Analysis of DNA strand-specific differential expression with high density tiling microarrays

BackgroundDNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol to exploit the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts.ResultsWe extensively filtered probes present in Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to generate custom Chip Description Files (CDF) in order to compare their efficiency. We experimentally tested the potential of our approach by measuring the differential expression of 4904 genes in the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress. The results showed a Pearson correlation coefficient of 0.943 between both platforms, indicating that TMAs are as reliable as EMAs for quantitative expression analysis. A significant advantage of TMAs over EMAs is the possibility of detecting non-annotated transcripts generated only under specific physiological conditions. To take full advantage of this property, we have used a target-labelling protocol that preserves the original polarity of the transcripts and, therefore, allows the strand-specific differential expression of non-annotated transcripts to be determined. By using a segmentation algorithm prior to generating the corresponding custom CDFs, we identified and quantitatively measured the expression of 510 transcripts longer than 180 nucleotides and not overlapping previously annotated ORFs that were differentially expressed at least 2-fold under oxidative stress.ConclusionsWe show that the information derived from TMA hybridization can be processed simultaneously for high-resolution qualitative and quantitative analysis of the differential expression of well-characterized genes and of previously non-annotated and antisense transcripts. The consistency of the performance of TMA, their genome-wide coverage and adaptability to updated genome annotations, and the possibility of measuring strand-specific differential expression makes them a tool of choice for the analysis of gene expression in any organism for which TMA platforms are available.

[1]  Scott A. Rifkin,et al.  A Gene Expression Map for the Euchromatic Genome of Drosophila melanogaster , 2004, Science.

[2]  Wei Zhou,et al.  Mapping the C. elegans noncoding transcriptome with a whole-genome tiling microarray. , 2007, Genome research.

[3]  Wolfgang Huber,et al.  A high-resolution map of transcription in the yeast genome. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Jürg Bähler,et al.  Whole-genome microarrays of fission yeast: characteristics, accuracy, reproducibility, and processing of array data , 2003, BMC Genomics.

[5]  L. Steinmetz,et al.  Bidirectional promoters generate pervasive transcription in yeast , 2009, Nature.

[6]  C. Ball,et al.  Repeatability of published microarray gene expression analyses , 2009, Nature Genetics.

[7]  Gunnar Rätsch,et al.  At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana , 2008, Genome Biology.

[8]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[9]  Christophe Malabat,et al.  Widespread bidirectional promoters are the major source of cryptic transcripts in yeast , 2009, Nature.

[10]  Mark Gerstein,et al.  Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping. , 2005, Trends in genetics : TIG.

[11]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[12]  S. Rasmussen,et al.  The transcriptionally active regions in the genome of Bacillus subtilis , 2009, Molecular microbiology.

[13]  G. Helt,et al.  Transcriptional Maps of 10 Human Chromosomes at 5-Nucleotide Resolution , 2005, Science.

[14]  Richard M. Karp,et al.  Efficient Randomized Pattern-Matching Algorithms , 1987, IBM J. Res. Dev..

[15]  Xiaole Shirley Liu,et al.  Getting Started in Tiling Microarray Analysis , 2007, PLoS Comput. Biol..

[16]  Dongrong Chen,et al.  Multiple pathways differentially regulate global oxidative stress responses in fission yeast. , 2008, Molecular biology of the cell.

[17]  I. Goodhead,et al.  Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution , 2008, Nature.

[18]  David Haussler,et al.  Forces Shaping the Fastest Evolving Regions in the Human Genome , 2006, PLoS genetics.

[19]  L. Steinmetz,et al.  Genome-wide allele- and strand-specific expression profiling , 2009, Molecular systems biology.

[20]  Jun Lu,et al.  Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: High-resolution annotation for microarrays , 2007, BMC Bioinform..