Selected article for: "RNA amount and viral sequence"

Author: Malboeuf, Christine M.; Yang, Xiao; Charlebois, Patrick; Qu, James; Berlin, Aaron M.; Casali, Monica; Pesko, Kendra N.; Boutwell, Christian L.; DeVincenzo, John P.; Ebel, Gregory D.; Allen, Todd M.; Zody, Michael C.; Henn, Matthew R.; Levin, Joshua Z.
Title: Complete viral RNA genome sequencing of ultra-low copy samples by sequence-independent amplification
  • Document date: 2012_9_8
  • ID: s76c5ebd_45
    Snippet: A major disadvantage for sequence-independent amplification of viral RNA samples is the large amount of host contamination that is amplified using these methods. We tested several different RNA sequence-independent amplification kits to evaluate their utility with viral clinical samples (unpublished results). The TransPlex Whole Transcriptome Amplification (WTA1-Sigma Aldrich, St. Louis, MO), Complete TransPlex Whole Transcriptome Amplification (.....
    Document: A major disadvantage for sequence-independent amplification of viral RNA samples is the large amount of host contamination that is amplified using these methods. We tested several different RNA sequence-independent amplification kits to evaluate their utility with viral clinical samples (unpublished results). The TransPlex Whole Transcriptome Amplification (WTA1-Sigma Aldrich, St. Louis, MO), Complete TransPlex Whole Transcriptome Amplification (WTA2-Sigma Aldrich), and SMARTer cDNA synthesis (Clontech, Mountain View, CA) kits produced 10-fold fewer reads aligning to the HIV genome for clinical samples compared with the Ovation RNA-Seq system (C. Malboeuf, unpublished data). The Ovation RNA-Seq system performed best, in part, due to it having the lowest percent of reads aligning to host contamination, particularly rRNA, which comprises >80% of total RNA. In our Ovation RNA-Seq experiments, <37% of the total reads from HIV and RSV clinical samples aligned to rRNA (Table 1) . This significant reduction in amplification of rRNA may have led to increased amplification of viral RNA thus allowing us to utilize only 5 million reads to generate full-length consensus assembles with >400-fold average coverage.

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