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Turkina VA, Orlova NG, Orlov YL. Biophysics education section and computational training discussion at VII Congress of Russian Biophysicists. Biophys Rev 2023; 15:807-809. [PMID: 37974980 PMCID: PMC10643721 DOI: 10.1007/s12551-023-01147-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 11/19/2023] Open
Abstract
We present commentaries on the section "Biophysical education" of the VII Congress of Russian Biophysicists. Presentations are briefly introduced along with the current problems of biophysical education and the educational approach development. We discuss educational course on bioinformatics based on the integration of online databases and the usage of internet platforms for functional annotation of genes and proteins.
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Affiliation(s)
- Vasilisa A. Turkina
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Nina G. Orlova
- Department of Mathematics, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
| | - Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
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2
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Das R, Tatarinova TV, Galieva ER, Orlov YL. Editorial: Association Between Individuals' Genomic Ancestry and Variation in Disease Susceptibility. Front Genet 2022; 13:831320. [PMID: 35186044 PMCID: PMC8847436 DOI: 10.3389/fgene.2022.831320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/14/2022] [Indexed: 12/18/2022] Open
Affiliation(s)
- Ranajit Das
- Yenepoya Research Centre, Yenepoya University, Mangalore, India
| | - Tatiana V Tatarinova
- Natural Science Division, La Verne University, La Verne, CA, United States.,Department of Fundamental Biology and Biotechnology, Siberian Federal University, Krasnoyarsk, Russia
| | - Elvira R Galieva
- Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Yuriy L Orlov
- Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.,Agrarian and Technological Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.,Institute of Digital Medicine, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
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3
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Orlov YL, Tatarinova TV, Oparina NY, Galieva ER, Baranova AV. Editorial: Bioinformatics of Genome Regulation, Volume I. Front Genet 2021; 12:803273. [PMID: 34938326 PMCID: PMC8687738 DOI: 10.3389/fgene.2021.803273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Yuriy L Orlov
- Institute of Digital Medicine, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Agrarian and Technological Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.,Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | | | - Nina Y Oparina
- Institute of Medicine, University of Gothenburg, Göteborg, Sweden
| | - Elvira R Galieva
- Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia
| | - Ancha V Baranova
- School of Systems Biology, George Mason University, Fairfax, VA, United States.,Research Centre for Medical Genetics, Moscow, Russia
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4
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Dergilev AI, Orlova NG, Dobrovolskaya OB, Orlov YL. Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data. J Integr Bioinform 2021; 19:jib-2020-0036. [PMID: 34953471 PMCID: PMC9069649 DOI: 10.1515/jib-2020-0036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 11/25/2021] [Indexed: 12/03/2022] Open
Abstract
The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in Arabidopsis thaliana genome were discussed here in detail.
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Affiliation(s)
- Arthur I. Dergilev
- Novosibirsk State University, 630090Novosibirsk, Russia
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090Novosibirsk, Russia
| | - Nina G. Orlova
- Financial University under the Government of the Russian Federation, 125993Moscow, Russia
- Moscow State Technical University of Civil Aviation, 125993Moscow, Russia
| | - Oxana B. Dobrovolskaya
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia,117198Moscow, Russia
| | - Yuriy L. Orlov
- Novosibirsk State University, 630090Novosibirsk, Russia
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia,117198Moscow, Russia
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), 119991Moscow, Russia
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Orlov YL, Anashkina AA, Tatarinova TV, Baranova AV. Editorial: Bioinformatics of Genome Regulation, Volume II. Front Genet 2021; 12:795257. [PMID: 34819949 PMCID: PMC8606529 DOI: 10.3389/fgene.2021.795257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Affiliation(s)
- Yuriy L Orlov
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Agrobiotechnology Department, Agrarian and Technological Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia
| | - Anastasia A Anashkina
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | | | - Ancha V Baranova
- School of Systems Biology, George Mason University, Fairfax, VA, United States.,Research Centre for Medical Genetics, Moscow, Russia
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Gubanova NV, Orlova NG, Dergilev AI, Oparina NY, Orlov YL. Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools. J Integr Bioinform 2021; 18:jib-2021-0031. [PMID: 34783229 PMCID: PMC8709738 DOI: 10.1515/jib-2021-0031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/18/2021] [Indexed: 12/13/2022] Open
Abstract
Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.
