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.
Tissue
TissueInfo vs. SAGEmap
GEA vs. SAGEmap
GEA vs. TissueInfo
Genesa
r2
Pb
Signsc
Genesa
r2
Pb
Signsc
Genesa
r2
Pb
Signsc
Brain
346
0.59
<0.01
92%
479
0.49
<0.01
84%
1431
0.43
<0.01
77%
Prostate
58
0.36
<0.01
76%
237
0.09
<0.01
59%
266
0.19
<0.01
63%
Vascular endothelium
13
0.29
0.6
92%
150
0.37
<0.01
73%
23
0.05
0.29
39%
Ovary
10
0.11
0.35
30%
175
0.03
0.2
49%
36
0.03
0.28
69%
Kidney
103
0.01
0.36
34%
140
0.00
0.78
50%
508
0.11
<0.01
55%
Pancreas
21
0.02
0.55
62%
141
0.00
0.59
48%
82
0.02
0.19
55%
a Number of genes compared. SAGEmap and TissueInfo data were only used for genes whose PEM scores were significant by χ2 test (P = 0.05), and only tags that could be mapped unambiguously to UniGene were used. b Two-tailed significance values for Pearson correlation coefficients. c Percentage of genes whose PEM scores have the same sign for the two methods being compared.
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.
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.
Brain
Prostate
Kidney
Vascular endothelium
Ovary
Pancreas
SAGE
326,481
150,116
67,923
110,460
96,911
64,577
EST
154,214
51,299
60,188
8,435
12,587
23,545
SAGE+EST
480,695
201,415
128,111
118,895
109,498
88,122
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.
%PEM a
UniGene cluster
LocusLink symbol
Description and references
76%
Hs.6535
n/a
novel cDNA clone, FLJ33742 fis, highly similar to Homo sapiens BNPI mRNA for brain-specific Na-dependent inorganic phosphate cotransporter (GenBank AK091061)
76%
Hs.143535
CAMK2A
calcium/calmodulin-dependent protein kinase, implied in control of fear and aggression [18]
stathmin-like 2; expressed in neuronal growth cones [46]
61%
Hs.226133
GAS7
growth arrest-specific 7; expressed in neurons and growth-arrested fibroblasts [47]
60%
Hs.155524
PNUTL2
septin 4; human orthologue of the mouse h5 brain protein [48]; involved in cell-division control; expressed in brain and, an alternatively spliced form, in heart [49]
60%
Hs.117546
NNAT
neuronatin; known to contain a 5' neurospecific silencer element which controls brain-specific expression [50]; possible role in brain development
60%
Hs.155247
ALDOC
brain-type aldolase; fructose-bisphosphate aldolase C [51]
60%
Hs.7357
CLIPR-59
microtubule-binding protein involved in Golgi targeting; known to be preferentially expressed in brain [52]
60%
Hs.74583
SPOCK2
sparc/osteonectin like; testican 2; calcium-binding proteoglycan specific to brain; expressed in many neuronal cell types in olfactory bulb, cerebral cortex, thalamus, hippocampus, cerebellum, and medulla [53]
60%
Hs.80395
MAL
hydrophobic integral membrane lipoprotein; a component of myelin [54]
60%
Hs.6349
BC008967
novel protein BC008967
60%
Hs.6755
RPIP8
RaP2 interacting protein; belongs to Ras family; expressed principally in brain [55]
60%
Hs.75090
SPRING
involved in synaptic exocytosis; expressed only in brain [56]
60%
Hs.90063
NCALD
neurocalcin delta; calcium-binding protein; expressed preferentially in brain [57]
58%
Hs.83384
S100B
calcium-binding protein S100; expressed in glia, astrocytes and neurons [58]
58%
Hs.104925
ENC1
ectodermal-neural cortex; p53 induced gene; expressed preferentially in brain, particularly in amygdala and hippocampus [59]
58%
Hs.7782
PNMA2
onconeuronal antigen MA2; expressed in brain and testis [60]
58%
Hs.8526
B3GNT6
beta-1,3-N-acetylglucosaminyltransferase 6; expressed preferentially in foetal and adult brain [61]
56%
Hs.125359
THY1
Thy-1 cell surface antigen; expressed in brain and on some T cells; neuronal expression pattern changes in development suggesting role in neurogenesis [62]
56%
Hs.194534
VAMP2
synaptobrevin 2; one of proteins involved in fusion of synaptic vesicles with the presynaptic membrane [63]
56%
Hs.31463
ELMO1
engulfment and cell motility 1; orthologue of C. elegans gene ced-12; has two isforms – 4.4 kb expressed ubiquitously and 2.4 kb expressed only in brain [64]
55%
Hs.3763
APBB1
amyloid beta (A4) precursor protein-binding; expressed preferentially in brain [65]
55%
Hs.286055
CHN2
