Postgenomic technologies for genomic and proteomic analysis in biological and medical research


S.A. Solodskikh, M.V. Gryaznova, Y.D. Dvoretskay, A. P. Gureev, A.V. Panevina, A.Y. Maslov, O.V. Serzhantova, A.A. Mikhailov, C. Chinopoulos , V.N. Popov

Over the 15 years since the decoding of the human genome a large number of individual genomes have been sequenced. Targeted sequencing – sequencing of select genome regions - has been widely used both in research and in medical practice. The use of various types of genetic analysis is starting to be used in daily clinical routine. At the same time, the price of sequencing decreases and as a result, the amount of genetic information available to researchers and physicians increases. These processes together determine the need for creation of databases for the centralized storage of genetic information which is crucial for synchronization and validation of the work of various institutions. One of the first such databases was the NCBI database created and supervised by the US National Center for Biotechnological Information (NCBI) in collaboration with the National Institute for Human Genome Research (NHGRI).
At the same time, the available methods for studying associations between DNA polymorphisms and various phenotypic manifestations do not cover the most important layer of regulation of biological processes - the proteome. The methods of high-throughput proteomic analysis that are to be developed will allow identifying driver mutations that make the greatest contribution to the phenotype of the studied object.
The application of an integrated analysis of the genome and proteome for the diagnosis and treatment of cancer pathologies is one of the most important research goals now. This approach will allow to identify new genetic biomarkers that could be used for reliable prediction of the treatment response, risks of the most important diseases, and the development of novel medications. This review shows recent advances in proteomic and genomic approaches to the development of more sensitive diagnostic and prognostic biomarkers that can be translated into improved clinical care and treatment of the disease.
Keywords: sequencing; genomics; transcriptomics; proteomics; socially significant diseases
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