Past curricula
2020/22
2019/21
2018/20
Semester 1
  • Data Analysis in R
  • Statistics *
  • Discrete Mathematics *
  • Programming in Python *
  • Advanced Statistics **
  • Algorithms and Data Structures **
  • Molecular Biology **
  • Applied Bioinformatics / Journal Club
  • Higher Mathematics (elective)
  • Introduction to Linux (elective)
  • Soft Skills Course ***
  • Creative Technologies / High Tech Business Creation / Thinking
  • Foreign Language in Professional Activity
  • Research Work
Semester 2
  • Systems Biology
  • Population and Medical Genetics
  • Scientific Python
  • Data Analysis in R
  • Methodology of Translational Research
  • Applied Artificial Intelligence
  • Foreign Language in Professional Activity
  • Research Work
    Semester 3
    • Structural Bioinformatics
    • Systems Biology
    • Biotechnology *
    • Molecular Phylogenetics *
    • Metagenomics *
    • Bioinformatics Algorithms **
    • Optimization Methods **
    • Advanced Machine Learning **
    • Research Work


    Semester 4 is for Research Internship and Master's Thesis
    * Track 1: Analysis in Biology and Medicine
    ** Track 2: Algorithmic Bioinformatics

    *** Negotiations, Influence and Conflict Management / Emotional Intelligence / Internationalization of Research
    Syllabuses
    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: Nikita Alexeev

        1. A quick refresher for classical probability: definitions and examples.
        2. Descriptive statistics: measures of central tendency and measures of variability.
        3. Mathematical modelling and statistical simulations, Monte Carlo method.
        4. Statistical hypothesis testing: main ideas and definitions. Goodness of fit hypothesis.
        5. First and second type errors. P-values. Power of a test.
        6. Goodness-of-fit chi-square test and Kolmogorov–Smirnov test: applicability, properties, and examples.
        7. Hypothesis of independence: examples and tests.
        8. Multiple testing problem. Bonferroni correction and Benjamini–Hochberg procedure.
        9. Elements of statistical learning: principal components analysis.
        10. Elements of statistical learning: cluster analysis.
            Data Analysis in R
            Teacher: Valeria Bogdanova

            R and Data Analysis basics
            1. Data types and structures
            2. Program flow, functions, etc.
            3. Scoping rules, Functional approach
            4. Shiny Basics
            5. Data import and cleaning
            6. Data manipulation (tidyr, dplyr)
            7. Data visualization (ggplot2)

            Data Analysis
            1. Exploratory and Descriptive analysis
            2. Basics of Statistical Inference
            3. Linear Regression (Simple, Multiple, Logistic)
            4. Methods of Classification
            5. Resampling
            6. Regularization
            7. PCA and Clustering
            8. Additional
            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
            Teacher: Pavel Dobrynin

            1. Cell structure and cell processes. Cell division. Mitosis and meiosis. Cell cycle.
            2. Molecular basis of genetics and inheritance. DNA. Chromosomes and chromatin. DNA recombination and crossover. Linkage groups.
            3. DNA replication. Replication processes in prokaryotes and eukaryotes. Reparation mechanisms.
            4. Central dogma of molecular biology. DNA and RNA functions. DNA transcription and RNA translation. Ribosomes.
            5. Definitions of gene. Gene in prokaryotes and eukaryotes. Splicing and alternative splicing. Operons. Gene regulation.
            6. Genome structure and organization. Chromosome organization. Genome rearrangements.
            7. 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 (ExAC & GnomAD).
            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
            Systems biology overview
            Teachers: Maxim Artyomov, Alexey Sergushichev, Konstantin Zaitsev

            1. Introduction to systems biology and immunology
            2. Introduction to gene expression analysis
            3. fGSEA, PCA, pathways, practice in Phantasus
            4. Single-cell RNA-sequencing basics
            5. Cancer Immunology, SCE application
            6. Aging single-cell RNA-seq data
            7. Introduction to metabolism
            8. Metabolism network analysis

            Gene expression analysis
            Teachers: Konstantin Zaitsev, Alexander Tkachenko

            1. Central dogma of molecular biology, transcription, qPCR, FISH
            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

