Skip to main content
Download PDF

Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases

BMC Genomics volume 4, Article number: 31 (2003) Cite this article

Abstract

Background

Extracting biological knowledge from large amounts of gene expression information deposited in public databases is a major challenge of the postgenomic era. Additional insights may be derived by data integration and cross-platform comparisons of expression profiles. However, database meta-analysis is complicated by differences in experimental technologies, data post-processing, database formats, and inconsistent gene and sample annotation.

Results

We have analysed expression profiles from three public databases: Gene Expression Atlas, SAGEmap and TissueInfo. These are repositories of oligonucleotide microarray, Serial Analysis of Gene Expression and Expressed Sequence Tag human gene expression data respectively. We devised a method, Preferential Expression Measure, to identify genes that are significantly over- or under-expressed in any given tissue. We examined intra- and inter-database consistency of Preferential Expression Measures. There was good correlation between replicate experiments of oligonucleotide microarray data, but there was less coherence in expression profiles as measured by Serial Analysis of Gene Expression and Expressed Sequence Tag counts. We investigated inter-database correlations for six tissue categories, for which data were present in the three databases. Significant positive correlations were found for brain, prostate and vascular endothelium but not for ovary, kidney, and pancreas.

Conclusion

We show that data from Gene Expression Atlas, SAGEmap and TissueInfo can be integrated using the UniGene gene index, and that expression profiles correlate relatively well when large numbers of tags are available or when tissue cellular composition is simple. Finally, in the case of brain, we demonstrate that when PEM values show good correlation, predictions of tissue-specific expression based on integrated data are very accurate.

Background

High-throughput expression profiling is a major tool for functional annotation of sequenced genomes. SAGEmap [1] and NCI60 cDNA microarray [2] datasets have been incorporated into the Ensembl and UCSC human genome browsers. Large-scale expression data have also been used to construct a transcriptional profile of adult skeletal muscle [3]; to identify genes preferentially expressed in prostate [4], granulocytes [5] and thyroid [6]; to discover markers of pathological states such as cancer [7], and for whole genome structure analysis such as identification of clusters of similarly expressed genes [8, 9].

Numerous public databases have been created to store large-scale expression data. These databases are, however, heterogeneous in format, sample annotation, and statistical post-processing of experimental results. Furthermore, the different databases store data from different experimental technologies. A cross-platform comparison between cDNA and oligonucleotide microarrays has demonstrated that data generated by different platforms may have poor correlation [10]. We investigated whether three public gene expression databases: the Gene Expression Atlas published by Su et al. [11], SAGEmap [1] and TissueInfo [12] (representing respectively oligonucleotide microarray, Serial Analysis of Gene Expression – SAGE, and Expressed Sequence Tags – ESTs) are internally consistent and complementary when used to compare expression profiles of human genes within six normal tissue categories, which were represented in the three databases.

The scope of this study is integration of expression data and meta-analysis of datasets. Our aim is to evaluate the congruence of publicly available expression datasets. It was not our intention to provide a technical benchmarking of different expression platforms, because such a benchmarking exercise would require controlled microarray, SAGE and EST experiments starting with identical RNA samples derived from the same tissue specimen, and would be beyond the resources of most laboratories.

Results and discussion

Preferential Expression Measure

We devised a Preferential Expression Measure (PEM) to score differential expression of genes in tissues. PEM describes the expression of a gene in a given tissue in relation to its expression in all tissues. Therefore, PEM controls for the fact that some genes are highly expressed across many tissues (housekeeping genes), and has the virtue of reporting a negative value for under-expressed genes, and a positive value for over-expressed genes. Large positive PEM scores for a gene in a particular tissue indicate that the gene is unusually highly expressed in that tissue, relative to its expression in other tissues. Large negative PEM scores indicate repression of a gene in a tissue.

For SAGEmap and TissueInfo we define PEM as log10(o/e), where o is the observed SAGE or EST tag count for a gene in a given tissue, and e the expected tag count under the null hypothesis of uniform expression in all tissues. e is calculated as (G * N/T) where G is the total number of tags ascribed to the gene, N is the total number of tags for the tissue, and T is the total number of tags in the dataset.

For example, carbonic anhydrase XI (UniGene cluster Hs.22777) is linked to a SAGE tag GTCGCTGAGA. This tag occurs 132 times in all normal tissue SAGEmap libraries. The total number of SAGE tags in the normal brain tissue category is 326,481, and the total number of tags in all normal tissue categories is 1,077,231. Therefore, if the distribution of this tag was uniform across all libraries, it would be expected to occur ~40 times (132 * 326,481/1,077,231) in brain libraries (expected count). The actual count in brain libraries is 124 (observed count) bringing the PEM value for this tag in brain to log10(o/e) = log10(132/40) = 0.49.

For microarray experiments, where the raw data is a continuous variable (a relative intensity of a gene-specific fluorescent signal) as opposed to a tag count, we defined PEM as log10(S/A), where S is the specific tissue signal for a gene and A is the arithmetic mean signal for the gene across all tissues.

Reproducibility and internal consistency

A major concern with all types of high-throughput expression data is reproducibility and internal consistency. To investigate this, for each of the three databases, we compared replicate experiments (where available) or equivalent tissues and measured the Pearson correlation coefficient for relative fluorescent signal intensity values assigned to the same probe in a pair of experiments. Gene Expression Atlas oligonucleotide microarray data had good correlation between repeat hybridizations of the same RNA sample (mean r2 = 0.94 ± 0.04; N = 45 pairwise comparisons) and, as expected, slightly lower values for repeat hybridizations of different RNA preparations from the same tissue type (mean r2 = 0.87 ± 0.06; N = 17 pairwise comparisons).

