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Sharing and Reusing Gene Expression Profiling Data in Neuroscience

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Abstract

As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.

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References

  • Aarnio, V., Paananen, J., & Wong, G. (2005). Analysis of microarray studies performed in the neurosciences. Journal of Molecular Neuroscience, 27, 261–268.

    Article  PubMed  CAS  Google Scholar 

  • Assou, S., Le Carrour, T., Tondeur, S., Strom, S., Gabelle, A., Marty, S., et al. (2007). A meta-analysis of human embryonic stem cells transcriptome integrated into a web-based expression atlas. Stem Cells (Dayton, OH), 25, 961–973.

    Article  CAS  Google Scholar 

  • Barnes, M., Freudenberg, J., Thompson, S., Aronow, B., & Pavlidis, P. (2005). Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms. Nucleic Acids Research, 33, 5914–5923.

    Article  PubMed  CAS  Google Scholar 

  • Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Rudnev, D., Evangelista, C., et al. (2007). NCBI GEO: Mining tens of millions of expression profiles-database and tools update. Nucleic Acids Research, 35, D760–D765.

    Article  PubMed  CAS  Google Scholar 

  • Berman, H., Henrick, K., Nakamura, H., & Markley, J. L. (2007). The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Research, 35, D301–D303.

    Article  PubMed  CAS  Google Scholar 

  • Bota, M., Dong, H. W., & Swanson, L. W. (2005). Brain architecture management system. Neuroinformatics, 3, 15–48.

    Article  PubMed  Google Scholar 

  • Bowden, D. M., & Dubach, M. F. (2003). NeuroNames 2002. Neuroinformatics, 1, 43–59.

    Article  PubMed  Google Scholar 

  • Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., et al. (2001). Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics, 29, 365–371.

    Article  PubMed  CAS  Google Scholar 

  • Breitling, R., Armengaud, P., Amtmann, A., & Herzyk, P. (2004). Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Letters, 573, 83–92.

    Article  PubMed  CAS  Google Scholar 

  • Brown, P. O., Eisen, M. B., & Varmus, H. E. (2003). Why PLoS became a publisher. PLoS Biology, 1, E36.

    Article  PubMed  Google Scholar 

  • Chesler, E. J., Lu, L., Shou, S., Qu, Y., Gu, J., Wang, J., et al. (2005). Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genetics, 37, 233–242.

    Article  PubMed  CAS  Google Scholar 

  • Chin, M. H., Geng, A. B., Khan, A. H., Qian, W. J., Petyuk, V. A., Boline, J., et al. (2007). A genome-scale map of expression for a mouse brain section obtained using voxelation. Physiological Genomics (in press).

  • Choi, J. K., Yu, U., Kim, S., & Yoo, O. J. (2003). Combining multiple microarray studies and modeling interstudy variation. Bioinformatics (Oxford, England), 19(Suppl 1), i84–i90.

    Article  Google Scholar 

  • Cooper, H., & Hedges, L. V. (1994). Handbook of research synthesis. New York: Russell Sage Foundation.

    Google Scholar 

  • Crasto, C. J., Marenco, L. N., Liu, N., Morse, T. M., Cheung, K. H., Lai, P. C., et al. (2007). SenseLab: New developments in disseminating neuroscience information. Briefings in Bioinformatics, 8, 150–162.

    Article  PubMed  CAS  Google Scholar 

  • Eckersley, P., Egan, G. F., Amari, S., Beltrame, F., Bennett, R., Bjaalie, J. G., et al. (2003). Neuroscience data and tool sharing: A legal and policy framework for neuroinformatics. Neuroinformatics, 1, 149–165.

    Article  PubMed  Google Scholar 

  • Edgar, R., Domrachev, M., & Lash, A. E. (2002). Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30, 207–210.

    Article  PubMed  CAS  Google Scholar 

  • Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America, 95, 14863–14868.

    Article  PubMed  CAS  Google Scholar 

  • Eisenstein, M. (2006). Microarrays: Quality control. Nature, 442, 1067–1070.

    Article  PubMed  CAS  Google Scholar 

  • Enard, W., Khaitovich, P., Klose, J., Zollner, S., Heissig, F., Giavalisco, P., et al. (2002). Intra- and interspecific variation in primate gene expression patterns. Science (New York, NY), 296, 340–343.

    CAS  Google Scholar 

  • Erraji-Benchekroun, L., Underwood, M. D., Arango, V., Galfalvy, H., Pavlidis, P., Smyrniotopoulos, P., et al. (2005). Molecular aging in human prefrontal cortex is selective and continuous throughout adult life. Biological Psychiatry, 57, 549–558.

    Article  PubMed  CAS  Google Scholar 

  • Galbraith, D. W. (2006). The daunting process of MIAME. Nature, 444, 31.

