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Combined analysis reveals a core set of cycling genes
Yong Lu*, Shaun Mahony†, Panayiotis V Benos†, Roni Rosenfeld‡,
Itamar Simon§, Linda L Breeden¶ and Ziv Bar-Joseph*‡
Addresses: *Department of Computer Science, Carnegie Mellon University, Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA. †Department
of Computational Biology, University of Pittsburgh Medical School, Lothrop Street, Pittsburgh, Pennsylvania 15213, USA. ‡Machine Learning
Department, Carnegie Mellon University, Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA. §Department of Molecular Biology, Hebrew
University Medical School, Jerusalem, Israel 91120. ¶Basic Sciences Division, Fred Hutchinson Cancer Center, Fairview Avenue N, Seattle,
Washington 98109, USA.
Correspondence: Ziv Bar-Joseph. Email: zivbj@cs.cmu.edu
© 2007 Lu et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Conservation of cycling genes<p>The simultaneous analysis of expression data from multiple species reveals a core set of conserved cycling genes that is much larger than prev ously th ght.</p>
Abstract
Background: Global transcript levels throughout the cell cycle have been characterized using
microarrays in several species. Early analysis of these experiments focused on individual species.
More recently, a number of studies have concluded that a surprisingly small number of genes
conserved in two or more species are periodically transcribed in these species. Combining and
comparing data from multiple species is challenging because of noise in expression data, the
different synchronization and scoring methods used, and the need to determine an accurate set of
homologs.
Results: To solve these problems, we developed and applied a new algorithm to analyze
expression data from multiple species simultaneously. Unlike previous studies, we find that more
than 20% of cycling genes in budding yeast have cycling homologs in fission yeast and 5% to 7% of
cycling genes in each of four species have cycling homologs in all other species. These conserved
cycling genes display much stronger cell cycle characteristics in several complementary high
throughput datasets.
Essentiality analysis for yeast and human genes confirms these findings. Motif analysis indicates
conservation in the corresponding regulatory mechanisms. Gene Ontology analysis and analysis of
the genes in the conserved sets sheds light on the evolution of specific subfunctions within the cell
cycle.
Conclusion: Our results indicate that the conservation in cyclic expression patterns is much
greater than was previously thought. These genes are highly enriched for most cell cycle categories,
and a large percentage of them are essential, supporting our claim that cross-species analysis can
identify the core set of cycling genes.
Published: 24 July 2007
Genome Biology 2007, 8:R146 (doi:10.1186/gb-2007-8-7-r146)
Received: 30 March 2007
Revised: 19 June 2007
Accepted: 24 July 2007
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2007/8/7/R146Genome Biology 2007, 8:R146
n

R146.2 Genome Biology 2007, Volume 8, Issue 7, Article R146 Lu et al. http://genomebiology.com/2007/8/7/R146
Background
The cell cycle is a series of linked, fundamentally conserved
processes that result in high-fidelity cell duplication. Global
transcript levels throughout the cell cycle have been charac-
terized using microarray expression data in several species.
These include humans [1], budding and fission yeast [2-6],
plants [7], and bacteria [8]. Early analysis of these experi-
ments focused on individual species. Hundreds of genes have
been identified whose transcripts oscillate during the cell
cycle, and in budding yeast it is estimated that 15% of all genes
are subject to this type of control. Despite this large cross-spe-
cies effort, a number of studies have concluded that a surpris-
ingly small number of genes conserved in two or more species
are periodically transcribed in these species. Rustici and cow-
orkers [4] compared fission and budding yeast expression
data. Dyczkowski and Vingron [9] compared three lists of
cycling genes (budding and fission yeast and human), and
Jensen and colleagues [10] added a fourth species (Arabidop-
sis). All three studies concluded that periodicity at the tran-
script level was conserved across species in only a small
number of cases.
When comparing cyclic expression patterns across species,
researchers face several challenges. In some cases the lists
derived for each species were generated using different
expression analysis methods. For example, the scoring meth-
ods used by Spellman [2] and Rustici [4] and their colleagues
are different, which makes direct comparison problematic.
Another challenge arises when determining the set of
homologs between the species being analyzed. Although
using curated databases results in a more accurate set of con-
served pairs, this analysis is limited to a small (and some-
times biased) set of genes. In addition, the binary assignment
(ortholog or not) in databases cannot account for more com-
plex similarity measures, which are often represented using a
more continuous value (for example, BLAST e-value). Relying
on the actual strength of homology may help when looking for
conserved sets. Finally, expression data are noisy. Repeated
experiments, even within the same species, often result in rel-
atively low agreement [5], and differences between species
may be even more problematic because radically different
synchronization procedures must be used [11]. Any combina-
tion of the above may bias the analysis and prevent the iden-
tification of an accurate set of conserved cycling genes.