Internal correlations for SAGEmap and TissueInfo were lower. Counts of identical tags were correlated in pairwise SAGEmap library comparisons yielding mean r2 values of 0.51 ± 0.25, N = 15 for brain; 0.26 ± 0.08, N = 6 for prostate; 0.89, N = 2 for vascular endothelium; 0.27, N = 2 for ovary; 0.83, N = 2 for kidney; and 0.97, N = 2 for pancreas.

For TissueInfo, we included tumour tissues to maximize the size of datasets and, therefore, minimize the sampling error. Counts of EST tags linked to the same UniGene cluster in pools of libraries were correlated. Comparison of brain TissueInfo libraries comprising 154,214 EST tags from normal tissue and 111,317 from tumour tissue, revealed an r2 of only 0.27.

Correlations among tissue profiles from Gene Expression Atlas, SAGEmap and TissueInfo

To compare different databases we grouped libraries into higher-level tissue categories, and calculated Pearson correlation coefficients for PEM scores for categories that were represented in all three databases. Tumour tissue libraries were not used in any of the interdatabase comparisons. UniGene was used as the common gene index to link entries from the three databases. There were six tissues available for comparison (Table 1). Significant positive correlations were found for brain, prostate and vascular endothelium (Figure 1) but not for ovary, kidney, and pancreas.

Table 1 r2 values for Pearson correlations between PEM scores computed from Gene Expression Atlas (GEA), SAGEmap and TissueInfo expression profiles for six human tissue categories, for which data were available in the three databases.
Full size table
Figure 1
figure 1

Correlations between the three databases Correlations between Gene Expression Atlas (GEA), SAGEmap, and TissueInfo preferential expression measures (PEM) for brain, prostate and vascular endothelium. Trend lines for linear regression and the corresponding r2 values are shown for each pairwise comparison.

Full size image

Because of sampling error, the total number of sequenced tags is a key determinant of reliability of SAGE and EST expression profiles. In our analysis, brain is the tissue category with the highest number of both SAGE and EST tags (Table 2) and accordingly shows the strongest correlations of PEM values among oligonucleotide microarray, SAGEmap, and TissueInfo (Table 1). Prostate, the tissue category with the second highest total number of tags, showed good correlation between SAGEmap and TissueInfo, but the correlation between the tag-based databases and oligonucleotide microarray data was weaker (r2 of 0.09 and 0.19 for SAGEmap and TissueInfo respectively).

Table 2 Number of SAGE and EST tags in tissue categories derived from SAGEmap and TissueInfo.
Full size table

We suggest that the relative complexity of a tissue is another important factor for comparison of high-throughput expression profiles deposited in public databases. Complex tissues consist of many cell types that may be mixed in different proportions in different samples. Expression profiles obtained from these tissues will be heterogeneous and consequently require a large number of tags and repeated hybridizations to detect signal in the noise. This is shown by the relatively strong correlations seen in SAGEmap/TissueInfo and SAGEmap/microarray correlations for vascular endothelium despite the small number of tags available (in comparison to brain, about 3-fold fewer SAGE tags and 18-fold fewer EST tags). SAGEmap and dbEST/TissueInfo vascular endothelium libraries are derived from in vitro cultures or homogenous primary isolates of vascular endothelial cells [13].

Correlations of PEM scores obtained from the three databases for kidney, pancreas, and ovary had very low r2 values (Table 1). However, measuring the correlation coefficient of PEM scores is a very rigorous test of congruence, because it not only requires that two datasets being compared identify the same sets of genes as being over- or under-expressed in the tissue, but also that the genes deviate from uniform expression to a similar degree. This may be an unrealistic expectation; for example, it is apparent from Figure 1 that PEM scores for SAGEmap in brain never exceed +1.19 (i.e., approximately 15-fold over-representation), which is probably due to the limited size of the SAGE database. A simpler test is to divide the plots into quadrants and measure the fraction of genes for which the two methods being compared give PEM scores with the same sign. For example, for brain tissue the PEM scores from SAGEmap and TissueInfo data are both positive for 134 genes, both negative for 184 genes, and in disagreement for only 28 genes (i.e., one method identifies the gene as significantly over-expressed in brain, and the other identifies it as significantly under-expressed) (Figure 1). Across all six tissues and three datasets, the PEM signs are in agreement for more than 50% of genes (the null expectation) in 13 of the 18 comparisons made (Table 1).

Tissue-specific genes

We showed previously that integrating two expression databases (SAGEmap and dbEST) provided accurate predictions of vascular endothelium-specific expression that were verified experimentally, whereas neither method alone was reliable [13]. Similarly, genes for which Gene Expression Atlas, SAGEmap and TissueInfo analyses all yield positive PEM scores should be very strong candidates for being tissue-specific. To test this hypothesis, we integrated PEM scores from the three types of expression profile to search for putative brain-specific genes. The 50 top-scoring genes were investigated through LocusLink and OMIM and of these 47 proved to be previously known brain-specific or preferentially brain expressed genes (Table 3). Three genes were novel, one of which was highly similar to a known brain-specific inorganic phosphate co-transporter. This confirms the very high reliability of integrated predictions of tissue-specific expression. The bottom-scoring ten genes for brain are also listed in Table 1. They have negative PEM values indicating under-representation in the brain transcriptome. Four of these genes are epithelial markers (EPCAM, KRT5, KRT18, and KRT19).

Table 3 Top 50 brain-specific and bottom ten brain underexpressed genes (in italics) identified by integrated PEM scores from Gene Expression Atlas, SAGEmap and TissueInfo.
Full size table

The top ten prostate-specific genes are listed in Table 4. As with the analysis for brain, combining Gene Expression Atlas, SAGEmap and TissueInfo data appears to identify prostate-specific genes successfully. Five genes were previously known to be expressed specifically by prostate epithelium (MSMB, ACPP, KLK3, AMD1 and RDH11). The other five are muscle-specific genes. This is not unexpected since the fibromuscular stroma surrounding prostatic glands is known to account for about half of the volume of the prostate [14].

