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Quantitative Analysis of Synthesized Nucleic Acid Pools

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Part of the SEMA SIMAI Springer Series book series (SEMA SIMAI,volume 7)

Abstract

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.

Keywords

  • 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.

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Correspondence to Ramon Xulvi-Brunet .

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Xulvi-Brunet, R., Campbell, G.W., Rajamani, S., Jiménez, J.I., Chen, I.A. (2016). Quantitative Analysis of Synthesized Nucleic Acid Pools. In: Carballido-Landeira, J., Escribano, B. (eds) Nonlinear Dynamics in Biological Systems. SEMA SIMAI Springer Series, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-33054-9_2

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