            Proteomics
            Teacher: Pavel Sinitcyn

            1. Introduction to Proteomics. Methods of protein analysis. Ionisation methods. Examples of mass analyzers. Top-down and Bottom-Up proteomics. DIA, DDA and Targeted proteomics. Tandem protein mass spectrometry.
            2. Protein quantification: LFQ, SILAC, TMT. Challenges of measuring protein concentrations. MS1 labeling - SILAC, NeuCode and chemical labeling. MS2 labeling - TMT и iTRAQ. Single Cell proteomics. Label-Free proteomics.
            3. Basics of MaxQuant and Perseus. 3D visualization HPLC+MS/MS. Analysis of HPLC+MS/MS.
            4. Proteomics application stories.

            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: Anastasia Gainullina

            1. Regular expressions. Metacharacters, special sequences and sets. "Greedy" and "lazy" quantifiers. Lookarounds. Protein database Prosite
            2. Biopython. Bio.Data, Bio.Alphabet. Bio.Seq, Bio.SeqRecord, Bio.SeqIO. Bio.Align, Bio.Blast, Bio.Phylo. Bio.PDB
            3. Numpy, pandas, seaborn. Numpy arrays, indexing/reshaping operations, time checking. Pandas data structures, tidy data concept, data wrangling. Visualization.
            4. Error handling. Error type hierarchy. Different types of clauses. Best practices for function organization
            5. Requests. Variety of databases. Concept of API. Popular data formats. Uniprot API queries
            6. Functional programming. Iterators, generators, comprehensions. Lambdas. Partial application of functions
            7. Pipelines and OS-level virtualization.
            8. Single cell in scanpy. Common data structures and conventional pipeline. Basic data representation.
            9. Introduction to machine learning.
              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.
              Molecular Phylogenetics
              Part 1. Phylogenetics
              Teacher: Mikhail Rayko

              1. Introduction. Necessary terminology. Acquaintance with trees.
              2. Alignment of nucleotide and protein sequences.
              3. Methods for constructing phylogenetic trees: MP, ME, NJ, ML.
              4. Testing the tree topology: bootstrap, supertrees.
              5. Bayesian methods in phylogenetics. Dating.
              6. Molecular Markers. Gene evolution and genome evolution.

              Part 2. Population Genetics
              Teacher: Yury Barbitoff

              1. Population genetics and genomics.
              2. The coalescent and its applications.
              3. Gene flow, admixture, and population histories.
              4. Natural selection and evolution.
              5. The impact of natural selection.
              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
              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 Methods
              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
                Semester 1
                • Data Analysis in R
                • Statistics
                • Discrete Mathematics
                • Programming in Python
                • Applied Bioinformatics / Journal Club
                • Soft Skills Course *
                • Creative Technologies / Project Management / Thinking
                • Foreign Language in Professional Activity
                • Research Work
                Semester 2
                • Systems Biology
                • Population and Medical Genetics
                • Scientific Python
                • Data Analysis in R
                • High Performance Computing
                • Applied Artificial Intelligence (online)
                • Foreign Language in Professional Activity
                • Research Work
                  Semester 3
                  • Structural Bioinformatics
                  • Systems Biology
                  • Metagenomics
                  • Molecular Phylogenetics
                  • Biotechnology
                  • Research Work


                  Semester 4 is for Research Internship and Master's Thesis
                  * Communication, Conflict Management and Influence Techniques / Emotional Intelligence / Business and Science Ethics, Research Management / Personal Efficiency and Time Management / Internationalization of Research / Effective Team Management
                  Syllabuses
                  Statistics
                  Teacher: Nikita Alexeev

                  1. Probability theory: basic concepts and definitions: events, independent events, random variables
                  2. Statistics: hypothesis, tests, significance, false positive and false negative errors.
                  3. P-values. Null hypothesis, alternative hypothesis, significance and statistical power.
                  4. Chi-square test of independence.
                  5. Chi-square goodness of fit test.
                  6. Tests for sample comparisons. Student's t-test and Mann-Whitney U-test.
                  7. Multiple comparisons problem. Genome-wide association study. Bonferroni correction and Benjamini-Hochberg Procedure.
                  8. Principal component analyses. Covariaton matrix, eigenvctors and eigenvalues.
                  9. Linear regression. Simple linear regression and multiple linear regression. Analyses of missing values.
                    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.
                    Data Analysis in R
                    Teacher: Valeria Bogdanova

