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, and attend a bioinformatics journal club. You will continue your project work and present results at the end of the semester.
3 semester
You will take four profile elective courses, continue to participate in the journal club, and take one course of the Soft Skills module. At the beginning of the semester you will choose the topic of your thesis project and start working on it.
This is the time you will devote entirely to writing your thesis and research internship.
1 semester
Profile courses
- Foundations of immunology. Antibodies and phagocytes. Cells of the immune system. T-cells.
- Transcription and RNA sequencing. Exploration and analysis of gene expression datasets.
- Introduction to single-cell RNA-sequencing
- Case studies from cancer immunology and aging immunology
- Immunometabolism. Metabolic network analysis in Cytoscape
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.
Profile courses of the "Data Analysis in Biology" track
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
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
- 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.
Profile courses of the Algorithmic Bioinformatics track
- 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.
- 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.
Elective and University-wide courses
The course is devoted to the study of the basics of higher mathematics and the analysis of typical problems of mathematical analysis.
The course is designed to teach you how to work locally and remotely with the GNU Linux operating system, and to familiarize you with the basic principles of operating systems.
Data Preprocessing and Statistics with R
The course will teach you the basic techniques of working with data, methods of their visualization and descriptive statistics, methods of data normalization and construction of target functions. And also practical skills of statistical data processing in R programming language to solve various analytical problems. You will learn the most important concepts of probability theory, on which many statistical constructions and conclusions are based.
Big Data: Storage Technologies and Introduction to Machine Learning (Python)
The course will teach you how to organize the storage of big data using relational DBMS and NoSQL storages and tell you about data structure design methods, data query languages and data processing techniques. It will also introduce you to types of machine learning and show you practical examples of solving problems using machine learning methods using Python. The main focus is on solving regression problems, classification problems, and clustering problems.
Introduction to Machine Learning (Python) and Advanced Machine Learning (Python)
This course introduces you to types of machine learning and shows practical examples of solving problems using machine learning methods using Python. The main focus is on solving regression problems, classification and clustering problems. You will also learn methods for reducing the dimensionality of the feature set and factor analysis from the course. Consider the support vector method and decision trees, ensembles of models, and another area of machine learning — reinforcement learning.
The course will teach you the basic techniques of working with data, methods of their visualization and descriptive statistics, methods of data normalization and construction of target functions. And also practical skills of statistical data processing in R programming language to solve various analytical problems. You will learn the most important concepts of probability theory, on which many statistical constructions and conclusions are based.
Big Data: Storage Technologies and Introduction to Machine Learning (Python)
The course will teach you how to organize the storage of big data using relational DBMS and NoSQL storages and tell you about data structure design methods, data query languages and data processing techniques. It will also introduce you to types of machine learning and show you practical examples of solving problems using machine learning methods using Python. The main focus is on solving regression problems, classification problems, and clustering problems.
Introduction to Machine Learning (Python) and Advanced Machine Learning (Python)
This course introduces you to types of machine learning and shows practical examples of solving problems using machine learning methods using Python. The main focus is on solving regression problems, classification and clustering problems. You will also learn methods for reducing the dimensionality of the feature set and factor analysis from the course. Consider the support vector method and decision trees, ensembles of models, and another area of machine learning — reinforcement learning.
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.
2 semester
Profile courses
- Transcriptomics
- 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.
- Epigenetics
- 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.
- 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.
- 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.
The course is designed to continue to teach the elements of programming in R to solve problems that arise in data processing.
1. Multivariate data. Measures of similarity and differences of multivariate data. Principles of working with matrices.
2. Distance calculation. Hierarchical clustering. Agglomerative clustering.
3. Nonmetric multidimensional scaling. Data imputation.
4. Principal Component Analysis (PCA). Matrix implementation of PCA. Factor loadings and ordinations. Properties of the components.