Table 4 Top ten prostate-specific genes identified by integrated PEM scores from Gene Expression Atlas, SAGEmap and TissueInfo.
Full size table

Conclusions

We show here that data from Gene Expression Atlas, SAGEmap and TissueInfo can be integrated when libraries are grouped into higher-level tissue categories and genes are mapped between datasets using the UniGene gene index. After integration, expression profiles between tissue categories represented in multiple databases can be compared using a measure of differential expression, PEM. Internal consistency of PEM scores is high for Gene Expression Atlas but poorer in the tissue categories derived from SAGEmap and TissueInfo. Between datasets, PEM values correlate relatively well when large numbers of SAGE and EST tags are available (brain, prostate) or when tissue cellular composition is simple (vascular endothelium), but in the other tissue categories examined the correlation is low. The usefulness of the integrated dataset is demonstrated by the accuracy with which brain- and prostate-specific genes could be identified. This suggests that similar accuracy could be achieved for other tissues if oligonucleotide microarray, SAGE, and EST experiments were performed on RNAs from the same well-annotated tissue samples of relatively simple cellular composition. This would also enable direct comparison and benchmarking of expression profiles obtained using different technologies. The integrated dataset, code and database schema are available on request from L.H.

Methods

Gene Expression Atlas

Affymetrix U95A oligonucleotide microarray [15] data for 12,587 consensus probes in 101 human tissue samples were downloaded from the Gene Expression Atlas website http://expression.gnf.org/data_public_U95.gz. Image analysis and normalization has been performed using Genechip 3.2 software (Affymetrix, Santa Clara, CA) by Su et al. [11]. The libraries were grouped into 47 higher-level tissue categories (averaging across duplicates and triplicates). Tumour libraries were grouped as separate to normal tissues and not used for further analysis.

SAGEmap

Tag frequencies for 132 SAGEmap libraries [1] were obtained from the project ftp site ftp://ftp.ncbi.nih.gov/pub/sage. After downloading, the libraries were grouped into 15 higher-level tissue categories (brain, prostate, kidney, colon, pancreas, ovary, breast, skin, peritoneum, stomach, blood, lung, liver, heart and vascular endothelium) and annotated according to their disease status (91 tumour tissues, 37 normal tissue libraries and four immortalised cell lines). Unless stated otherwise, only normal tissue libraries (total of 1,077,231 tags) were used for further analysis.

TissueInfo

The TissueInfo [12] database links ESTs from dbEST to the tissue where there are expressed by assigning a tissue-key to each EST entry. The dataset was downloaded from the TissueInfo website http://icb.mssm.edu/tissueinfo/local-inst.xml, which is updated daily, on 27 February 2002. Our aim was to group EST into major tissues with sufficient numbers of ESTs for statistically meaningful analysis. Therefore, some TissueInfo categories were grouped together into higher-level tissue categories. For example hypothalamus was also annotated as brain. We excluded ESTs that were annotated as mitochondrial libraries, tumour libraries, or multiple tissues. This procedure resulted in 48 higher-level tissue categories, 27 of which had EST counts in excess of 10,000 (the number of ESTs we accepted as the threshold for dataset size). We also included vascular endothelium (8,435 ESTs) because of the high number of SAGE tags available for this category (110,460) and its simple cellular composition.

Mapping

Six higher-level tissue categories were available for comparison between Gene Expression Atlas, SAGEmap, and TissueInfo (Table 1). An integrated MySQL database was set up and gene identifiers from the UniGene gene index were used to link expression data from the three datasets. Only UniGene entries with more than one EST (62,008 entries) were considered as valid clusters. UniGene mapping was provided as part of both SAGEmap and TissueInfo datasets. To map oligonucleotide microarray probes, probe consensus sequences were searched against longest representative sequence of each UniGene cluster using BLASTN with the cutoff parameter E = 10e-20. Alignments were accepted if the percentage identity was higher than 94% and length was at least 99 bp or 90% of the length of the query, or when percentage identity was 100% and length more than 49 bp [11].

When linking UniGene clusters to SAGEmap data, UniGenes mapping to more than SAGE tag sequence were excluded (25% of clusters). The size of the Unigene dataset linked to SAGEmap was, therefore, reduced from 21,224 to 15,872 clusters. Similarly, Unigene clusters mapping to more than one Affymetrix probe were excluded (22% of clusters). The size of the Unigene dataset linked to Gene Expression Atlas data was, therefore, reduced from 9,402 to 7,375 clusters. The overlaps between UniGenes linked to GEA and SAGEmap, GEA and TissueInfo, and SAGEmap and TissueInfo were 2742, 6758 and 15872 clusters respectively. Therefore:

- 63% and 8% of UniGene clusters linked to GEA could not be linked to SAGEmap and TissueInfo respectively;

- 83% and 0% of UniGene clusters linked to SAGEmap could not be linked to GEA and TissueInfo respectively;

- 74% and 89% of UniGene clusters linked to TissueInfo could not be linked to SAGEmap and GEA respectively.

Statistics

Gene expression can be estimated by counting gene-specific SAGE and EST tags but, as a stochastic process, these estimates are subject to sampling error. The size of the sampled dataset can have a dramatic influence on reliability of the data. Two independent groups have established χ2 as the best statistical test to use in tag sampling experiments [16, 17]. We excluded from subsequent comparative analysis all tissue/gene data for which the expected number of tags (e) was less than 5, which is the lower limit of reliability for the χ2 statistic. We used χ2 to filter the PEM score for both tag-based methods: unless significantly different from expectation (with P = 0.05) we concluded that we had no confidence in either the sign or the magnitude of the apparent deviation from uniform expression. A relatively low significance threshold of 0.05 was used to maximize the number of values available for inter-database comparison. Because χ2 test is only used on a case-by-case basis for preliminary selection of informative datapoints, a multiple testing correction such as Bonferroni correction was not applicable.