Here we use an algorithm that analyzes data from all species
concurrently. This differs from previous methods that per-
formed this analysis separately for each species and then
looked at the overlap. Our method overcomes many of the
obstacles discussed above. We use the same scoring method
for all species, and include parameters that allow a gene in
one species to influence the score of a homologous gene (in
either the same or in another species). These parameters are
continuous and depend on the similarity between the genes.
They allow for one to many and for many to many mappings
between genes; they also allow higher quality expression data
in one species to improve the quality of the data for other
species.
We analyze expression data from four species: budding [2]
and fission yeast [4-6], human [1], and plants [7]. Our pri-
mary goal is to determine sets of genes that are conserved in
sequence and at the transcript level between all and subsets of
these species. Our findings indicate that the set of conserved
cycling genes is much larger than was previously thought.
These findings are validated and explained using a large
number of complementary high throughput datasets.
Results and discussion
Combined analysis of cell cycle expression data
We developed an algorithm for combining sequence and
expression data in order to identify cycling genes [12]. The
algorithm uses probabilistic graphical models, and in partic-
ular Markov random fields, to combine these data sources.
Genes are represented as nodes in the graph and are con-
nected by edges to other genes (in the same species and all
other species), based on their sequence similarity as deter-
mined by a BLAST score (Figure 1a). Each node (gene) is
assigned an initial cycling score that is determined from
expression data using a method from de Lichtenberg and
coworkers [13]. Starting with this score, we propagate infor-
mation along the edges of the graph until convergence. Thus,
if a node with a medium to high score is connected to a set of
Method overviewFigure 1 (see following page)
Method overview. (a) Genes (nodes in the graph) are connected to other genes based on sequence similarity. Species identity is indicated by shape of
nodes. Genes are also connected to a 'score node', which represents cycling expression score. Information is propagated along the edges until
convergence. Genes are assigned a posterior score and a cut-off is applied to select the top genes for each species. (b) The subgraph containing the
selected genes is further analyzed by identifying multidomain homology cliques. Examples of identified cliques of conserved genes are presented in panels c
to f. (c) Cyclins. Fission yeast Cig2 promotes the onset of S phase [45]. Human Ccna2 is part of the G2 checkpoint [46]. (d) Cdc6/Cdc18 is a conserved
and essential component of pre-replication complexes (pre-RCs). Orc1 is the largest subunit of the origin recognition complex (ORC), which binds
specifically to replication origins and triggers the assembly of pre-RCs [47]. (e) TOG related proteins, a family of microtubule-associated proteins (MAPs).
Proteins in this group localize to the plus-end tips of microtubules and are essential for spindle pole organization. Alp14 is a component of the Mad2-
dependent spindle checkpoint cascade sharing redundant functions with Dis1. Mutants with both genes knocked out are nonviable [48]. (f,g) Microtubule
component clique and expression profiles for fission yeast Nda3 in eight experiments [4-6]. Nda3, a known cell division gene [49], obtains a high cycling
score but is not one of the 600 top cycling fission genes based on expression analysis. Using our method, its score is correctly elevated because its Genome Biology 2007, 8:R146
sequence similarity to high scoring genes.

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Human Budding yeast Fission yeast Arabidopsis Score node


CYCB3;1
CCNF
CCNE1
CCNB1
CCNA2
cig1
cig2
cdc13
CLB6
CLB5
CLB1
CLB4
CLB2
CYCA1;2
CYCA2;1
CYCB1;4
At4g35620
CYC1BAT
At2g17620

Highest cycling score
Lowest cycling score
CDC6
ORC1L
CDC6
cdc18
ORC1
CDC6
CKAP5
dis1alp14
STU2 MOR1
(e)
TUBA3
TUBG1
TUBA2
TUBA1
nda2
nda3
TUB4
TUB2
TUB6
TUB5
nda3
Time (min)
E
xp
re
ss
io
n
le
ve
l
0 50 100 150 200 250
−0.6
−0.4
−0.2
0.0
0.2
0.4
Cdc25−1 −0.6
−0.4
−0.2
0.0
0.2
0.4
Cdc25−2−0.6
−0.4
−0.2
0.0
0.2
0.4
Cdc25−2−swap
nda3
Time (min)
E
xp
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ss
io
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l
0 100 200 300 400 500
0.0
0.5
Cdc25
0.0
0.5
Elutriation a
0.0
0.5
Elutriation b
nda3
Time (min)
E
xp
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io
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100 200 300 400
−0.2
−0.1
0.0
0.1
0.2
Cdc25
−0.2
−0.1
0.0
0.1
0.2
Wild Type
(g)
(a)
(c)
(f)
(d)
(b)n
Genome Biology 2007, 8:R146
Figure 1 (see legend on previous page)

R146.4 Genome Biology 2007, Volume 8, Issue 7, Article R146 Lu et al. http://genomebiology.com/2007/8/7/R146
nodes with high scores, then the information from the neigh-
boring nodes can be used to elevate our belief in the assign-
ment of this node, and vice versa. This method allows us to
identify several cycling genes that can be missed in an analy-
sis focused on a single species as a result of expression noise
(Figure 1 and Additional data file 1 [Supporting Figures 1 to
3]). Similarly, genes with marginal scores that are only con-
nected to low scoring genes can be filtered out of the cycling
gene lists. Once the algorithm converges each gene is
assigned a posterior cycling score between 0 and 1. For com-
parison reasons, we select for each species a set of genes with
roughly equal size to those used in the original reports
(although the identity of these genes is different), remove all
other genes from the graph, and consider only the subgraph
induced by the selected genes. This graph is analyzed to iden-
tify multidomain homology cliques [14] (Figure 1b-e). Each of
these cliques is then analyzed to determine the set of species
included. These findings are reported as three cell cycle con-
servation (CCC) sets with conservation across two (budding
and fission yeast), three (yeasts and human cells), or all four
species.
