Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays
<p>Abstract</p> <p>Background</p> <p>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 decr...
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doaj-5e30d81138f04f6b8343235124d959612020-11-25T01:39:16ZengBMCBMC Genomics1471-21642010-06-0111138310.1186/1471-2164-11-383Comparison and calibration of transcriptome data from RNA-Seq and tiling arraysReinke ValerieSasidharan RajkumarHillier LaDeana WHabegger LukasSboner AndreaRozowsky JoelKoppstein DavidAgarwal AshishWaterston Robert HGerstein Mark<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of <it>C. elegans</it>. 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.</p> <p>Conclusions</p> <p>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.</p> http://www.biomedcentral.com/1471-2164/11/383 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Reinke Valerie Sasidharan Rajkumar Hillier LaDeana W Habegger Lukas Sboner Andrea Rozowsky Joel Koppstein David Agarwal Ashish Waterston Robert H Gerstein Mark |
spellingShingle |
Reinke Valerie Sasidharan Rajkumar Hillier LaDeana W Habegger Lukas Sboner Andrea Rozowsky Joel Koppstein David Agarwal Ashish Waterston Robert H Gerstein Mark Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays BMC Genomics |
author_facet |
Reinke Valerie Sasidharan Rajkumar Hillier LaDeana W Habegger Lukas Sboner Andrea Rozowsky Joel Koppstein David Agarwal Ashish Waterston Robert H Gerstein Mark |
author_sort |
Reinke Valerie |
title |
Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays |
title_short |
Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays |
title_full |
Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays |
title_fullStr |
Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays |
title_full_unstemmed |
Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays |
title_sort |
comparison and calibration of transcriptome data from rna-seq and tiling arrays |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2010-06-01 |
description |
<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of <it>C. elegans</it>. 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.</p> <p>Conclusions</p> <p>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.</p> |
url |
http://www.biomedcentral.com/1471-2164/11/383 |
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