Resumen de cDNA Hybrid Capture Improves Transcriptome Analysis on Low-Input and Archived Samples

Christopher R. Cabanski, Vincent Magrini, Malachi Griffith, Obi L. Griffith, Sean McGrath, Jin Zhang, Jason Walker, Amy Ly, Ryan T. Demeter, Robert S. Fulton, Winnie W. Pong, David. H. Gutmann, Ramaswamy Govindan, Elaine R. Mardis, Christopher A. Maher

• The use of massively parallel sequencing for studying RNA expression has greatly enhanced our understanding of the transcriptome through the myriad ways these data can be characterized. In particular, clinical samples provide important insights about RNA expression in health and disease, yet these studies can be complicated by RNA degradation that results from the use of formalin as a clinical preservative and by the limited amounts of RNA often available from these precious samples. In this study we describe the combined use of RNA sequencing with an exome capture selection step to enhance the yield of on-exon sequencing read data when compared with RNA sequencing alone. In particular, the exome capture step preserves the dynamic range of expression, permitting differential comparisons and validation of expressed mutations from limited and FFPE preserved samples, while reducing the data generation requirement. We conclude that cDNA hybrid capture has the potential to significantly improve transcriptome analysis from low-yield FFPE material.