See Materials and methods and Additional data file 3 for fur-
ther details on our graph-based algorithm and on clique anal-
ysis. Also see our supporting website [15] for a complete list of
genes identified using our algorithm.
Analysis of identified cycling genes
Our method combines expression and sequence data. This
raises an obvious question; is the quality of our lists compara-
ble to the quality of previous lists that relied on expression
data alone? In other words, does our method sacrifice the
accuracy with respect to the set of cycling genes in each spe-
cies in order to obtain a larger set of conserved genes?
A possible way to assess the quality of such lists is by compar-
ing them with other high-throughput data sources [13]. For
example, protein-DNA binding data are available for nine
budding yeast transcription factors that are known to be
involved in cell cycle specific transcription [16]. It is expected
that many cycling genes would be bound by these factors.
When comparing the genes in our list with the original list [2],
we find that both exhibit a threefold enrichment for these
interactions compared with a random gene list (Figure 2a).
Stationary phase expression experiments yield similar results
(Figure 2b). Similarly, both our list and the original list [1] of
cycling human genes are enriched for binding of known cell
cycle factors (Nrf1 and E2f2; Additional data file 1 [Support-
ing Figure 4d]). Genes on both lists exhibit lower expression
levels in nonproliferating tissues (Figure 2c) and higher
expression levels in cancer cells (Additional data file 1 [Sup-
porting Figure 4c]). Expression data for fission yeast and
Arabidopsis support our list for these species as well (Figure
2d-e).
Combined, these results indicate that the species-specific lists
derived using our method are comparable in quality to those
of previously reported cell cycle gene lists. Additional data file
2 (Supporting Tables 1 to 3) presents the percentage overlap
between the lists of cycling genes identified using our method
and previously reported cycling gene lists for the four species.
Conserved cycling genes
Figure 3a presents the number of conserved genes for the dif-
ferent evolutionary distances represented in our datasets.
About 21% of the budding and fission yeast cycling genes
reside in cliques containing genes from these two species
(CCC2). When adding human genes, roughly 10% of cycling
yeast genes and 8% of cycling human genes are included in
such cliques (CCC3). Finally, between 5% and 7% of cycling
genes in all four species are conserved in sequence and
expression (CCC4). Additional data file 2 (Supporting Tables
4 to 10) presents the list of genes assigned to CCC4 and CCC3
for each of the species. We note that although our original
sequence similarity criterion was based on BLAST e-values,
following the clique analysis the resulting sets are in very
good agreement with curated homology databases [17]. For
example, 82% of budding yeast genes in CCC2 have a curated
fission yeast homolog in CCC2. Similarly, 82% of fission yeast
genes in CCC2 have a curated budding yeast homolog in
CCC2. See our supporting website [15] for complete hom-
ology references.
