Published online before print
August 12, 2003, 10.1101/gr.1649303
Genome Res. 13:1998-2004, 2003
©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00
The Human Transcriptome Map Reveals Extremes in Gene Density, Intron Length, GC Content, and Repeat Pattern for Domains of Highly and Weakly Expressed Genes
Rogier Versteeg,1,6,
Barbera D.C. van Schaik1,2,
Marinus F. van Batenburg4,5,
Marco Roos1,
Ramin Monajemi2,
Huib Caron1,3,
Harmen J. Bussemaker5 and
Antoine H.C. van Kampen2
1 Department of Human Genetics, Academic Medical Center, University of
Amsterdam, 1100DE Amsterdam, The Netherlands
2 Department of Bioinformatics, Academic Medical Center, University of
Amsterdam, 1100DE Amsterdam, The Netherlands
3 Department of Paediatric Oncology/EKZ, Academic Medical Center, University
of Amsterdam, 1100DE Amsterdam, The Netherlands
4 Swammerdam Institute for Life Sciences, University of Amsterdam, The
Netherlands
5 Department of Biological Sciences and Center for Computational Biology and
Bioinformatics, Columbia University, New York 10027, USA
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ABSTRACT
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The chromosomal gene expression profiles established by the Human
Transcriptome Map (HTM) revealed a clustering of highly expressed genes in
about 30 domains, called ridges. To physically characterize ridges, we
constructed a new HTM based on the draft human genome sequence (HTMseq).
Expression of 25,003 genes can be analyzed online in a multitude of tissues
(http://bioinfo.amc.uva.nl/HTMseq).
Ridges are found to be very gene-dense domains with a high GC content, a high
SINE repeat density, and a low LINE repeat density. Genes in ridges have
significantly shorter introns than genes outside of ridges. The HTMseq also
identifies a significant clustering of weakly expressed genes in domains with
fully opposite characteristics (antiridges). Both types of domains are open to
tissue-specific expression regulation, but the maximal expression levels in
ridges are considerably higher than in antiridges. Ridges are therefore an
integral part of a higher order structure in the genome related to
transcriptional regulation.
About 120 Serial Analysis of Gene Expression (SAGE) libraries with 6
million transcript tags are available publicly and provide a quantitative mRNA
expression profile for a range of tissues and cell lines
(Velculescu et al. 1995 ;
Lal et al. 1999). Integration
of these data with the radiation hybrid map of the genome has visualizedthe
expression profiles of 19,000 genes in the context of their chromosomal map
position (Caron et al. 2001).
Highly expressed genes were foundto cluster in about 30 ridges. Some of these
showed a high gene density per centiRay, but this analysis was limited by the
inherent errors and ambiguities of the human radiation hybrid map. Ridges have
an overall high expression in all analyzed tissues, as confirmed in two recent
studies (Fujii et al. 2002;
Lercher et al. 2002). These
data raised the question as to whether ridges represent domains with a higher
order structure related to transcriptional regulation.
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RESULTS
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To analyze the genomic structure of ridges, we designed a Human
Transcriptome Map based on the draft human genome sequence (HTMseq). Briefly,
the databases of the public human genome project
(Lander et al. 2001) as
provided by the UCSC Genome Project
(http://genome.ucsc.edu/)
were integrated with mRNA expression levels as established by SAGE (see
Supplemental Information, available at
www.genome.org,
for details). The UCSC database lists all identified transcripts with a
genomic map position. We related these transcripts to UniGene clusters. The
SAGE tags for 52,256 of these UniGene clusters were identified using AMCtagmap
(Caron et al. 2001). Many
UniGene clusters map to multiple genomic positions due to the occurrence of
pseudogenes and the erroneous clustering of unrelated transcripts in UniGene
clusters. We designed a procedure to split UniGene clusters according to
chromosomal positions, resulting in a total of 70,751 map positions.
Individual transcripts were subsequently assigned to those split clusters that
have a matching genomic sequence. UniGene clusters were also frequently found
to overlap. We considered overlapping clusters for which we confirmedan
identical strandassignment to be fragments of one larger gene and merged them.
Finally, UniGene clusters without intron–exon structure were rejected as
potential pseudogenes. This procedure resulted in a chromosomal map with
25,003 transcriptional units (TUs), for which the expression levels are known
from SAGE libraries. This sequence-based version of the Human Transcriptome
Map (HTMseq) is available as a Web application
(http://bioinfo.amc.uva.nl/HTMseq).
