Journal
NATURE BIOTECHNOLOGY
Volume 32, Issue 9, Pages 903-914Publisher
NATURE PORTFOLIO
DOI: 10.1038/nbt.2957
Keywords
-
Categories
Funding
- China's Program of Global Experts
- US National Institutes of Health (NIH) [R01CA163256, R01HG006798, R01NS076465, R44HG005297, U54CA119338, PO1HG00205, R24GM102656]
- Intramural Research Program of the NIH, National Library of Medicine, National Institute of Environmental Health Sciences (NIEHS) [Z01 ES102345-04]
- Shriners Research Grant [85500]
- Australia National Health and Medical Research Council (NHMRC) [1023454]
- Victorian State Government Operational Infrastructure Support (Australia)
- National 973 Key Basic Research Program of China [2010CB945401]
- National Natural Science Foundation of China [31240038, 31071162]
- Science and Technology Commission of Shanghai Municipality [11DZ2260300]
- Biotechnology and Biological Sciences Research Council [BB/I025840/1, BBS/E/D/20310000, BB/I000771/1, BB/J020265/1] Funding Source: researchfish
- Grants-in-Aid for Scientific Research [25241016] Funding Source: KAKEN
- BBSRC [BB/I025840/1, BB/I000771/1, BBS/E/D/20310000, BB/J020265/1] Funding Source: UKRI
Ask authors/readers for more resources
We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available