Curriculum 2021/23
Semester 1
Profile courses
  • Introduction to Bioinformatics
  • Statistics *
  • Discrete Mathematics *
  • Programming in Python *
  • Advanced Statistics **
  • Algorithms and Data Structures **
  • Molecular Biology **
  • Research Work
Electives
  • Higher Mathematics
  • Introduction to Linux
General courses
  • Data Processing and Analysis
  • Creative Technologies/ High Tech Business Creation/ Thinking
  • Foreign Language in Professional Activity
Semester 2
Profile courses
  • Systems Biology
  • Population and Medical Genetics
  • Scientific Python
  • Applied Statistics
  • Applied Bioinformatics (Journal Сlub)
  • Research Work
General courses
  • Methodology of Translational Research
  • Applied Artificial Intelligence
  • Foreign Language in Professional Activity
    Semester 3
    Profile courses
    • Structural Bioinformatics
    • Biotechnology *
    • Comparative Genomics *
    • Microbial Omics *
    • Bioinformatics Algorithms **
    • Optimization & Sampling **
    • Advanced Machine Learning **
    • Applied Bioinformatics (Journal Сlub)
    • Research Work
    Soft Skills course
    • Negotiations, Influence and Conflict Management/ Emotional Intelligence/ International Research Management Essentials/ Life in Science: Guide for Young Researchers


    Semester 4 is for Research Internship and Master's Thesis
    +
    Applied Bioinformatics (Journal Сlub)
    * Track 1: Data Analysis in Biology and Medicine
    ** Track 2: Algorithmic Bioinformatics
    Syllabuses
    Introduction to Bioinformatics
    Teachers: Alexey Sergushichev, Maxim Artyomov

    1. Foundations of immunology. Antibodies and phagocytes. Cells of the immune system. T-cells.
    2. Transcription and RNA sequencing. Exploration and analysis of gene expression datasets.
    3. Introduction to single-cell RNA-sequencing
    4. Case studies from cancer immunology and aging immunology
    5. Immunometabolism. Metabolic network analysis in Cytoscape
        Statistics
        Teacher: Alexey Zabelkin

        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 technics, 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 Statistics
            Teacher: Anastasia Abramova

            1. Sample, estimates. Descriptive statistics. Unbiased estimator, consistent estimator. Distribution of empirical E, D.
            2. MLE, method of moments. Confidence intervals.
            3. One-sample hypothesis testing: one-tailed/two-tailed alternatives.
            4. One-sample hypothesis testing: parametric/non-parametric criteria.
            5. Two-sample hypothesis testing. Correlation coefficients.
            6. Linear regression, ANOVA.
            7. Multiple hypothesis testing: correction methods, FDR.
                Programming in Python
                Teacher: Vladimir Sukhov

                1. Python Basics. Numbers. Strings. Boolean Type. Variables and rules for naming variables. Arithmetic Operators. Comparison operators. Logical operators.
                2. Control Flow. Indentation.
                3. Data structures. List. Set. Dictionary. Tuple.
                4. Functions.
                5. Functional programming.
                6. Call stack. Namespace.
                7. Modules, Files.
                8. Python Classes. Attributes, methods. Instantiation. self parameter. Underscores.
                9. Inheritance. Parent class and Child class. Subclass attributes. Multiple inheritance. Method Resolution Order (MRO).
                10. Iterators. Generators. Errors. Exceptions.
                Molecular Biology
                Teachers: Pavel Dobrynin, Anna Zhuk

                1. Cell structure and cell processes. Cell division. Mitosis and meiosis. Cell cycle.
                2. Molecular basis of genetics and inheritance. Central dogma of molecular biology. DNA and RNA functions.
                3. DNA replication. Replication processes in prokaryotes and eukaryotes. Transcription and translation. RNA splicing and processing. DNA damage and mutations. DNA repair.
                4. Definitions of gene. Mendel's principles of heredity. The chromosome theory of inheritance. Gene regulation in prokaryotes and eukaryotes.
                5. Genome structure and organization. Chromosome organization. Genome rearrangements.
                6. Genetic diversity. Sources of genetics diversity. Inbreeding and mutational load.
                Discrete Mathematics
                Teacher: Artem Ivanov