Expression can be monitored for whole chromosomes
(Fig. 1, left graph) or for any
chromosomal subregion (Fig.
4A,B, below) and expression levels can be analyzed for 120 SAGE
libraries from a range of tissues or for combinations of SAGE libraries of a
specific tissue. The map serves as a powerful tool for analyzing expression
profiles of chromosomal regions implicated in cancer or tissue-specific gene
expression in a chromosomal context.
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Figure 1 Profiles of gene expression, gene density, inverse intron length, GC
content, SINE density, and inverse LINE density of human chromosome 9. The
left color chart shows expression data from 62 SAGE libraries of
50,000 or more transcript tags. Each horizontal line is a transcription unit
(TU). Vertical rows are individual SAGE libraries. The levels of expression
are given by a color code, ranging from zero to the here chosen maximum of 25
or more tags/100,000 transcript tags in a library; Green profiles: moving
medians of the gene expression levels, gene density, inverse intron length, GC
content, SINE density and inverse LINE density at window size 49. Expression
levels are taken from all combined SAGE libraries, totaling to 5,959,046 tags
(SAGE all tissues). See Figure 6 of the Supplemental Information for profiles
of all human chromosomes.
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Figure 4 Genes in ridges and antiridges can have tissue-specific expression
patterns. (A) Expression pattern of 25 genes in a ridge on chromosome
9. The region of the ridge is indicated in
Figure 1 by a red bar. The
figure is taken from the HTMseq Web site and represents the concise view level
of the application. Each horizontal line gives the TU number, the start
nucleotide position of the gene on chromosome 9, the level of expression of
the gene in 16 tissue-type SAGE libraries, and the description line of the TU.
The level of expression is given by a color code ranging from 0–25
tags/100,000 tags in the SAGE library (see color scale at top). The
SAGE libraries shown are (1) brain normal; (2) brain normal cerebellum; (3)
brain normal white matter; (4) breast normal; (5) colon normal; (6) heart
normal; (7) kidney normal; (8) leucocyte normal; (9) liver normal; (10) lung
normal; (11) ovary normal; (12) pancreas normal; (13) peritoneum normal; (14)
prostate normal; (15) vascular endothelium normal; (16) all normal tissues;
(B) The same analysis for an antiridge on chromosome 9 (indicated by
blue bar in Fig. 1). The SAGE
libraries are as indicated above.
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Ridges Are Characterized by High Gene Density, High GC Content, and
Short Introns
The chromosomal expression profiles of SAGE libraries established by the
HTMseq confirmed the previously observed clustering of highly expressed genes.
Figure 1 (left) shows the
expression profile of chromosome 9 in 62 SAGE libraries of at least 50,000
transcript tags each, generated from many different tissues. To visualize
ridges, we computed median expression levels in moving windows of consecutive
genes along the chromosomes (Caron et al.
2001). We initially analyzed the patterns for window size 49 (w =
49). Regions of high gene expression are clearly visible, as shown for
chromosome 9 in Figure 1 (for
other chromosomes, see Fig. 6 in the Suppl. Information). The clustering is
highly significant (P < 10–20, see
below), confirming the organization of the genome in ridges. The initial
analysis of the human genome sequence
(Lander et al. 2001) revealed a
statistical relationship between gene density and GC content, whereas GC-rich
genomic sequences were also found to correlate with short intron length. We
therefore calculated the gene density, average intron length per gene, and GC
content in blocks of 20,000 bp encompassing each gene. The moving medians of
the gene density and GC content (w = 49) show a striking similarity to the
patterns of gene expression (Fig.
1; Suppl. Information). The expression patterns are also similar
to the inverse of the intron length, implying that genes in ridges have very
short introns compared with genes outside of ridges
(Fig. 1). We found a
significant positive correlation between gene expression and gene density
(P < 10–45), GC content (P
< 10–211), and (inverse) intron length
(P < 3.10–12; Spearman rank
correlation test; Press et al.
1997). This correlation was established for individual genes (w =
1) and was even more significant for higher window sizes. Two-dimensional
histograms of SAGE rank versus gene density rank (w = 49) suggest that the
correlation is not uniform over the range of both quantities, but that the
main contributions come from the high andlow extremes
(Fig. 2A). The same holds for
inverse intron length and GC content (Fig.
2B,C). Accordingly, ridges show a significant enrichment for genes
with short introns, high gene density, or high GC content (all P
values < 10–25 in a Kolmogorow-Smirnov test,
see Fig. 8 of the Suppl. Information).
