Advertisement

Analysis of AmpliSeq RNA-Sequencing Enrichment Panels

  • Marek S. Wiewiorka
  • Alicja Szabelska
  • Michal J. Okoniewski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9124)

Abstract

This study presents a proof of concept of encoding genomic signatures in the AmpliSeq technology. The samples of patients with a disease and healthy ones have been processed using an AmpliSeq RNA sequencing kit of a custom design, that include 290 amplicons, sequenced using an IonTorrent machine. The read count data show the sufficient coverage in most of the chosen amplicons, which results in a good separability between the disease patients and healthy donors. In addition, several amplicons allow for checking useful genomics variants (SNPs), whenever the coverage level permits. The paper presents a machine-learning classifier evaluation of the answer to the question of difference between the patients and healthy donors, based upon the AmpliSeq panel data. The outcome confirms the potential utility of similar RNA amplicon kits in the research and clinical practice to encode gene expression signatures of diseases and their phenotypes.

Keywords

Genomics Transcriptomics Amplicon sequencing Classification Genomic signatures 

Notes

Acknowledgements

We are grateful to Kelli Bramlett, Jeoffrey Schageman, and Daniel Williams from LifeTech for discussion on AmpliSeq technology, to Andreas Tobler for coordinating the collaboration and to Marzanna Künzli-Gontarczyk, Daria Bochenek and Josias Brito Frazao for the help in the sequencing library prep and discussion on the lab aspects of the study. This work was supported by the grants Sciex.ch (nr. 11.182 to AS and MO, and nr 12.289 to MW and MO).

References

  1. 1.
    Clark, M.J., Chen, R., Lam, H.Y.K., Karczewski, K.J., Chen, R., Euskirchen, G., et al.: Performance comparison of exome DNA sequencing technologies. Nat. Biotechnol. 29(10), 908–914 (2011). doi: 10.1038/nbt.1975 CrossRefGoogle Scholar
  2. 2.
    Sulonen, A.-M., Ellonen, P., Almusa, H., Lepistoe, M., Eldfors, S., Hannula, S., et al.: Comparison of solution-based exome capture methods for next generation sequencing. Genome Biol. 12(9), R94 (2011). doi: 10.1186/gb-2011-12-9-r94 CrossRefGoogle Scholar
  3. 3.
    Rachwal, P.A., Rose, H.L., Cox, V., Lukaszewski, R.A., Murch, A.L., Weller, S.A.: The potential of TaqMan array cards for detection of multiple biological agents by real-time PCR. PloS One 7(4), e35971 (2012). doi: 10.1371/journal.pone.0035971 CrossRefGoogle Scholar
  4. 4.
    Yuan, J., Reed, A., Chen, F., Stewart, C.N.: Statistical analysis of real-time PCR data. BMC Bioinform. 7(1), 85–85 (2005). doi: 10.1186/1471-2105-7-85 CrossRefGoogle Scholar
  5. 5.
    Okoniewski, M.J., Meienberg, J., Patrignani, A., Szabelska, A., Matyas, G., Schlapbach, R.: Precise breakpoint localization of large genomic deletions using PacBio and lllumina next-generation sequencers. BioTechniques 54(2), 98–100 (2013). doi: 10.2144/000113992 CrossRefGoogle Scholar
  6. 6.
    Veer, L.V., Dai, H., Van De Vijver, M.J., He, Y.D.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002)CrossRefGoogle Scholar
  7. 7.
    Van De Vijver, M.J., He, Y.D., Veer, L.V., et al.: A gene-expression signature as a predictor of survival in breast cancer. New Engl. J. Med. N 347, 1999–2009 (2002). doi: 10.1056/NEJMoa021967 CrossRefGoogle Scholar
  8. 8.
    Zhou, T., Zhang, W., Sweiss, N.J., Chen, E.S., Moller, D.R., Knox, K.S., et al.: Peripheral blood gene expression as a novel genomic biomarker in complicated sarcoidosis. PloS One 7(9), e44818 (2012). doi: 10.1371/journal.pone.0044818 CrossRefGoogle Scholar
  9. 9.
    Trapnell, C., Pachter, L., Salzberg, S.L.: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (Oxford, England) 25(9), 1105–1111 (2009). doi: 10.1093/bioinformatics/btp120 CrossRefGoogle Scholar
  10. 10.
    Leniewska, A., Okoniewski, M.J.: rnaSeqMap: a bioconductor package for RNA sequencing data exploration. BMC Bioinform. 12, 200 (2011). doi: 10.1186/1471-2105-12-200 CrossRefGoogle Scholar
  11. 11.
    Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al.: The sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England) 25(16), 2078–2079 (2009). doi: 10.1093/bioinformatics/btp352 CrossRefGoogle Scholar
  12. 12.
    Anders, S., McCarthy, D.J., Chen, Y., Okoniewski, M., Smyth, G.K., Huber, W., Robinson, M.D.: Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat. Protoc. 8, 1765–1786 (2013). http://arxiv.org/abs/1302.3685 CrossRefGoogle Scholar
  13. 13.
    Li, J., Tibshirani, R.: Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data. Stat. Methods Med. Res. 22(5), 519–536 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marek S. Wiewiorka
    • 1
  • Alicja Szabelska
    • 2
  • Michal J. Okoniewski
    • 3
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland
  2. 2.Department of Mathematical and Statistical MethodsPoznan University of Life SciencesPoznan Poland
  3. 3.Scientific IT ServicesSwiss Federal Institute of Technology (ETH Zurich)ZurichSwitzerland

Personalised recommendations