    Article  PubMed  CAS  Google Scholar 

  • Ge, X., Yamamoto, S., Tsutsumi, S., Midorikawa, Y., Ihara, S., Wang, S. M., et al. (2005). Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics, 86, 127–141.

    Article  PubMed  CAS  Google Scholar 

  • Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., et al. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology, 5, R80.

    Article  PubMed  Google Scholar 

  • Glorioso, C., Sabatini, M., Unger, T., Hashimoto, T., Monteggia, L. M., Lewis, D.A., et al. (2006). Specificity and timing of neocortical transcriptome changes in response to BDNF gene ablation during embryogenesis or adulthood. Molecular Psychiatry, 11, 633–648.

    Article  PubMed  CAS  Google Scholar 

  • Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science (New York, NY), 286, 531–537.

    CAS  Google Scholar 

  • Gong, S., Zheng, C., Doughty, M. L., Losos, K., Didkovsky, N., Schambra, U. B., et al. (2003). A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature, 425, 917–925.

    Article  PubMed  CAS  Google Scholar 

  • Gu, J., & Gu, X. (2003). Induced gene expression in human brain after the split from chimpanzee. Trends Genetics, 19, 63–65.

    Article  CAS  Google Scholar 

  • Gu, J., & Gu, X. (2004). Further statistical analysis for genome-wide expression evolution in primate brain/liver/fibroblast tissues. Human Genomics, 1, 247–254.

    PubMed  CAS  Google Scholar 

  • Hakak, Y., Walker, J. R., Li, C., Wong, W. H., Davis, K. L., Buxbaum, J. D., et al. (2001). Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 98, 4746–4751.

    Article  PubMed  CAS  Google Scholar 

  • Hsieh, W. P., Chu, T. M., Wolfinger, R. D., & Gibson, G. (2003). Mixed-model reanalysis of primate data suggests tissue and species biases in oligonucleotide-based gene expression profiles. Genetics, 165, 747–757.

    PubMed  CAS  Google Scholar 

  • Hunter, J. E., & Schmidt, F. L. (1990). Methods of meta-analysis. London: Sage.

    Google Scholar 

  • Iwamoto, K., Bundo, M., Washizuka, S., Kakiuchi, C., & Kato, T. (2004a). Expression of HSPF1 and LIM in the lymphoblastoid cells derived from patients with bipolar disorder and schizophrenia. Journal of Human Genetics, 49, 227–231.

    Article  PubMed  CAS  Google Scholar 

  • Iwamoto, K., Kakiuchi, C., Bundo, M., Ikeda, K., & Kato, T. (2004b). Molecular characterization of bipolar disorder by comparing gene expression profiles of postmortem brains of major mental disorders. Molecular Psychiatry, 9, 406–416.

    Article  PubMed  CAS  Google Scholar 

  • Jurata, L. W., Bukhman, Y. V., Charles, V., Capriglione, F., Bullard, J., Lemire, A. L., et al. (2004). Comparison of microarray-based mRNA profiling technologies for identification of psychiatric disease and drug signatures. Journal of Neuroscience Methods, 138, 173–188.

    Article  PubMed  CAS  Google Scholar 

  • Kapushesky, M., Kemmeren, P., Culhane, A. C., Durinck, S., Ihmels, J., Korner, C., et al. (2004). Expression Profiler: Next generation—an online platform for analysis of microarray data. Nucleic Acids Research, 32, W465–W470.

    Article  PubMed  CAS  Google Scholar 

  • Koslow, S. H. (2000). Should the neuroscience community make a paradigm shift to sharing primary data? Nature Neuroscience, 3, 863–865.

    Article  PubMed  CAS  Google Scholar 

  • Koslow, S. H. (2005). Discovery and integrative neuroscience. Clinical EEG Neuroscience, 36, 55–63.

    Google Scholar 

  • Larsson, O., Wennmalm, K., & Sandberg, R. (2006). Comparative microarray analysis. Omics, 10, 381–397.

    Article  PubMed  CAS  Google Scholar 

  • Lee, H. K., Hsu, A.K., Sajdak, J., Qin, J., & Pavlidis, P. (2004). Coexpression analysis of human genes across many microarray data sets. Genome Research, 14, 1085–1094.

    Article  PubMed  CAS  Google Scholar 

  • Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., et al. (2007). Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445, 168–176.

    Article  PubMed  CAS  Google Scholar 

  • Lein, E. S., Zhao, X., & Gage, F. H. (2004). Defining a molecular atlas of the hippocampus using DNA microarrays and high-throughput in situ hybridization. Journal of Neuroscience, 24, 3879–3889.