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Figure 2 Analysis of the rank correlation between median gene expression and median
GC content (A), median inverse intron length (B), median
gene density (C), median SINE density (D), and median
inverse LINE density (E). The shade of each square corresponds to the
number of genes. The values obtained for window size 49 are shown.
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Visualizing Position and Length Scale Simultaneously in
Ridgeograms
Significant clustering of highly expressed genes can result either from
clustering of a large number of genes with a moderately high expression, or
from clustering of a more limited set of genes with an extremely high
expression. We developed a method for identifying ridges that takes this
variability into account by considering all window sizes simultaneously (for
details see Suppl. Information). We computed median expression levels of all
window sizes up to the largest possible size (viz. the number of TUs on a
given chromosome). For each window size, we determined a threshold value above
which the moving median is significantly higher than expected for a random
distribution of genes along the genome. We imposed a false discovery rate
(Benjamini and Hochberg 1995)
of 5% on the basis of sampling over a large series of genomes with randomly
permuted gene orders. Figure 3A
shows ridgeograms in which the red regions summarize the location of moving
medians above the thresholds for each window size. They horizontally give the
gene order on each chromosome and vertically show the window size increasing
from 19 to the maximal window size. In the range of window sizes from
19–59 genes, we find 1359 distinct TUs with a median expression value
above threshold, which is highly significant for a nonrandom distribution of
highly expressed genes along the genome (P <
10–20, see Methods). Chromosome 3 contains an
example of a ridge consisting of a limited number of genes with very high
expression, whereas the ridges on chromosome 14 have more moderate expression
levels, but consist of a very large number of genes. Chromosome 6 has a
pronounced ridge characterized by both very high gene expression and a large
number of genes.
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Figure 3 Identification of chromosomal regions of significant extremes in gene
expression, gene density, intron length, and GC content. (A)
Ridgeograms of the gene expression of all human chromosomes showing the ridges
(red) and antiridges (blue) as function of window size. The horizontal axis of
each triangle gives the position of the TUs along each chromosome
(left, top of the p arm; right, distal end of the q arm).
The vertical axis gives the window size, increasing from window size 19 until
the maximum possible window size for each chromosome; (B) Ridgeogram
of chromosome 6 together with significance plots of extremes in GC content,
inverse intron length and gene density.
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Weakly Expressed Genes Cluster in Antiridges
We used the same procedure to analyze whether genes with a very weak
expression level also cluster in the genome. We found430 genes (w =
19–59) with a moving median of significantly decreased expression
(antiridges, indicated in blue in Fig.
3A). Chromosomes 4, 8, 13, and 18 have a significantly decreased
expression as a whole. The median expression of genes in ridges is 16 times as
high as in antiridges (128 compared with 8 transcripts/gene/100,000
mRNAs).
We applied the same procedure to assess clustering of genes with short
introns, high GC content or regions of high gene density. For all three
entities, we find a significant clustering in the genome, as shown for
chromosome 6 in Figure 3B (red
regions). Also, all three opposite characteristics, that is, long introns, low
GC content, and low gene density, significantly cluster in the genome (blue
spots in Fig. 3A). Analysis of
all chromosomes (see Fig. 7 of the Suppl. Information) identified 4199 genes
in significantly gene denser domains than could be expected by chance (w =
19–59). These domains were 5.8 times gene denser than the genepoor
domains. The significant clusters of genes with short introns include 2801
genes (w = 19–59). Their average intron length is 4.1 times shorter than
in the clusters of genes with very long introns (11,915 bp. vs. 2879 bp.).
There are 8534 genes that significantly cluster in GC-rich domains (w =
19–59), illustrating that these domains are larger than the other
domains. Their average GC content is 50.0%, whereas the clusters of low GC
density show an average GC content of 39.8%. The patterns of the significant
clusters of extreme values for gene expression, gene density, intron length,
and GC content are highly similar (Fig. 7 of the Suppl. Information).
Ridges Are Enriched in SINE Repeats and Antiridges in LINE
Repeats
As a start, for a further analysis of sequence characteristics of ridges
and antiridges, we analyzed the type of repeat sequences in the domains. SINE
and LINE repeat densities were calculated in blocks of 20,000 bp. The repeat
density was related to each gene in these blocks and moving medians (w = 49)
along the chromosomes were calculated.
Figure 1 shows the values of
the SINE repeats and the inverse value for the LINE repeats for chromosome 9.
