RNA-Seq Accuracy Comprehensive Assessment, Duplicability and Knowledge Content by the Sequencing Internal Control Consortium
Bioinformatics Research Group, Boku University Vienna, Vienna, Austria
- Corresponding Author:
- Leming Shi
Bioinformatics Research Group
Boku University Vienna, Vienna, Austria
Email: [email protected]
Received date: September 15, 2014; Accepted date: December 23, 2014; Published date: December 26, 2014
Citation: Shi L (2015) RNA-Seq Accuracy Comprehensive Assessment, Duplicability and Knowledge Content by the Sequencing Internal Control Consortium. J Tissue Sci Eng 6:146. doi:10.4172/2157-7552.1000146
Copyright: © 2015 Shi L. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
We gift primary results from the Sequencing Internal Control (SEQC) project, coordinated by the America Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites victimization reference RNA samples with intrinsical controls, we have a tendency to assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression identification and compare it to microarray and quantitative PCR (qPCR) information victimization complementary metrics. in the slightest degree sequencing depths, we have a tendency to discover unannotated exon-exon junctions, with >80% valid by qPCR. We discover that measurements of relative expression area unit correct and reproducible across sites and platforms if specific filters area unit used. In distinction, RNA-seq and microarrays don't offer correct absolute measurements, and gene-specific biases area unit determined for all examined platforms, together with qPCR. Activity performance depends on the platform and information analysis pipeline, and variation is giant for transcript-level identification. The whole SEQC information sets, comprising >100 billion reads (10Tb), offer distinctive resources for evaluating RNA-seq analyses for clinical and regulative settings.