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DNA Microarrays in the Undergraduate Microbiology Lab: Experimentation and Handling Large Datasets in as Few as Six Weeks

    Author: David B. Kushner1,*
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    Affiliations: 1: Department of Biology, Dickinson College, Carlisle, Pennsylvania 17013
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Published 17 May 2007
    • *Corresponding author. Mailing address: Department of Biology, Dickinson College, P. O. Box 1773, Carlisle, Pennsylvania 17013. Phone: (717) 245-1328. Fax: (717) 245-1130. E-mail: [email protected].
    • Copyright © 2007, American Society for Microbiology. All Rights Reserved.
    Source: J. Microbiol. Biol. Educ. May 2007 vol. 8 no. 1 3-12. doi:10.1128/193578807X14285807361449
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    Abstract:

    DNA microarrays have significantly impacted the study of gene expression on a genome-wide level but also have forced a more global consideration of research questions. As such, it has become critical to introduce undergraduate students to genomics approaches to research. A challenge with performing a DNA microarray experiment in the teaching lab is determining the time required for the study and how to handle the voluminous data generated. At an unexpectedly low cost, a 6-week, project-based lab module has been developed that provides 3 weeks for wet lab (hands-on work with the DNA microarrays) and 3 weeks for dry lab (analyzing data, using databases to help with data analysis, and considering the meaning of data within the large dataset). Options exist for extending the number of weeks dedicated to the project, but 6 weeks is sufficient for providing an introduction to both experimental genomics and data analysis. Students indicate that being able to both perform array experiments and thoroughly analyze data enriches their understanding of genomics and the complexity of biological systems.

References & Citations

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2. Campbell AM 2002 Meeting report: genomics in the undergraduate curriculum—rocket science or basic science? Cell Biol Educ 1 70 72 10.1187/cbe.02-06-0014 12459791 128541 http://dx.doi.org/10.1187/cbe.02-06-0014
3. Campbell AM 2003 Public access for teaching genomics, proteomics, and bioinformatics Cell Biol Educ 2 98 111 10.1187/cbe.03-02-0007 12888845 162192 http://dx.doi.org/10.1187/cbe.03-02-0007
4. Campbell AM, Eckdahl TT, Fowlks E, Heyer LJ, Hoopes LLM, Ledbetter ML, Rosenwald AG 2006 Genome Consortium for Active Teaching (GCAT) Science 311 1103 1104 10.1126/science.1121955 16497918 http://dx.doi.org/10.1126/science.1121955
5. Fare TL, Coffey EM, Dai H, He YD, Kessler DA, Kilian KA, Koch JE, LeProust E, Marton MJ, Meyer MR, Stoughton RB, Tokiwa GY, Wang Y 2003 Effects of atmospheric ozone on microarray data quality Anal Chem 75 4672 4675 10.1021/ac034241b 14632079 http://dx.doi.org/10.1021/ac034241b
6. Hancock D, Nguyen LL, Denyer GS, Johnston JM 2006 Microarrays for undergraduate classes Biochem Mol Biol Educ 34 432 437 10.1002/bmb.2006.494034062682 21638741 http://dx.doi.org/10.1002/bmb.2006.494034062682
7. Heyer LJ, Moskowitz DZ, Abele JA, Karnik P, Choi D, Campbell AM, Oldham EE, Akin BK 2005 MAGIC Tool: integrated microarray data analysis Bioinformatics 21 2114 2115 10.1093/bioinformatics/bti247 15647303 http://dx.doi.org/10.1093/bioinformatics/bti247
8. Ideker T 2004 Systems biology 101—what you need to know Nature Biotechnol 22 473 475 10.1038/nbt0404-473 http://dx.doi.org/10.1038/nbt0404-473
9. Kirschner MW 2005 The meaning of systems biology Cell 121 503 504 10.1016/j.cell.2005.05.005 15907462 http://dx.doi.org/10.1016/j.cell.2005.05.005
10. Kling J 2006 Working the systems Science 311 1305 1306 10.1126/science.311.5765.1305 16513987 http://dx.doi.org/10.1126/science.311.5765.1305
11. Lashkari DA, DeRisi JL, McCusker JH, Namath AF, Gentile C, Hwang SY, Brown PO, Davis RW 1997 Yeast microarrays for genome wide parallel genetic and gene expression analysis Proc Natl Acad Sci USA 94 13057 13062 10.1073/pnas.94.24.13057 9371799 http://dx.doi.org/10.1073/pnas.94.24.13057
12. Lok C 2005 Thinking outside the cell Nature 438 706 707 10.1038/nj7068-706a 16365935 http://dx.doi.org/10.1038/nj7068-706a
13. National Research Council, Committee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century 2003 BIO2010: transforming undergraduate education for future research biologists National Academy Press Washington, D.C.
14. Schena M, Shalon D, Davis RW, Brown PO 1995 Quantitative monitoring of gene expression patterns with a complementary DNA microarray Science 270 467 470 10.1126/science.270.5235.467 7569999 http://dx.doi.org/10.1126/science.270.5235.467