chimerin 2; expressed preferentially in cerebellum in granule cells [66]
55%
Hs.12305
DKFZP566B183
novel protein DKFZP566B183
-50%
Hs.80988
COL6A3
collagen type VI alpha 3 chain; type 3 alpha chain of a beaded filament collagen found in most connective tissues[67]
-50%
Hs.79914
LUM
lumican, a keratan sulfate proteoglycan known to be abundant in the corneal stroma and collagenous matrices of the heart, skeletal muscle, aorta, and intervertebral discs[68]
claudin 4; expressed in kidney, small intestine, lung, heart, liver, and skeletal muscle; no expression was found in brain or spleen[72]
-57%
Hs.81892
KIAA0101
identified in the Kazusa large-scale cDNA sequencing project [73]; northern blot revealed expression in many tissues but signal is completely absent from brainhttp://www.kazusa.or.jp/huge/gfpage/KIAA0101/
-58%
Hs.195850
KRT5
keratin 5; expressed in many epithelia including mammary epithelial cells and squamous epithelia lining the upper digestive tract[74]
keratin 19; found predominantly in the periderm – the transient layer that surrounds the developing epidermis[76]
a Integrated PEM score, calculated as the average of PEMGEA/PEMGEAmax, PEMSAGEMAP/PEMSAGEMAPmax, PEMTISSUEINFO/PEMTISSUEINFOmax, where "max" refers to the maximum PEM value encountered in the tissue. This weighting scheme was used because brain microarray PEM values were several fold higher than those for SAGE and EST (PEMGEAmax = 5.21, PEMSAGEMAPmax = 1.19, PEMTISSUEINFOmax = 1.15)
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.
%PEM a
UniGene cluster
LocusLink symbol
Description and references
95%
Hs.183752
MSMB
beta microseminoprotein; synthesized by the epithelial cells of the prostate gland and secreted into the seminal plasma [77]
a Prostate PEMGEAmax = 4.30, PEMSAGEMAPmax = 1.97, and PEMTISSUEINFOmax = 1.61
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.
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
Declarations
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.
Authors’ Affiliations
(1)
Department of Genetics, Smurfit Institute, University of Dublin Trinity College
References
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
Itoh K, Okubo K, Utiyama H, Hirano T, Yoshii J, Matsubara K: Expression profile of active genes in granulocytes. Blood. 1998, 92: 1432-1441.PubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.PubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
Yan Y, Lagenaur C, Narayanan V: Molecular cloning of M6: identification of a PLP/DM20 gene family. Neuron. 1993, 11: 423-431.View ArticlePubMedGoogle Scholar
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.PubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.PubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
Rall S. C., Jr., Weisgraber KH, Mahley RW: Human apolipoprotein E. The complete amino acid sequence. J Biol Chem. 1982, 257: 4171-4178.PubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
Oliva D, Cali L, Feo S, Giallongo A: Complete structure of the human gene encoding neuron-specific enolase. Genomics. 1991, 10: 157-165.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticleGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.PubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralPubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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 CentralView ArticlePubMedGoogle Scholar
Kulesh DA, Oshima RG: Complete structure of the gene for human keratin 18. Genomics. 1989, 4: 339-347.View ArticlePubMedGoogle Scholar
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 CentralPubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
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.View ArticlePubMedGoogle Scholar
Maric SC, Crozat A, Janne OA: Structure and organization of the human S-adenosylmethionine decarboxylase gene. J Biol Chem. 1992, 267: 18915-18923.PubMedGoogle Scholar
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.PubMedGoogle Scholar
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.PubMedGoogle Scholar
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