RNA序列
计算生物学
基因表达谱
核糖核酸
生物
DNA微阵列
深度测序
基因
基因表达
遗传学
转录组
基因组
作者
Zhenqiang Su,Paweł P. Łabaj,Sheng Li,Jean Thierry‐Mieg,Danielle Thierry‐Mieg,Wei Shi,Charles Wang,Gary P. Schroth,Robert A. Setterquist,John F. Thompson,Wendell Jones,Wenzhong Xiao,Weihong Xu,Roderick V. Jensen,Reagan Kelly,Joshua Xu,Ana Conesa,Cesare Furlanello,Hanlin Gao,Huixiao Hong
摘要
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.
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