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Science 291 (5507): 1289-1292

Copyright © 2001 by the American Association for the Advancement of Science

The Human Transcriptome Map: Clustering of Highly Expressed Genes in Chromosomal Domains

Huib Caron,12 Barbera van Schaik,13 Merlijn van der Mee,3 Frank Baas,4 Gregory Riggins,6 Peter van Sluis,1 Marie-Christine Hermus,1 Ronald van Asperen,1 Kathy Boon,1 P. A. Voûte,2 Siem Heisterkamp,5 Antoine van Kampen,3 Rogier Versteeg1

The chromosomal position of human genes is rapidly being established. We integrated these mapping data with genome-wide messenger RNA expression profiles as provided by SAGE (serial analysis of gene expression). Over 2.45 million SAGE transcript tags, including 160,000 tags of neuroblastomas, are presently known for 12 tissue types. We developed algorithms to assign these tags to UniGene clusters and their chromosomal position. The resulting Human Transcriptome Map generates gene expression profiles for any chromosomal region in 12 normal and pathologic tissue types. The map reveals a clustering of highly expressed genes to specific chromosomal regions. It provides a tool to search for genes that are overexpressed or silenced in cancer.

1 Department of Human Genetics,
2 Department of Pediatric Oncology, Emma Children's Hospital, Academic Medical Center, University of Amsterdam, Post Office Box 22700, 1100 DE Amsterdam, Netherlands.
3 Bioinformatics Laboratory,
4 Neurozintuigen Laboratory,
5 Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands.
6 Department of Pathology and Department of Genetics, Duke University Medical Center, Durham, NC 27710, USA.


GeneMap'99 (1) gives the chromosomal position of 45,049 human expressed sequence tags (ESTs) and genes belonging to 24,106 UniGene clusters. To obtain an expression profile of these genes, we made use of the SAGE technology and databases. SAGE can quantitatively identify all transcripts expressed in a tissue or cell line (2). It is based on the extraction of a 10-base pair (bp) tag from a fixed position in each transcript and the sequencing of thousands of these tags. Software programs and databases support the identification of the mRNAs corresponding to the tags in a SAGE library. However, this step is prone to errors, and tag assignment requires manual verification. The National Center for Biotechnology Information (NCBI) SAGEmap database has electronically extracted tags from mRNAs and ESTs in UniGene clusters. A manual check of 156 tags extracted from 30 UniGene clusters showed that wrong tags mainly stemmed from sequence errors in ESTs and from errors in their 5' and 3' orientations. We developed algorithms to select 3'-end clones of 713,489 ESTs assigned to UniGene clusters and identified their tags. Sequence comparison algorithms discarded tags caused by sequence errors while preserving tags from alternative transcripts or single nucleotide polymorphisms [see supplementary information for AMCtagmap details (3)]. We identified reliable tags for 18,954 of the 24,106 UniGene clusters mapped on GeneMap'99. Manual analysis of 287 tags extracted from 86 UniGene clusters from intervals of chromosomes 1 and 22 showed an error rate of 6.2% in our electronic tag identification algorithms. To check for errors in UniGene clustering, we verified tags on the available sequenced P1-derived artificial chromosomes (PACs) of the mapped markers and annotated them accordingly [see legend to Fig. 2 and supplementary information (3)].
Fig. 2. Extended interval view of a chromosome 2p region showing neuroblastoma-specific overexpression of the neighboring genes N-myc (UniGene Hs. 25960) and DDX-1 (UniGene Hs. 78580). A small part of the interval D2S287 to D2S2375 is shown. The left columns show the marker and centiray position as defined on GeneMap'99. The right side shows the UniGene number, tag sequence, and the description of the UniGene cluster. Expression levels in the libraries are normalized per 100,000 tags and shown by colored bars with a range from 0 to 15. Numbers give the tag counts per 100,000 tags. The tags are annotated by symbols. To identify tags produced by hybrid UniGene clusters, we analyzed for each marker of GeneMap'99 the corresponding PAC sequenced in the Human Genome Project, as well as two adjacent PACs. Tags that are present on these PACs are from ESTs belonging to the mapped marker and are marked by P in a light green box. Tags not present on these PACs are probably derived from a contaminating EST not belonging to the mapped marker and are marked by P in a red box [see Web site (4)]. This check is not yet available for all markers. Tags belonging to more than one UniGene cluster are marked by 2/3 or >3 in a yellow box. The expression levels of tags belonging to more than three clusters are not shown and are not used in the totals of the concise interval maps and the whole chromosome maps. Tags from ESTs of opposite orientation in the UniGene cluster are marked with AS in a purple box. [View Larger Version of this Image (20K GIF file)]

