Published online before print
April 14, 2003, 10.1101/gr.737703
Vol 13, Issue 5, 875-882, May 2003
LETTER
Genomic Gene Clustering Analysis of Pathways in Eukaryotes
Jennifer M. Lee and
Erik L.L. Sonnhammer1
Center for Genomics and Bioinformatics, Karolinska Institutet, S171
77 Stockholm, Sweden
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ABSTRACT
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Genomic clustering of genes in a pathway is commonly found in
prokaryotes due to transcriptional operons, but these are not present
in most eukaryotes. Yet, there might be clustering to a lesser extent
of pathway members in eukaryotic genomes, that assist coregulation of a
set of functionally cooperating genes. We analyzed five sequenced
eukaryotic genomes for clustering of genes assigned to the same pathway
in the KEGG database. Between 98% and 30% of the analyzed pathways in
a genome were found to exhibit significantly higher clustering levels
than expected by chance. In descending order by the level of
clustering, the genomes studied were Saccharomyces cerevisiae,
Homo sapiens, Caenorhabditis elegans,
Arabidopsis thaliana, and Drosophila melanogaster.
Surprisingly, there is not much agreement between genomes in terms of
which pathways are most clustered. Only seven of 69 pathways found in
all species were significantly clustered in all five of them. This
species-specific pattern of pathway clustering may reflect adaptations
or evolutionary events unique to a particular lineage. We note that
although operons are common in C. elegans, only 58% of the
pathways showed significant clustering, which is less than in human.
Virtually all pathways in S. cerevisiae showed significant
clustering.
Cotranscription of genes in operons, which is the
norm in prokaryotes, has not been observed in most eukaryotes. The
known exceptions are nematodes and trypanosomes, but these operons are
different from prokaryotic operons in terms of both mechanism and gene
content (Blumenthal 1998 ). Genes in prokaryotic operons are frequently
found to be functionally related and involved in the same pathway
(e.g., Lawrence 1997; Overbeek et al. 1999).
There are however numerous examples in prokaryotes of functionally
related nonhomologous genes that are found in close proximity in the
genome even when not part of an operon. Overbeek et al. (1999)
identified clusters that are conserved between bacterial genomes of
functionally related genes. Kolesov et al. (2001) developed a method
called SNAP (similarity-neighborhood approach) that uses orthology
relations between genomes together with neighbor relations within a
genome to infer cycles of functionally related genes. A correlation
between gene proximity and function was also observed by Yanai et al.
(2002), who discovered that pairs of genes that are adjacent in
multiple genomes are often functionally related, also in
genomes where they are not adjacent.
Lathe et al. (2000) determined that certain pairs of genes were found
adjacent to each other in a broad range of bacterial species. Although
rearrangements of gene order occurs regularly in bacteria, gene
neighborhoods tend to be somewhat conserved. A set of functionally
related genes maintained in close proximity in the genome was termed
‘uber-operon.’ Ribosomal genes in bacteria were found to be organized
in uber-operons. Each ribosomal gene had other ribosomal genes as
neighbors in all 15 genomes studied, although the complement of genes
found in the uber-operons in each genome were not necessarily the same
as those found in other genomes. Two other functionally related groups
of genes were found in uber-operons as well. Lathe et al. (2000) showed
that uber-operons can be used to correctly predict function of
‘hypothetical’ proteins.
Operons that have been detected in eukaryotes differ from those in
prokaryotes in that eukaryotic polycistronic mRNAs are not translatable
directly as in prokaryotes (Kozak 1999). Polycistronic mRNAs have been
detected in Trypanosomes (Johnson et al. 1987) and C.
elegans (Spieth et al. 1993) but are processed into monocistronic
mRNA before translation occurs. Zorio et al. (1994) estimated that
about 25% of C. elegans genes are organized in operons. They
have observed trans-splicing to two leader sequences in the C.
elegans genome. Blumenthal (1998) found that genes in operons are
frequently due to tandem duplication and are similar at the sequence
level as well as being involved in similar functions. However, genes
have also been found in C. elegans operons that are not
similar at the sequence level but are known to be functionally related
(Blumenthal 1998). Although polycistronic transcription units have yet
to be found in yeast, Zhang and Smith (1998) found functionally related
and adjacent genes. Such gene pairs are generally controlled by the
same promoter region, located between the two genes on opposite
strands. In addition, Kruglyak and Tang (2000) ascertained
that there is a much higher correlation of expression patterns for
adjacent genes in yeast than for randomly selected genes.
