Open Access Highly Accessed Research article

Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays

Ashish Agarwal1, David Koppstein1, Joel Rozowsky1, Andrea Sboner1, Lukas Habegger1, LaDeana W Hillier3, Rajkumar Sasidharan1, Valerie Reinke4, Robert H Waterston3 and Mark Gerstein12*

Author Affiliations

1 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA

2 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA

3 Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA

4 Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA

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BMC Genomics 2010, 11:383  doi:10.1186/1471-2164-11-383

Published: 17 June 2010

Abstract

Background

Tiling arrays have been the tool of choice for probing an organism's transcriptome without prior assumptions about the transcribed regions, but RNA-Seq is becoming a viable alternative as the costs of sequencing continue to decrease. Understanding the relative merits of these technologies will help researchers select the appropriate technology for their needs.

Results

Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of C. elegans. We find that the raw signals from these two technologies are reasonably well correlated but that RNA-Seq outperforms tiling arrays in several respects, notably in exon boundary detection and dynamic range of expression. By exploring the accuracy of sequencing as a function of depth of coverage, we found that about 4 million reads are required to match the sensitivity of two tiling array replicates. The effects of cross-hybridization were analyzed using a "nearest neighbor" classifier applied to array probes; we describe a method for determining potential "black list" regions whose signals are unreliable. Finally, we propose a strategy for using RNA-Seq data as a gold standard set to calibrate tiling array data. All tiling array and RNA-Seq data sets have been submitted to the modENCODE Data Coordinating Center.

Conclusions

Tiling arrays effectively detect transcript expression levels at a low cost for many species while RNA-Seq provides greater accuracy in several regards. Researchers will need to carefully select the technology appropriate to the biological investigations they are undertaking. It will also be important to reconsider a comparison such as ours as sequencing technologies continue to evolve.