To compare the results from any two databases for a given tissue, we plotted the PEM scores for all genes for which data was available from the two experiments, but excluding SAGE and EST data with non-significant χ2. We calculated the correlation coefficient (r) and report r2, which represents the proportion of the variability in the data that is explained by the correlation.

Database versions

Gene Expression Atlas, Affymetrix human Genechip U95A http://expression.gnf.org

SAGEmap, January 2002 http://www.ncbi.nlm.nih.gov/SAGE/

TissueInfo, 27 February 2002 http://icb.mssm.edu/crt/tissueinfowebservice.xml

UniGene, Build 148 http://www.ncbi.nlm.nih.gov/UniGene/

Abbreviations

NCI60 –:

set of 60 human cancer cell lines used by the National Cancer Institute for chemotherapeutic drug screens

UCSC –:

University of California Santa Cruz

cDNA –:

complementary DNA

SAGE –:

Serial Analysis of Gene Expression

EST –:

Expressed Sequence Tag

PEM –:

Preferential Expression Measure

BLAST –:

Basic Local Alignment Search Tool

OMIM –:

Online Mendelian Inheritance in Man

MySQL –:

open source relational database

References

  1. Lash AE, Tolstoshev CM, Wagner L, Schuler GD, Strausberg RL, Riggins GJ, Altschul SF: SAGEmap: a public gene expression resource. Genome Res. 2000, 10: 1051-1060. 10.1101/gr.10.7.1051.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  2. Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, Van de Rijn M, Waltham M, Pergamenschikov A, Lee JC, Lashkari D, Shalon D, Myers TG, Weinstein JN, Botstein D, Brown PO: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000, 24: 227-235. 10.1038/73432.

    CAS  Article  PubMed  Google Scholar 

  3. Bortoluzzi S, d'Alessi F, Romualdi C, Danieli GA: The human adult skeletal muscle transcriptional profile reconstructed by a novel computational approach. Genome Res. 2000, 10: 344-349. 10.1101/gr.10.3.344.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  4. Vasmatzis G, Essand M, Brinkmann U, Lee B, Pastan I: Discovery of three genes specifically expressed in human prostate by expressed sequence tag database analysis. Proc Natl Acad Sci U S A. 1998, 95: 300-304. 10.1073/pnas.95.1.300.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  5. Itoh K, Okubo K, Utiyama H, Hirano T, Yoshii J, Matsubara K: Expression profile of active genes in granulocytes. Blood. 1998, 92: 1432-1441.

    CAS  PubMed  Google Scholar 

  6. Moreno JC, Pauws E, van Kampen AH, Jedlickova M, de Vijlder JJ, Ris-Stalpers C: Cloning of tissue-specific genes using serial analysis of gene expression and a novel computational substraction approach. Genomics. 2001, 75: 70-76. 10.1006/geno.2001.6586.

    CAS  Article  PubMed  Google Scholar 

  7. St Croix B, Rago C, Velculescu V, Traverso G, Romans KE, Montgomery E, Lal A, Riggins GJ, Lengauer C, Vogelstein B, Kinzler KW: Genes expressed in human tumor endothelium. Science. 2000, 289: 1197-1202. 10.1126/science.289.5482.1197.

    CAS  Article  PubMed  Google Scholar 

  8. Caron H, van Schaik B, van der Mee M, Baas F, Riggins G, van Sluis P, Hermus MC, van Asperen R, Boon K, Voute PA, Heisterkamp S, van Kampen A, Versteeg R: The human transcriptome map: clustering of highly expressed genes in chromosomal domains. Science. 2001, 291: 1289-1292. 10.1126/science.1056794.

    CAS  Article  PubMed  Google Scholar 

  9. Lercher MJ, Urrutia AO, Hurst LD: Clustering of housekeeping genes provides a unified model of gene order in the human genome. Nat Genet. 2002, 31: 180-183. 10.1038/ng887.

    CAS  Article  PubMed  Google Scholar 

  10. Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics. 2002, 18: 405-412. 10.1093/bioinformatics/18.3.405.

    CAS  Article  PubMed  Google Scholar 

  11. Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A, Patapoutian A, Hampton GM, Schultz PG, Hogenesch JB: Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci U S A. 2002, 99: 4465-4470. 10.1073/pnas.012025199.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  12. Skrabanek L, Campagne F: TissueInfo: high-throughput identification of tissue expression profiles and specificity. Nucleic Acids Res. 2001, 29: E102-2. 10.1093/nar/29.21.e102.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  13. Huminiecki L, Bicknell R: In silico cloning of novel endothelial-specific genes. Genome Res. 2000, 10: 1796-1806. 10.1101/gr.150700.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  14. Farnsworth WE: Prostate stroma: physiology. Prostate. 1999, 38: 60-72. 10.1002/(SICI)1097-0045(19990101)38:1<60::AID-PROS8>3.3.CO;2-V.

    CAS  Article  PubMed  Google Scholar 

  15. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996, 14: 1675-1680.

    CAS  Article  PubMed  Google Scholar 

  16. Man MZ, Wang X, Wang Y: POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics. 2000, 16: 953-959. 10.1093/bioinformatics/16.11.953.

    CAS  Article  PubMed  Google Scholar 

  17. Romualdi C, Bortoluzzi S, Danieli GA: Detecting differentially expressed genes in multiple tag sampling experiments: comparative evaluation of statistical tests. Hum Mol Genet. 2001, 10: 2133-2141. 10.1093/hmg/10.19.2133.

    CAS  Article  PubMed  Google Scholar 

  18. Chen C, Rainnie DG, Greene RW, Tonegawa S: Abnormal fear response and aggressive behavior in mutant mice deficient for alpha-calcium-calmodulin kinase II. Science. 1994, 266: 291-294.

