Advanced studying the formation of stable secondary DNA structures as applied to DNA technologies

DNA oligonucleotides are essential components of a high number of technologies in molecular biology which are based on DNA and RNA hybridization. Such DNA hybridization-based experimental methods as multiplex polymerase chain reaction, microarray analysis, NanoString multiplex analysis, next-generation targeted sequencing, and similar approaches require the use of complex mixtures of oligonucleotides (primers and probes) in one tube. Single-stranded DNA molecules also tend to bind to themselves. The probability of such nonspecific binding increases depending on the degree of analysis complexity. Moreover, there is a necessity to revise existing approaches to the development of certain hybridization probes and primers for existing DNA detection technologies. First of all, it is necessary for such technologies as standard or quantitative PCR with various DNA amplification methods for the detection of a specific amplicon using hybridization probes, as well as for isothermal DNA amplification methods that combine many nested primers and fluorescent probes. Revision is needed to accurately determine the melting temperature for both complementary DNA duplexes and DNA duplexes with the presence of non-complementary bases through the use of machine learning methods.
The main objective of the project is to conduct study of stable secondary structures of nucleic acids based on experimental data on DNA/DNA hybridization for complex single-stranded DNA mixtures using a machine learning approach. The development of bioinformatics tools that implement machine learning approaches for calculating the basic thermodynamics of secondary DNA structures as applied to DNA detection and amplification technologies.
The algorithms developed by us will enable to explain the observed artifacts in mega-multiplex DNA amplification and predict thermodynamic prognoses regarding the melting of DNA duplexes. Software to be developed will enable to detect nucleic acid interactions for the design of individual oligonucleotides and their mixtures, which are characterized by the weakest possible cross-interactions. Model experiments in DNA hybridization and determination of melting temperatures using synthetic DNA structures will be carried out. Studies of stable secondary nucleic acid structures will be carried out based on experimental data on DNA/DNA duplexes for complex single-stranded DNA mixtures. Bioinformatics tools implementing a machine learning approach for calculating the basic thermodynamics of secondary DNA structures as applied to DNA detection and amplification will be developed. Algorithms, which implement machine learning approaches, will be developed for the design of PCR primers, probes, microchips. Online applications will be installed on the server. At least 3 articles will be published in peer-reviewed scientific journals from academic publishing company (Springer Nature, Cell Press, PLOS, PeerJ, MDPI, Oxford Press, Frontiers and Elsevier), included in the Q1-Q3 quartile in the Web of Science database with a CiteScore percentile in the Scopus database not less than 50.

the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan