1 semester
Your study will begin with an introduction to bioinformatics. Next, students with a background in biology will take courses in mathematics and programming, while computer scientists will take courses in biology, advanced statistics, and algorithms. This semester you will also choose a semester project topic and supervisor, and begin the research work.
You will study medical genetics, several course modules in systems biology and Scientific Python. Continue to work on your project and present your results at the end of the semester.
3 semester
You will take four elective courses, and study one course of the Soft Skills module. At the beginning of the semester you will also choose the topic of your thesis project and start working on it.
This is the time you will devote entirely to writing your thesis.
1 semester
- What is bioinformatics and systems biology. Historical background. Areas of bioinformatics. Bioinformatics in the AI era
- Sequencing technologies. Research applications working in Linux
- Genomic data processing
1. Introduction to statistics: variable types, basic distributions and hypotheses examples
2. Calculating data statistics: central and variance tendency measures, correlation
3. Data visualization: distribution visualization techniques, multidimensional data, heatmaps, Venn diagrams
4. Data visualization practice: visualizing experiment data, explanatory data analysis (EDA)
5. Testing mean equality one-sample hypotheses with simulations
6. Testing goodness of fit hypothesis with simulations
7. Testing independency hypothesis with simulations
8. Sampling methods, sources of bias
9. Distributions: Bernoulli, binomial, geometrical, uniform, normal, exponential, Poisson
10. Types of errors and power of tests
11. Multiple comparisons problem
12. Regression analysis: linear regression using least squares
Advanced topics:
2. Calculating data statistics: central and variance tendency measures, correlation
3. Data visualization: distribution visualization techniques, multidimensional data, heatmaps, Venn diagrams
4. Data visualization practice: visualizing experiment data, explanatory data analysis (EDA)
5. Testing mean equality one-sample hypotheses with simulations
6. Testing goodness of fit hypothesis with simulations
7. Testing independency hypothesis with simulations
8. Sampling methods, sources of bias
9. Distributions: Bernoulli, binomial, geometrical, uniform, normal, exponential, Poisson
10. Types of errors and power of tests
11. Multiple comparisons problem
12. Regression analysis: linear regression using least squares
Advanced topics:
- Sample, estimates. Descriptive statistics. Unbiased estimator, consistent estimator. Distribution of empirical E, D.
- MLE, method of moments. Confidence intervals.
- One-sample hypothesis testing: one-tailed/two-tailed alternatives.
- One-sample hypothesis testing: parametric/non-parametric criteria.
- Two-sample hypothesis testing. Correlation coefficients.
- Linear regression, ANOVA.
- Multiple hypothesis testing: correction methods, FDR.
- Introduction to set theory: definitions, set operations and properties
- Boolean functions, boolean logic and proof methods
- Combinatorics: combinations, permutations, pigeonhole principle
- Combinatorial generation
- Asymptotic analysis of algorithms
- Sorting algorithms
- Dynamic programming: definitions, subtask optimization, basic problems
- Dynamic programming in bioinformatics: local and global alignments
- Dynamic programming: RNA’s secondary structure prediction
- Graph theory: definitions, DFS, BFS
- Graph theory: algorithms, de Bruijn graph
- Python Basics. Numbers. Strings. Boolean Type. Variables and rules for naming variables. Arithmetic Operators. Comparison operators. Logical operators.
- Control Flow. Indentation.
- Data structures. List. Set. Dictionary. Tuple.
- Functions.
- Functional programming.
- Call stack. Namespace.
- Modules, Files.
- Python Classes. Attributes, methods. Instantiation. self parameter. Underscores.
- Inheritance. Parent class and Child class. Subclass attributes. Multiple inheritance. Method Resolution Order (MRO).
- Iterators. Generators. Errors. Exceptions.
- Complexity
- Sorting and Binary Search
- DigitSort and Stacks
- Queues, Dequeue, Heap, Amortized time
- Vector continuation, BST, AVL, Treap
- Treap and Treap with hidden keys
- Dynamic Programming. Part 1.
- Dynamic Programming. Part 2.
- Dynamic Programming. Part 3.
- Dynamic Programming. Part 4.
- String Hashes
- Depth-First Search
- Eulerian cycle, Breadth-First Search, Dijkstra
- Ford-Bellman, Floyd
- Cell structure and cell processes. Cell division. Mitosis and meiosis. Cell cycle.
- Molecular basis of genetics and inheritance. Central dogma of molecular biology. DNA and RNA functions.
- DNA replication. Replication processes in prokaryotes and eukaryotes. Transcription and translation. RNA splicing and processing. DNA damage and mutations. DNA repair.
- Definitions of the gene. Mendel’s principles of heredity. The chromosome theory of inheritance. Gene regulation in prokaryotes and eukaryotes.
- Genome structure and organization. Chromosome organization. Genome rearrangements.
- Genetic diversity. Sources of genetic diversity. Inbreeding and mutational load.