It thus seems that in most eukaryotes, the transcription factor
machinery is sufficient for ensuring coregulation, and that
colocalization in the genome is not a general requirement.
Consequently, there should be no selection for lining up coregulated
genes directly next to each other in the genome. However, there could
exist some degree of selection for keeping coregulated genes in the
same region of a chromosome, for instance to make them available to
transcription more efficiently as a group. This is supported by the
fact that coregulated human genes are often linked functionally in
"synexpression" groups (Niehrs and Pollet 1999). In addition, genes
which are coexpressed may be found clustered in eukaryotes. Highly
expressed genes in humans have been found to be colocalized in the
genome (Caron et al. 2001). Spellman and Rubin (2002) determined that
about 20% of Drosophilia genes fall into clusters of
coexpressed groups.
We wanted to use the currently available eukaryotic genome sequences to
investigate the levels of clustering within pathways. The research
presented here is based on genes in metabolic pathways as defined in
the Kyoto Encyclopedia for Genes and Genomes (KEGG), with missing
enzymes filled in by homology. The species included in this study were:
Homo sapiens, Caenorhabditis elegans, Drosophila
melanogaster, Arabidopsis thaliana, and Saccharomyces
cerevisiae. We analyzed which pathways are significantly clustered
in each species, and whether the clustered pathways are the same in
different species. The clustering level was measured by calculating the
overall degree of gene colocalization compared to random. The
‘clusters’ are thus not necessarily compact, but may correspond to
rather large regions with high concentrations of pathway members,
although nonmembers may also be present in such regions. In
addition we propose a procedure to delineate actual clusters of genes,
but do not prove those here. The clustering statistics and other
auxiliary information can be found at
ftp://ftp.cgb.ki.se/pub/data/pathwayclusters.
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RESULTS
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The pathways used in this study are the metabolic pathways in KEGG
that include genes from at least one of the five species investigated
here. Pathways that were incomplete in a genome were "repaired" by
searching for homologs of the missing genes known in other species,
using a stringent cutoff. We collected data for 105 different pathways
in total. The number of pathways with data in each individual genome
ranged from 79–98 (see Table 1).
To determine whether genes in a pathway are found in closer proximity
than expected by chance, we developed a formula for calculating a
clustering score. Such a formula needs to fulfill a set of criteria:
(1) The score should increase monotonously with increased proximity,
(2) the score between genes on different chromosomes needs to be
defined and to be the same as the average score between two randomly
placed genes on an average chromosome, and (3) the score should be
normalized for genome and pathway size to allow comparative studies. To
estimate the significance of the observed average clustering score of a
pathway in a genome, it was compared to the distribution of 200
iterations of placing the same number of genes on randomly picked known
gene positions. Friedman and Hughes (2001) used a similar approach for
determining whether observed gene patterns are expected by chance.
A potential source of overestimating the amount of clustering is the
fact that genes frequently undergo tandem duplications. In our
analysis, such tandem pairs would be assigned to the same pathway if
the duplication was recent, yet would not necessarily reflect
functional coupling. Both genes may have the same function, or one gene
may not have any function (pseudogene). Such cases of recent tandem
duplication can be omitted from the analysis by counting any stretch of
adjacent genes with the same EC number and greater than 60% identity
as one gene. We call this mode of analysis "merged", and present
both merged and unmerged results. The merged results should be seen as
a conservative estimate, as some neighbors may be truly distinct genes
in a pathway.
Table 1 shows the number of pathways found to be significantly
clustered in each genome. We used three times the distance between the
3rd quartile and the median of the random cluster score distribution as
the threshold for significance. This approach was used because it makes
no assumptions about the shape of the distribution. As seen in Figures
1 and 2, a more
stringent significance criterion would have produced essentially the
same result, because most significantly clustered pathways are far
above the cutoff. To verify that the result is not expected by chance,
we randomized the gene content of each pathway such that the gene
number was kept constant but the gene locations were picked randomly
from all genes in the organism. Between 10% and 12% of the
randomized pathways reached our significance cutoff, whereas the
observed fractions of significantly clustered pathways ranged between
30% and 98% (both using merged data).
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Figure 1. Clustering results for four pathways, illustrating different levels in
clustering score observed among different genomes, pathways, and
merged/unmerged data. Pathway 00361 is significantly clustered in all
species, whereas the other pathways show varying degrees of clustering.
In S. cerevisiae, all but two pathways showed significant
clustering (using merged data). Note that the scale of the clustering
score is logarithmic.