                1. Introduction to set theory: definitions, set operations and properties
                2. Boolean functions, boolean logic and proof methods
                3. Combinatorics: combinations, permutations, pigeonhole principle
                4. Combinatorial generation
                5. Asymptotic analysis of algorithms
                6. Sorting algorithms
                7. Dynamic programming: definitions, subtask optimization, basic problems
                8. Dynamic programming in bioinformatics: local and global alignments
                9. Dynamic programming: RNA's secondary structure prediction
                10. Graph theory: definitions, DFS, BFS
                11. Graph theory: algorithms, de Bruijn graph
                Algorithms and Data Structures
                Teacher: Vitaly Aksenov

                1. Complexity
                2. Sorting and Binary Search
                3. DigitSort and Stacks
                4. Queues, Dequeue, Heap, Amortized time
                5. Vector continuation, BST, AVL, Treap
                6. Treap and Treap with hidden keys
                7. Dynamic Programming. Part 1.
                8. Dynamic Programming. Part 2.
                9. Dynamic Programming. Part 3.
                10. Dynamic Programming. Part 4.
                11. String Hashes
                12. Depth-First Search
                13. Eulerian cycle, Breadth-First Search, Dijkstra
                14. Ford-Bellman, Floyd
                Population and Medical Genetics
                Teacher: Mykyta Artomov

                1. Where it all started. Solving a mystery of inheritance. Mendel and post-Mendel era
                2. Pre-genome era. Mapping of the first human disease gene. Huntingnton's disease.
                3. RFPL, microsatellite and genetic linkage. Pedigree and linkage analysis. DNA forensics.
                4. Human Genome Project. SNP map of the human genome. DNA variation.
                5. Linkage disequilibrium and genome-wide association studies. HapMap project.
                6. GWAS concepts and approaches.
                7. Hands on GWAS tutorial.
                8. GWAS discussion and resources. Polygenic risk scores. UK biobank.
                9. Next generation sequencing. Exome sequencing.
                10. GATK pipeline.
                11. Variant annotations, selection pressure metrics. Large scale sequencing resources.
                12. Rare variant association studies
                13. Hands-on analysis of exome sequencing data.
                14. Cancer genetics. TCGA project.
                15. Case-control matching challenge.
                Systems Biology
                Gene expression analysis
                Teachers: Konstantin Zaitsev, Alexander Tkachenko

                1. Central dogma of molecular biology, structure of gene, types of RNA, structure of RNA, transcription, reverse transcription, FACS
                2. Microarray: quantification, normalization, basic analysis
                3. RNA-seq: alignment, quantification, QC, normalization, basic analysis
                4. Overall quality control: PCA, clustering, outlier detection
                5. Overall quality control: batch correction
                6. Differential expression: limma for microarray, Deseq2 for RNA-seq
                7. Downstream analysis: pathway/gene set enrichment analysis
                8. Downstream analysis: gene expression deconvolution
                9. Transcriptome assembly, functional annotation
                10. Single-cell transcriptomics: Seurat basic analysis
                11. Single-cell transcriptomics: Trajectory analysis, RNA velocity, optimal transport
                12. Visual data exploration: phantasus, JBR genome browser
                13. Experimental design of gene expression study


                Epigenetics practice
                Teachers: Oleg Shpynov, Roman Chernyatchik

                1. ChIP-seq. QC. Alignment. Visualization. Peak calling.
                2. Downstream. Genomic regions manipulation. Associated/closest gene annotation. Coverage profile. Functional genome annotation. Motif analysis TF. Pathway enrichment analysis. Similar datasets.
                3. Methylation. BS-Seq Preprocessing. BS-Seq Downstream. Bis-SNP.
                Scientific Python
                Teacher: Denis Kleverov

                1. Basic text processing with python. Regular expressions
                2. Code organization. Error handling and debugging
                3. Exploratory data analysis with python. Data manipulation packages
                4. Visual data analysis with python. Data visualization packages
                5. 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.
                6. Biological data processing with python. Biopython and Scanpy
                7. Scientific project organization. Virtualization, Pipelines and Web applications. Docker. Introduction to pipeline development using Snakemake
                8. Machine learning possibilities in python. Advanced tools for data processing. Introduction to neural networks with python.
                  Structural Bioinformatics
                  Teachers: Ferdinand Molnar, Karina Pats

                  1. Defining bioinformatics and structural bioinformatics.
                  2. Fundamentals of macromolecular organization and structure. Hierarchical levels of protein organization. Protein 3D structure. Protein Domains. Protein Folds.
                  3. 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.
                  4. 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.
                  5. Experimental approaches in structural biology: Determination of macromolecular structures. X-ray crystallography. Nuclear Magnetic Resonance. Cryo-electron microscopy. Hydrogen-deuterium exchange.
                  6. Protein databases. The Protein Databank (PDB), UniProtKB, InterPro, HMMER, PDBe, EMDB, EMPIAR, IntAct, Complex Portal, Reactome.
                  7. Computer-aided drug design: approaches and methods. Structure-based drug design. Ligand-based drug design. De novo design.
                  8. Basics of molecular modeling. Preparation of protein structure. Molecular docking, molecular dynamics.
                  Biotechnology
                  Teacher: Alexander Tkachenko