    CAS  Article  PubMed  Google Scholar 

  19. Martin-Vasallo P, Dackowski W, Emanuel JR, Levenson R: Identification of a putative isoform of the Na,K-ATPase beta subunit. Primary structure and tissue-specific expression. J Biol Chem. 1989, 264: 4613-4618.

    CAS  PubMed  Google Scholar 

  20. Pagliusi S, Antonicek H, Gloor S, Frank R, Moos M, Schachner M: Identification of a cDNA clone specific for the neural cell adhesion molecule AMOG. J Neurosci Res. 1989, 22: 113-119.

    CAS  Article  PubMed  Google Scholar 

  21. Lien LL, Feener CA, Fischbach N, Kunkel LM: Cloning of human microtubule-associated protein 1B and the identification of a related gene on chromosome 15. Genomics. 1994, 22: 273-280. 10.1006/geno.1994.1384.

    CAS  Article  PubMed  Google Scholar 

  22. Hu K, Carroll J, Fedorovich S, Rickman C, Sukhodub A, Davletov B: Vesicular restriction of synaptobrevin suggests a role for calcium in membrane fusion. Nature. 2002, 415: 646-650. 10.1038/415646a.

    CAS  Article  PubMed  Google Scholar 

  23. Polymeropoulos MH, Ide S, Soares MB, Lennon GG: Sequence characterization and genetic mapping of the human VSNL1 gene, a homologue of the rat visinin-like peptide RNVP1. Genomics. 1995, 29: 273-275. 10.1006/geno.1995.1244.

    CAS  Article  PubMed  Google Scholar 

  24. Diehl HJ, Schaich M, Budzinski RM, Stoffel W: Individual exons encode the integral membrane domains of human myelin proteolipid protein. Proc Natl Acad Sci U S A. 1986, 83: 9807-9811.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  25. Jiang M, Gold MS, Boulay G, Spicher K, Peyton M, Brabet P, Srinivasan Y, Rudolph U, Ellison G, Birnbaumer L: Multiple neurological abnormalities in mice deficient in the G protein Go. Proc Natl Acad Sci U S A. 1998, 95: 3269-3274. 10.1073/pnas.95.6.3269.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  26. Strathmann M, Wilkie TM, Simon MI: Alternative splicing produces transcripts encoding two forms of the alpha subunit of GTP-binding protein Go. Proc Natl Acad Sci U S A. 1990, 87: 6477-6481.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  27. Yan Y, Lagenaur C, Narayanan V: Molecular cloning of M6: identification of a PLP/DM20 gene family. Neuron. 1993, 11: 423-431.

    CAS  Article  PubMed  Google Scholar 

  28. Eylar EH, Brostoff S, Hashim G, Caccam J, Burnett P: Basic A1 protein of the myelin membrane. The complete amino acid sequence. J Biol Chem. 1971, 246: 5770-5784.

    CAS  PubMed  Google Scholar 

  29. Wasco W, Bupp K, Magendantz M, Gusella JF, Tanzi RE, Solomon F: Identification of a mouse brain cDNA that encodes a protein related to the Alzheimer disease-associated amyloid beta protein precursor. Proc Natl Acad Sci U S A. 1992, 89: 10758-10762.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  30. Kedra D, Pan HQ, Seroussi E, Fransson I, Guilbaud C, Collins JE, Dunham I, Blennow E, Roe BA, Piehl F, Dumanski JP: Characterization of the human synaptogyrin gene family. Hum Genet. 1998, 103: 131-141. 10.1007/s004390050795.

    CAS  Article  PubMed  Google Scholar 

  31. Fernandez-Chacon R, Konigstorfer A, Gerber SH, Garcia J, Matos MF, Stevens CF, Brose N, Rizo J, Rosenmund C, Sudhof TC: Synaptotagmin I functions as a calcium regulator of release probability. Nature. 2001, 410: 41-49. 10.1038/35065004.

    CAS  Article  PubMed  Google Scholar 

  32. Nakagawa H, Koyama K, Murata Y, Morito M, Akiyama T, Nakamura Y: APCL, a central nervous system-specific homologue of adenomatous polyposis coli tumor suppressor, binds to p53-binding protein 2 and translocates it to the perinucleus. Cancer Res. 2000, 60: 101-105.

    CAS  PubMed  Google Scholar 

  33. Strittmatter SM, Fankhauser C, Huang PL, Mashimo H, Fishman MC: Neuronal pathfinding is abnormal in mice lacking the neuronal growth cone protein GAP-43. Cell. 1995, 80: 445-452.

    CAS  Article  PubMed  Google Scholar 

  34. Bellingham J, Gregory-Evans K, Gregory-Evans CY: Sequence and tissue expression of a novel human carbonic anhydrase-related protein, CARP-2, mapping to chromosome 19q13.3. Biochem Biophys Res Commun. 1998, 253: 364-367. 10.1006/bbrc.1998.9449.

    CAS  Article  PubMed  Google Scholar 

  35. Pevsner J, Hsu SC, Scheller RH: n-Sec1: a neural-specific syntaxin-binding protein. Proc Natl Acad Sci U S A. 1994, 91: 1445-1449.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  36. Schweitzer B, Taylor V, Welcher AA, McClelland M, Suter U: Neural membrane protein 35 (NMP35): a novel member of a gene family which is highly expressed in the adult nervous system. Mol Cell Neurosci. 1998, 11: 260-273. 10.1006/mcne.1998.0697.

    CAS  Article  PubMed  Google Scholar 

  37. Bajjalieh SM, Peterson K, Linial M, Scheller RH: Brain contains two forms of synaptic vesicle protein 2. Proc Natl Acad Sci U S A. 1993, 90: 2150-2154.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  38. Zhou RH, Kokame K, Tsukamoto Y, Yutani C, Kato H, Miyata T: Characterization of the human NDRG gene family: a newly identified member, NDRG4, is specifically expressed in brain and heart. Genomics. 2001, 73: 86-97. 10.1006/geno.2000.6496.