The course is devoted to the study of the basics of higher mathematics and the analysis of typical problems of mathematical analysis.
The course develops an AI-first mindset and teaches students to use artificial intelligence as a tool for cognitive augmentation in research and professional practice. The main focus is advanced AI-powered decision support: working with open data sources, automating analytical reports, and extracting insights from unstructured information such as texts, documents, and reports. Students learn prompt engineering, data literacy, and agentic AI system design, moving from intuitive chatbot usage to conscious management of AI workflows, including context design, output verification, and ethical technology adoption. Acting as AI solution architects, students decompose complex tasks into structured workflows and orchestrate AI agents to solve them. The course culminates in the creation of a digital product, AI agent, or research project demonstrating the ability to address complex analytical challenges through human–AI collaboration.
Critical Thinking Skills (advanced)
This course focuses on the important skills of critical thinking, with consideration for their application in scientific and educational as well as conflict situations. You will learn how to assess the relevance of information, how to construct a persuasive argument, how to detect errors in reasoning, how to avoid them, etc.
High Tech Business Creation
In the course you will be able to try yourself in the role of an entrepreneur, create and develop an interdisciplinary business project. The main objectives of the course are to study the specifics of developing a high-tech project and assess its readiness for commercialization in the real sector of the economy.
Introduction to Conceptual Thinking
A course on the basic principles of analysis, idea generation, creative processes, information structuring and system building, providing the knowledge to create breakthrough and innovative projects in various fields of science and business.
This course focuses on the important skills of critical thinking, with consideration for their application in scientific and educational as well as conflict situations. You will learn how to assess the relevance of information, how to construct a persuasive argument, how to detect errors in reasoning, how to avoid them, etc.
High Tech Business Creation
In the course you will be able to try yourself in the role of an entrepreneur, create and develop an interdisciplinary business project. The main objectives of the course are to study the specifics of developing a high-tech project and assess its readiness for commercialization in the real sector of the economy.
Introduction to Conceptual Thinking
A course on the basic principles of analysis, idea generation, creative processes, information structuring and system building, providing the knowledge to create breakthrough and innovative projects in various fields of science and business.
In-depth English course. You will learn the basics of intercultural communication in the scientific world, equip yourself with new theoretical knowledge, improve and develop your writing and speaking skills, and learn how to write, read and think critically.
You will learn how to conduct a full-fledged research study in bioinformatics and systems biology individually or in teams. The result will be a semester-long report.
2 semester
- Where it all started. Solving a mystery of inheritance. Mendel and post-Mendel era
- Pre-genome era. Mapping of the first human disease gene. Huntingnton's disease.
- RFPL, microsatellite and genetic linkage. Pedigree and linkage analysis. DNA forensics.
- Human Genome Project. SNP map of the human genome. DNA variation.
- Linkage disequilibrium and genome-wide association studies. HapMap project.
- GWAS concepts and approaches.
- Hands on GWAS tutorial.
- GWAS discussion and resources. Polygenic risk scores. UK biobank.
- Next generation sequencing. Exome sequencing.
- GATK pipeline.
- Variant annotations, selection pressure metrics. Large scale sequencing resources.
- Rare variant association studies
- Hands-on analysis of exome sequencing data.
- Cancer genetics. TCGA project.
- Case-control matching challenge.
- Central dogma of molecular biology, structure of gene, types of RNA, structure of RNA, transcription, reverse transcription, FACS
- RNA-seq: alignment, quantification, QC, normalization, basic analysis
- Overall quality control: PCA, clustering, outlier detection, batch correction
- Differential expression (DE), Deseq2 for RNA-seq
- Downstream analysis: pathway/gene set enrichment analysis
- Transcriptome assembly, functional annotation
- Single-cell transcriptomics
- Spatial transcriptomics
- Visual data exploration: Phantasus, JBR genome browser
- Experimental design of gene expression study
Proteomics
- Introduction to Proteomics. Methods of protein analysis. Ionization methods. Examples of mass analyzers. Top-down and Bottom-Up proteomics. DIA, DDA and Targeted proteomics. Tandem protein mass spectrometry.
- Protein quantification: LFQ, SILAC, TMT. Challenges of measuring protein concentrations. Labeling. Single Cell proteomics. Label-Free proteomics.
- Basics of MaxQuant and Perseus. 3D visualization.
- Proteomics applications.
- ChIP-seq. QC. Alignment. Visualization. Peak calling. Spectrometry.
- Downstream. Genomic regions manipulation. Associated/closest gene annotation. Coverage profile. Functional genome annotation. Motif analysis TF. Pathway enrichment analysis. Similar datasets.
- Methylation. BS-Seq Preprocessing. BS-Seq Downstream. Bis-SNP.
- Basic text processing with python. Regular expressions
- Code organization. Error handling and debugging
- Exploratory data analysis with python. Data manipulation packages
- Visual data analysis with python. Data visualization packages
- Functional programming with python. Method oriented programming. Functional programming paradigm. Inerating over objects. Iterators in python. Working with infinite objects. Generators in python. Python functional possibilities.