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2007-05-17
2019-01-17

Abstract:

DNA microarrays have significantly impacted the study of gene expression on a genome-wide level but also have forced a more global consideration of research questions. As such, it has become critical to introduce undergraduate students to genomics approaches to research. A challenge with performing a DNA microarray experiment in the teaching lab is determining the time required for the study and how to handle the voluminous data generated. At an unexpectedly low cost, a 6-week, project-based lab module has been developed that provides 3 weeks for wet lab (hands-on work with the DNA microarrays) and 3 weeks for dry lab (analyzing data, using databases to help with data analysis, and considering the meaning of data within the large dataset). Options exist for extending the number of weeks dedicated to the project, but 6 weeks is sufficient for providing an introduction to both experimental genomics and data analysis. Students indicate that being able to both perform array experiments and thoroughly analyze data enriches their understanding of genomics and the complexity of biological systems.

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Figures

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FIG. 1

DNA microarray experimental setup. Control (ctrl) and experimental (expt) RNA were each reverse transcribed with Cy3 or Cy5 tagged primers (four total reactions) in week 2 of the wet lab work. Distinct pairs of cDNAs were hybridized to distinct portions of the microarray (as indicated in the figure). A sequential incubation with Cy dyes generated signal. The results of the dye reversal can be seen in the sample array image (right). The two yellow spots in the center and upper right of both halves of the sample array indicate similar expression of the gene in both conditions; no signal indicates no expression of the gene in either condition; expression of the two genes in the experimental condition is seen using both dyes via the dye reversal.

Source: J. Microbiol. Biol. Educ. May 2007 vol. 8 no. 1 3-12. doi:10.1128/193578807X14285807361449
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FIG. 3

Portion of a student’s table (from fall 2005) containing information obtained from online databases. The molecular function, biological process, and cellular compartment categories relate to gene ontology. Protein localization information came from the yeast GFP-fusion localization database, and protein-protein interactions information came via BioGRID. As seen in the gene ontology columns, traceable author statement (TAS), inferred from direct assay (IDA), inferred from sequence or structural similarity (ISS), and inferred from mutant phenotype (IMP) are evidence codes for how the annotation was generated.

Source: J. Microbiol. Biol. Educ. May 2007 vol. 8 no. 1 3-12. doi:10.1128/193578807X14285807361449
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Image of FIG. 2

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FIG. 2

Figures provided to students to quiz them on understanding trends in gene expression data. (A) This figure was used in 2003 when asking students to consider trends in gene expression over time (reprinted from ( 3 ) with permission of the publisher. (B) This figure was used in 2004 and 2005 when asking students to directly consider relationships between genes and their levels of expression over time.

Source: J. Microbiol. Biol. Educ. May 2007 vol. 8 no. 1 3-12. doi:10.1128/193578807X14285807361449
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