    CAS  Article  PubMed  Google Scholar 

  39. Craxton M: Genomic analysis of synaptotagmin genes. Genomics. 2001, 77: 43-49. 10.1006/geno.2001.6619.

    CAS  Article  PubMed  Google Scholar 

  40. Li SH, McInnis MG, Margolis RL, Antonarakis SE, Ross CA: Novel triplet repeat containing genes in human brain: cloning, expression, and length polymorphisms. Genomics. 1993, 16: 572-579. 10.1006/geno.1993.1232.

    CAS  Article  PubMed  Google Scholar 

  41. Zemni R, Bienvenu T, Vinet MC, Sefiani A, Carrie A, Billuart P, McDonell N, Couvert P, Francis F, Chafey P, Fauchereau F, Friocourt G, Portes V, Cardona A, Frints S, Meindl A, Brandau O, Ronce N, Moraine C, Bokhoven H, Ropers HH, Sudbrak R, Kahn A, Fryns JP, Beldjord C, Chelly J: A new gene involved in X-linked mental retardation identified by analysis of an X;2 balanced translocation. Nat Genet. 2000, 24: 167-170. 10.1038/72829.

    CAS  Article  PubMed  Google Scholar 

  42. Rall S. C., Jr., Weisgraber KH, Mahley RW: Human apolipoprotein E. The complete amino acid sequence. J Biol Chem. 1982, 257: 4171-4178.

    CAS  PubMed  Google Scholar 

  43. van der Bliek AM, Redelmeier TE, Damke H, Tisdale EJ, Meyerowitz EM, Schmid SL: Mutations in human dynamin block an intermediate stage in coated vesicle formation. J Cell Biol. 1993, 122: 553-563.

    CAS  Article  PubMed  Google Scholar 

  44. Reddy TR, Li X, Jones Y, Ellisman MH, Ching GY, Liem RK, Wong-Staal F: Specific interaction of HTLV tax protein and a human type IV neuronal intermediate filament protein. Proc Natl Acad Sci U S A. 1998, 95: 702-707. 10.1073/pnas.95.2.702.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  45. Oliva D, Cali L, Feo S, Giallongo A: Complete structure of the human gene encoding neuron-specific enolase. Genomics. 1991, 10: 157-165.

    CAS  Article  PubMed  Google Scholar 

  46. Stein R, Mori N, Matthews K, Lo LC, Anderson DJ: The NGF-inducible SCG10 mRNA encodes a novel membrane-bound protein present in growth cones and abundant in developing neurons. Neuron. 1988, 1: 463-476.

    CAS  Article  PubMed  Google Scholar 

  47. Ju YT, Chang AC, She BR, Tsaur ML, Hwang HM, Chao CC, Cohen SN, Lin-Chao S: gas7: A gene expressed preferentially in growth-arrested fibroblasts and terminally differentiated Purkinje neurons affects neurite formation. Proc Natl Acad Sci U S A. 1998, 95: 11423-11428. 10.1073/pnas.95.19.11423.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  48. Zieger B, Tran H, Hainmann I, Wunderle D, Zgaga-Griesz A, Blaser S, Ware J: Characterization and expression analysis of two human septin genes, PNUTL1 and PNUTL2. Gene. 2000, 261: 197-203. 10.1016/S0378-1119(00)00527-8.

    CAS  Article  PubMed  Google Scholar 

  49. Tanaka M, Tanaka T, Kijima H, Itoh J, Matsuda T, Hori S, Yamamoto M: Characterization of tissue- and cell-type-specific expression of a novel human septin family gene, Bradeion. Biochem Biophys Res Commun. 2001, 286: 547-553. 10.1006/bbrc.2001.5413.

    CAS  Article  PubMed  Google Scholar 

  50. Dou D, Joseph R: Cloning of human neuronatin gene and its localization to chromosome-20q 11.2-12: the deduced protein is a novel "proteolipid'. Brain Res. 1996, 723: 8-22. 10.1016/0006-8993(96)00167-9.

    CAS  Article  PubMed  Google Scholar 

  51. Kukita A, Mukai T, Miyata T, Hori K: The structure of brain-specific rat aldolase C mRNA and the evolution of aldolase isozyme genes. Eur J Biochem. 1988, 171: 471-478.

    CAS  Article  PubMed  Google Scholar 

  52. Perez F, Pernet-Gallay K, Nizak C, Goodson HV, Kreis TE, Goud B: CLIPR-59, a new trans-Golgi/TGN cytoplasmic linker protein belonging to the CLIP-170 family. J Cell Biol. 2002, 156: 631-642. 10.1083/jcb.200111003.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  53. Vannahme C, Schubel S, Herud M, Gosling S, Hulsmann H, Paulsson M, Hartmann U, Maurer P: Molecular cloning of testican-2: defining a novel calcium-binding proteoglycan family expressed in brain. J Neurochem. 1999, 73: 12-20. 10.1046/j.1471-4159.1999.0730012.x.

    CAS  Article  PubMed  Google Scholar 

  54. Frank M: MAL, a proteolipid in glycosphingolipid enriched domains: functional implications in myelin and beyond. Prog Neurobiol. 2000, 60: 531-544. 10.1016/S0301-0082(99)00039-8.

    CAS  Article  PubMed  Google Scholar 

  55. Janoueix-Lerosey I, Pasheva E, de Tand MF, Tavitian A, de Gunzburg J: Identification of a specific effector of the small GTP-binding protein Rap2. Eur J Biochem. 1998, 252: 290-298. 10.1046/j.1432-1327.1998.2520290.x.

    CAS  Article  PubMed  Google Scholar 

  56. Li Y, Chin LS, Weigel C, Li L: Spring, a novel RING finger protein that regulates synaptic vesicle exocytosis. J Biol Chem. 2001, 276: 40824-40833. 10.1074/jbc.M106141200.

