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Analyzing Gene Expression Data from Microarray and Next‐Generation DNA Sequencing Transcriptome Profiling Assays Using GeneSifter Analysis Edition

Sandra Porter1,2,  N. Eric Olson2,  Todd Smith2

1Digital World Biology, Seattle, Washington
2Geospiza, Inc., Seattle, Washington


Publication Name: 
Current Protocols in Bioinformatics
Unit Number: 
UNIT 7.14
DOI: 
10.1002/0471250953.bi0714s27
Online Posting Date: 
September, 2009
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Abstract

Transcription profiling with microarrays has become a standard procedure for comparing the levels of gene expression between pairs of samples, or multiple samples following different experimental treatments. New technologies, collectively known as next-generation DNA sequencing methods, are also starting to be used for transcriptome analysis. These technologies, with their low background, large capacity for data collection, and dynamic range, provide a powerful and complementary tool to the assays that formerly relied on microarrays. In this chapter, we describe two protocols for working with microarray data from pairs of samples and samples treated with multiple conditions, and discuss alternative protocols for carrying out similar analyses with next-generation DNA sequencing data from two different instrument platforms (Illumina GA and Applied Biosystems SOLiD). Curr. Protoc. Bioinform. 27:7.14.1-7.14.35. © 2009 by John Wiley & Sons, Inc.

Keywords: gene expression; microarray; RNA-Seq; transcriptome; GeneSifter Analysis Edition; next-generation DNA sequencing

     
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Table of Contents

  • Introduction
  • Basic Protocol 1: Comparing Gene Expression from Paired Sample Data Obtained from Microarray Experiments
  • Alternate Protocol 1: Compare Gene Expression from Paired Samples Obtained from Transcriptome Profiling Assays by Next-Generation DNA Sequencing
  • Basic Protocol 2: Comparing Gene Expression from Microarray Experiments with Multiple Conditions
  • Alternate Protocol 2: Compare Gene Expression from Next-Generation DNA Sequencing Data Obtained from Multiple Conditions
  • Literature Cited
  • Figures
  • Tables
  • Topics
    • Bioinformatics
    • Gene Expression
    • Genetics and Genomics
    • Molecular Biology
    • Nucleic Acid Chemistry
    • RNA
     
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Figures

  • Figure 7.14.1
    Overview of the process for a pairwise comparison.

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  • Figure 7.14.2
    Setting up a pairwise comparison.

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  • Figure 7.14.3
    Analyzing the results from a pairwise comparison.

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  • Figure 7.14.4
    Scatter plot.

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  • Figure 7.14.5
    KEGG pathway results.

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  • Figure 7.14.6
    Gene ontology reports.

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  • Figure 7.14.7
    Analysis results from NGS data, obtained from an ABI SOLiD instrument.

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  • Figure 7.14.8
    Gene summaries for microarray and NGS data. A gene summary from a microarray sample is shown in the top half of the image and a summary for a sample analyzed by NGS is shown in the bottom half. Note the difference between the intensity and quality values.

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  • Figure 7.14.9
    Overview of an experiment comparing multiple conditions.

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  • Figure 7.14.10
    Box plot.

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  • Figure 7.14.11
    Box plots from a multiple-condition experiment. (A) Box plots from the six conditions that were compared in Basic Protocol 2. Each plot represents the averaged data from the four to five replicates from each treatment. (B) Box plots from biological replicates. Replicates from the AIN-76, 0 lead samples are shown.

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  • Figure 7.14.12
    Analyzing the results from comparing multiple samples.

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  • Figure 7.14.13
    Gene-specific navigation.

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  • Figure 7.14.14
    Illumina data.

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  • Figure 7.14.15
    Partitioning and silhouette data from a Next Gen experiment.

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Literature Cited

Literature Cited
    Barrett, T., Troup, D.B., Wilhite, S.E., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I.F., Soboleva, A., Tomashevsky, M., Marshall, K.A., Phillippy, K.H., Sherman, P.M., Muertter, R.N., and Edgar, R. 2009. NCBI GEO: Archive for high-throughput functional genomic data. Nucleic Acids Res. 37:D885-D890.
    Kaufman, L. and Rousseeuw, P. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., New York.
    Kozul, C.D., Nomikos, A.P., Hampton, T.H., Warnke, L.A., Gosse, J.A., Davey, J.C., Thorpe, J.E., Jackson, B.P., Ihnat, M.A., and Hamilton, J.W. 2008. Laboratory diet profoundly alters gene expression and confounds genomic analysis in mouse liver and lung. Chem. Biol. Interact. 173:129-140.
    Li, H. and Durbin, R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics E-pub May 18.
    Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M., and Gilad, Y. 2008. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18:1509-1517.
    Millenaar, F.F., Okyere, J., May, S.T., van Zanten, M., Voesenek, L.A., and Peeters, A.J. 2006. How to decide Different methods of calculating gene expression from short oligonucleotide array data will give different results. BMC Bioinformatics 7:137.
    Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., and Wold, B. 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5:621-628.
    Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B.B., Siddiqui, A., Lao, K., and Surani, M.A. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 5:377-382.
    Wang, Z., Gerstein, M., and Snyder, M. 2009. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 10:57-63.
    Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., Dicuccio, M., Edgar, R., Federhen, S., Feolo, M., Geer, L.Y., Helmberg, W., Kapustin, Y., Khovayko, O., Landsman, D., Lipman, D.J., Madden, T.L., Maglott, D.R., Miller, V., Ostell, J., Pruitt, K.D., Schuler, G.D., Shumway, M., Sequeira, E., Sherry, S.T., Sirotkin, K., Souvorov, A., Starchenko, G., Tatusov, R.L., Tatusova, T.A., Wagner, L., and Yaschenko, E. 2008. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 36:D13-D21.
 Internet Resources
    http://www.geospiza.com/Support/datacenter.shtml

The microarray data center at Geospiza, Inc. A diverse set of microarray data sets and tutorials on using GSAE are available from this page.

    http://www.ncbi.nlm.nih.gov/geo/

The NCBI GEO (Gene Expression Omnibus) database. GEO is a convenient place to find both microarray and Next Gen transcriptome datasets.

    http://www.ebi.ac.uk/microarray/

The ArrayExpress database from the European Bioinformatics Institute. Both microarray and Next Gen transcriptome data can be obtained here.

    http://www.ncbi.nlm.nih.gov/sra/

The NCBI SRA (Short Read Archive) database. Some Next Gen transcriptome data can be obtained here.

     
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