                    R and Data Analysis basics
                    1. Data types and structures
                    2. Program flow, functions, etc.
                    3. Scoping rules, Functional approach
                    4. Shiny Basics
                    5. Data import and cleaning
                    6. Data manipulation (tidyr, dplyr)
                    7. Data visualization (ggplot2)

                    Data Analysis
                    1. Exploratory and Descriptive analysis
                    2. Basics of Statistical Inference
                    3. Linear Regression (Simple, Multiple, Logistic)
                    4. Methods of Classification
                    5. Resampling
                    6. Regularization
                    7. PCA and Clustering
                    8. Additional
                    Discrete Mathematics
                    Teacher: Artem Vasiliev

                    1. Introduction to set theory and boolean functions
                    2. Boolean logic and proof methods
                    3. Combinatorics
                    4. Combinatorial generation
                    5. Asymptotic analysis of algorithms
                    6. Sorting algorithms
                    7. Dynamic programming
                    8. Graph theory: basic concepts
                    9. Graph theory: graph algorithms
                    Systems Biology
                    Systems biology overview
                    Teachers: Maxim Artyomov, Alexey Sergushichev, Konstantin Zaitsev, Oleg Shpynov, Roman Chernyatchik

                    1. Introduction to systems biology and immunology
                    2. Introduction to gene expression analysis. Interactive analysis in Phantasus: working with public datasets
                    3. Introduction to single cell RNA-sequencing
                    4. Introduction to epigenetics
                    5. Introduction to metabolism

                    Gene expression analysis

                    Teacher: Konstantin Zaitsev

                    1. Central dogma of molecular biology, transcription, qPCR, FISH
                    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

                    Proteomics
                    Teacher: Pavel Sinitcyn

                    1. Introduction to Proteomics. Methods of protein analysis. Ionisation methods. Examples of mass analyzers. Top-down and Bottom-Up proteomics. DIA, DDA and Targeted proteomics. Tandem protein mass spectrometry.
                    2. Protein quantification: LFQ, SILAC, TMT. Challenges of measuring protein concentrations. MS1 labeling - SILAC, NeuCode and chemical labeling. MS2 labeling - TMT и iTRAQ. Single Cell proteomics. Label-Free proteomics.
                    3. Basics of MaxQuant and Perseus. 3D visualization HPLC+MS/MS. Analysis of HPLC+MS/MS.
                    4. Proteomics application stories.

                    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.
                    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 (ExAC & GnomAD).
                    12. Rare variant association studies
                    13. Hands-on analysis of exome sequencing data.
                    14. Cancer genetics. TCGA project.
                    15. Case-control matching challenge.
                    Scientific Python
                    Teacher: Anastasia Gainullina

                    1. Regular expressions. Metacharacters, special sequences and sets. "Greedy" and "lazy" quantifiers. Lookarounds. Protein database Prosite
                    2. Biopython. Bio.Data, Bio.Alphabet. Bio.Seq, Bio.SeqRecord, Bio.SeqIO. Bio.Align, Bio.Blast, Bio.Phylo. Bio.PDB
                    3. Numpy, pandas, seaborn. Numpy arrays, indexing/reshaping operations, time checking. Pandas data structures, tidy data concept, data wrangling. Visualization.
                    4. Error handling. Error type hierarchy. Different types of clauses. Best practices for function organization
                    5. Requests. Variety of databases. Concept of API. Popular data formats. Uniprot API queries
                    6. Functional programming. Iterators, generators, comprehensions. Lambdas. Partial application of functions
                    7. Pipelines and OS-level virtualization.
                    8. Single cell in scanpy. Common data structures and conventional pipeline. Basic data representation.
                    9. Introduction to machine learning.
                      Structural Bioinformatics
                      Teacher: Ferdinand Molnar

                      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. Structure based drug design: Hit identification.
                      7. Protein databases.
                      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.
                      Molecular Phylogenetics
                      Teachers: Mikhail Rayko, Yury Barbitoff