    CAS  Article  PubMed  Google Scholar 

  57. Wang W, Zhou Z, Zhao W, Huang Y, Tang R, Ying K, Xie Y, Mao Y: Molecular cloning, mapping and characterization of the human neurocalcin delta gene (NCALD). Biochim Biophys Acta. 2001, 1518: 162-167. 10.1016/S0167-4781(00)00290-6.

    CAS  Article  PubMed  Google Scholar 

  58. Vives V, Alonso G, Solal AC, Joubert D, Legraverend C: Visualization of S100B-positive neurons and glia in the central nervous system of EGFP transgenic mice. J Comp Neurol. 2003, 457: 404-419. 10.1002/cne.10552.

    CAS  Article  PubMed  Google Scholar 

  59. Hernandez MC, Andres-Barquin PJ, Holt I, Israel MA: Cloning of human ENC-1 and evaluation of its expression and regulation in nervous system tumors. Exp Cell Res. 1998, 242: 470-477. 10.1006/excr.1998.4109.

    CAS  Article  PubMed  Google Scholar 

  60. Dalmau J, Gultekin SH, Voltz R, Hoard R, DesChamps T, Balmaceda C, Batchelor T, Gerstner E, Eichen J, Frennier J, Posner JB, Rosenfeld MR: Ma1, a novel neuron- and testis-specific protein, is recognized by the serum of patients with paraneoplastic neurological disorders. Brain. 1999, 122 ( Pt 1): 27-39. 10.1093/brain/122.1.27.

    CAS  Article  Google Scholar 

  61. Sasaki K, Kurata-Miura K, Ujita M, Angata K, Nakagawa S, Sekine S, Nishi T, Fukuda M: Expression cloning of cDNA encoding a human beta-1,3-N-acetylglucosaminyltransferase that is essential for poly-N-acetyllactosamine synthesis. Proc Natl Acad Sci U S A. 1997, 94: 14294-14299. 10.1073/pnas.94.26.14294.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  62. Morris R: Thy-1 in developing nervous tissue. Dev Neurosci. 1985, 7: 133-160.

    CAS  Article  PubMed  Google Scholar 

  63. Hunt JM, Bommert K, Charlton MP, Kistner A, Habermann E, Augustine GJ, Betz H: A post-docking role for synaptobrevin in synaptic vesicle fusion. Neuron. 1994, 12: 1269-1279.

    CAS  Article  PubMed  Google Scholar 

  64. Gumienny TL, Brugnera E, Tosello-Trampont AC, Kinchen JM, Haney LB, Nishiwaki K, Walk SF, Nemergut ME, Macara IG, Francis R, Schedl T, Qin Y, Van Aelst L, Hengartner MO, Ravichandran KS: CED-12/ELMO, a novel member of the CrkII/Dock180/Rac pathway, is required for phagocytosis and cell migration. Cell. 2001, 107: 27-41.

    CAS  Article  PubMed  Google Scholar 

  65. Bressler SL, Gray MD, Sopher BL, Hu Q, Hearn MG, Pham DG, Dinulos MB, Fukuchi K, Sisodia SS, Miller MA, Disteche CM, Martin GM: cDNA cloning and chromosome mapping of the human Fe65 gene: interaction of the conserved cytoplasmic domains of the human beta-amyloid precursor protein and its homologues with the mouse Fe65 protein. Hum Mol Genet. 1996, 5: 1589-1598. 10.1093/hmg/5.10.1589.

    CAS  Article  PubMed  Google Scholar 

  66. Leung T, How BE, Manser E, Lim L: Cerebellar beta 2-chimaerin, a GTPase-activating protein for p21 ras-related rac is specifically expressed in granule cells and has a unique N-terminal SH2 domain. J Biol Chem. 1994, 269: 12888-12892.

    CAS  PubMed  Google Scholar 

  67. Jobsis GJ, Keizers H, Vreijling JP, de Visser M, Speer MC, Wolterman RA, Baas F, Bolhuis PA: Type VI collagen mutations in Bethlem myopathy, an autosomal dominant myopathy with contractures. Nat Genet. 1996, 14: 113-115.

    CAS  Article  PubMed  Google Scholar 

  68. Grover J, Chen XN, Korenberg JR, Roughley PJ: The human lumican gene. Organization, chromosomal location, and expression in articular cartilage. J Biol Chem. 1995, 270: 21942-21949. 10.1074/jbc.270.37.21942.

    CAS  Article  PubMed  Google Scholar 

  69. Todd SC, Doctor VS, Levy S: Sequences and expression of six new members of the tetraspanin/TM4SF family. Biochim Biophys Acta. 1998, 1399: 101-104. 10.1016/S0167-4781(98)00087-6.

    CAS  Article  PubMed  Google Scholar 

  70. Spurr NK, Durbin H, Sheer D, Parkar M, Bobrow L, Bodmer WF: Characterization and chromosomal assignment of a human cell surface antigen defined by the monoclonal antibody AUAI. Int J Cancer. 1986, 38: 631-636.

    CAS  Article  PubMed  Google Scholar 

  71. Lai EC, Kao FT, Law ML, Woo SL: Assignment of the alpha 1-antitrypsin gene and a sequence-related gene to human chromosome 14 by molecular hybridization. Am J Hum Genet. 1983, 35: 385-392.

    PubMed Central  CAS  PubMed  Google Scholar 

  72. Katahira J, Sugiyama H, Inoue N, Horiguchi Y, Matsuda M, Sugimoto N: Clostridium perfringens enterotoxin utilizes two structurally related membrane proteins as functional receptors in vivo. J Biol Chem. 1997, 272: 26652-26658. 10.1074/jbc.272.42.26652.