The Human Transcriptome Map [for Web site, see (4)] uses these tag assignments to relate 2.31 million tags in public SAGE libraries (NCBI SAGEmap database) (5) and 160,000 tags in our neuroblastoma SAGE libraries to the UniGene clusters mapped in GeneMap'99. The Human Transcriptome Map shows expression profiles for any chromosomal region in 12 tissue types. SAGE libraries of a specific tissue were combined into tissue-specific libraries (e.g., normal colon). We included tissues for which 100,000 or more tags were available, as most transcripts in a tissue are represented in a library of this size (6). Five libraries represent normal tissues (colon epithelium, brain, mammary gland, ovary, and prostate), and seven libraries represent tumor tissues (neuroblastoma, glioblastoma, medulloblastoma, and carcinomas of colon, ovary, breast, and prostate). The Human Transcriptome Map has three levels of resolution. The "whole chromosome view" shows gene expression per chromosome (Fig. 1). Each horizontal blue or red bar represents the expression level of a UniGene cluster. UniGene clusters mapped by several markers are shown only once, at the position of the highest reliability (1). The identity, map position, and precise expression of the genes are shown in the "concise interval view." The highest resolution is given by the "extended interval view," where expression levels are shown for all individual tags of a gene (Fig. 2).


Fig. 1. Whole chromosome view of expression levels of the 1208 UniGene clusters mapped to chromosome 11 on the GB4 radiation hybrid map of GeneMap'99. Each unit on the vertical axis represents one UniGene cluster. UniGene clusters mapped by several markers are only shown once, at the position of the highest lod score (the logarithm of the odds ratio for linkage). Only clusters for which we could extract a tag with our algorithms are included. Expression is shown for SAGE libraries of 8 out of the 12 available tissue types. Expression levels in the libraries are normalized per 100,000 tags. Expression levels from 0 to 15 tags are shown by horizontal blue bars. Tag frequencies over 15 are shown by red bars. The blue-only section to the right represents a moving median with a window size of 39 UniGene clusters generated from the expression levels in "all tissues." Green bars indicate RIDGEs. The boxed region shows the tissue-specific expression of a cluster of five metalloproteinases and two apoptosis inhibitors in normal breast tissue and breast cancer tissue. [View Larger Version of this Image (29K GIF file)]

The whole chromosome views reveal a higher order organization of the genome, as there is a strong clustering of highly expressed genes. Chromosome 11 has several large regions of high gene expression, interspersed with regions where gene expression is low (Fig. 1). This pattern is observed in all 12 tissues. An application of a moving median with a window size of 39 genes to the chromosome 11 map even more clearly visualizes the expression differences (Fig. 1, blue graph to the right). Most chromosomes show these clusters of highly expressed genes, which we call RIDGEs (regions of increased gene expression) (Fig. 3). A quantitative definition of RIDGEs is not straightforward, as there is a continuum from small to very large clusters. We analyzed whether RIDGEs can be explained by a random variation in the distribution of highly expressed genes among the 18,954 genes of the Human Transcriptome Map. When defined as regions in which 10 consecutive moving medians have a lower limit of four times the genomic median, we identify 27 RIDGEs (green bars in Figs. 1 and 3). The probability of observing this number of RIDGEs under a random permutation of the order of the 18,954 genes is very low [P = 10-12; see supplementary information (3)]. In addition, Bayesian statistical modeling without prior cluster definition showed that a model of nonrandom distribution provided the best fit with the observed clustering. These analyses show that RIDGEs most likely represent a higher order structure in the genome.


Fig. 3. Regional expression profiles for 23 human chromosomes show a clustering of highly expressed genes in RIDGEs. Expression levels are shown as a moving median with a window size of 39 genes. There are 74 regions with one or more consecutive moving medians that have a lower limit of four times the genomic median; 27 of them have a length of at least 10 consecutive moving medians (indicated by green bars). [View Larger Version of this Image (26K GIF file)]

