Abstract Number:
2630
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Lianbo Yu (1)
Institutions:
(1) The Ohio State University, N/A
First Author:
Presenting Author:
Abstract Text:
With the widespread RNA-seq applications of different sequencing platforms in biomedical science research in recent years, a systematic evaluation of RNA-seq data quality is crucial and timely. The Sequencing Quality Control (SEQC) project is a large-scale community effort for assessing the performance of RNA-seq technology across different platforms and multiple laboratories, where reference RNA samples with multiple replicates were sequenced at twelve laboratories using three sequencing platforms. Different from the SEQC project, we performed an independent and comprehensive analysis of RNA-seq data of the SEQC project to assess sequencing reproducibility across platforms, sequencing sites, sample replicates, and FlowCells respectively. With the employment of graphical tools and statistical models, our systemic analysis supports a distinctive conclusion that reproducibility across platforms and sequencing sites are not acceptable, while reproducibility across sample replicates and FlowCells are acceptable.
Keywords:
SEQC|Reproducibility| | | |
Sponsors:
Biometrics Section
Tracks:
Genomics, Metabolomics, Microbiome and NextGen Sequencing
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