
regression models (see Figure 1B for examples), which were thenused to map individual data intrinsic scores to LLS scores ina continuous manner, allowing calculation of LLS scores for genepairs lacking annotation. In general, quadratic curve fits tended tooverscore gene pairs with the highest data-intrinsic scores (e.g.,
those with the highest correlation coefficients) during theextrapolation to unannotated genes; sigmoidal fits provided
equivalent quality regression models, but were more conservative
for the highest scoring cases.
For integrating LLS scores from different data sets, we employed
the weighted sum method [1] in order to take into accountcorrelations among the data sets. The published weighted sum
method was modified by using linearly decaying weights for
additional datasets, and by including a new free parameter, T,
which represents a minimum LLS threshold on the data sets being
integrated. The weighted sum (WS) integrating multiple likelihoodscores of functional association for a gene-pair was calculated as:

WS~LLS0 z

Inferring gene functional linkages from mRNA
expression data
Gene functional linkages were inferred from mRNA expression
data deposited in the Stanford Microarray Database (SMD) by
July 2005 [26]. Co-expression relationships were measured as thePearson correlation coefficient (PCC) between pairs of genes’
mRNA expression vectors, accepting only PCC values statisticallysignificant at the 99% confidence level by t-test. From the set ofgene pairs with significant PCC scores, we excluded pairs with
cDNA sequence homology (defined as a BLAST E-value,1024
and percentage nucleotide sequence identity .70% over thealigned regions [51]) in order to reduce false positive co-expression
linkages caused by cross-hybridization on the DNA microarrays.
As demonstrated previously [1], overall recall/precision of
expression-derived linkages can be improved by analyzing subsets
of arrays independently, rather than as a single composite
expression vector. We tested a total of 581 DNA microarrayexperiments comprising 18 sets, as defined by SMD (Tables 2
and 3). We found that 14 SMD sets, containing a total of 500
array experiments, exhibited a significant correlation between
PCC and the log likelihood score; we considered only these data
sets further.
We introduced two additional parameters to improve coexpression inferences: a threshold for the minimum observedchange in mRNA levels across the set of array experiments (R inTable 2), and a threshold for the minimum number of microarrayexperiments with expression values greater than R (M in Table 2).
Thus, only genes that are differentially expressed by at least R-fold(in either direction) on at least M microarrays in the given data set
will be considered for co-expression linkages. These parametersconsiderably reduce the linkage false positive rate by removing
genes that do not vary across the set of arrays being analyzed,
under the premise that genes that are expressed at a constant levelacross the tested conditions are not likely to be relevant to theconditions of the experiments or to participate in strong coexpression relationships. These filters therefore remove false
positive linkages derived from experimental noise and drift inotherwise unchanging baseline expression levels. We optimized thetwo thresholds for each set of SMD arrays, maximizing the area
under a curve plotting the number of genes incorporated in theinferred linkages versus cumulative log likelihood score of thelinkages (Table 2).
In order to include otherwise robust co-expression linkages
missed by these analyses, we also concatenated all 500 experiments
derived from the 14 selected SMD data sets and derived coexpression linkages from these concatenated expression vectors.
These linkages plus those from each of the 14 SMD subsets were
integrated by the weighted sum method.

n
X
LLSi
, for all LLS§T,
D: i
i~1

where LLS0 represents the maximum LLS score for a given genepair, D is a free parameter determining the decay rate of theweight for secondary evidence, and i is the rank order index of LLS
scores, ranking gene pairs starting from the second highest LLS
with descending magnitude for all n remaining LLS scores. For
integration, we consider only LLS scores above the threshold T,
thereby excluding noisy low scoring linkages. The free parameter
D ranges from 1 to +‘, and is optimized to maximize overall
performance (measured as the area under a recall-precision curve)of the integrated model. As the optimal value of D approaches +‘,WS approaches LLS0, and lower scoring LLS scores do not provide
any additional likelihood, as appropriate when all data sets arecompletely dependent. We independently explicitly test theperformance of a naı̈ve Bayesian integration of the LLS scores(here, simply the sum of the LLS scores for a given gene pair), thenselect the integration approach maximizing the area under a plotof LLS versus gene pairs incorporated in the network.
Regarding the choice of linear versus exponential decay ofconfidence in secondary evidence, we observe better performance(measured by recall-precision analysis) using the linear model
when accompanied by more extensive secondary evidence andimproved filtering of false positive linkages prior to integration. In
YeastNet v.1, more low-scoring false-positive linkages wereincorporated, and their contributions as secondary evidence weremore strongly down-weighted under the exponential model.
However, in YeastNet v. 2, new filters (in particular, newprobabilistic scores for protein interactions and the introductionof thresholds for DNA microarray data) down-weight or remove
many false positive associations prior to integration. The addition
PLoS ONE | www.plosone.org

