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.

Project goal: The aim of the project is to develop and test software for the design of targeted gene panels for long fragment sequencing using nanopore technology.

Project description: To develop software for the design and optimization of compatible primer sets for long distance multiplex PCR for the target genes. To develop a platform for comprehensive analysis of third generation amplicon sequencing data and analysis of identified variants. Investigate and evaluate the genotyping of mutations in the BRCA1/2, KRAS, NRAS, BRAF, PIK3CA and EGFR genes in tumour samples for the identification of rare variants using a gene panel for targeted third-generation sequencing analysis with ONT. To evaluate the genotyping of mutations in key biomarker genes in tumour samples for the identification of rare variants using targeted third-generation sequencing analysis. Mutational profiles will be identified and correlated with clinical outcomes (overall response rate; progression-free survival; overall survival).

PI: Ruslan Kalendar

Expected results: The project will focus on the development of software for the design of gene panels using nanopore sequencing of the human genome that can be interpreted and applied by researchers and public health organizations. A diagnostic oncopanel will be developed as a precision assay based on targeted third-generation sequencing using Oxford Nanopore Technologies' genomic profiling solution to detect key biomarkers such as BRCA1/2, KRAS, NRAS, BRAF, PIK3CA and EGFR. A platform will be developed to comprehensively analyze amplicon sequencing data and analyze identified variants. A decision support tool will be used to match gene variants detected in the sequences with databases of known biomarkers, related therapies, clinical trials and guidelines. The results of this project will have important implications for medicine and public health.

Methodology: We will use the available "National Laboratory Astana" collection of specimen and data of full genome sequencing of patients and healthy individuals recruited in the Republic of Kazakhstan. Amplicon sequencing using Oxford Nanopore Technologies (ONT). Bioinformatics methods using machine learning and molecular biology techniques.

Contacts: ruslan.kalendar@nu.edu.kz

Chromosome-scale assembly of Kazakh individuals using modern genomics approaches

Project goal: Creation of a chromosomal-level assembly of whole genomes of individuals of the Kazakh population using data from long and short sequencing reads, as well as data from bionano optical genomic maps and Hi-C chromosomal conformation

Project description: The idea of the project is to use genomic data obtained from modern high-throughput genomic platforms and bioinformatics methods to create the best chromosomal-level assembly of whole genomes of individuals of the Kazakh population and make it available for exploitation as a reference population-oriented genome in various biomedical investigations.

Project facilitators: PI: Ulykbek Kairov;
Project participants: Asset Daniyarov, Askhat Molkenov, Kuanysh Sarkytbayev, Anel Ordabayeva.

Realisation period: 2024-2026

Expected results: - The quality of long and short sequencing reads will be assessed for subsequent de novo assembly of whole genomes;

- De novo assembly of whole genomes of Kazakhstani individuals will be carried out based on data from long sequencing reads;

- Integration of data from short sequencing reads will be carried out to polish and improve the assembly of whole genomes;

- Hybrid integration of long-read assemblies with bionano genomic maps will be carried out to improve draft genomic assemblies;

- Hi-C chromosomal conformation data will be mapped on the reference genome and hybrid genomic assemblies;

- The quality of draft and hybrid genome assemblies will be assessed;

- Comparative analysis of the resulting draft and hybrid assemblies will be carried out with genomic data from other populations and individuals;

- The results of the best assemblies will be deposited in open access, making them available to use in comparative, population, and biomedical analysis by researchers in the field of genomics and bioinformatics;