Ridges are clearly enriched for SINE repeats, but depleted for LINE repeats.
Similar profiles were found for the other chromosomes (see Fig. 6 of the
Suppl. Information). A significant positive correlation was found between
ranks of individual genes (w = 1) for gene expression and SINE repeat density
(P < 10–100) as well as (inverse) LINE
repeat density (P < 10–72; Spearman
rank correlation test; Press et al.
1997). Two-dimensional histograms of SAGE rank versus SINE density
rank and inverse LINE density ranks (w = 49) confirms the correlations
(Fig. 2D,E).
Ridges and Antiridges Are Open to Tissue-Specific Regulation
Consistent with our previous radiation-hybrid based analysis
(Caron et al. 2001), the HTMseq
shows that the median gene expression in ridges is high in all analyzed
tissues. This is evident from the expression pattern of the 62 SAGE libraries
of a range of tissues shown in the left panel of
Figure 1. We further visualized
this for chromosome 6 by analyzing the gene expression pattern (w = 49) for
six different compound SAGE libraries of specific tissues (Fig. 8 of the
Suppl. Information). All tissues show increased gene expression in the ridge
domain, and peaks are found in corresponding chromosomal regions. The high
median expression of genes in ridges in all SAGE libraries
(Fig. 1) suggests that their
products are required in bulk for basic cellular functioning. Clusters of
highly expressed genes were proposed to represent domains of housekeeping
genes (Lercher et al. 2002).
The HTMseq application enables close inspection of every chromosomal region
(Fig. 4). Detailed inspection
of a ridge on chromosome 9 (marked by a red bar in
Fig. 1) shows that individual
gene expression in 15 normal tissues can vary strongly from gene to gene and
from tissue to tissue (Fig.
4A). Antiridges show a similar pattern. Most genes in an antiridge
on chromosome 9 (blue bar in Fig.
1) are weakly or not at all expressed in normal tissues, but some
genes have a fair expression in one or more tissues
(Fig. 4B). This suggests that
ridges are not the exclusive domain of house-keeping genes, but encode
tissue-specific genes as well. This is further supported by a genome-wide
analysis of the variability in gene expression. In SAGE libraries of 50,000 or
more mRNA tags (n = 57), we determined the maximum expression level
of each gene observed in any library, as well as the average expression level
in all libraries. The moving medians (w = 49) of these two values for
chromosome 9 reveal two highly similar graphs, each following the pattern of
ridges and antiridges (Fig. 5).
Therefore, ridges appear to be open to normal regulation by tissue-specific
transcription factors.
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Figure 5 Both maximal and average gene expressions in a series of SAGE libraries
follow the pattern of ridges and antiridges. The profiles of average (blue)
and maximal (red) gene expression as found in 57 SAGE libraries of minimally
50,000 SAGE tags are shown for chromosome 9 (w = 49).
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DISCUSSION
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The significant clustering and correlation of extremes in gene expression,
gene density, intron length, GC content, and repeat density identified a
series of physical characteristics of ridges and defines a domain-like
structure in the genome. These analyses integrate some previously observed
statistical correlations between these parameters. The initial analysis of the
human genome sequence described correlations between GC content and intron
length and between GC content and gene density
(Lander et al. 2001). A study
of EST frequencies in dbEST showed that highly expressed genes have on average
shorter introns (Castillo-Davis et al.
2002). Our physical analysis of ridges integrates all of these
parameters in one higher order structure of the genome, closely related to
gene expression regulation. The evolutionary age of ridges is unknown, as they
have only been analyzed in the human genome. A similar analysis relating SAGE
expression profiles to the yeast genome did not identify a ridge-like
organization (Velculescu et al.
1997). Higher order structures implicated in gene regulation were
observed in Drosophila, but they regarded spatial clusters of 10 to
30 genes with a dynamic expression pattern that correlated in different
developmental stages and conditions
(Spellman and Rubin 2002).
Smaller clusters of mainly two or three adjacent genes with a correlated
expression in varying tissues were identified in yeast
(Cohen et al. 2000) and
Caenorhabditis elegans (Roy et
al. 2002). Such clusters, therefore, can have a high overall
expression in one tissue, and a weak overall expression in another tissue. One
of the remarkable observations of the human transcriptome map is that the
patterns of highly and weakly expressed chromosomal regions are similar in all
analyzed tissues. This does not exclude that spatial clusters of co-regulated
genes also exist in the human genome, but we have not addressed this question
in this study.