Inferring gene functional linkages fromexperimental protein-protein interaction data
Physical protein-protein interactions (PPI) and genetic interactions(GI) were collected from the Database of Interacting Proteins(DIP) small-scale experiment set (downloaded March 2003) [30],
BioGRID (downloaded on June 2006) in which all interactions aresupported by literature curation [29] and literature collection by
MIPS [43]. These interactions are highly confident, because
genetic interaction screens inherently provide low false-positive
11

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

Phylogenetic profiles were constructed from these comparisonsand analyzed as in [69] with the following modifications. We
found the profiles corresponding to major phylogenetic groups oforganisms varied widely in their utility for deriving functional geneassociations. In particular, inclusion of eukaryotic and archaeal
genomes did not significantly improve performance. Instead, we
found the best performance—measured as the performancemaximizing the area under a plot of LLS versus the number ofgenes participating in the linkages—by inferring functional
linkages from a profile constructed only from bacterial genomes.
For discretizing BLAST E-values prior to calculation of mutual
information between phylogenetic profiles, we binned by equal
numbers of examples rather than by equal intervals of E-values,
accounting for the non-uniform distribution of BLAST E-values.
We observed the best results from using 3 bins.

rates (Type I errors), and all physical interactions in these sets arederived from small-scale studies. Additional physical interactions
were collected from published genome-scale screens using mass
spectrometry analyses of affinity-purified protein complexes [52–54] or high throughput yeast two hybrid (Y2H) assays [20,21,55–
57].
We applied a quantitative error model developed for PPI data
sets [41,58] in order to assign probabilistic confidence scores toeach PPI or GI gene pair. Instead of modeling simple binary baitprey interactions for yeast two hybrid assays, inferred binaryinteractions from mass spectrometry analysis of affinity-purified
protein complex [22], or binary genetic interactions, we calculated
the hypergeometric probability of interaction between two
proteins by random chance, assigning a probability (p-value) tothe pair as:p(#interactions§kjn,m,N)~

min
(n,m)
X

Inferring gene functional linkages from literatureminingp(ijn,m,N),i~k

We identified functional linkages by mining the scientific literature(specifically, Medline abstracts) using the co-citation approach[70,71] as in [1]. We analyzed a set of N = 29,135 Medline
abstracts that included the word ‘‘Saccharomyces cerevisiae’’ in the
abstract for perfect matches to either the standardized names orcommon names (or their synonyms) of 5,794 yeast genes.

wherenp(ijn,m,N)~

!

i

N{n
m{i
!
N

!
~

Inferring gene functional linkages from protein
tertiary structuremn!ðN{nÞ!m!ðN{mÞ!
,
ðn{iÞ!i!ðm{iÞ!ðN{n{mziÞ!N!

Functional linkages were also inferred from physical interactions
predicted between proteins pairs based upon modeling their 3dimensional structures into X-ray crystal structures of homologous
protein complexes. We used the tertiary structure predictions
reported by Aloy and Russell [72], using the reported P-values as
the internal measure of confidence in the interactions.

and where k is the number of interactions observed between
proteins A and B in the complete interaction data set (e.g.,depending on the data set, counting the number of yeast twohybrid interactions, mass spectrometry co-purifications, or genetic
interactions involving both A and B), n is the number of observed
interactions involving protein A, m is the number of observed
interactions involving protein B, and N is total number ofexperiments with $1 interaction measured (e.g., depending on
the data set, counting the number of total detected yeast twohybrid interactions, the number of pull-down experiments with atleast one interaction prey identified by mass spectrometry analysis,or the number of total detected genetic interactions). Using thismeasure, interactions between proteins with many different
interacting partners (i.e., frequent interactors) have a highprobability of occurring by random chance, indicating either
promiscuous or strongly context-dependent interactions. Givenprobable association with many other partners, such cases
therefore receive a correspondingly low confidence in the genepair’s specific interaction with each other.