To test the agreement of our conserved lists with complemen-
tary high-throughput datasets, we have repeated and
extended our analysis discussed above but focusing only on
genes included in CCC3 and CCC4. As Figures 2 and 3 and
Additional data file 1 (Supporting Figure 4) show, CCC3 and
CCC4 genes exhibit much stronger cell cycle characteristics
when compared with the original set of cycling genes for each
Analysis of cycling genes using complementary high throughput datasetsFigure 2 (see following page)
Analysis of cycling genes using complementary high throughput datasets. (a) Number of interactions between cycling genes and nine cell cycle
transcription factors. (b) Average expression level of sets of budding yeast genes in stationary phase (data from Gasch and coworkers [50]). (c)
Expression levels of human genes in normal tissues, using data presented by Shyamsundar and colleagues [51] (also see Additional data file 1 [Supporting
Figure 4]). Genes in the conserved set have lower expression levels for most nonproliferating normal tissues when compared with the full list and the list
presented by Whitfield and coworkers [1]. For 26 out of 36 normal tissues this difference is significant with a P value < 0.05. (d) Arabidopsis cells in
developmental arrest experiments [52]. Flower cells in the mutants stop growing after stage 11, whereas cells in the stem grow normally. Again, the
conserved set is expressed at lower levels in developmental arrest (P = 0.027 at stages 11 and 12; P = 0.003 at stages 13 and 14). (e) Expression data from Genome Biology 2007, 8:R146
studying sexual differentiation and mating in fission yeast [53].

R146.6 Genome Biology 2007, Volume 8, Issue 7, Article R146 Lu et al. http://genomebiology.com/2007/8/7/R146
species. For example, in a protein-protein interaction dataset
for budding yeast [18,19], genes in CCC3 are involved in ten
times more pair-wise interactions when compared with a ran-
dom set of similar size from the full set of cycling genes (Fig-
ure 3c). This indicates that these genes have long been
involved in the same function. Similarly, the percentages of
human genes bound by two cell cycle transcription factors are
much higher for the CCC3 and CCC4 sets (Nrf1 and E2f2 [20];
Additional data file 1 [Supporting Figure 4d]). Also, for
humans the CCC3 and CCC4 sets are much more repressed in
several nonproliferating tissues when compared with the full
set of cycling human genes (Figure 2c). Similarly, CCC4 genes
exhibit stronger cell cycle characteristics in Arabidopsis and
fission yeast expression experiments (Figure 2d-e).
We have also repeated our analysis by comparing our lists
with subsets of cycling genes with high amplitude in each of
the four species. As shown in Additional data file 1 (Support-
ing Figure 5), high amplitude genes exhibit similar cell cycle
characteristics to the CCC3 and CCC4 sets for human and
plants. However, for the two yeasts these high amplitude
genes are more similar to the full set of cycling genes. This
indicates that expression analysis alone cannot be used to
identify this core set of genes.
Motif analysis for budding and fission yeast genes
To further validate our findings of a large overlap between the
cycling genes in the two yeast species, we turned to motif
analysis. Several transcription factors are conserved between
budding and fission yeast [21]. A possible explanation for
expression conservation (or lack thereof) is in the conserva-
tion (or lack of conservation) of a binding motif for these
cycling genes.
We started by looking at genes bound by the budding yeast
factor Swi6, which regulates transcription at the G1/S transi-
tion [22]. We extracted three lists for this factor. The first,
denoted BY6, contained cycling budding yeast genes in CCC2
determined to be bound by Swi6 [23]. The second list,
denoted FY6C, contained fission yeast genes that both were in
CCC2 and had homologs in BY6. These genes were deter-
mined to be cycling and conserved by our method. The third
list (FY6NC) contained noncycling fission yeast genes with
cycling budding yeast homologs bound by Swi6. This latter
list serves as a negative control because it contains genes that
have lost their cycling status between the two species. Four
motif finders were run on each dataset; SOMBRERO [24,25],
BioProspector [26], Consensus [27], and AlignACE [28] (see
Materials and methods, below, for details). All four motif
finding algorithms were able to identify the Swi6 motif in BY6
and FY6C, indicating that this motif is conserved between the
two species, at least for some of the conserved cycling genes
(Additional data file 1 [Supporting Figures 6 and 7]). In sharp
contrast, none of these motif finders was able to identify the
Swi6 motif in the upstream regions of genes in FY6NC.
Mechanistic similarities and differences between cell
cycle regulation in budding and fission yeast
We have extended the motif analysis discussed above to study
ten additional transcription factors that were determined to
play a key role in regulating cycling genes in budding yeast
[3,16]. For each of these factors we extracted all cycling bud-
ding yeast genes determined to be bound by this factor [23]
and their fission yeast homologs. As we did for Swi6, we fur-
ther divided the fission yeast genes into two sets; the first con-
tains fission yeast genes in CCC2 and the second (a negative
control list) contains noncycling fission yeast homologs of
cycling budding yeast genes. Next, we ran the four motif find-
ers on each dataset.