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Figure 2. Distribution of pathway clustering scores (using merged data). The
x-axis represents how much higher from the median the score is
in terms of difference between the median and the 3rd quartile. Also
shown is the score distribution from randomly chosen pathways (from all
organisms) in which the genes were picked randomly from all genes in
the particular organism.
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The number of pathways with significant clustering is consistently
lower using merged data. In yeast there is only a 2% difference,
whereas in human, worm, and Arabidopsis the drop is more
substantial, yet no more than about one-quarter of the original number.
In fly we see the largest effect, with 40% of the clustered pathways
dropping to an insignificant clustering score in the merged data set.
Differences were found between pathways as well. Data from a few
selected pathways are shown in detail in Figure 1. The distribution of
clustering scores expressed as 3rd quartile-median ranges (3QMRs) for
all pathways and genomes is shown in Figure 2. For yeast, human, and
worm, the bulk of the pathways fall in very significant regions.
Plots of actual gene locations in a pathway are shown in Figure
3, illustrating the difference between two
pathways with the same number of genes, one with significant clustering
and one without. In cases like this, it can be useful to define loose
clusters of genes as groups that form a string with genomic distances
less than a threshold value. We calculated such thresholds individually
for each genome by counting distances for 1000 pairs of randomly placed
genes on the same chromosome. These pairwise distances appeared to
approximate a normal distribution, and we determined the average and
standard deviation for a random pair of genes in each genome (Table
2). To define a loose cluster of genes, we
used the average random distance minus three standard deviations as a
threshold (99% significance level). Such loose clusters are shown for
two pathways in Figure 3. It is clear that the clustering is a
relatively diffuse large-scale phenomenon, and not immediately striking
to the eye, but after closer inspection the significantly clustered
pathway does contain many more nearby genes than the insignificant one.
The degree of clustering measured as fraction of genes in loose
clusters in significantly and insignificantly clustered pathways for
the five species was: H. sapiens: 50% / 18%; D.
melanogaster: 68% / 45%; C. elegans: 74% / 44%;
A. thaliana: 52% / 44%; S. cerevisiae:
37% / 0%.
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Figure 3. Distribution of genes in pathways with and without significant
clustering in A. thaliana. Pathway 00360 has a highly
significant clustering score, whereas pathway 03020 does not. Merged
data were used. The boxes represent putative loose gene clusters.
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Recent tandem duplications sometimes gave rise to extremely high
clustering scores. In most cases when a pathway had an unmerged cluster
score higher than 1000, this number dropped substantially after
merging, although not necessarily to an insignificant level. Hence
recent tandem duplication can only be partially responsible for the
high levels of clustering observed.
To what extent is clustering of a pathway conserved between different
genomes? Although pathway data are inherently incomplete, we have
enough coverage to analyze crude patterns of shared clustering. Table
3 shows the number of pathways shared among
different genomes, divided into categories of the total number of
genomes containing the pathway. Of the 69 pathways found in all five
genomes, only seven were significantly clustered in all of them. (Using
unmerged data, 18 of them were clustered.) A complete list of pathways
including which species we have data for, number of genes in the
pathway, and clustering scores can be found on the Web site table.
Trusting the merged data more, we conclude that universally clustered
pathways are relatively rare. The seven universally clustered pathways
are: glycolysis, aminoacyl-tRNA biosynthesis, ATP synthase, DNA
polymerase, hexachlorocyclohexane degradation, cyanoamino acid
metabolism, and photosynthesis (which in KEGG includes ATP synthesis,
explaining why it can be found in all organisms).
It may be expected that pathways with many genes exhibit higher
clustering scores than pathways with few members. This should not have
an effect when comparing to the randomized clustering scores, but could
influence the relative comparison of pathways. A plot of gene number in
the pathway versus its clustering score (Fig.
4) indicates that no clear correlation
between them exists; there are pathways with high scores and few genes
as well as pathways with low scores and many genes.
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Figure 4. Relationship between clustering score per gene and number of genes in
the pathway (merged data).
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DISCUSSION
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Operons or clusters of genes which are linked functionally are found
frequently in prokaryotes. Identification of operons and uber-operons
has been documented in prokaryotes (Overbeek et al. 1999; Salgado et
al. 2000; Kolesov et al. 2001; Tamames 2001). Salgado et al. (2000)
found that they could predict genes which are in an operon by
functional relation of closely placed genes in bacterial genomes and,
in addition, found cases of genes which were not in an operon but were
adjacent, transcribed in the same direction (a directon),
and are functionally related. We have investigated genes involved in
pathways in eukaryotes to determine whether similar observations can be
detected here. In all genomes studied, we frequently found that genes
in a pathway are in closer proximity than would be expected by chance.
