Quantitative Analysis of Synthesized Nucleic Acid Pools

  • Ramon Xulvi-Brunet
  • Gregory W. Campbell
  • Sudha Rajamani
  • José I. Jiménez
  • Irene A. Chen
Part of the SEMA SIMAI Springer Series book series (SEMA SIMAI, volume 7)


Experimental evolution of RNA (or DNA) is a powerful method to isolate sequences with useful function (e.g., catalytic RNA), discover fundamental features of the sequence-activity relationship (i.e., the fitness landscape), and map evolutionary pathways or functional optimization strategies. However, the limitations of current sequencing technology create a significant undersampling problem which impedes our ability to measure the true distribution of unique sequences. In addition, synthetic sequence pools contain a non-random distribution of nucleotides. Here, we present and analyze simple models to approximate the true sequence distribution. We also provide tools that compensate for sequencing errors and other biases that occur during sample processing. We describe our implementation of these algorithms in the Galaxy bioinformatics platform.


Unique Sequence Selection Experiment Fitness Landscape Adaptor Ligation Initial Pool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Strack, R.L., Disney, M.D., Jaffrey, S.R.: A superfolding Spinach2 reveals the dynamic nature of trinucleotide repeat-containing RNA. Nat. Methods 10, 1219–1924 (2013)CrossRefGoogle Scholar
  2. 2.
    Dolan, G.F., Akoopie, A., Muller, U.F.: A faster triphosphorylation ribozyme. PLoS ONE 10, e0142559 (2015)CrossRefGoogle Scholar
  3. 3.
    Chan, L., Tram, K., Gysbers, R., Gu, J., Li, Y.: Sequence mutation and structural alteration transform a noncatalytic DNA sequence into an efficient RNA-cleaving DNAzyme. J. Mol. Evol. 85, 245–253 (2015)CrossRefGoogle Scholar
  4. 4.
    Sefah, K., Phillips, J.A., Xiong, X., Meng, L., Van Simaeys, D., Chen, H., Martin, J., Tan, W.: Nucleic acid aptamers for biosensors and bio-analytical applications. Analyst 134, 1765–1775 (2009)ADSCrossRefGoogle Scholar
  5. 5.
    Martini, L., Meyer, A.J., Ellefson, J.W., Milligan, J.N., Forlin, M., Ellington, A.D., Mansy, S.S.: In vitro selection for small-molecule-triggered strand displacement and riboswitch activity. ACS Synth. Biol. 4, 1144–1150 (2015)CrossRefGoogle Scholar
  6. 6.
    Jimenez, J.I., Xulvi-Brunet, R., Campbell, G.W., Turk-MacLeod, R., Chen, I.A.: Comprehensive experimental fitness landscape and evolutionary network for small RNA. Proc. Natl. Acad. Sci. U. S. A. 110, 14984–14989 (2013)ADSCrossRefGoogle Scholar
  7. 7.
    Ellington, A., Pollard Jr., J.D.: Synthesis and purification of oligonucleotides. In: Current Protocols in Molecular Biology, Chap. 2, Unit 2.11. Wiley, New York (2001)Google Scholar
  8. 8.
    Edwards, A.W.F.: Likelihood. Johns Hopkins University Press, Baltimore (1992). Expanded editionGoogle Scholar
  9. 9.
    Acinas, S.G., Sarma-Rupavtarm, R., Klepac-Ceraj, V., Polz, M.F.: PCR-induced sequence artifacts and bias: insights from comparison of two 16S rRNA clone libraries constructed from the same sample. Appl. Environ. Microbiol. 71, 8966–8969 (2005)CrossRefGoogle Scholar
  10. 10.
    Aird, D., Ross, M.G., Chen, W., Danielsson, M., Fennell, T., Russ, C., Jaffe, D.B., Nusbaum, C., Gnirke, A.: Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011)CrossRefGoogle Scholar
  11. 11.
    Bennett, S.: Solexa Ltd. Pharmacogenomics 5, 433–438 (2004)CrossRefGoogle Scholar
  12. 12.
    Meacham, F., Boffelli, D., Dhahbi, J., Martin, D.I.K., Singer, M., Pachter, L.: Identification and correction of systematic error in high-throughput sequence data. MBC Bioinform. 12, 451 (2011)Google Scholar
  13. 13.
    Nakamura, K., Oshima, T., Morimoto, T., Ikeda, S., Yoshikawa, H., Shiwa, Y., Ishikawa, S., Linak, M. C., Hirai, A., Takahashi, H., Altaf-Ul-Amin, Md., Ogasawara, N., Kanaya, S.: Sequence-specific error profile of Illumina sequencers. Nucl. Acids Res. 39 (13), e90 (2011)Google Scholar
  14. 14.
    Dohm, J.C., Lottaz, C., Borodina, T., Himmelbauer, H.: Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucl. Acids Res. 36 (16), e105 (2008)CrossRefGoogle Scholar
  15. 15.
    Schirmer, M., Ijaz, U.Z., D’Amore, R., Hall, N., Sloan, W.T., Quince, C.: Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucl. Acids Res. 43 (6), e37 (2015)CrossRefGoogle Scholar
  16. 16.
    England, T.E., Uhlenbeck, O.C.: Enzymatic oligoribonucleotide synthesis with T4 RNA ligase. Biochemistry 17, 2069–2076 (1978)CrossRefGoogle Scholar
  17. 17.
    Middleton, T., Herlihy, W.C., Schimmel, P.R., Munro, H.N.: Synthesis and purification of oligoribonucleotides using T4 RNA ligase and reverse-phase chromatography. Anal. Biochem. 144, 110–117 (1985)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ramon Xulvi-Brunet
    • 1
    • 2
  • Gregory W. Campbell
    • 2
  • Sudha Rajamani
    • 3
  • José I. Jiménez
    • 4
  • Irene A. Chen
    • 2
  1. 1.Departamento de Física, Facultad de CienciasEscuela Politécnica NacionalQuitoEcuador
  2. 2.Department of Chemistry and BiochemistryUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Indian Institute of Science Education and ResearchPuneIndia
  4. 4.Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK

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