- Biological data processing with python. Biopython and Scanpy
- Scientific project organization. Virtualization, Pipelines and Web applications. Docker. Introduction to pipeline development using Snakemake
- Machine learning possibilities in python. Advanced tools for data processing. Introduction to neural networks with python.
- Sequence alignment
- Genome assembly
- Hidden Markov Models
- Phylogeny reconstruction
- Genome rearrangements
- Active module problem
- Algorithms for gene set enrichment analysis
- Privacy-preserving genome-wide association studies
- Reconstruction of evolutionary histories
- Reconstruction of gene regulatory networks
In-depth English or Russian course. You will learn the basics of intercultural communication in the scientific world, equip yourself with new theoretical knowledge, improve and develop your writing and speaking skills, and learn how to write, read and think critically.
3 courses from the list (micro-modules):
- Mediation and Dispute Resolution
- Art & Math of Decision Making
- Stress Management
- Empathetic Communication
- Pitches and Speeches
- Evidence-based Approach to Career Management
- Launching and Relaunching Career
You will learn how to conduct a full-fledged scientific research in bioinformatics and systems biology individually or in teams. The result will be a semester-long report and a public defense of the project. The results may be published in a scientific journal.
3 semester
- Defining bioinformatics and structural bioinformatics.
- Fundamentals of macromolecular organization and structure. Hierarchical levels of protein organization. Protein 3D structure. Protein Domains. Protein Folds.
- Analysis of macromolecule. Sequence and structural alignment. Amino acid substitutions, amino acid replacement matrices. Quality of protein structures, Torsion angles and Ramachadran plot. Function from structure: Structure-function relationship and analysis.
- Prediction and modeling of macromolecules. Homology and similarity of proteins, quality assessments of homology models. Molecular dynamics and docking, Monte Carlo simulations. Protein folding and energetics.
- Experimental approaches in structural biology: Determination of macromolecular structures. X-ray crystallography. Nuclear Magnetic Resonance. Cryo-electron microscopy. Hydrogen-deuterium exchange.
- Protein databases. The Protein Databank (PDB), UniProtKB, InterPro, HMMER, PDBe, EMDB, EMPIAR, IntAct, Complex Portal, Reactome.
- Computer-aided drug design: approaches and methods. Structure-based drug design. Ligand-based drug design. De novo design.
- Basics of molecular modeling. Preparation of protein structure. Molecular docking, molecular dynamics.
The course is aimed at studying modern methods of working with genomic data, which allow to study the features of structure and evolution of various organisms.
- Introduction to population genetics. Hardy-Weinberg equilibrium
- Driving forces of microevolution. Mutations and gene flow
- Analysis of gene flow and admixture in natural populations
- Genetic drift: models and consequences
- The coalescent theory and its application to population history analysis
- Natural selection and its impact on genetic variation
- Searching for positive and negative selection in sequence data
- Comparative genomics
The course provides basic skills in systems-biological analysis of microbial communities and develops an understanding of methods for studying these data. It is divided into two modules: molecular phylogenetics and metagenomics.
Part 1. Molecular Phylogenetics
Part 1. Molecular Phylogenetics
- Introduction. Tree representation.
- Multiple sequence alignment, databases. Eutils, Biopython.
- Distance methods.
- Maximum Likelihood
- Bayesian methods.
- Introduction. Communities, sampling, history, etc.
- 16S analysis. From reads to ASV table.
- Analysis of communities: diversity, indices, differential abundance.
- WGS, shotgun metagenomiсs. Assembly, binning.
- Taxonomic composition, k-mer analysis (Kraken, mash, etc).
- Practice on real data.
- Introduction to Machine Learning
- Non-parametric Models. Nadaraya-Watson kernel regression.
- Linear Models. Gradient Algorithms.
- Support Vector Machine. Karush-Kuhn-Tucker Condition
- Probabilistic Classifiers
- Trees and Ensembles. Boosting algorithms
- Neural Networks
- Optimization algorithms for Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Network
- Dimensionality reduction
- Label Irregularity
- Clustering
- Generative Models
- Best Practices in ML
1 course from the list (big modules):
- Negotiations, Influence and Conflict Management
- Emotional Intelligence
- Mediation and Dispute Resolution
- Art & Math of Decision Making
- Stress Management
- Empathetic Communication
- Pitches and Speeches
- Evidence-based Approach to Career Management
- Launching and Relaunching Career
Beginning work on an individual thesis project in bioinformatics and systems biology. The result will be a semester report and an interim presentation.
4 semester
Continuation of work on the research project in the field of bioinformatics and systems biology. Three stages of pre-defense: January, April, May; final defense in June. After defense, many results are published in scientific journals.
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MASTER'S PROGRAM
Bioinformatics
and systems biology
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