The results are presented in Table 1 and Additional data file 1
(Supporting Figures 6 to 17). In Table 1 we report on the
number of motif finders that identified the correct motif for
each factor and on the percentage of genes in the set that con-
tained this motif. Similar to the results obtained for Swi6, the
other two G1/S factors, namely Swi4 and Mbp1, exhibit the
optimal motif conservation pattern; the expected motifs are
found in both the fission yeast cell cycle genes and the positive
control of conserved budding yeast cell cycle genes, but are
not found in the negative control set of noncycling fission
yeast genes. Motif scan analysis (Additional data file 2 [Sup-
porting Table 11]) confirms the results for these factors. For
G2/M, the Fkh2 sets display similar, although less significant,
pattern (two of four motif finders identified the correct motif
for the cycling set). However, Fkh1 and Fkh2 motifs also
appear, although less strongly, in the negative control sets. In
total, FKH-like motifs are present in eight of the 11 negative
control datasets. The M/G1 phase analysis is complicated by
small dataset size. This may result from the lack of conserva-
tion between the two species for this phase [21]. As a result,
motif match for this set is either weak (Swi5) or nonexistent
(Mcm1 and Yox1).
Conservation of cycling genesFigure 3 (see following page)
Conservation of cycling genes. (a) Percentage of conserved cycling genes in the four species. (b) Enrichment of cell cycle related Gene Ontology GO
terms between all cycling genes and the CCC3 set in budding yeast, fission yeast, and humans. (c) Yeast protein-protein interactions [18]. We counted the
number of interactions within a random set of 80 cycling yeast genes. In all, 1,000 sets were sampled. The histogram on the left plots the number of
interactions observed for these sets. X represents internal interactions with the CCC3 set, which has significantly more internal interactions.Genome Biology 2007, 8:R146

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500 (100%) 800 (100%) 600 (100%) 1000 (100%)
CCC3
72 (9.0%)
68 (11.3%)
83 (8.3%)
CCC4
39 (7.8%)
37 (6.2%)
39 (4.9%)
52 (5.2%)
CCC2
154 (19.3%)
140 (23.3%)
Arabidopsis S. cerevisiae S. pombe H. Sapiens
−log10(pval)
Cell cycle
Chromatin assembly or disassembly
DNA replication initiation
DNA replication
DNA unwinding during replication
DNA metabolism
Regulation of cyclin−dependent protein kinase activity
Microtubule−based process
M phase
Cell division
Regulation of cell cycle
Cytoskeleton organization and biogenesis
DNA repair
Cell budding
Meiosis
Cell wall organization and biogenesis
0 5 101520
Budding
0 5 101520
Fission
0 5 101520
Human
All cycling
CCC3

Pairwise interaction
between conserved cell cycle genes
Number of pairwise interactions
Fr
eq
ue
nc
y
0 5 10 15 20 25 30
0
10
0
30
0
x
Interactions between
conserved
cell cycle genes
(a)
(b)
(c) n
Genome Biology 2007, 8:R146
Figure 3 (see legend on previous page)

R146.8 Genome Biology 2007, Volume 8, Issue 7, Article R146 Lu et al. http://genomebiology.com/2007/8/7/R146
The biologic importance of the core set of cycling
genes
To further validate that genes in CCC3 and CCC4 are core
cycling genes, we studied their importance using deletion
data. Surprisingly, only 15% of cycling yeast genes are essen-
tial in rich media conditions [29], which is roughly equal to
the overall percentage of essential yeast genes (18%). How-
ever, as Figure 4 shows, 35% of budding yeast genes in the
CCC3 list and 46% of the genes in the CCC4 lists are essential.
To test whether similar result could be obtained using only
sequence data (without expression data for the other species),
we extracted from the full list of cycling budding yeast genes
those with homologs in all other species, without taking into
account their cycling status in these other species. Although
this increased the percentage of essential genes (to 27%),
these percentages remained well below those achieved for
CCC4, which uses the expression data.
We have also carried out similar analyses for human genes
using data from RNA interference (RNAi) experiments [30].
In these experiments 24,373 genes were knocked down using
RNAi and assessed for phenotypic influence on cell growth.
For 1,152 (4.7%) of the genes, the resulting knockdown cells
presented phenotypic growth defects. As Mukherji and cow-
orkers [30] note in their report, roughly 6% of cycling human
genes reported by Whitfield and colleagues [1] are included in
this list. Similar to the process we conducted in yeast, we con-
sidered sequence data only and extracted from the Whitfield
list those genes with homologs in the other three species. For
this list, the percentage of genes increases to 10%. Again, the
most enriched lists are obtained when using the CCC3 and
CCC4 sets. For these, the percentage climbs to 16% (CCC3)
and 17% (CCC4). These findings highlight the importance of
the conserved set and support our conclusion that it contains
key cycling genes.
Conserved protein complexes regulated by the cell cycle
To determine cell cycle regulated protein complexes con-
served between these species, we searched for protein com-
plexes with one or more subunits in the CCC3 set using high-
throughput protein-protein interaction data. This type of data
is thus far only available in budding yeast [18,19]. Additional
data file 2 (Supporting Table 12) and Additional data file 1
(Supporting Figure 18) present some of the protein com-
plexes that we identified. Some of these complexes are known
to regulate important events in the cell cycle. For example, the
origin recognition complex (ORC) is a well conserved com-
plex that is involved in the initiation of DNA synthesis [31].