                      Theoretical lectures
                      1. Introduction. Necessary terminology. Acquaintance with trees.
                      2. Alignment of nucleotide and protein sequences.
                      3. Methods for constructing phylogenetic trees: MP, ME, NJ, ML.
                      4. Testing the tree topology: bootstrap, supertrees.
                      5. Bayesian methods in phylogenetics. Dating
                      6. Molecular Markers. Gene evolution and genome evolution.
                      Practical exercises
                      1. Graphical display of phylogenetic trees using R and Python.
                      2. Work with NCBI databases
                      3. Multiple sequence alignment tools.
                      4. Cleaning the alignment. Evolution model testing.
                      5. Comparison of tree reconstruction algorithms.
                      6. Tree topology verification. Bootstrap analysis .
                      7. Bayesian methods in phylogenetics.
                      8. Discussion and debriefing.
                      Semester 1
                      • Data Analysis in R
                      • Statistics
                      • Discrete Mathematics
                      • Programming in Python
                      • Applied Bioinformatics / Journal Club
                      • Soft Skills Course
                      • Project Management
                      • Foreign Language in Professional Activity
                      • Research Work
                      Semester 2
                      • Systems Biology
                      • Population and Medical Genetics
                      • Scientific Python
                      • Applied Statistics
                      • High Performance Computing
                      • Applied Artificial Intelligence (online)
                      • Foreign Language in Professional Activity
                      • Research Work
                        Semester 3
                        • Structural Bioinformatics
                        • Metagenomics
                        • Molecular Phylogenetics
                        • Biotechnology
                        • Applied Bioinformatics / Journal Club
                        • Research Work

                        Semester 4 is for Research Internship and Master's Thesis
                        Syllabuses
                        Statistics
                        Teacher: Nikita Alexeev

                        1. Probability theory: basic concepts and definitions: events, independent events, random variables
                        2. Statistics: hypothesis, tests, significance, false positive and false negative errors.
                        3. P-values. Null hypothesis, alternative hypothesis, significance and statistical power.
                        4. Chi-square test of independence.
                        5. Chi-square goodness of fit test.
                        6. Tests for sample comparisons. Student's t-test and Mann-Whitney U-test.
                        7. Multiple comparisons problem. Genome-wide association study. Bonferroni correction and Benjamini-Hochberg Procedure.
                        8. Principal component analyses. Covariaton matrix, eigenvctors and eigenvalues.
                        9. Linear regression. Simple linear regression and multiple linear regression. Analyses of missing values.
                          Programming in Python
                          Teacher: Dmitriy Yakutov

                          1. Common syntax and basic data types
                          2. More data types
                          3. Modules
                          4. Functions and functional programming
                          5. Recursion
                          6. Numpy module
                          7. Classes as data types
                          8. OOP basics
                          Data Analysis in R
                          Teacher: Valeria Bogdanova

                          Introduction to R
                          1. Data types and structures
                          2. Program flow, functions, etc.
                          3. Scoping rules, Functional approach
                          4. Shiny Basics
                          Data Analysis basics
                          1. Data import and cleaning
                          2. Data manipulation (tidyr, dplyr)
                          3. Data visualization (ggplot2)
                          Statistics basics
                          1. Exploratory and descriptive analysis
                          2. Linear Regression
                          Discrete Mathematics
                          Teacher: Artem Vasiliev

                          1. Introduction to set theory and boolean functions
                          2. Boolean logic and proof methods
                          3. Combinatorics
                          4. Combinatorial generation
                          5. Asymptotic analysis of algorithms
                          6. Sorting algorithms
                          7. Dynamic programming
                          8. Graph theory: basic concepts
                          9. Graph theory: graph algorithms
                          Systems Biology
                          Systems biology overview
                          Teachers: Maxim Artyomov, Alexey Sergushichev, Konstantin Zaitsev, Oleg Shpynov, Roman Chernyatchik
                          1. Introduction to systems biology and immunology.
                          2. Introduction to gene expression analysis. Interactive analysis in Phantasus: working with public datasets.
                          3. Introduction to single cell RNA-sequencing.
                          4. Introduction to epigenetics.
                          5. Introduction to metabolism.

