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Science 16 February 2001:
Vol. 291. no. 5507, pp. 1289 - 1292
DOI: 10.1126/science.1056794
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Reports

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.

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23 October 2000; accepted 11 January 2001
10.1126/science.1056794
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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
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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
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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
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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
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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
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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
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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
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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
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Gene Order and Dynamic Domains.
S. T. Kosak and M. Groudine (2004)
Science 306, 644-647
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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
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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
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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
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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
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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
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Form follows function: the genomic organization of cellular differentiation.
S. T. Kosak and M. Groudine (2004)
Genes & Dev. 18, 1371-1384
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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
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Genomic Regionality in Rates of Evolution Is Not Explained by Clustering of Genes of Comparable Expression Profile.
M. J. Lercher, J.-V. Chamary, and L. D. Hurst (2004)
Genome Res. 14, 1002-1013
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Methods for Transcriptional Profiling in Plants. Be Fruitful and Replicate.
B. C. Meyers, D. W. Galbraith, T. Nelson, and V. Agrawal (2004)
Plant Physiology 135, 637-652
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A gene atlas of the mouse and human protein-encoding transcriptomes.
A. I. Su, T. Wiltshire, S. Batalov, H. Lapp, K. A. Ching, D. Block, J. Zhang, R. Soden, M. Hayakawa, G. Kreiman, et al. (2004)
PNAS 101, 6062-6067
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Calculating the statistical significance of physical clusters of co-regulated genes in the genome: the role of chromatin in domain-wide gene regulation.
C.-F. Chang, K.-M. Wai, and H. G. Patterton (2004)
Nucleic Acids Res. 32, 1798-1807
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Promyelocytic leukemia nuclear bodies associate with transcriptionally active genomic regions.
J. Wang, C. Shiels, P. Sasieni, P. J. Wu, S. A. Islam, P. S. Freemont, and D. Sheer (2004)
J. Cell Biol. 164, 515-526
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Global optimization of cerebral cortex layout.
C. Cherniak, Z. Mokhtarzada, R. Rodriguez-Esteban, and K. Changizi (2004)
PNAS 101, 1081-1086
   Abstract »    Full Text »    PDF »
A genomic perspective on protein tyrosine phosphatases: gene structure, pseudogenes, and genetic disease linkage.
J. N. ANDERSEN, P. G. JANSEN, S. M. ECHWALD, O. H. MORTENSEN, T. FUKADA, R. DEL VECCHIO, N. K. TONKS, and N. P. H. MOLLER (2004)
FASEB J 18, 8-30
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An Abundance of Bidirectional Promoters in the Human Genome.
N. D. Trinklein, S. F. Aldred, S. J. Hartman, D. I. Schroeder, R. P. Otillar, and R. M. Myers (2004)
Genome Res. 14, 62-66
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In Silico Study of Transcriptome Genetic Variation in Outbred Populations.
M. Perez-Enciso (2004)
Genetics 166, 547-554
   Abstract »    Full Text »    PDF »
Moving forward with chemical mutagenesis in the mouse.
T. P. O'Brien and W. N. Frankel (2004)
J. Physiol. 554, 13-21
   Abstract »    Full Text »    PDF »
A Gene Expression Map of the Arabidopsis Root.