Analysis of RIDGEs for physical characteristics suggests that many of them have a high gene density. Chromosome 18 is, on average, weakly expressed, and only 385 genes have been mapped to it on GeneMap'99. The equally large chromosome 19 consists of a succession of RIDGEs and harbors 937 mapped genes (Fig. 3). Although many human genes are still unmapped, the difference in gene density of chromosomes 18 and 19 is supported by CpG island density analyses (7). The correlation between RIDGEs and gene density is even more suggestive for chromosomes 3 and 6 (Fig. 4). The RIDGE on chromosome 6 corresponds to the major histocompatibility complex (MHC) region. A correlation between gene expression and density of mapped genes is found for 50 to 60% of the RIDGEs [Web fig. 1 (3)]. Typical RIDGEs count 6 to 30 mapped genes per centiray, compared to 1 to 2 mapped genes per centiray for weakly transcribed regions. In RIDGEs, average expression levels per gene are up to seven times that of the genomic average. This suggests that in RIDGEs, transcription per unit length of DNA is 20 to 200 times that in weakly expressed regions. About 40 to 50% of the RIDGEs are not gene dense. These RIDGEs preferentially map to telomeres, which is remarkable in light of the observed telomeric silencing in yeast (8, 9). Chromosomes 4, 13, 18, and 21 show an overall low gene expression and are devoid of RIDGEs (Fig. 3). The latter three chromosomes are responsible for most constitutional trisomies, suggesting that the low expression and low gene density could limit the lethality of an extra copy of them.


Fig. 4. Comparison of median gene expression levels and gene density for chromosomes 3 and 6. The left diagrams of each chromosome show the expression levels as a moving median with a window size of 39 UniGene clusters. The right diagram of each chromosome shows gene density. For each UniGene cluster, we calculated the average distance between adjacent clusters in a window of 39 adjacent UniGene clusters. The inverse of this value is shown (inverse centirays per gene). [View Larger Version of this Image (17K GIF file)]

The Human Transcriptome Map provides a tool to identify candidate genes that are overexpressed or silenced in cancer tissue. Neuroblastomas frequently show amplification of the distal chromosome 2p region, which targets the N-myc oncogene (10). Comparison of the whole chromosome views of chromosome 2p shows overexpression of two adjacent genes in neuroblastoma SAGE libraries. The extended interval view identifies these genes as N-myc and the often coamplified neighboring gene DDX-1 (Fig. 2). Therefore, global positional information of chromosomal defects is sufficient to identify candidate oncogenes (11). Also, tumor-specific down-regulation can be detected. Examples are a cluster of five matrix metalloproteinases on chromosome 11 [348 to 353 centirays (cR)] that are down-regulated in breast cancer tissue (Fig. 1, box); the E-cadherin tumor suppressor gene on chromosome 16 (406 cR) that is down-regulated in breast cancer tissue, as compared to normal breast tissue; and five carcinoembryonic antigen-related cell adhesion molecule genes on chromosome 19 (238 to 244 cR) that are down-regulated in colon carcinoma tissue, as compared to normal colon tissue (4).

Potential error sources in the Human Transcriptome Map are clustering errors in UniGene and the assignment of wrong tags to UniGene clusters. Our algorithms assign ~6.2% erroneous tags to UniGene clusters. The influence of these errors is probably attenuated. Assuming a total of 100,000 genes with 2 tags each, 200,000 tags would represent all human genes. Because there are >1 million variants of a 10-bp tag sequence, ~80% of the erroneously extracted tags will not match tags present in SAGE libraries and therefore will not influence overall expression profiles. However, individual tags and expression levels of UniGene clusters may harbor errors and require experimental confirmation. To test whether errors in UniGene clustering and mapping to GeneMap'99 may influence our observation of RIDGEs, we constructed a sequence-based expression map for the annotated chromosome 21 sequence and for a 4.3-Mb annotated contig of the MHC region on chromosome 6 (12, 13). Also, these maps showed that the MHC region is a pronounced RIDGE, whereas chromosome 21 is devoid of RIDGEs and has an overall weak gene expression [see Web fig. 4 for maps (3)]. Therefore, the higher order structure of the genome observed with the Human Transcriptome Map will largely be correct. The existence of RIDGEs is unanticipated, as a comparable SAGE-based transcriptome map for yeast showed an even distribution over the genome of highly and weakly expressed genes (8). Because the Human Transcriptome Map identifies different types of transcription domains, it can now be analyzed as to how they relate to known nuclear substructures, such as nuclear speckles, PML bodies, and coiled bodies (14-16). Definition of the position of tags to the full chromosomal sequences will further increase the resolution of the transcriptome map. Incorporation of the growing number of SAGE libraries from different tissues and various developmental stages will extend the overview of gene expression profiles in the human body.

