Nonlinear manipulation and analysis of large DNA datasets

Meiying Cui, Xueping Zhao, Francesco V. Reddavide, Michelle Patino Gaillez, Stephan Heiden, Luca Mannocci, Michael Thompson, Yixin Zhang

Research output: Journal PublicationArticlepeer-review


Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNA-based neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, a biased competition method for nonlinear manipulation and analysis of mixtures of DNA sequences was developed. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among a myriad of different DNA sequences was carried out to reduce the information entropy. The method could be used for various oligonucleotide-encoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiments with DNA-encoded chemical libraries against protein targets.

Original languageEnglish
Pages (from-to)8974-8985
Number of pages12
JournalNucleic Acids Research
Issue number15
Publication statusPublished - 26 Aug 2022
Externally publishedYes

ASJC Scopus subject areas

  • Genetics


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