    CAS  Article  PubMed  Google Scholar 

  73. Nagase T, Miyajima N, Tanaka A, Sazuka T, Seki N, Sato S, Tabata S, Ishikawa K, Kawarabayasi Y, Kotani H: Prediction of the coding sequences of unidentified human genes. III. The coding sequences of 40 new genes (KIAA0081-KIAA0120) deduced by analysis of cDNA clones from human cell line KG-1 (supplement). DNA Res. 1995, 2: 51-59.

    CAS  Article  PubMed  Google Scholar 

  74. Trask DK, Band V, Zajchowski DA, Yaswen P, Suh T, Sager R: Keratins as markers that distinguish normal and tumor-derived mammary epithelial cells. Proc Natl Acad Sci U S A. 1990, 87: 2319-2323.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  75. Kulesh DA, Oshima RG: Complete structure of the gene for human keratin 18. Genomics. 1989, 4: 339-347.

    CAS  Article  PubMed  Google Scholar 

  76. Bader BL, Magin TM, Hatzfeld M, Franke WW: Amino acid sequence and gene organization of cytokeratin no. 19, an exceptional tail-less intermediate filament protein. Embo J. 1986, 5: 1865-1875.

    PubMed Central  CAS  PubMed  Google Scholar 

  77. Mbikay M, Nolet S, Fournier S, Benjannet S, Chapdelaine P, Paradis G, Dube JY, Tremblay R, Lazure C, Seidah NG: Molecular cloning and sequence of the cDNA for a 94-amino-acid seminal plasma protein secreted by the human prostate. DNA. 1987, 6: 23-29.

    CAS  Article  PubMed  Google Scholar 

  78. Sharief FS, Lee H, Leuderman MM, Lundwall A, Deaven LL, Lee CL, Li SS: Human prostatic acid phosphatase: cDNA cloning, gene mapping and protein sequence homology with lysosomal acid phosphatase. Biochem Biophys Res Commun. 1989, 160: 79-86.

    CAS  Article  PubMed  Google Scholar 

  79. Lundwall A, Lilja H: Molecular cloning of human prostate specific antigen cDNA. FEBS Lett. 1987, 214: 317-322. 10.1016/0014-5793(87)80078-9.

    CAS  Article  PubMed  Google Scholar 

  80. Matsuoka R, Yoshida MC, Furutani Y, Imamura S, Kanda N, Yanagisawa M, Masaki T, Takao A: Human smooth muscle myosin heavy chain gene mapped to chromosomal region 16q12. Am J Med Genet. 1993, 46: 61-67.

    CAS  Article  PubMed  Google Scholar 

  81. Laing NG, Wilton SD, Akkari PA, Dorosz S, Boundy K, Kneebone C, Blumbergs P, White S, Watkins H, Love DR: A mutation in the alpha tropomyosin gene TPM3 associated with autosomal dominant nemaline myopathy. Nat Genet. 1995, 9: 75-79.

    CAS  Article  PubMed  Google Scholar 

  82. Maestrini E, Patrosso C, Mancini M, Rivella S, Rocchi M, Repetto M, Villa A, Frattini A, Zoppe M, Vezzoni P: Mapping of two genes encoding isoforms of the actin binding protein ABP-280, a dystrophin like protein, to Xq28 and to chromosome 7. Hum Mol Genet. 1993, 2: 761-766.

    CAS  Article  PubMed  Google Scholar 

  83. Solway J, Seltzer J, Samaha FF, Kim S, Alger LE, Niu Q, Morrisey EE, Ip HS, Parmacek MS: Structure and expression of a smooth muscle cell-specific gene, SM22 alpha. J Biol Chem. 1995, 270: 13460-13469. 10.1074/jbc.270.22.13460.

    CAS  Article  PubMed  Google Scholar 

  84. Maric SC, Crozat A, Janne OA: Structure and organization of the human S-adenosylmethionine decarboxylase gene. J Biol Chem. 1992, 267: 18915-18923.

    CAS  PubMed  Google Scholar 

  85. Kumar CC, Chang C: Human smooth muscle myosin light chain-2 gene expression is repressed in ras transformed fibroblast cells. Cell Growth Differ. 1992, 3: 1-10. 10.1002/pat.1992.220030101.

    PubMed  Google Scholar 

  86. Lin B, White JT, Ferguson C, Wang S, Vessella R, Bumgarner R, True LD, Hood L, Nelson PS: Prostate short-chain dehydrogenase reductase 1 (PSDR1): a new member of the short-chain steroid dehydrogenase/reductase family highly expressed in normal and neoplastic prostate epithelium. Cancer Res. 2001, 61: 1611-1618.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by Science Foundation Ireland. We would like to thank Dr. Keith Ching, Genome Informatics Group, Novartis Genomics Institute for providing us with mapping of Affymetrix microarray probes to UniGene clusters, and Dr. Greg Singer for helpful discussions.

Author information

Authors and Affiliations

  1. Department of Genetics, Smurfit Institute, University of Dublin Trinity College, Dublin 2, Ireland

    Lukasz Huminiecki, Andrew T Lloyd & Kenneth H Wolfe

Authors
  1. Lukasz Huminiecki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew T Lloyd
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenneth H Wolfe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lukasz Huminiecki.

Additional information

Authors' contributions

LH study design, SAGE database, Affymetrix database, correlations, manuscript draft, preparation of figures and tables

AL statistical methods, TissueInfo database, Affymetrix database, manuscript draft

KW study design, coordination, manuscript review

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Huminiecki, L., Lloyd, A.T. & Wolfe, K.H. Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases. BMC Genomics 4, 31 (2003). https://doi.org/10.1186/1471-2164-4-31

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1471-2164-4-31

Keywords

  • Oligonucleotide Microarray
  • UniGene Cluster
  • Tissue Category
  • Unigene Dataset
  • UCSC Human Genome Browser

BMC Genomics

ISSN: 1471-2164

Contact us