    Article  PubMed  CAS  Google Scholar 

  • Li, H., Lu, L., Manly, K. F., Chesler, E. J., Bao, L., Wang, J., et al. (2005). Inferring gene transcriptional modulatory relations: A genetical genomics approach. Human Molecular Genetics, 14, 1119–1125.

    Article  PubMed  CAS  Google Scholar 

  • Magdaleno, S., Jensen, P., Brumwell, C. L., Seal, A., Lehman, K., Asbury, A., et al. (2006). BGEM: An in situ hybridization database of gene expression in the embryonic and adult mouse nervous system. PLoS Biology, 4, e86.

    Article  PubMed  Google Scholar 

  • McCarroll, S. A., Murphy, C. T., Zou, S., Pletcher, S. D., Chin, C. S., Jan, Y. N., et al. (2004). Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nature Genetics, 36, 197–204.

    Article  PubMed  CAS  Google Scholar 

  • Mirnics, K., & Pevsner, J. (2004). Progress in the use of microarray technology to study the neurobiology of disease. Nature Neuroscience, 7, 434–439.

    Article  PubMed  CAS  Google Scholar 

  • Moreau, Y., Aerts, S., De Moor, B., De Strooper, B., & Dabrowski, M. (2003). Comparison and meta-analysis of microarray data: From the bench to the computer desk. Trends in Genetics, 19, 570–577.

    Article  PubMed  CAS  Google Scholar 

  • Mulligan, M. K., Ponomarev, I., Hitzemann, R. J., Belknap, J. K., Tabakoff, B., Harris, R. A., et al. (2006). Toward understanding the genetics of alcohol drinking through transcriptome meta-analysis. Proceedings of the National Academy of Sciences of the United States of America, 103, 6368–6373.

    Article  PubMed  CAS  Google Scholar 

  • Pan, F., Chiu, C. H., Pulapura, S., Mehan, M. R., Nunez-Iglesias, J., Zhang, K., et al. (2007). Gene Aging Nexus: A web database and data mining platform for microarray data on aging. Nucleic Acids Research, 35, D756–D759.

    Article  PubMed  CAS  Google Scholar 

  • Parkinson, H., Kapushesky, M., Shojatalab, M., Abeygunawardena, N., Coulson, R., Farne, A., et al. (2007). ArrayExpress—a public database of microarray experiments and gene expression profiles. Nucleic Acids Research, 35, D747–D750.

    Article  PubMed  CAS  Google Scholar 

  • Pavlidis, P., & Noble, W. S. (2001). Analysis of strain and regional variation in gene expression in mouse brain. Genome Biology, 2, RESEARCH0042.

  • Perkins, D. O., Jeffries, C. D., Jarskog, L. F., Thomson, J. M., Woods, K., Newman, M. A., et al. (2007). MicroRNA expression in the prefrontal cortex of individuals with schizophrenia and schizoaffective disorder. Genome Biology, 8, R27.

    Article  PubMed  Google Scholar 

  • Polesskaya, O. O., Haroutunian, V., Davis, K. L., Hernandez, I., & Sokolov, B. P. (2003). Novel putative nonprotein-coding RNA gene from 11q14 displays decreased expression in brains of patients with schizophrenia. Journal of Neuroscience Research, 74, 111–122.

    Article  PubMed  CAS  Google Scholar 

  • Ponomarev, I., Maiya, R., Harnett, M. T., Schafer, G. L., Ryabinin, A. E., Blednov, Y. A., et al. (2006). Transcriptional signatures of cellular plasticity in mice lacking the alpha 1 subunit of GABA(A) receptors. Journal of Neuroscience, 26, 5673–5683.

    Article  PubMed  CAS  Google Scholar 

  • Rhodes, D. R., Barrette, T. R., Rubin, M. A., Ghosh, D., & Chinnaiyan, A. M. (2002). Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Research, 62, 4427–4433.

    PubMed  CAS  Google Scholar 

  • Rhodes, D. R., & Chinnaiyan, A. M. (2004). Bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers. Annals of the New York Academy of Sciences, 1020, 32–40.

    Article  PubMed  CAS  Google Scholar 

  • Rhodes, D. R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., et al. (2004). ONCOMINE: A cancer microarray database and integrated data-mining platform. Neoplasia (New York, NY), 6, 1–6.

    CAS  Google Scholar 

  • Sandberg, R., Yasuda, R., Pankratz, D. G., Carter, T. A., Del Rio, J. A., Wodicka, L., et al. (2000). Regional and strain-specific gene expression mapping in the adult mouse brain. Proceedings of the National Academy of Sciences of the United States of America, 97, 11038–11043.

    Article  PubMed  CAS  Google Scholar 

  • Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., Guhathakurta, D., et al. (2005). An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genetics, 37, 710–717.