Summary of integration
The final integrated gene network incorporates 10 fairly distinctive
types of data: 1) small-scale protein physical interactions fromliterature curation, 2) co-citation evidence, 3) mRNA coexpression, 4) genetic interactions, 5) protein complexes derived
from affinity-purification followed by mass spectrometry, 6) highthroughput yeast two hybrid analyses, 7) gene neighbors, 8)phylogenetic profiles, 9) Rosetta Stone protein linkages, and 10)
inferred interactions from tertiary structural modeling (Table 4).
The following pseudo-code summarizes the benchmarking andintegration of these data:
1. For DNA microarray data
1.1. For each set of yeast DNA microarrays (corresponding to
all arrays from a given category defined in SMD)
1.1.1.
Calculate the mean-centered Pearson correlation
coefficient (PCC) between all pairs of genes’
expression profiles
1.1.1.1. Calculate (by t-test) the minimum correlation
coefficient for 99% confidence given the # ofexperiments in the set. For further analyses,
consider only pairs meeting this criterion.
1.1.1.2. Eliminate all potential cross-hybridization pairs
defined by cDNA BLAST score (E-value,1024
and nucleotide sequence identity .70%), thenevaluate the regression between PCC and thelog likelihood score (LLS) of sharing Gene
Ontology biological process annotations

Inferring gene functional linkages from genome
context
We employ three genome context methods for inferring functional
linkages from genome sequences: phylogenetic profiling (PG) [59–61], the Rosetta Stone protein (RS) (or gene-fusion) method[59,62–64], and gene neighbors [3,65,66]. Linkages for eachmethod were derived from analysis of a database of 149 genomes(117 bacteria, 16 archaea, and 16 eukaryotes).
Briefly, each yeast protein sequence was compared to every
other sequence using the program BLASTP with default settings[67]. Rosetta Stone linkages and gene neighbor linkages were
calculated from these comparisons as in [68] and [3], respectively.
PLoS ONE | www.plosone.org

12

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

1.1.1.2.1. Reject set if no relationship is evident
between PCC and LLS
1.1.1.3. Filter genes considered in the correlation
analysis by requiring each gene to exhibit
significant expression changes (e.g., .R-fold,typically ,1.5-fold) in at least M microarrayexperiments across the data set. Optimize these2 parameters by recall-precision analysis, maximizing the area under a plot of LLS versus # ofgenes participating in the linkages.
1.1.1.4. Fit regression (typically sigmoid) between PCC
and LLS, considering only genes passing the
optimized filtering criteria (1.1.1.3) and only
gene pairs whose correlation exceeds the 99%
confidence level (1.1.1.1).
1.1.1.5. Using regression fit, assign LLS scores to allgene pairs whose correlation exceeds the 99%
confidence level, including unannotated genepairs.
1.1.1.6. Select minimum LLS threshold from inflection
point of regression model. Retain only LLS
scores/gene pairs surpassing threshold.
1.2. Consider all sets of yeast DNA microarrays passing thefilter of 1.1.1.2.1 as a single composite set and analyze as in
1.1 and subsections. (This step helps reconstruct linkages
for globally co-expressed genes, such as housekeeping
genes.)
1.3. Integrate LLS scores from all analyses of DNA microarrays
1.3.1.
Calculate the weighted sum of LLS scores for eachgene pair across the analyses of DNA microarraysets
1.3.2.
Optimize the choice of the weighting parameters
D and T using recall-precision analysis bymaximizing the area under a plot of LLS versus# of genes participating in the linkages. Compare
to naı̈ve Bayesian integration, and choose fromweighted integration versus naı̈ve Bayes by recallprecision analysis.
2. For each set of protein-protein physical interaction data (massspectrometry analyses of purified complexes, genome-scale
yeast 2 hybrid analysis, small-scale data collected by literature
curation) and genetic interaction data (collected by literature
curation)
2.1. Fit regressions between LLS and data-intrinsic scores(–log(hypergeometric probability of interaction))
2.2. Using regression fit(s), assign LLS scores to all interacting gene pairs, including unannotated gene pairs
2.3. Integrate LLS scores from homogeneous types ofdetection methods using weighted sum method (e.g.,
integrate LLS from three major mass spectrometry
analysis of complex [52–54] into a single integrated setof gene linkages from all mass spectrometry analyses),
optimizing D and T parameters by recall-precision
analysis. Compare to naı̈ve Bayesian integration, andchoose from weighted integration versus naı̈ve Bayes byrecall-precision analysis.