REFERENCES AND NOTES

  1. P. Deloukas, et al., Science 282, 744 (1998) .
  2. V. E. Velculescu, L. Zhang, B. Vogelstein, K. W. Kinzler, Science 270, 484 (1995) .
  3. Supplemental Web material is available at www.sciencemag.org/cgi/content/full/291/5507/1289/DC1.
  4. The Human Transcriptome Map is available at http://bioinfo.amc.uva.nl/HTM/.
  5. A. Lal, et al., Cancer Res. 59, 5403 (1999) .
  6. V. E. Velculescu, et al., Nature Genet. 23, 387 (1999) .
  7. J. M. Craig and W. A. Bickmore, Nature Genet. 7, 376 (1994) .
  8. V. E. Velculescu, et al., Cell 88, 243 (1997) .
  9. D. E. Gottschling, O. M. Aparicio, B. L. Billington, V. A. Zakian, Cell 63, 751 (1990) .
  10. M. Schwab, et al., Nature 305, 245 (1983) .
  11. N. Spieker et al., Genomics, in press.
  12. The MHC Sequencing Consortium, Nature 401, 921 (1999) .
  13. M. Hattori, et al., Nature 405, 311 (2000) .
  14. D. G. Wansink, et al., J. Cell Biol. 122, 283 (1993) .
  15. X. Wei, S. Somanathan, J. Samarabandu, R. Berezney, J. Cell Biol. 146, 543 (1999) .
  16. D. A. Jackson, F. J. Iborra, E. M. Manders, P. R. Cook, Mol. Biol. Cell 9, 1523 (1998) .
  17. We thank A. Luyf and A. Sha Sawari for their help in computational analyses, A. Lash for use of the SAGEmap database and help in tag analyses, and E. Roos for expert digital imaging. Supported by grants from the Stichting Kindergeneeskundig Kankeronderzoek, the A. Meelmeijer Fund, and the Dutch Cancer Society. H.C. is a fellow of the Dutch Royal Academy of Sciences.
23 October 2000; accepted 11 January 2001
10.1126/science.1056794
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   Abstract »    Full Text »    PDF »
Viral Determinants of Integration Site Preferences of Simian Immunodeficiency Virus-Based Vectors.
H. Monse, S. Laufs, S. Kuate, W. J. Zeller, S. Fruehauf, and K. Uberla (2006)
J. Virol. 80, 8145-8150
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Active Chromatin Hub of the Mouse {alpha}-Globin Locus Forms in a Transcription Factory of Clustered Housekeeping Genes.
G.-L. Zhou, L. Xin, W. Song, L.-J. Di, G. Liu, X.-S. Wu, D.-P. Liu, and C.-C. Liang (2006)
Mol. Cell. Biol. 26, 5096-5105
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BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments..
F. Al-Shahrour, P. Minguez, J. Tarraga, D. Montaner, E. Alloza, J. M. Vaquerizas, L. Conde, C. Blaschke, J. Vera, and J. Dopazo (2006)
Nucleic Acids Res. 34, W472-W476
   Abstract »    Full Text »    PDF »
Next station in microarray data analysis: GEPAS..
D. Montaner, J. Tarraga, J. Huerta-Cepas, J. Burguet, J. M. Vaquerizas, L. Conde, P. Minguez, J. Vera, S. Mukherjee, J. Valls, et al. (2006)
Nucleic Acids Res. 34, W486-W491
   Abstract »    Full Text »    PDF »
Evidence for variation in abundance of antisense transcripts between multicellular animals but no relationship between antisense transcription and organismic complexity.
M. Sun, L. D. Hurst, G. G. Carmichael, and J. Chen (2006)
Genome Res. 16, 922-933
   Abstract »    Full Text »    PDF »
Strong Regional Biases in Nucleotide Substitution in the Chicken Genome.
M. T. Webster, E. Axelsson, and H. Ellegren (2006)
Mol. Biol. Evol. 23, 1203-1216
   Abstract »    Full Text »    PDF »
Profiling the Epigenome using MSDK (Methylation-Sensitive Digital Karyotyping).
K. Polyak (2006)
Am. Assoc. Cancer Res. Educ. Book 2006, 199-201
   Full Text »    PDF »
Genomic Organization of gypsy Chromatin Insulators in Drosophila melanogaster.
E. Ramos, D. Ghosh, E. Baxter, and V. G. Corces (2006)
Genetics 172, 2337-2349
   Abstract »    Full Text »    PDF »
ChroCoLoc: an application for calculating the probability of co-localization of microarray gene expression.
J. Blake, C. Schwager, M. Kapushesky, and A. Brazma (2006)
Bioinformatics 22, 765-767
   Abstract »    Full Text »    PDF »