    Article  PubMed  CAS  Google Scholar 

  • Shields, R. (2006). MIAME, we have a problem. Trends in Genetics, 22, 65–66.

    Article  PubMed  CAS  Google Scholar 

  • Sibille, E., Su, J., Leman, S., Le Guisquet, A. M., Ibarguen-Vargas, Y., Joeyen-Waldorf, J., et al. (2007). Lack of serotonin(1B) receptor expression leads to age-related motor dysfunction, early onset of brain molecular aging and reduced longevity. Molecular Psychiatry (in press).

  • Siddiqui, A. S., Khattra, J., Delaney, A. D., Zhao, Y., Astell, C., Asano, J., et al. (2005). A mouse atlas of gene expression: Large-scale digital gene-expression profiles from precisely defined developing C57BL/6J mouse tissues and cells. Proceedings of the National Academy of Sciences of the United States of America, 102, 18485–18490.

    Article  PubMed  CAS  Google Scholar 

  • Sokolov, B. P., Jiang, L. X., Trivedi, N. S., & Aston, C. (2003). Transcription profiling reveals mitochondrial, ubiquitin and signaling systems abnormalities in postmortem brains from subjects with a history of alcohol abuse or dependence. Journal of Neuroscience Research, 72, 756–767.

    Article  PubMed  CAS  Google Scholar 

  • Spellman, P. T., Miller, M., Stewart, J., Troup, C., Sarkans, U., Chervitz, S., et al. (2002). Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biology, 3, RESEARCH0046.

  • Stansberg, C., Vik-Mo, A. O., Holdhus, R., Breilid, H., Srebro, B., Petersen, K., et al. (2007). Gene expression profiles in rat brain disclose CNS signature genes and regional patterns of functional specialisation. BioMed Central Genomics, 8.

  • Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003). A gene-coexpression network for global discovery of conserved genetic modules. Science (New York, NY), 302, 249–255.

    CAS  Google Scholar 

  • Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., et al. (2004). A gene atlas of the mouse and human protein-encoding transcriptomes. Proceedings of the National Academy of Sciences of the United States of America, 101, 6062–6067.

    Article  PubMed  CAS  Google Scholar 

  • Sugino, K., Hempel, C. M., Miller, M. N., Hattox, A. M., Shapiro, P., Wu, C., et al. (2006). Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nature Neuroscience, 9, 99–107.

    Article  PubMed  CAS  Google Scholar 

  • Sunkin, S. M. (2006). Towards the integration of spatially and temporally resolved murine gene expression databases. Trends in Genetics, 22, 211–217.

    Article  PubMed  CAS  Google Scholar 

  • Tkachev, D., Mimmack, M. L., Ryan, M. M., Wayland, M., Freeman, T., Jones, P. B., et al. (2003). Oligodendrocyte dysfunction in schizophrenia and bipolar disorder. Lancet, 362, 798–805.

    Article  PubMed  CAS  Google Scholar 

  • Vazquez-Chona, F. R., Khan, A. N., Chan, C. K., Moore, A. N., Dash, P. K., Hernandez, M. R., et al. (2005). Genetic networks controlling retinal injury. Molecular Vision, 11, 958–970.

    PubMed  CAS  Google Scholar 

  • Watson, R. (2007). EC to promote open access publishing. Builders Merchants Journal (Clinical Research Education), 334, 389.

    Article  Google Scholar 

  • Williams, R. W. (2006). Expression genetics and the phenotype revolution. Mammalian Genome, 17, 496–502.

    Article  PubMed  Google Scholar 

  • Zapala, M. A., Hovatta, I., Ellison, J. A., Wodicka, L., Del Rio, J. A., Tennant, R., et al. (2005). Adult mouse brain gene expression patterns bear an embryologic imprint. Proceedings of the National Academy of Sciences of the United States of America, 102, 10357–10362.

    Article  PubMed  CAS  Google Scholar 

  • Zhang, W., Morris, Q. D., Chang, R., Shai, O., Bakowski, M. A., Mitsakakis, N., et al. (2004). The functional landscape of mouse gene expression. Journal of Biology, 3, 21.

    Article  PubMed  Google Scholar 

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Acknowledgements

We are grateful to the Etienne Sibille and the anonymous reviewers for helpful suggestions, and to Tanya Barrett and the rest of the GEO staff for their assistance with the use of GEO. We are indebted to the many groups who generously provide expression data. Supported by NIH GM076990 and a Michael Smith Foundation for Health Research Career Award to P.P.

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Correspondence to Paul Pavlidis.

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Wan, X., Pavlidis, P. Sharing and Reusing Gene Expression Profiling Data in Neuroscience. Neuroinform 5, 161–175 (2007). https://doi.org/10.1007/s12021-007-0012-5

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