TY - JOUR
T1 - Multi-platform assessment of transcriptional profiling technologies utilizing a precise probe mapping methodology
AU - Yu, Jinsheng
AU - Cliften, Paul F.
AU - Juehne, Twyla I.
AU - Sinnwell, Toni M.
AU - Sawyer, Chris S.
AU - Sharma, Mala
AU - Lutz, Andrew
AU - Tycksen, Eric
AU - Johnson, Mark R.
AU - Minton, Matthew R.
AU - Klotz, Elliott T.
AU - Schriefer, Andrew E.
AU - Yang, Wei
AU - Heinz, Michael E.
AU - Crosby, Seth D.
AU - Head, Richard D.
N1 - Funding Information:
The authors would like to thank Dr. Rebecca Aft for kindly providing the normal bone marrow RNA utilized in this study. The Genome Technology Access Center is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant# UL1RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. This publication is solely the responsibility of the authors and does not necessarily represent the official view of NCRR or NIH.
Publisher Copyright:
© 2015 Yu et al.
PY - 2015/9/18
Y1 - 2015/9/18
N2 - Background: The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed "transcript pattern". A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes. Results: In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% fold-change concordance with the gold standard qRT-PCR (TaqMan). Conclusions: This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations.
AB - Background: The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed "transcript pattern". A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes. Results: In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% fold-change concordance with the gold standard qRT-PCR (TaqMan). Conclusions: This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations.
KW - Fold-change
KW - Microarray
KW - RNA-seq
KW - TaqMan assay
KW - Transcript pattern
UR - http://www.scopus.com/inward/record.url?scp=84961166046&partnerID=8YFLogxK
U2 - 10.1186/s12864-015-1913-6
DO - 10.1186/s12864-015-1913-6
M3 - Article
C2 - 26385698
AN - SCOPUS:84961166046
SN - 1471-2164
VL - 16
JO - BMC Genomics
JF - BMC Genomics
IS - 1
M1 - 710
ER -