3. For co-citation, phylogenetic profiles, Rosetta-stone proteins,
gene neighbors data, and inferred protein interactions fromprotein tertiary structure
3.1. Fit regressions between LLS and data-intrinsic scores (–log(random probability of co-citation), mutual information of phylogenetic profiles, (–log(random probabilityof gene-fusion), –log(random probability of being geneneighbors, and original P score as in [72], respectively)
3.2. Using regression fit(s), assign LLS scores to all co-cited(or co-inherited or co-neighboring) gene pairs, including
unannotated gene pairs
4. Integrate all linkages using the weighted sum method,
optimizing the choice of D and T parameters by recallprecision analysis. Compare to naı̈ve Bayesian integration, andchoose from weighted integration versus naı̈ve Bayes by recallprecision analysis.

Experimental validation of yeast ribosomal
biogenesis genes
Yeast strains were cultured in YPD (1% yeast extract, 2% peptone,
2% dextrose) at either 20uC or 30uC. The puf6D haploid MATa
deletion strain [36] and PUF6, NMD3, and TDH1 TAP-tagged
haploid MATa strains [40] were obtained from Open Biosystems.
For polysome profile analysis, yeast strains were cultured to
OD600 0.3–0.5, and 100 mg/ml cycloheximide (Sigma) was added
to each culture. Cultures were immediately cooled with ice, and all
subsequent steps were performed on ice or at 4uC. Each cell pellet
was washed once with lysis buffer (20 mM Tris pH 7.4, 20 mM
KCl, 5 mM MgCl2, 100 mg/ml cycloheximide, 12 mM bmercaptoethanol). The cells were pelleted, resuspended in one
volume lysis buffer with protease inhibitors (2 mg/ml leupeptin,
2 mg/ml aprotinin, 1 mg/ml bestatin, 1 mg/ml pepstatin A;
obtained from MP Biomedicals Inc.), and lysed with glass beads.
Crude lysates were centrifuged at 15,000g for 10 minutes. Fifteen
OD260 units of each supernatant were loaded onto continuous
12 ml 7 to 47% sucrose gradients in lysis buffer without proteaseinhibitors, as in [73]. After a 2.5-h spin at 40,000 rpm ina Beckman SW40 rotor, the sucrose gradient was fractionated andabsorbance at 254 nm was measured. For TAP-tagged strains,
fractions were collected, and proteins were precipitated with 10%
cold trichloroacetic acid and washed with 100% cold acetone.
For analysis of co-sedimentation with ribosomes, precipitatedproteins were resuspended in 20 ml Laemmli buffer, and 2 ml ofeach sample was deposited onto a nitrocellulose membrane. TAPtagged proteins were detected with a PAP antibody (Rockland
Immunochemicals, Inc.) and electrochemiluminescence (ECL; GE
Amersham).

ACKNOWLEDGMENTS
We thank Arlen Johnson for assistance with polysome profile analysis, andOrly Alter and Vishy Iyer for critical discussion of microarray analyses.

Author Contributions
Conceived and designed the experiments: EM IL ZL. Performed theexperiments: IL ZL. Analyzed the data: IL. Wrote the paper: EM IL.

REFERENCES
1. Lee I, Date SV, Adai AT, Marcotte EM (2004) A probabilistic functional
network of yeast genes. Science 306: 1555–1558.

PLoS ONE | www.plosone.org

2. Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, et al. (2001) BIND–
The Biomolecular Interaction Network Database. Nucleic Acids Res 29: 242–245.