                          Working with RNA-sequencing data
                          Teacher: Alexey Serushichev
                          1. RNA-sequencing quantification pipelines
                          2. Analysis of gene expression data in R

                          Proteomics

                          Teacher: Pavel Sinitcyn
                          1. Introduction to proteomics. Experimental protocols.
                          2. Proteomics quantification.
                          3. Analysis of proteomics data in Max Quant.
                          4. Proteomics extras: posttranslational modifications, protein-protein interactions, spatial proteomics.
                          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 (ExAC & GnomAD).
                          12. Rare variant association studies
                          13. Hands-on analysis of exome sequencing data.
                          14. Cancer genetics. TCGA project.
                          15. Case-control matching challenge.
                          Scientific Python
                          Teacher: Anastasia Gainullina

                          1. Regular expressions. Metacharacters, special sequences and sets. "Greedy" and "lazy" quantifiers. Lookarounds. Protein database Prosite
                          2. Biopython. Bio.Data, Bio.Alphabet. Bio.Seq, Bio.SeqRecord, Bio.SeqIO. Bio.Align, Bio.Blast, Bio.Phylo. Bio.PDB
                          3. Numpy, pandas, seaborn. Numpy arrays, indexing/reshaping operations, time checking. Pandas data structures, tidy data concept, data wrangling. Visualization.
                          4. Error handling. Error type hierarchy. Different types of clauses. Best practices for function organization
                          5. Requests. Variety of databases. Concept of API. Popular data formats. Uniprot API queries
                          6. Functional programming. Iterators, generators, comprehensions. Lambdas. Partial application of functions
                          7. Pipelines and OS-level virtualization.
                          8. Single cell in scanpy. Common data structures and conventional pipeline. Basic data representation.
                          9. Introduction to machine learning.
                            Structural Bioinformatics
                            Teacher: Ferdinand Molnar

                            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. Structure based drug design: Hit identification.
                            7. Protein databases.
                            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.
                            Molecular Phylogenetics
                            Teachers: Mikhail Rayko, Yury Barbitoff

                            Theoretical lectures
                            1. Introduction. Necessary terminology. Acquaintance with trees.
                            2. Alignment of nucleotide and protein sequences.
                            3. Methods for constructing phylogenetic trees: MP, ME, NJ, ML.
                            4. Testing the tree topology: bootstrap, supertrees.
                            5. Bayesian methods in phylogenetics. Dating
                            6. Molecular Markers. Gene evolution and genome evolution.
                            Practical exercises
                            1. Graphical display of phylogenetic trees using R and Python.
                            2. Work with NCBI databases
                            3. Multiple sequence alignment tools.
                            4. Cleaning the alignment. Evolution model testing.
                            5. Comparison of tree reconstruction algorithms.
                            6. Tree topology verification. Bootstrap analysis .
                            7. Bayesian methods in phylogenetics.
                            8. Discussion and debriefing.
                            Metagenomics
                            Part 1. Teacher: Ilia Korvigo
                            1. A primer on linear algebra I: Algebraic structures, Vector spaces
                            2. A primer on linear algebra II: Coordinate systems, Linear maps, Covariance
                            3. A primer on compositional data analysis: Compositions as equivalence classes, Aitchison simplex, Principles of compositional data analysis, Generating systems on the simplex, Isomorphisms and isometries
                            4. Introduction to metagenomics: Shotgun metagenomes, Amplicon libraries
                            5. Taxonomic annotation: Reference databases, Alignment-based annotation, Machine learning methods, Phylogenetic placement
                            6. Statistical analyses I: Diversity indices, Ordination, Location tests, Handling zeros
                            7. Statistical analyses II: ILR balances, Feature selection, Mixed effects linear models
                            8. Statistical analyses III: Dirichlet-multinomial regression, Logistic-normal-multinomial models

                            Part 2. Teachers: Alexander Tyakht, Daria Kashtanova, Vera Odintsova, Natalya Klimenko
                            1. Human microbiome. Analysis of 16S-data
                            2. Shotgun metagenomics
                            3. Clinical applications of metagenomics
                            4. Statistical analysis in metagenomics
                            5. Machine learning in metagenomics