The genes that are ‘clustered’ are much more distant than those
detected in prokaryotes; however there may still be an advantage for
functionally related eukaryotic genes to be close in a genome even if
separated by other genes. In some cases, clustering of functionally
related genes appears to be due to recent tandem duplications. In a few
cases, merging tandemly duplicated genes (with the same EC number)
caused the clustering score to drop to insignificant levels, but
generally similar clustering scores were found in both data sets. In
other words, most genes in a pathway that are adjacent in the genome do
not come from recent tandem duplications. Some of the observed
clustering effect could stem from local duplications followed by
functional divergence. However, given the generally high motility of
genes in a genome, this effect can hardly explain the high levels of
clustering observed; it seems instead that the clustering of pathway
members must be a selected trait. In further support of this, there is
no detectable correlation between sequence similarity and clustering
score in our data (data not shown).
Lawrence (1999) proposed a selfish operon model, which suggests that
genes are clustered in operons to benefit the genes and not the
organism. In this regard, genes, especially in prokaryotes, may be
transmitted by both vertical and horizontal transfer and remain as an
intact functional unit. Glansdorff (1999) suggested that operons arose
in thermophilic bacteria, where it is important for proteins to complex
very quickly in order to become more stable, less toxic, or as a means
of facilitating protein interactions. An argument against the selfish
operon model is that operons are poorly conserved between different
organisms, and perhaps operons are simply selected for to promote
coregulation and efficient transcription and translation (Lathe et al.
2000).
Prokaryotic genomes tend to be small and do not need to be as tightly
packed as eukaryotic chromosomes. The eukaryotic genome requires
extensive compaction of DNA to fit into the nucleus. Packing is
accomplished by wrapping the DNA around histones and other proteins.
When genes are transcribed, a great deal of unpacking of regions of DNA
is required. The region of DNA which is unfolded contains a number of
genes, and keeping functionally related genes near, even if not
adjacent, may ease the burden of unpacking of DNA in the cell for
transcription. Evidence of such clusters of coexpressed genes have been
detected in human, fly, and worm. These clusters contain 10–30 genes
in fly and 2–5 in worm (Roy et al. 2002; Spellman and Rubin 2002).
Eukaryotic operons have been detected in various forms. In C.
elegans, the splice leader for the second gene in polycistronic
transcription units has only been detected in the genus
Caenorhabditis and not in other closely related genera (Spieth
et al. 1993). This may indicate that polycistronic transcription units
are a relatively novel feature in this genome and may have been
selected for in recent evolution. It does not appear to increase the
amount of genomic clustering of functionally linked genes, as C.
elegans pathways are not more clustered than in human.
Huynen et al. (2001) found that gene pairs are less conserved in
eukaryotes than prokaryotes, where a gene pair is defined as two
adjacent genes. In eukaryotes, conserved gene order was not found at a
higher rate than in randomized genomes when looking solely at adjacent
genes. Evidence obtained in the present study indicates that clustering
of genes may not be limited to adjacent genes, and that gene order
conservation studies should be expanded to include larger areas than
the immediate neighborhood.
In the present study the question remains as to why clustering of genes
in pathways may occur at a higher level in certain eukaryotes and less
in others. Specifically, why is clustering observed at a very high
level in S. cerevisiae and at a low level in D.
melanogaster? A study by Ranz et al. (2001) demonstrated that the
Drosophila genome shows a higher rate of chromosomal evolution
than other eukaryotes. This may contribute to more dispersion of gene
clusters than in other eukaryotes. Similar analysis of additional
eukaryotic genomes as they become available may shed more light on
questions raised by our research.
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METHODS
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Genomic Data
Collections of all known and predicted proteins for each species
in this study were taken directly from the genome sequencing sites in
February 2001 (Protein databases). The species included and databases
from which information was collected are: H. sapiens
(www.ensemble.org), C. elegans (www.wormbase.org), D.
melanogaster (www.flybase.org), A. thaliana
(www.arabidopsis.org), and S. cerevisiae
(genome-www.stanford.edu/Saccharomyces). For all proteins in each
genome, we stored the protein identifier, the genomic location, and the
sequence. We also created separate files of gene names and genomic
locations to facilitate search of genomic locations (Locations tables).