Retroviral vector integration deregulates gene expression but has no consequence on the biology and function of transplanted T cells.
A. Recchia, C. Bonini, Z. Magnani, F. Urbinati, D. Sartori, S. Muraro, E. Tagliafico, A. Bondanza, M. T. L. Stanghellini, M. Bernardi, et al. (2006)
PNAS 103, 1457-1462
   Abstract »    Full Text »    PDF »
Deregulation of common genes by c-Myc and its direct target, MT-MC1.
K. R. Rogulski, D. E. Cohen, D. L. Corcoran, P. V. Benos, and E. V. Prochownik (2005)
PNAS 102, 18968-18973
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Drosophila melanogaster: A case study of a model genomic sequence and its consequences.
M. Ashburner and C. M. Bergman (2005)
Genome Res. 15, 1661-1667
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Structure and function of the human genome.
P. F.R. Little (2005)
Genome Res. 15, 1759-1766
   Abstract »    Full Text »    PDF »
Scale-free networks in cell biology.
R. Albert (2005)
J. Cell Sci. 118, 4947-4957
   Abstract »    Full Text »    PDF »
Natural antisense transcripts: sound or silence?.
A. Werner and A. Berdal (2005)
Physiol Genomics 23, 125-131
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A microarray analysis of the rice transcriptome and its comparison to Arabidopsis.
L. Ma, C. Chen, X. Liu, Y. Jiao, N. Su, L. Li, X. Wang, M. Cao, N. Sun, X. Zhang, et al. (2005)
Genome Res. 15, 1274-1283
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Global analysis of IL-2 target genes: identification of chromosomal clusters of expressed genes.
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. (2005)
Int. Immunol. 17, 1009-1021
   Abstract »    Full Text »    PDF »
WebGestalt: an integrated system for exploring gene sets in various biological contexts.
B. Zhang, S. Kirov, and J. Snoddy (2005)
Nucleic Acids Res. 33, W741-W748
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Profiling of oxygen-modulated gene expression in early human placenta by systematic sequencing of suppressive subtractive hybridization products.
F. Mondon, T.-M. Mignot, R. Rebourcet, H. Jammes, J.-L. Danan, F. Ferre, and D. Vaiman (2005)
Physiol Genomics 22, 99-107
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A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors.
A. M. Levin, D. Ghosh, K. R. Cho, and S. L. R. Kardia (2005)
Bioinformatics 21, 2867-2874
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Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues.
T. Mijalski, A. Harder, T. Halder, M. Kersten, M. Horsch, T. M. Strom, H. V. Liebscher, F. Lottspeich, M. H. de Angelis, and J. Beckers (2005)
PNAS 102, 8621-8626
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Male-Driven Biased Gene Conversion Governs the Evolution of Base Composition in Human Alu Repeats.
M. T. Webster, N. G. C. Smith, L. Hultin-Rosenberg, P. F. Arndt, and H. Ellegren (2005)
Mol. Biol. Evol. 22, 1468-1474
   Abstract »    Full Text »    PDF »
Local Coexpression Domains of Two to Four Genes in the Genome of Arabidopsis.
X.-Y. Ren, M. W.E.J. Fiers, W. J. Stiekema, and J.-P. Nap (2005)
Plant Physiology 138, 923-934
   Abstract »    Full Text »    PDF »
Organ-Specific Expression of Arabidopsis Genome during Development.
L. Ma, N. Sun, X. Liu, Y. Jiao, H. Zhao, and X. W. Deng (2005)
Plant Physiology 138, 80-91
   Abstract »    Full Text »    PDF »
Clusters of Co-expressed Genes in Mammalian Genomes Are Conserved by Natural Selection.
G. A. C. Singer, A. T. Lloyd, L. B. Huminiecki, and K. H. Wolfe (2005)
Mol. Biol. Evol. 22, 767-775
   Abstract »    Full Text »    PDF »
Visualizing Chromosomes as Transcriptome Correlation Maps: Evidence of Chromosomal Domains Containing Co-expressed Genes--A Study of 130 Invasive Ductal Breast Carcinomas.