13

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

34. Gu W, Deng Y, Zenklusen D, Singer RH (2004) A new yeast PUF family
protein, Puf6p, represses ASH1 mRNA translation and is required for its
localization. Genes Dev 18: 1452–1465.
35. Wade CH, Umbarger MA, McAlear MA (2006) The budding yeast rRNA andribosome biosynthesis (RRB) regulon contains over 200 genes. Yeast 23:
293–306.
36. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, et al. (1999)
Functional characterization of the S. cerevisiae genome by gene deletion andparallel analysis. Science 285: 901–906.
37. Rotenberg MO, Moritz M, Woolford JL Jr (1988) Depletion of Saccharomyces
cerevisiae ribosomal protein L16 causes a decrease in 60S ribosomal subunits
and formation of half-mer polyribosomes. Genes Dev 2: 160–172.
38. Ho JH, Johnson AW (1999) NMD3 encodes an essential cytoplasmic protein
required for stable 60S ribosomal subunits in Saccharomyces cerevisiae. Mol
Cell Biol 19: 2389–2399.
39. Adams CC, Jakovljevic J, Roman J, Harnpicharnchai P, Woolford JL Jr (2002)
Saccharomyces cerevisiae nucleolar protein Nop7p is necessary for biogenesis of
60S ribosomal subunits. Rna 8: 150–165.
40. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, et al. (2003)
Global analysis of protein expression in yeast. Nature 425: 737–741.
41. Hart GT, Lee I, Marcotte EM (2007) A high-accuracy consensus map of yeast
protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics 8: 236.
42. Cherry JM, Adler C, Ball C, Chervitz SA, Dwight SS, et al. (1998) SGD:
Saccharomyces Genome Database. Nucleic Acids Res 26: 73–79.
43. Mewes HW, Frishman D, Guldener U, Mannhaupt G, Mayer K, et al. (2002)
MIPS: a database for genomes and protein sequences. Nucleic Acids Res 30:
31–34.
44. Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein
families. Science 278: 631–637.
45. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, et al. (2003)
The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4: 41.
46. Efron B (1983) Estimating the error rate of a prediction rule: improvement on
cross-validation. J Am Stat Assoc 78: 316–331.
47. Efron B, Tibshirani R (1993) An introduction to the bootstrap. New York:
Chapman & Hall. 439 p.
48. Sima C, Braga-Neto U, Dougherty ER (2005) Superior feature-set ranking forsmall samples using bolstered error estimation. Bioinformatics 21: 1046–1054.
49. Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample
microarray classification? Bioinformatics 20: 374–380.
50. Witten IH, Frank E (2005) Data Mining: Practical Machine Learning Tools andTechniques: Morgan Kaufmann. 560 p.
51. Carlson MW (2002) Surveying yeast genomics diversity using cDNA
microarrays. MS thesis, University of Texas.
52. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, et al. (2006) Proteome
survey reveals modularity of the yeast cell machinery. Nature 440: 631–636.
53. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, et al. (2002) Systematic
identification of protein complexes in Saccharomyces cerevisiae by mass
spectrometry. Nature 415: 180–183.
54. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, et al. (2006) Global landscapeof protein complexes in the yeast Saccharomyces cerevisiae. Nature 440:
637–643.
55. Fromont-Racine M, Mayes AE, Brunet-Simon A, Rain JC, Colley A, et al.
(2000) Genome-wide protein interaction screens reveal functional networks
involving Sm-like proteins. Yeast 17: 95–110.
56. Newman JR, Wolf E, Kim PS (2000) A computationally directed screen
identifying interacting coiled coils from Saccharomyces cerevisiae. Proc Natl
Acad Sci U S A 97: 13203–13208.
57. Tong AH, Drees B, Nardelli G, Bader GD, Brannetti B, et al. (2002) A
combined experimental and computational strategy to define protein interaction
networks for peptide recognition modules. Science 295: 321–324.
58. Lee I, Narayanaswamy R, Marcotte EM (2007) Bioinformatic prediction of yeast
gene function. In: Stansfield I, ed. Yeast Gene Analysis: Elsevier Press. pp
597–628.
59. Huynen M, Snel B, Lathe W 3rd, Bork P (2000) Predicting protein function by
genomic context: quantitative evaluation and qualitative inferences. Genome
Res 10: 1204–1210.
60. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO (1999)
Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci U S A 96: 4285–4288.
61. Wolf YI, Rogozin IB, Kondrashov AS, Koonin EV (2001) Genome alignment,
evolution of prokaryotic genome organization, and prediction of gene function
using genomic context. Genome Res 11: 356–372.
62. Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA (1999) Protein interaction
maps for complete genomes based on gene fusion events. Nature 402: 86–90.
63. Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, et al. (1999)
Detecting protein function and protein-protein interactions from genome
sequences. Science 285: 751–753.
64. Yanai I, Derti A, DeLisi C (2001) Genes linked by fusion events are generally of
the same functional category: a systematic analysis of 30 microbial genomes.
Proc Natl Acad Sci U S A 98: 7940–7945.

