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Chao H, Zhang S, Hu Y, Ni Q, Xin S, Zhao L, Ivanisenko VA, Orlov YL, Chen M. Integrating omics databases for enhanced crop breeding. J Integr Bioinform 2023; 20:jib-2023-0012. [PMID: 37486120 PMCID: PMC10777369 DOI: 10.1515/jib-2023-0012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
Crop plant breeding involves selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value. With the development of high-throughput technologies, such as genomics, transcriptomics, and metabolomics, crop breeding has entered a new era. However, to effectively use these technologies, integration of multi-omics data from different databases is required. Integration of omics data provides a comprehensive understanding of the biological processes underlying plant traits and their interactions. This review highlights the importance of integrating omics databases in crop plant breeding, discusses available omics data and databases, describes integration challenges, and highlights recent developments and potential benefits. Taken together, the integration of omics databases is a critical step towards enhancing crop plant breeding and improving global food security.
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Affiliation(s)
- Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Shilong Zhang
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Yueming Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Qingyang Ni
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Saige Xin
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Liang Zhao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Vladimir A. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
| | - Yuriy L. Orlov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow117198, Russia
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow119991, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
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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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Orlov YL, Orlova NG. Bioinformatics tools for the sequence complexity estimates. Biophys Rev 2023; 15:1367-1378. [PMID: 37974990 PMCID: PMC10643780 DOI: 10.1007/s12551-023-01140-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 09/01/2023] [Indexed: 11/19/2023] Open
Abstract
We review current methods and bioinformatics tools for the text complexity estimates (information and entropy measures). The search DNA regions with extreme statistical characteristics such as low complexity regions are important for biophysical models of chromosome function and gene transcription regulation in genome scale. We discuss the complexity profiling for segmentation and delineation of genome sequences, search for genome repeats and transposable elements, and applications to next-generation sequencing reads. We review the complexity methods and new applications fields: analysis of mutation hotspots loci, analysis of short sequencing reads with quality control, and alignment-free genome comparisons. The algorithms implementing various numerical measures of text complexity estimates including combinatorial and linguistic measures have been developed before genome sequencing era. The series of tools to estimate sequence complexity use compression approaches, mainly by modification of Lempel-Ziv compression. Most of the tools are available online providing large-scale service for whole genome analysis. Novel machine learning applications for classification of complete genome sequences also include sequence compression and complexity algorithms. We present comparison of the complexity methods on the different sequence sets, the applications for gene transcription regulatory regions analysis. Furthermore, we discuss approaches and application of sequence complexity for proteins. The complexity measures for amino acid sequences could be calculated by the same entropy and compression-based algorithms. But the functional and evolutionary roles of low complexity regions in protein have specific features differing from DNA. The tools for protein sequence complexity aimed for protein structural constraints. It was shown that low complexity regions in protein sequences are conservative in evolution and have important biological and structural functions. Finally, we summarize recent findings in large scale genome complexity comparison and applications for coronavirus genome analysis.
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Affiliation(s)
- Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow, 119991 Russia
- Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
| | - Nina G. Orlova
- Department of Mathematics, Financial University under the Government of the Russian Federation, Moscow, 125167 Russia
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Orlov YL, Chen M, Kolchanov NA, Hofestädt R. BGRS: bioinformatics of genome regulation and data integration. J Integr Bioinform 2023; 20:jib-2023-0032. [PMID: 37972410 PMCID: PMC10757072 DOI: 10.1515/jib-2023-0032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
Affiliation(s)
- Yuriy L. Orlov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090Novosibirsk, Russia
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991Moscow, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198Moscow, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Nikolay A. Kolchanov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090Novosibirsk, Russia
| | - Ralf Hofestädt
- Faculty of Technology, Bielefeld University, Bielefeld, Germany
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Orlov YL, Anashkina AA, Kumeiko VV, Chen M, Kolchanov NA. Research Topics of the Bioinformatics of Gene Regulation. Int J Mol Sci 2023; 24:ijms24108774. [PMID: 37240120 DOI: 10.3390/ijms24108774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
The study of gene expression regulation raises the challenge of developing bioinformatics tools and algorithms, demanding data integration [...].
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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
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples' Friendship University of Russia, 117198 Moscow, Russia
| | - 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
| | - Vadim V Kumeiko
- Institute of Life Sciences and Biomedicine, Far Eastern Federal University, 690922 Vladivostok, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Nikolay A Kolchanov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
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Orlov YL, Ivanisenko VA, Dobrovolskaya OB, Chen M. Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics. Int J Mol Sci 2022; 23:ijms23126759. [PMID: 35743200 PMCID: PMC9223720 DOI: 10.3390/ijms23126759] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 11/16/2022] Open
Abstract
The study of molecular mechanisms of plant stress response is important for agrobiotechnology applications as it was discussed at series of recent bioinformatics conferences [...].
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Affiliation(s)
- Yuriy L. Orlov
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia;
- 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
- Correspondence:
| | | | - Oxana B. Dobrovolskaya
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia;
- Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia;
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China;
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