title: Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays.
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.
authors.affiliation: Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
authors.forename: Ashish
authors.initials: A
authors.lastname: Agarwal
authors.forename: David
authors.initials: D
authors.lastname: Koppstein
authors.forename: Joel
authors.initials: J
authors.lastname: Rozowsky
authors.forename: Andrea
authors.initials: A
authors.lastname: Sboner
authors.forename: Lukas
authors.initials: L
authors.lastname: Habegger
authors.forename: Ladeana W
authors.initials: LW
authors.lastname: Hillier
authors.forename: Rajkumar
authors.initials: R
authors.lastname: Sasidharan
authors.forename: Valerie
authors.initials: V
authors.lastname: Reinke
authors.forename: Robert H
authors.initials: RH
authors.lastname: Waterston
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D000818:Animals; D001482:Base Composition; D017173:Caenorhabditis elegans / Q000235:genetics; D002138:Calibration; D030541:Databases, Genetic; D005091:Exons / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011544:Pseudogenes / Q000235:genetics; D012313:RNA / Q000235:genetics*
chemical_list: D012313:RNA