5. Multivariate analysis of variance. Limitations and applications.
6. Correlations between multivariate matrices.
7. Methods for identifying marker features. Graphical interpretation of data.
1. Multivariate data. Measures of similarity and differences of multivariate data. Principles of working with matrices.
2. Distance calculation. Hierarchical clustering. Agglomerative clustering.
3. Nonmetric multidimensional scaling. Data imputation.
4. Principal Component Analysis (PCA). Matrix implementation of PCA. Factor loadings and ordinations. Properties of the components.
5. Multivariate analysis of variance. Limitations and applications.
6. Correlations between multivariate matrices.
7. Methods for identifying marker features. Graphical interpretation of data.
In this course, you will learn about current research from various fields of bioinformatics, how to analyze scientific articles, and presentation skills.
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.
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
Profile courses (4 courses to select from the list)
- 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.
- Genetic engineering, synthetic biology.
- Basic genetic engineering.
- Gene delivery, genome editing.
- Gene synthesis, high-throughput cloning.
- Genome synthesis, synthetic signaling circuits and metabolic pathways.
- Basic genetic engineering.
- Protein engineering, drug design
- Protein design, peptide design.
- Antibody design, directed evolution.
- Ligand design, small molecule library creation and optimization.
- CAR-T cells.
- Protein design, peptide design.
- Cell biotechnology, neurotechnology and neural engineering.
- Stem cells, regenerative medicine.
- Organoids, organ-on-a-chip, 3D bioprinting.
- Brain-machine interfaces, neuroprosthetics.
- Stem cells, regenerative medicine.
- Applied biotechnology.
- Microbial biotechnology, industrial biotechnology.
- Plant biotechnology, agricultural biotechnology, biofuel.
- Biotechnology of animals, transgenic animals.
- Microbial biotechnology, industrial biotechnology.
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.
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.
- 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
- Introduction to optimization
- Necessary and sufficient conditions for optimality
- Lagrange multipliers method
- Gradient descent method
- Binary and ternary search
- Newton and quasi-Newton method
- Linear programming
- Mixed-integer programming
- Lagrangian relaxation
- MIP formulations. Tips and tricks
- Rejection sampling
- Monte-Carlo Markov Chain
- Simulated annealing
- Genetic programming
- Evolution strategies
- 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
Other courses
In this course, you will learn about current research from various fields of bioinformatics, how to analyze scientific articles, and presentation skills.
Negotiations, Influence and Conflict Management
The course focuses on understanding how to plan, design, and reach mutually beneficial agreements. You will learn the best modern approaches to negotiation, as well as the common biases and pitfalls that can accompany the negotiation process.
International Research Management Essentials
In the course, you'll learn step-by-step how to conduct research projects and prepare them for the final defense. You will also learn and acquire some of the most important 21st-century skills, from solving complex problems to making data-driven decisions.
Life in Science: Guide for Young Researchers
In the course you will develop several skills: to write scientific articles and choose journals for their publication, navigate the leading rating systems and strategically approach the construction of your academic reputation, design a public statement taking into account its goals and the characteristics of the target audience, work in a team and organize its work, to find the necessary resources for scientific work (grants, scholarships) and competently apply for their receipt.
Emotional Intelligence
In the course you will learn how to perceive and manage emotions, deepen your understanding of your emotional life, needs and motivation. You will also be able to understand others better and improve your effective communication skills.
The course focuses on understanding how to plan, design, and reach mutually beneficial agreements. You will learn the best modern approaches to negotiation, as well as the common biases and pitfalls that can accompany the negotiation process.
International Research Management Essentials
In the course, you'll learn step-by-step how to conduct research projects and prepare them for the final defense. You will also learn and acquire some of the most important 21st-century skills, from solving complex problems to making data-driven decisions.
Life in Science: Guide for Young Researchers
In the course you will develop several skills: to write scientific articles and choose journals for their publication, navigate the leading rating systems and strategically approach the construction of your academic reputation, design a public statement taking into account its goals and the characteristics of the target audience, work in a team and organize its work, to find the necessary resources for scientific work (grants, scholarships) and competently apply for their receipt.
Emotional Intelligence
In the course you will learn how to perceive and manage emotions, deepen your understanding of your emotional life, needs and motivation. You will also be able to understand others better and improve your effective communication skills.
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
and systems biology