3. Bowers PM, Pellegrini M, Thompson MJ, Fierro J, Yeates TO, et al. (2004)
Prolinks: a database of protein functional linkages derived from coevolution.
Genome Biol 5: R35.
4. Gunsalus KC, Ge H, Schetter AJ, Goldberg DS, Han JD, et al. (2005) Predictivemodels of molecular machines involved in Caenorhabditis elegans early
embryogenesis. Nature 436: 861–865.
5. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, et al. (2003) A Bayesian
networks approach for predicting protein-protein interactions from genomic
data. Science 302: 449–453.
6. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D (1999) A
combined algorithm for genome-wide prediction of protein function. Nature
402: 83–86.
7. Mellor JC, Yanai I, Clodfelter KH, Mintseris J, DeLisi C (2002) Predictome:
a database of putative functional links between proteins. Nucleic Acids Res 30:
306–309.
8. Myers CL, Robson D, Wible A, Hibbs MA, Chiriac C, et al. (2005) Discovery of
biological networks from diverse functional genomic data. Genome Biol 6:
R114.
9. Rhodes DR, Tomlins SA, Varambally S, Mahavisno V, Barrette T, et al. (2005)
Probabilistic model of the human protein-protein interaction network. Nat
Biotechnol 23: 951–959.
10. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, et al. (2003) STRING:
a database of predicted functional associations between proteins. Nucleic Acids
Res 31: 258–261.
11. Zhong W, Sternberg PW (2006) Genome-wide prediction of C. elegans genetic
interactions. Science 311: 1481–1484.
12. Nariai N, Kolaczyk ED, Kasif S (2007) Probabilistic protein function prediction
from heterogeneous genome-wide data. PLoS ONE 2: e337.
13. Fraser AG, Marcotte EM (2004) A probabilistic view of gene function. Nature
Genetics 36: 559–564.
14. Lee I, Marcotte EM (2007) Effects of functional bias on supervised learning ofa gene network model. In: McDermott J, Samudrala R, Bumgarner R, eds.
Methods in Molecular Biology: Computational Systems Biology. Totowa: The
Humana press Inc.
15. Myers CL, Barrett DR, Hibbs MA, Huttenhower C, Troyanskaya OG (2006)
Finding function: evaluation methods for functional genomic data. BMC
Genomics 7: 187.
16. Huttenhower C, Hibbs M, Myers C, Troyanskaya OG (2006) A scalable method
for integration and functional analysis of multiple microarray datasets.
Bioinformatics 22: 2890–2897.
17. Dwight SS, Harris MA, Dolinski K, Ball CA, Binkley G, et al. (2002)
Saccharomyces Genome Database (SGD) provides secondary gene annotation
using the Gene Ontology (GO). Nucleic Acids Res 30: 69–72.
18. Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at
GenomeNet. Nucleic Acids Res 30: 42–46.
19. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, et al. (2000)
Genomic expression programs in the response of yeast cells to environmental
changes. Mol Biol Cell 11: 4241–4257.
20. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, et al. (2001) A comprehensive
two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad
Sci U S A 98: 4569–4574.
21. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, et al. (2000) A
comprehensive analysis of protein-protein interactions in Saccharomyces
cerevisiae. Nature 403: 623–627.
22. Bader GD, Hogue CW (2002) Analyzing yeast protein-protein interaction dataobtained from different sources. Nat Biotechnol 20: 991–997.
23. Kelley R, Ideker T (2005) Systematic interpretation of genetic interactions using
protein networks. Nat Biotechnol 23: 561–566.
24. Sangster TA, Lindquist S, Queitsch C (2004) Under cover: causes, effects andimplications of Hsp90-mediated genetic capacitance. Bioessays 26: 348–362.
25. Boone C, Bussey H, Andrews BJ (2007) Exploring genetic interactions andnetworks with yeast. Nat Rev Genet 8: 437–449.
26. Gollub J, Ball CA, Binkley G, Demeter J, Finkelstein DB, et al. (2003) The
Stanford Microarray Database: data access and quality assessment tools. Nucleic
Acids Res 31: 94–96.
27. Giaever G, Chu AM, Ni L, Connelly C, Riles L, et al. (2002) Functional
profiling of the Saccharomyces cerevisiae genome. Nature 418: 387–391.
28. Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality inprotein networks. Nature 411: 41–42.
29. Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hon GC, et al. (2006)
Comprehensive curation and analysis of global interaction networks in
Saccharomyces cerevisiae. J Biol 5: 11.
30. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, et al. (2002) DIP, theDatabase of Interacting Proteins: a research tool for studying cellular networks ofprotein interactions. Nucleic Acids Res 30: 303–305.
31. Hoang T, Peng WT, Vanrobays E, Krogan N, Hiley S, et al. (2005) Esf2p, a U3associated factor required for small-subunit processome assembly and compaction. Mol Cell Biol 25: 5523–5534.
32. Horsey EW, Jakovljevic J, Miles TD, Harnpicharnchai P, Woolford JL Jr (2004)
Role of the yeast Rrp1 protein in the dynamics of pre-ribosome maturation. Rna
10: 813–827.
33. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, et al. (2003) Global
analysis of protein localization in budding yeast. Nature 425: 686–691.