Other examples are the cohesin complex, which is responsible
for binding the sister chromatids during mitosis after S phase
[32], and the ribonucleoside-diphosphate reductase (RNR)
complex, which is involved in the maintenance of the cellular
pool of dNTPs [33].
Gene Ontology analysis of conserved cycling genes
The CCC3 list gives us our first look at the conserved core of
periodically transcribed genes across evolution. Even though
CCC3 contains relatively few genes (0.4% to 1.3% of the total
number of genes for each species), many of these genes play a
role in key processes required for growth. Using Gene Ontol-
Table 1
Summary of motif-finding results
Budding yeast
phase
Transcription
factor
Fission yeast cell cycle
genes
Negative control
(fission yeast non-cell-cycle
genes)
Positive control
(conserved budding yeast
cell-cycle genes)
Extended positive control
(all budding yeast CC
genes)
G1/S Swi4 4 43% 0 0% 4 96% 4 98%
Swi6 4 97% 0 0% 4 100% 4 83%
Mbp1 4 59% 0 0% 4 93% 4 91%
G2/M Fkh1 0 0% 2 22% 1 62% 3 67%
Fkh2 2 45% 2 24% 1 74% 2 67%
Ndd1 0 0% 0 0% 4 100% 4 100%
M/G1 Mcm1a 0 0% 0 0% 3 87% 4 88%
Ace2 4b 86% 0 0% 0b 0% 4 88%
Swi5 ~2b 100% 0 0% ~2b 75% 1 0%
Yox1 0b 0% 0b 0% 3b 86% 3 86%
Yhp1 0b 0% 0b 0% 1b 0% ~1b 67%
Motif analysis of the conserved cycling genes in budding and fission yeast. For each set and each factor we list the number of motif finders (up to four)
that identified the correct motif. Each motif finder often recovers multiple correct motifs, and each motif is associated with a list of predicted
instances in promoter regions. We report the percentage of promoters that contain instances predicted by at least one-third of the correct motifs.
The first and third columns are the CCC2 genes in budding and fission yeast, respectively. The second column is non-cycling fission yeast genes with Genome Biology 2007, 8:R146
homolog cycling budding yeast genes. See Additional data file 3 for further details. aMcm1 regulates genes in G2/M and M/G1. bThese datasets contain
ten genes or fewer. ~, weak matches to the known motif.

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ogy (GO) analysis [34], we identified categories that were
enriched in this set. For budding yeast these categories
include cell cycle (P = 3 × 10-15), DNA replication (P = 2 × 10-
13), and mitosis (P = 1 × 10-7). Similar enrichments were found
for human conserved cycling genes and for fission yeast. For
example, cell cycle (P = 5 × 10-17), DNA replication (P = 7 × 10-
14) and cell division (P = 2 × 10-9) are enriched in humans, and
cell cycle (P = 10-9) and chromatin assembly/disassembly (P
= 10-9) are enriched in fission yeast. Figure 3 and Additional
data file 2 (Supporting Tables 13 to 21) present P values for
the various GO categories.
centage of cycling genes. This indicates that many of the genes
associated with these functions have been conserved in cyclic
expression between the species. These include categories
related to DNA metabolism (P = 5.7 × 10-12 for CCC3 and P =
1.1 × 10-6 for the full list) and chromatin assembly (P = 3.7 ×
10-5 versus P = 0.01). In contrast, there are a number of cate-
gories that are much more enriched in the full list, indicating
that they have probably evolved, or at least greatly expanded,
in the individual species. These include categories such as
mitosis for fission yeast (P = 1.6 × 10-4 versus P = 3.6 × 10-7)
and the cell wall category, which exhibits a great deal of spe-
cies-specific variation between the budding yeast, the fission
yeast, and metazoans [35]. For the human list, DNA repair
and chromosome segregation were more significantly
enriched in the full set (P = 5.9 × 10-4 versus P = 7.3 × 10-5, and
P > 0.1 versus P = 9.0 × 10-8, respectively). Although these
functions are conserved across organisms, our analysis
indicates that many of these genes are cycling only in human
cells, perhaps indicating that these functions have been
adapted to accommodate the longer cell cycle.
