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Updated: Jun 10, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
Published on: May 28, 2021
Bioinformatics in Russia: history and present-day landscape.
Muhammad A Nawaz1,2, Igor E Pamirsky1,3, Kirill S Golokhvast1,3
1Advanced Engineering School (Agrobiotek), National Research Tomsk State University, Lenin Ave, 36, Tomsk Oblast, Tomsk 634050, Russia.
Bioinformatics research in Russia is rapidly advancing, focusing on genetics, metagenomics, and large-scale sequencing projects. Future growth depends on increased funding, data sharing, and developing a critical mass of bioinformaticians.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
- Proteomics
Background:
- Bioinformatics is crucial for molecular biology research.
- The status of bioinformatics in Russia was previously unknown.
- Research is driven by IT, pharmaceuticals, biotechnology, and agriculture.
Purpose of the Study:
- To review the history of bioinformatics in Russia.
- To present the current landscape of Russian bioinformatics research.
- To highlight future directions and challenges in the field.
Main Methods:
- Historical review of bioinformatics in Russia.
- Analysis of current research trends and industry drivers.
- Identification of key areas for future development and challenges.
Main Results:
- Bioinformatics in Russia has gained momentum over three decades, particularly in protein and nucleic acid research.
- Active centers for genomics, proteomics, and bioinformatics exist across Russia.
- Notable developments include software tools, high-performance computing, and large-scale sequencing projects like the 100,000 human genomes initiative.
Conclusions:
- Russian bioinformatics is characterized by research in genetics, metagenomics, OMICs, medical informatics, and structural bioinformatics.
- Increasing government funding, policy changes, and the establishment of a National Genomic Information Database are positive developments.
- Future focus on eukaryotic genome sequencing, data sharing platforms, biological modeling, machine learning, and biostatistics is recommended to align with global progress.
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