Pathway Data
Pathways were initially based on the metabolic pathways defined in
the KEGG (http://www.genome.ad.jp/kegg/kegg/html) for each organism
included in our analysis. Each step in a pathway is identified by an EC
number, which in turn is linked to genes in the individual organisms.
Because KEGG is not complete with all current genomic data, we searched
for missing genes in each pathway. This was accomplished by using genes
from each species known to be involved in each step of each pathway and
by using BLAST (Altschul et al. 1990) to search for homologs in our
collections of proteins from the other species involved in this study.
BLAST results were run through MSPcrunch (Sonnhammer and Durbin 1994)
with a cutoff of 60% Identity. The 60% cutoff was chosen after
analyzing rates of false-positive and false-negative assignments of EC
numbers at different levels of identity in BLAST matches. As shown in
Figure 5, a cutoff of 60% removes nearly
all matches (99.4%) between genes with different EC numbers across the
genomes studied. Many matches between genes with the same EC number
fall below this cutoff, but the bulk of these are distantly related
genes that by chance have the same function. We did not use ortholog
finding programs to identify enzymes in other species belonging to an
EC group, as many enzymes which perform the same function in different
species are not always detectable as orthologs. Sequences that were
previously not included and had matches above the cutoff to a sequence
in another species with the "missing" EC number were assigned this
EC number. This EC number was kept associated with the sequence in
further analyses of "repaired" pathways. The fractions of genes in
a pathway that came from such repair assignments ranged from 20% to
46%.
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Figure 5. Comparison of percent identity in BLAST matches of enzymes with the
same EC number to those with different EC numbers, across all organisms
and pathways in this study. Less than 0.6% of all pairs with different
EC numbers had an identity above 60%.
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Identification of Genomic Locations
Genomic locations were retrieved simply by searching the Location
tables for all genes in a pathway and appending the chromosome and
base-pair position information to the gene name and EC number. The
analysis following was then done in two different ways. Gene locations
as they were found were used, and in addition, genes with the same EC
and which are at least 60% identical which are located adjacently,
presumably from tandem duplication, were merged and counted as a single
gene. "Adjacent" was defined as a pair with no intervening genes on
either strand. All analysis following was performed with both the
merged and unmerged files.
Calculation of Pathway Clustering Score
The clustering score was determined by a pairwise analysis of all
genes in each pathway. For each pair we first determined whether the
genes were on the same chromosome. The score for pairs of genes on the
same chromosome was calculated with the following equation:
For genes on different chromosomes:
For genes on different chromosomes, the average length of the
two chromosomes was taken as a substitute for a real distance that
represents the maximum distance between genes on these chromosomes. We
divided the average chromosome length by the pairwise distance to
produce pair scores close to one for randomized locations and higher
scores for close genes. The pathway clustering score is the sum of all
pairwise scores divided by the number of genes in the pathway as
normalization. Although there are alternative ways to calculate
distances and clustering scores, the actual method should not matter
when comparing to randomized data. We chose this method for reasons of
convenience.
Comparison to Random
To determine whether the observed clustering score for each pathway
could be found by chance, "randomized pathways" were generated for
each pathway. Gene locations were randomly picked from all pathways in
the Location tables, but matched the actual number of genes in each
pathway and species. A clustering score was calculated with the
identical method as the actual pathways. Two hundred iterations of
random pathways were done to generate a distribution and determine the
1st and 3rd quartiles and the median.
Defining a Loose Cluster
We were able to group genes in each pathway into loose clusters by
calculating a clustering cut-off distance. This was accomplished by
extracting pairwise distances of neighboring genes on the same
chromosome from the Location tables, and determining the average
distance (see Table 2). All such distances generated approximately
normal distributions, hence we used the average distance minus three
standard deviations as a significant distance cut-off for loose gene
clusters. A loose cluster was defined as a group of pathway members in
which all member genes are closer than this cut-off. The corresponding
genomic region may well contain other genes that are not considered
members in the KEGG.
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WEB SITE REFERENCES
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www.genome.ad.jp/kegg/kegg/html; KEGG.
www.ensemble.org; ENSEMBL.
www.wormbase.org; WORMBASE.
www.flybase.org; FLYBASE.
www.arabidopsis.org; TAIR.
genome-www.stanford.edu/Saccharomyces; SGD.
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Acknowledgements
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This work was supported by a grant from Pharmacia
Corporation.