Analysis of specific CCC3 genes
Partial functional knowledge is available for all but one
(YPL247C) of the 72 budding yeast genes on CCC3. Sixteen of
these genes encode products that are involved in DNA repli-
cation and another 23 are involved in chromosome organiza-
tion and biogenesis. These include structural components
(Mcms, tubulins, and histones) as well as regulatory proteins
(cyclins, Cdc20, and Cin8). The mcm2 (cdc19) and mcm6
genes were previously known to be cyclic subunits of the
highly conserved Mcm pre-replication complex in fission
yeast [5,36]. Our combined analysis indicates that two other
genes (mcm3 and mcm5) may also be periodic, similar to the
budding yeast and human Mcm subunits. Another large class
of conserved cyclic genes is involved in chromosome segrega-
tion (ASE1, KIP1, NUM1, and STU2) and cytokinesis (MOB1,
HOF1, KEL2, and IQG1). In addition, the list includes factors
that affect transcription globally (ARP7 and TUP1) and spe-
cifically (ACE2, FKH2, and HCM1). Interestingly, the S phase
specific transcription factor Hcm1 has a conserved cyclic
transcript, as do 22 of its predicted targets [3]. The fact that
nearly 30% of the budding yeast CCC3 genes are potential tar-
gets of Hcm1 is consistent with the known role of Hcm1 in reg-
ulating genes involved in chromosome dynamics [3,37].
There is only a small number of genes in CCC3 that are not
obviously involved in cell cycle specific processes. These
genes include three involved in metal homeostasis (SMF2,
SMF3, and CTH2), some cell wall proteins (FIG2, AGA1, and
SED1) and alkaline phosphatase (PHO8). These gene prod-
ucts could be involved in unknown aspects of the cell division
cycle, or they could be evolutionarily related to other cell cycle
proteins.
The importance of the core cycling genesFigure 4
The importance of the core cycling genes. (a) Percentage of essential
genes in different sets of budding yeast genes [29]. Although 18% of
budding yeast genes are essential, only 15% of cycling genes are essential.
Our analysis resolves this apparent contradiction by showing that the
conserved cycling genes lists contain a much higher percentage of essential
genes (35% and 46% for CCC3 and CCC4). Sequence alone cannot
account for this high percentage (27%), indicating the importance of the
combined analysis. (b) Similar analysis for the human lists using data from
RNA interference knockdown experiments [30].
Percentage of essential budding yeast genes
Pe
rc
en
ta
ge
o
f e
ss
e
n
tia
l g
en
es
0
10
20
30
40
50 Spellman (15.3%)
Spellman w/ homologs (27.0%)
Our list (15.2%)
CCC3 (34.7%)
CCC4 (45.9%)
All genes (17.9%)
(a)
Percentage of human genes strongly effecting cell cycle progression
Pe
rc
en
ta
ge
o
f e
ss
e
n
tia
l g
en
es
0
5
10
15
20 Whitfield (5.6%)Whitfield w/ homologs (9.7%)
Our list (7.4%)
CCC3 (15.7%)
CCC4 (17.3%)
All genes (4.7%)
(b) n
Genome Biology 2007, 8:R146
Some categories were more enriched in the CCC3 set than in
the full list. For these categories, CCC3 contains a dispropor-
tionate number of genes when compared with the overall per-

R146.10 Genome Biology 2007, Volume 8, Issue 7, Article R146 Lu et al. http://genomebiology.com/2007/8/7/R146
Conclusion
By applying a combined analysis, coupled with an unbiased
homology metric, we were able to identify a large set of genes
as conserved in sequence and cycling status between four dif-
ferent species: budding and fission yeast, human, and Arabi-
dopsis.
A number of previous efforts to compare cycling gene lists
derived independently for each species concluded that only a
small number of genes are conserved between these species.
For example, Rustici and coworkers [4] concluded that only
5% to 10% of cycling budding yeast genes have a cycling
homolog in fission yeast. Jensen and colleagues [10] identi-
fied only five orthologous groups as conserved between the
four species (about 1% of the cycling genes) and only eight
groups (2%) between the three species of CCC3. The differ-
ences between these conclusions can be attributed to differ-
ences in the analysis of the expression and sequence data, as
mentioned in the Introduction (above). We note, however,
that the results presented by Oliva and coworkers [5] provide
partial support to our conclusions. Although they did not
carry out a complete conservation analysis, they found that 72
of their top 200 cycling fission yeast genes (36%) had a
cycling homolog in budding yeast. Earlier work that used
clustering methods to look at global expression similarities
between species also supports our findings regarding the
extent of expression conservation [38,39]. Although our anal-
ysis identifies a larger fraction of conserved cycling tran-
scripts than does that conducted by Jensen and colleagues
[10], we find the same striking co-occurrence of cell cycle spe-
cific phosphorylation of the gene products they encode. As
Additional data file 2 (Supporting Table 22) shows, when
using data on Cdk1 phosphorylation [40] we find that 65% of
tested CCC3 gene products are phosphorylated by Cdk1. This
percentage is twice the percentage of phosphorylated gene
products from the full set of tested cycling genes (33%) and
eight times higher than the percentage of tested random
genes (8%). These finding reinforces the view that there is a
conserved core of genes that are regulated at multiple levels
during the cell cycle in most eukaryotic cells.