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Affiliation(s)
- Natalya V Gubanova
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Nina G Orlova
- Financial University under the Government of the Russian Federation, 119991 Moscow, Russia.,Moscow State Technical University of Civil Aviation, 125993 Moscow, Russia
| | | | | | - Yuriy L Orlov
- Novosibirsk State University, 630090 Novosibirsk, Russia.,The Digital Health Institute, I.M.Sechenov First Moscow State Medical University of the Russian Ministry of Health, 119991 Moscow, Russia
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Orlov YL, Anashkina AA. Life: Computational Genomics Applications in Life Sciences. Life (Basel) 2021; 11:life11111211. [PMID: 34833087 PMCID: PMC8622464 DOI: 10.3390/life11111211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 01/19/2023] Open
Affiliation(s)
- Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
- Correspondence:
| | - Anastasia A. Anashkina
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
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Medical Genetics, Genomics and Bioinformatics Aid in Understanding Molecular Mechanisms of Human Diseases. Int J Mol Sci 2021; 22:ijms22189962. [PMID: 34576125 PMCID: PMC8467458 DOI: 10.3390/ijms22189962] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 12/14/2022] Open
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Analysis of Homozygous-by-Descent (HBD) Segments for Purebred and Crossbred Pigs in Russia. Life (Basel) 2021; 11:life11080861. [PMID: 34440604 PMCID: PMC8400874 DOI: 10.3390/life11080861] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/11/2021] [Accepted: 08/16/2021] [Indexed: 12/30/2022] Open
Abstract
Intensive selection raises the efficiency of pig farming considerably, but it also promotes the accumulation of homozygosity, which can lead to an increase in inbreeding and the accumulation of deleterious variation. The analysis of segments homozygous-by-descent (HBD) and non-HBD segments in purebred and crossbred pigs is of great interest. Research was carried out on 657 pigs, of which there were Large White (LW, n = 280), Landrace (LR, n = 218) and F1 female (♂LR × ♀LW) (F1, n = 159). Genotyping was performed using the GeneSeek® GGP Porcine HD Genomic Profiler v1 (Illumina Inc., USA). To identify HBD segments and estimate autozygosity (inbreeding coefficient), we used the multiple HBD classes model. LW pigs exhibited 50,420 HBD segments, an average of 180 per animal; LR pigs exhibited 33,586 HBD segments, an average of 154 per animal; F1 pigs exhibited 21,068 HBD segments, an average of 132 per animal. The longest HBD segments in LW were presented in SSC1, SSC13 and SSC15; in LR, in SSC1; and in F1, in SSC15. In these segments, 3898 SNPs localized in 1252 genes were identified. These areas overlap with 441 QTLs (SSC1—238 QTLs; SSC13—101 QTLs; and SSC15—102 QTLs), including 174 QTLs for meat and carcass traits (84 QTLs—fatness), 127 QTLs for reproduction traits (100 QTLs—litter traits), 101 for production traits (69 QTLs—growth and 30 QTLs—feed intake), 21 QTLs for exterior traits (9 QTLs—conformation) and 18 QTLs for health traits (13 QTLs—blood parameters). Thirty SNPs were missense variants. Whilst estimating the potential for deleterious variation, six SNPs localized in the NEDD4, SEC11C, DCP1A, CCT8, PKP4 and TENM3 genes were identified, which may show deleterious variation. A high frequency of potential deleterious variation was noted for LR in DCP1A, and for LW in TENM3 and PKP4. In all cases, the genotype frequencies in F1 were intermediate between LR and LW. The findings presented in our work show the promise of genome scanning for HBD as a strategy for studying population history, identifying genomic regions and genes associated with important economic traits, as well as deleterious variation.
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