PLoS ONE | www.plosone.org

14

October 2007 | Issue 10 | e988

Yeast Functional Gene Network

71. Stapley BJ, Benoit G (2000) Biobibliometrics: information retrieval andvisualization from co-occurrences of gene names in Medline abstracts. Pac
Symp Biocomput. pp 529–540.
72. Aloy P, Russell RB (2003) InterPreTS: protein interaction prediction throughtertiary structure. Bioinformatics 19: 161–162.
73. Baim SB, Pietras DF, Eustice DC, Sherman F (1985) A mutation allowing anmRNA secondary structure diminishes translation of Saccharomyces cerevisiae
iso-1-cytochrome c. Mol Cell Biol 5: 1839–1846.
74. Tong AH, Lesage G, Bader GD, Ding H, Xu H, et al. (2004) Global mapping of
the yeast genetic interaction network. Science 303: 808–813.
75. Shapira M, Segal E, Botstein D (2004) Disruption of yeast forkhead-associatedcell cycle transcription by oxidative stress. Mol Biol Cell 15: 5659–5669.
76. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis anddisplay of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:
14863–14868.
77. Brauer MJ, Saldanha AJ, Dolinski K, Botstein D (2005) Homeostatic adjustment
and metabolic remodeling in glucose-limited yeast cultures. Mol Biol Cell 16:
2503–2517.

65. Dandekar T, Snel B, Huynen M, Bork P (1998) Conservation of gene order:
a fingerprint of proteins that physically interact. Trends Biochem Sci 23:
324–328.
66. Overbeek R, Fonstein M, D’Souza M, Pusch GD, Maltsev N (1999) The use ofgene clusters to infer functional coupling. Proc Natl Acad Sci U S A 96:
2896–2901.
67. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped
BLAST and PSI-BLAST: a new generation of protein database search
programs. Nucleic Acids Res 25: 3389–3402.
68. Marcotte CJ, Marcotte EM (2002) Predicting functional linkages from genefusions with confidence. Appl Bioinformatics 1: 93–100.
69. Date SV, Marcotte EM (2003) Discovery of uncharacterized cellular systems bygenome-wide analysis of functional linkages. Nat Biotechnol 21: 1055–1062.
70. Jenssen TK, Laegreid A, Komorowski J, Hovig E (2001) A literature network ofhuman genes for high-throughput analysis of gene expression. Nat Genet 28:
21–28.

PLoS ONE | www.plosone.org

15

October 2007 | Issue 10 | e988