Our results are strongly supported by the fact that genes con-
served in two or more species display much stronger cell cycle
characteristics than the full list for each species. They also
show extensive interactions within the set, and almost half of
the CCC4 yeast genes are essential. These observations and
GO analysis indicates that these genes are crucial compo-
nents of the cell cycle system. Combined, these findings sup-
port our claim that the lists we derive contain a core
conserved set of cycling genes.
Our findings indicate that combined analysis of expression
and sequence data leads to refined lists containing a core set
using expression experiments in multiple species, including
immune response and circadian rhythm.
Materials and methods
Assigning cyclic status to genes
We applied a probabilistic graphical model to combine micro-
array expression data and sequence data for identification of
cycling genes, as described in Lu and coworkers [12]. We used
microarray expression data reported by Spellman [2], Rustici
[4], Oliva [5], Peng [6], Whitfield [1], and Menges [7] and
their coworkers. We downloaded protein sequences from the
National Center for Biotechnology Information website [41].
The method starts by using gene specific expression data to
compute a cycling score based on both the amplitude and
periodicity [13]. We run BLASTALL [42] to calculate bit
scores between all pairs of sequences, as was done by Sharan
and coworkers [43]. We use a Markov random field to model
the joint likelihood of the data. The (hidden) cycling status of
each gene is represented by a node in the graph, and two
nodes are connected by an edge if the bit score for the two
genes is above a threshold. We define potential functions on
nodes to capture information from the cycling scores, where
we assume the scores of cycling and the noncycling genes fol-
low a mixture of extreme value distributions, and define
potential functions on edges to capture the correlation of
cycling statuses between similar genes. The posterior beliefs
of the cycling status of the genes are estimated using loopy
belief propagation algorithm. Finally, we rank the genes by
their posterior and use the name number as were used in the
original papers (500 for Arabidopsis, 800 for budding yeast,
600 for fission yeast genes, and 1,000 human genes). See
Additional data file 3 for complete details.
Identifying conserved sets
Genes identified as cycling in each species were used to iden-
tify conserved sets of cycling genes. This is done using the
Markov clustering algorithm (MCL) [14] as follows. First, we
start with the graph of all cycling genes. Edges in the graph
are defined based on the bit score cut-off, as mentioned
above. Second, for any connected subgraph in this graph, we
use MCL to break it into smaller subgraphs if it has more than
30 nodes. Third, repeat the previous step until all connected
subgraphs have at most 30 nodes.
Next, we assign genes to different conserved sets based on the
other species represented in the subgraph to which they
belong. The numbers of genes in the conserved sets are shown
in Figure 3, in which the sets are organized as a tree reflecting
the evolutionary relation between the four species.
Motif discovery
For each gene in the lists, the appropriate intergenic regionGenome Biology 2007, 8:R146
of system specific genes. Although we have focused here on
the cell cycle, such an analysis can be carried out to study a
number of other biologic systems that have been profiled
was extracted from the budding or fission yeast genome. Four
motif finders were run on each dataset: SOMBRERO [24,25],
Consensus [27], BioProspector [26], and AlignACE [28]. Both

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atiohttp://genomebiology.com/2007/8/7/R146 Genome Biology 2007, Volume 8, Issue 7, Article R146 Lu et al. R
SOMBRERO and BioProspector require a background model,
and the background was constructed from all intergenic
regions (in the appropriate genome) for both cases. SOM-
BRERO was run using default settings, and simultaneously
using all known yeast motifs as an appropriate source of prior
knowledge. Consensus and BioProspector were run using
default settings requiring the top ten motifs to be reported.
AlignACE was run using default settings (using a seed motif
length of 10), and provided with the background intergenic
GC content (31.45% for fission and 35.3% for budding). See
Additional data file 3 for further details.
P value analysis
GO enrichment P values were computed using STEM [44],
which relies on hyper-geometric distribution. Corrected P
values were computed by permutation analysis using STEM.
Additional data files
The following additional data are available with the online
version of this manuscript. Additional data file 1 provides
supporting figures. Additional data file 2 provides supporting
tables. Additional data file 3 provides further details regard-
ing the methods used and results generated.
Additional data file 1Supporting figuresProvided are s pporting figures.Click h re for file 2t bl s tables.3methodologic detailsmethodologic details.
Acknowledgements
This work was partially supported by NSF CAREER award 0448453 to ZBJ,
by NSF Grant 0225656, and by a tobacco settlement grant from the Penn-
sylvania Department of Health.
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