A major question arising from the physical characterization of the human
ridges is how the expression levels in the domains are established. Two
extremes can be envisaged, which are not mutually exclusive. In the simplest
scenario, genes in ridges are autonomous and exclusively controlled by their
own cis-acting regulatory sequences. This possibility is in line with
a classical view on the basis of early transgenic mice studies: Expression of
a  -globin transgene under control of its own locus control region was
equivalent to the endogenous gene, independent of the chromosomal integration
site (Talbot et al. 1989).
When extrapolated to genes in general, ridges should represent clusters of
highly expressed genes that are individually controlled by their own
regulatory sequences. This model predicts that their clustering during
evolution functioned to streamline posttranscriptional events like splicing
and mRNA transport. Space constraints in the densely populated ridges could
have limited intron lengths. However, the view that gene expression is solely
regulated by cis-acting elements and independent of nuclear position
is based on studies of a few genes only and -globin may not be
representative for the majority of genes, as it is, when active, one of the
most highly expressed genes in the genome (cf. SAGE library GSM709 at
http://www.ncbi.nlm.nih.gov/SAGE/,
in which -globin represents 4% of all transcript tags).
The alternative view brought forward by the finding that transcriptional
domains are related to major physical parameters of the genome, is that ridges
globally govern the expression levels of their embedded genes. At the same
time, we observe that the expression level of genes within ridges is variable
and can be regulated in a tissue-specific way. This would imply a dual
regulation of expression levels. Expression levels would be determined by
specific transcription factors controlling individual genes, as well as by
factors that exert their control at the level of ridges. Such factors could
govern chromatin conformation, position in the nucleus, or local
concentrations of general transcription factors.
The chromosomal profiles show a striking enrichment of ridges for SINE
repeats, whereas they are depleted for LINE repeats. A correlation between
SINE repeat density, GC content, and gene density was observed previously, as
well as a correlation between LINE density and AT-rich regions (for review,
see Lander et al. 2001). The
depletion of LINE repeats in the GC-rich ridges and the increased density in
AT-rich regions can mechanistically be explained by the preferred cleavage
site of LINE endonuclease (TTTTA) and functionally by describing LINE repeats
as endo-symbionts that avoid deleterious integrations in gene-dense ridge
domains. However, the concordance of SINE-rich domains and ridges is less easy
to interpret. Although the evolutionary age of ridges is unknown, the
co-occurrence of a series of elementary chromosomal parameters in ridges
suggests them to be a fundamental structure in the genome, which we expect to
be evolutionarily older than the relatively young SINE elements of the genome.
Alu repeats represent the majority of SINE elements and most of them
integrated <75 Mya (Lander et al.
2001). If ridges are more ancient structures, why are they
targeted by SINE repeats? Interestingly, the HIV virus was found recently to
have a strong preference for integration in vivo in gene-dense regions and the
map of the integration sites suggests a pattern corresponding to ridges
(Schroder et al. 2002). HIV
integration in vitro follows a random pattern, indicating that the preferred
integration in vivo in gene-dense domains is not sequence dependent, but a
result of in vivo properties of the genome, like chromatin conformation or
transcriptional activity (Schroder et al.
2002). A similar mechanism may explain frequent SINE integrations
in ridges. A further analysis of repeat-subtypes in ridges and antiridges, as
well as an analysis of the evolutionary age of ridges, is necessary to
understand these patterns. The HTMseq enables the analysis of transcriptional
domains at the sequence level, which might identify sequence elements with a
regulatory role in maintaining these higher order structures and in the
organization of long-range transcriptional domains. In addition, the HTMseq
provides a powerful tool to analyze cancer-specific gene expression in a
chromosomal context.
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METHODS
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(See Supplemental Information)
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Acknowledgements
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This research was supportedby grants from the Netherlands Organization for
Scientific Research (NWO BMI 050.50.201), the A. Meelmeijer fund ,and the
Stichting Kindergeneeskundig Kankeronderzoek (SKK). H.J.B. was partly
supported by NIH grant LM007276.
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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Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.1649303. Article published online
before print in August 2003.
[Supplemental material is available online at www.genome.org. The HTMseq
application is available online at http://bioinfo.amc.uva.nl/HTMseq].
6 Corresponding author.
E-MAIL
R.Versteeg{at}AMC.UVA.NL;
FAX 0031-20-6918626. 
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Received March 12, 2003;
accepted in revised format June 16, 2003.
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