K. Birnbaum, D. E. Shasha, J. Y. Wang, J. W. Jung, G. M. Lambert, D. W. Galbraith, and P. N. Benfey (2003)
Science 302, 1956-1960
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Development of an enhanced GFP-based dual-color reporter to facilitate genetic screens for the recovery of mutations in mice.
A. C. Frank, K. A. Meyers, I. C. Welsh, and T. P. O'Brien (2003)
PNAS 100, 14103-14108
   Abstract »    Full Text »    PDF »
The eukaryotic genome: a system regulated at different hierarchical levels.
R. van Driel, P. F. Fransz, and P. J. Verschure (2003)
J. Cell Sci. 116, 4067-4075
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A unification of mosaic structures in the human genome.
M. J. Lercher, A. O. Urrutia, A. Pavlicek, and L. D. Hurst (2003)
Hum. Mol. Genet. 12, 2411-2415
   Abstract »    Full Text »    PDF »
The Signature of Selection Mediated by Expression on Human Genes.
A. O. Urrutia and L. D. Hurst (2003)
Genome Res. 13, 2260-2264
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The Human Transcriptome Map Reveals Extremes in Gene Density, Intron Length, GC Content, and Repeat Pattern for Domains of Highly and Weakly Expressed Genes.
R. Versteeg,, B. D.C. van Schaik, M. F. van Batenburg, M. Roos, R. Monajemi, H. Caron, H. J. Bussemaker, and A. H.C. van Kampen (2003)
Genome Res. 13, 1998-2004
   Abstract »    Full Text »    PDF »
Visualization of chromatin domains created by the gypsy insulator of Drosophila.
K. Byrd and V. G. Corces (2003)
J. Cell Biol. 162, 565-574
   Abstract »    Full Text »    PDF »
Combining Gene Expression and Molecular Marker Information for Mapping Complex Trait Genes: A Simulation Study.
M. Perez-Enciso, M. A. Toro, M. Tenenhaus, and D. Gianola (2003)
Genetics 164, 1597-1606
   Abstract »    Full Text »    PDF »
Genome Function and Nuclear Architecture: From Gene Expression to Nanoscience.
T. P. O'Brien, C. J. Bult, C. Cremer, M. Grunze, B. B. Knowles, J. Langowski, J. McNally, T. Pederson, J. C. Politz, A. Pombo, et al. (2003)
Genome Res. 13, 1029-1041
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Genomic Gene Clustering Analysis of Pathways in Eukaryotes.
J. M. Lee and E. L.L. Sonnhammer (2003)
Genome Res. 13, 875-882
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Senescence-specific gene expression fingerprints reveal cell-type-dependent physical clustering of up-regulated chromosomal loci.
H. Zhang, K.-H. Pan, and S. N. Cohen (2003)
PNAS 100, 3251-3256
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The transcriptional activity of human Chromosome 22.
J. L. Rinn, G. Euskirchen, P. Bertone, R. Martone, N. M. Luscombe, S. Hartman, P. M. Harrison, F. K. Nelson, P. Miller, M. Gerstein, et al. (2003)
Genes & Dev. 17, 529-540
   Abstract »    Full Text »    PDF »
Transcriptome analysis of human autosomal trisomy.
D. R. FitzPatrick, J. Ramsay, N. I. McGill, M. Shade, A. D. Carothers, and N. D. Hastie (2002)
Hum. Mol. Genet. 11, 3249-3256
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Gene density and transcription influence the localization of chromatin outside of chromosome territories detectable by FISH.
N. L. Mahy, P. E. Perry, and W. A. Bickmore (2002)
J. Cell Biol. 159, 753-763
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A genetic and genomic analysis identifies a cluster of genes associated with hematopoietic cell turnover.
G. de Haan, L. V. Bystrykh, E. Weersing, B. Dontje, H. Geiger, N. Ivanova, I. R. Lemischka, E. Vellenga, and G. Van Zant (2002)
Blood 100, 2056-2062
   Abstract »    Full Text »    PDF »
Clusters of transcription-coupled repair in the human genome.
J. Surralles, M. J. Ramirez, R. Marcos, A. T. Natarajan, and L. H. F. Mullenders (2002)
PNAS 99, 10571-10574
   Abstract »    Full Text »    PDF »
General Statistics of Stochastic Process of Gene Expression in Eukaryotic Cells.
V. A. Kuznetsov, G. D. Knott, and R. F. Bonner (2002)
Genetics 161, 1321-1332
   Abstract »    Full Text »    PDF »
Long-Range Heterogeneity at the 3' Ends of Human mRNAs.
C. Iseli, B. J. Stevenson, S. J. de Souza, H. B. Samaia, A. A. Camargo, K. H. Buetow, R. L. Strausberg, A. J.G. Simpson, P. Bucher, and C. V. Jongeneel (2002)
Genome Res. 12, 1068-1074
   Abstract »    Full Text »    PDF »
Mapping QTLs Regulating Morpho-physiological Traits and Yield: Case Studies, Shortcomings and Perspectives in Drought-stressed Maize.
R. TUBEROSA, S. SALVI, M. C. SANGUINETI, P. LANDI, M. MACCAFERRI, and S. CONTI (2002)
Ann. Bot. 89, 941-963
   Abstract »    Full Text »    PDF »


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Science. ISSN 0036-8075 (print), 1095-9203 (online)