F. Reyal, N. Stransky, I. Bernard-Pierrot, A. Vincent-Salomon, Y. de Rycke, P. Elvin, A. Cassidy, A. Graham, C. Spraggon, Y. Desille, et al. (2005)
Cancer Res. 65, 1376-1383
   Abstract »    Full Text »    PDF »
Relationship between gene expression and GC-content in mammals: statistical significance and biological relevance.
M. Semon, D. Mouchiroud, and L. Duret (2005)
Hum. Mol. Genet. 14, 421-427
   Abstract »    Full Text »    PDF »
Wavelet Transformations of Tumor Expression Profiles Reveals a Pervasive Genome-Wide Imprinting of Aneuploidy on the Cancer Transcriptome.
A. Aggarwal, S. H. Leong, C. Lee, O. L. Kon, and P. Tan (2005)
Cancer Res. 65, 186-194
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Comparison of the chicken and turkey genomes reveals a higher rate of nucleotide divergence on microchromosomes than macrochromosomes.
E. Axelsson, M. T. Webster, N. G.C. Smith, D. W. Burt, and H. Ellegren (2005)
Genome Res. 15, 120-125
   Abstract »    Full Text »    PDF »
Generic Features of Tertiary Chromatin Structure as Detected in Natural Chromosomes.
W. G. Muller, D. Rieder, G. Kreth, C. Cremer, Z. Trajanoski, and J. G. McNally (2004)
Mol. Cell. Biol. 24, 9359-9370
   Abstract »    Full Text »    PDF »
Genome-Wide Analyses of Avian Sarcoma Virus Integration Sites.
A. Narezkina, K. D. Taganov, S. Litwin, R. Stoyanova, J. Hayashi, C. Seeger, A. M. Skalka, and R. A. Katz (2004)
J. Virol. 78, 11656-11663
   Abstract »    Full Text »    PDF »
Gene Order and Dynamic Domains.
S. T. Kosak and M. Groudine (2004)
Science 306, 644-647
   Abstract »    Full Text »    PDF »
A comparative analysis of transcribed genes in the mouse hypothalamus and neocortex reveals chromosomal clustering.
W.-M. Boon, T. Beissbarth, L. Hyde, G. Smyth, J. Gunnersen, D. A. Denton, H. Scott, and S.-S. Tan (2004)
PNAS 101, 14972-14977
   Abstract »    Full Text »    PDF »
Chromosome Transfer Induced Aneuploidy Results in Complex Dysregulation of the Cellular Transcriptome in Immortalized and Cancer Cells.
M. B. Upender, J. K. Habermann, L. M. McShane, E. L. Korn, J. C. Barrett, M. J. Difilippantonio, and T. Ried (2004)
Cancer Res. 64, 6941-6949
   Abstract »    Full Text »    PDF »
Gene Expression, Synteny, and Local Similarity in Human Noncoding Mutation Rates.
M. T. Webster, N. G.C. Smith, M. J. Lercher, and H. Ellegren (2004)
Mol. Biol. Evol. 21, 1820-1830
   Abstract »    Full Text »    PDF »
Over 20% of human transcripts might form sense-antisense pairs.
J. Chen, M. Sun, W. J. Kent, X. Huang, H. Xie, W. Wang, G. Zhou, R. Z. Shi, and J. D. Rowley (2004)
Nucleic Acids Res. 32, 4812-4820
   Abstract »    Full Text »    PDF »
The Use of MPSS for Whole-Genome Transcriptional Analysis in Arabidopsis.
B. C. Meyers, S. S. Tej, T. H. Vu, C. D. Haudenschild, V. Agrawal, S. B. Edberg, H. Ghazal, and S. Decola (2004)
Genome Res. 14, 1641-1653
   Abstract »    Full Text »    PDF »
Increasing the efficiency of SAGE adaptor ligation bydirected ligation chemistry.
A. P. So, R. F. B. Turner, and C. A. Haynes (2004)
Nucleic Acids Res. 32, e96
   Abstract »    Full Text »    PDF »
Comparative Sequence and X-Inactivation Analyses of a Domain of Escape in Human Xp11.2 and the Conserved Segment in Mouse.
K. D. Tsuchiya, J. M. Greally, Y. Yi, K. P. Noel, J.-P. Truong, and C. M. Disteche (2004)
Genome Res. 14, 1275-1284
   Abstract »    Full Text »    PDF »
Form follows function: the genomic organization of cellular differentiation.
S. T. Kosak and M. Groudine (2004)
Genes & Dev. 18, 1371-1384
   Abstract »    Full Text »    PDF »
A naturally occurring C-terminal truncated isoform of the latent nuclear antigen of Kaposi's sarcoma-associated herpesvirus does not associate with viral episomal DNA.
M. Canham and S. J. Talbot (2004)
J. Gen. Virol. 85, 1363-1369
   Abstract »    Full Text »    PDF »

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