Curriculum 2019/21
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
  • Course Project
    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
    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
      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.
        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.