The publication costs of this article were
defrayed in part by payment of page charges. This article must
therefore be hereby marked "advertisement" in accordance with 18
USC section 1734 solely to indicate this fact.
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Footnotes
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1 Corresponding author. 
E-MAIL erik.sonnhammer{at}cgb.ki.se; FAX +46 8 337983.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.737703. Article published online before print in April 2003.
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Received August 28, 2002;
accepted in revised format January 28, 2003.
13:875-882 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00
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C. Hall and F. S. Dietrich
The Reacquisition of Biotin Prototrophy in Saccharomyces cerevisiae Involved Horizontal Gene Transfer, Gene Duplication and Gene Clustering
Genetics,
December 1, 2007;
177(4):
2293 - 2307.
[Abstract]
[Full Text]
[PDF]
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Y. Lu, X. He, and S. Zhong
Cross-species microarray analysis with the OSCAR system suggests an INSR->Pax6->NQO1 neuro-protective pathway in aging and Alzheimer's disease
Nucleic Acids Res.,
July 13, 2007;
35(suppl_2):
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[Abstract]
[Full Text]
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G. Yi, S.-H. Sze, and M. R. Thon
Identifying clusters of functionally related genes in genomes
Bioinformatics,
May 1, 2007;
23(9):
1053 - 1060.
[Abstract]
[Full Text]
[PDF]
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H. Inaoka, Y. Fukuoka, and I. S. Kohane
Evidence of spatially bound gene regulation in Mus musculus: Decreased gene expression proximal to microRNA genomic location
PNAS,
March 20, 2007;
104(12):
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[Abstract]
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D. Gebhart, A. K. Bahrami, and A. Sil
Identification of a Copper-Inducible Promoter for Use in Ectopic Expression in the Fungal Pathogen Histoplasma capsulatum
Eukaryot. Cell,
June 1, 2006;
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[Abstract]
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S. Harju, P. A. Navas, G. Stamatoyannopoulos, and K. R. Peterson
Genome Architecture of the Human {beta}-Globin Locus Affects Developmental Regulation of Gene Expression
Mol. Cell. Biol.,
October 15, 2005;
25(20):
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[Abstract]
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[PDF]
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P. E. Kovanen, L. Young, A. Al-Shami, V. Rovella, C. A. Pise-Masison, M. F. Radonovich, J. Powell, J. Fu, J. N. Brady, P. J. Munson, et al.
Global analysis of IL-2 target genes: identification of chromosomal clusters of expressed genes
Int. Immunol.,
August 1, 2005;
17(8):
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[Abstract]
[Full Text]
[PDF]
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R. J. Wisser, Q. Sun, S. H. Hulbert, S. Kresovich, and R. J. Nelson
Identification and Characterization of Regions of the Rice Genome Associated With Broad-Spectrum, Quantitative Disease Resistance
Genetics,
April 1, 2005;
169(4):
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[Abstract]
[Full Text]
[PDF]
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G. A. C. Singer, A. T. Lloyd, L. B. Huminiecki, and K. H. Wolfe
Clusters of Co-expressed Genes in Mammalian Genomes Are Conserved by Natural Selection
Mol. Biol. Evol.,
March 1, 2005;
22(3):
767 - 775.
[Abstract]
[Full Text]
[PDF]
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W.-M. Boon, T. Beissbarth, L. Hyde, G. Smyth, J. Gunnersen, D. A. Denton, H. Scott, and S.-S. Tan
A comparative analysis of transcribed genes in the mouse hypothalamus and neocortex reveals chromosomal clustering
PNAS,
October 12, 2004;
101(41):
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[Abstract]
[Full Text]
[PDF]
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S. A. Teichmann and R. A. Veitia
Genes Encoding Subunits of Stable Complexes Are Clustered on the Yeast Chromosomes: An Interpretation From a Dosage Balance Perspective
Genetics,
August 1, 2004;
167(4):
2121 - 2125.
[Abstract]
[Full Text]
[PDF]
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G. Blanc and K. H. Wolfe
Functional Divergence of Duplicated Genes Formed by Polyploidy during Arabidopsis Evolution
PLANT CELL,
July 1, 2004;
16(7):
1679 - 1691.
[Abstract]
[Full Text]
[PDF]
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E. J.B. Williams and D. J. Bowles
Coexpression of Neighboring Genes in the Genome of Arabidopsis thaliana
Genome Res.,
June 1, 2004;
14(6):
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[Abstract]
[Full Text]
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