                  Part 1. Genetic engineering, synthetic biology. This part tells about nucleic acid manipulation methods used to create genetic constructs of different complexity.
                  1. Basic genetic engineering.
                  2. Gene delivery, genome editing.
                  3. Gene synthesis, high-throughput cloning.
                  4. Genome synthesis, synthetic signaling circuits and metabolic pathways.

                  Part 2. Protein engineering, drug design. This part tells about protein and ligand design: creation of new and modification of existing proteins and small molecules.
                  1. Protein design, peptide design.
                  2. Antibody design, directed evolution.
                  3. Ligand design, small molecule library creation and optimization.
                  4. CAR-T cells.

                  Part 3. Cell biotechnology, neurotechnology and neural engineering. This part tells about cell culture technology applications in biotechnology and medicine, artificial organ models, and brain-machine interfaces.
                  1. Stem cells, regenerative medicine.
                  2. Organoids, organ-on-a-chip, 3D bioprinting.
                  3. Brain-machine interfaces, neuroprosthetics.

                  Part 4. Applied biotechnology. This part tells about specific practical applications of biotechnology in industry and agriculture.
                  1. Microbial biotechnology, industrial biotechnology.
                  2. Plant biotechnology, agricultural biotechnology, biofuel.
                  3. Biotechnology of animals, transgenic animals.
                  Bioinformatics Algorithms
                  Teachers: Alexey Sergushichev and others

                  1. Sequence alignment
                  2. Genome assembly
                  3. Hidden Markov Models
                  4. Phylogeny reconstruction
                  5. Genome rearrangements
                  6. Active module problem
                  7. Algorithms for gene set enrichment analysis
                  8. Privacy-preserving genome-wide association studies
                  9. Reconstruction of evolutionary histories
                  10. Reconstruction of gene regulatory networks
                  Optimization & Sampling
                  Teacher: Alexander Loboda

                  1. Introduction to optimization
                  2. Necessary and sufficient conditions for optimality
                  3. Lagrange multipliers method
                  4. Gradient descent method
                  5. Binary and ternary search
                  6. Newton and quasi-Newton method
                  7. Linear programming
                  8. Mixed-integer programming
                  9. Lagrangian relaxation
                  10. MIP formulations. Tips and tricks
                  11. Rejection sampling
                  12. Monte-Carlo Markov Chain
                  13. Simulated anneanling
                  14. Genetic programming
                  15. Evolution strategies
                  Advanced Machine Learning
                  Teacher: Sergey Muravyov

                  1. Introduction to Machine Learning
                  2. Non-parametric Models. Nadaraya-Watson kernel regression.
                  3. Linear Models. Gradient Algorithms.
                  4. Support Vector Machine. Karush-Kuhn-Tucker Condition
                  5. Probabilistic Classifiers
                  6. Trees and Ensembles. Boosting algorithms
                  7. Neural Networks
                  8. Optimization algorithms for Neural Networks
                  9. Convolutional Neural Networks
                  10. Recurrent Neural Network
                  11. Dimensionality reduction
                  12. Label Irregularity
                  13. Clustering
                  14. Generative Models
                  15. Best Practices in ML
                  Comparative Genomics
                  Preliminary syllabus

                  1. Structure of the genome. Functional elements in genome. Epigenetic modifications of genome. Eukaryotic organelles genomes.
                  2. Genomic technologies. Methods for DNA sequencing. Genome annotation. Genome sequencing projects.
                  3. Molecular phylogenetics. Systems of biological classification. Phylogenetic tree.
                  4. Comparative genomic approaches. De novo genome assembly. Assessing sequence similarity. Software used for comparative genomics. Phylogenetic analysis.
                  Microbial Omics
                  Preliminary syllabus

                  1. Metagenomics. Amplicon metagenomics. Shotgun metagenomics. Genome-resolved metagenomics. Metagenomic assembly.
                  2. Phylogenomics and Phylogenetics. Phylogenomics and pangenomics.
                  3. Pangenomics. Metapangenomics. Horizontal gene transfer.
                  4. Bacterial population genetics. Single-cell genomics.