From October to December, our master students participate in a series of introductory methods courses in small groups. In these courses you learn fundamental techniques applied in current research and meet members of the participating research groups. Three different course types differ in length and concept: The 4-day courses with proteins and nucleic acids introduce you to integrate various and basic and advanced techniques in the style of short projects. Several 2-day courses provide you with hands-on experience with a variety of different methods as indicated below. In addition, you are offered a choice of two (out of four) 5-day special courses (week 8-9) with an integrated concept of lectures and hands-on experiments in the fields of intergrated structural biology, electron microscopy (including 3-D cryo EM), NMR spectroscopy, mass spectrometry, proteomics and metabolomics. Two addition course weeks focus on the introduction to bioinformatics including topics such as next generation sequencing, protein bioinformatics, comparative sequence analysis, gene ontologies and biological networks. These courses are preceded by an introduction to R in five course modules starting already during the orientation weeks in September. Please click on the different modules and respective course topics in order to see more details.
Methods
Day 2: Preparation of permeabilized mammalian cells (fractionation into soluble contents and membrane-enclosed compartments); in vitro targeting of fluorescent signal-bearing proteins to selected compartments (nuclei, mitochondria, possibly peroxisomes); analysis by fluorescence microscopy.
Day 2: Analysis of the column fractions: dot blot, acidification assay of synaptic vesicles by a dual-wavelength spectrophotometer.
- statistical thinking
- qualitative vs quantitative analysis
- statistical vocabulary: e.g. random variable, sample, population, probability, independence etc,
- distributions: what types of distributions are relevant in Biology; characteristics of distributions; revisit mean, median, standard deviation etc,
- hypothesis testing (frequentist): which tests are available/applicable (1-2 for discrete, 1-2 for continuous); null hypothesis and alternative hypothesis; p-value; significance; interpretation of test results; statistical vs practical significance; concept of multiple testing
- correlation, causation and confounders: correlation vs causation, non-parametric correlation (generally beyond Pearson)
- linear regression; confidence; goodness of fit; interpretation
- data visualization and presentation: need for data cleaning -> human factors (units, dates, inconsistent annotation of data); different types of plots; how to display distributions; box plots vs. full distribution; pros and cons for different visualization approaches (how to miss effects, how to create wrong effects, when not to use bar plots)
- statistics vs. intuition
- reproducibility of statistical analysis: code reviews – good practice in writing analysis code
- Quality controls and normalization of single-cell RNA-seq
- Dimension reduction (PCA, tSNE, UMAP)
- Cell-type assignment in single-cell RNA-seq data
- Spatial transcriptomics; identification of spatially variable genes
- Day 1: Intro to Python with exercises: Installing Python, elementary syntax (indentation, ranges, dictionaries), if-then-else, for loops, objects and classes, function calls;
- Day 2: Pytorch: matrices and tensors, k-means clustering; logistic regression; random forests; training and test sets, data leakage; ROC analysis, precision/recall, FDR
- Day 3: Intro to neural networks as universal function approximators; gradient descent in high dimensions, local optima, stochastic gradient descent; autodiff
- Day 4: Deep learning: image classification on MNIST and ImageNet10 using Pytorch
This course is intended to serve as an introduction to integrated modelling of proteins and macromolecular complexes. The lectures will cover the theoretical basis of the most frequently used structural biology techniques and how structural data is derived using these methods. The practical sessions will provide hands-on experience on how structural data can be interpreted (model building) and results from several methods can be combined across scales.
Monday: General lecture on Electron Microscopy
Tuesday: Electron Optics
Wednesday: Image processing I (Tomography)
Thursday: Image processing II (Single particle EM)
Friday: Image processing III (Validation & Visualization)
Monday: Group 1: Techniques of molecular electron microscopy: negative stain preparation, cryo preparation, imaging.; Group 2: Techniques of cellular electron microscopy: Embedding of specimen, acquisition of tomograms
Tuesday: Group 2: Techniques of molecular electron microscopy: negative stain preparation, cryo preparation, imaging.; Group 1: Techniques of cellular electron microscopy: Embedding of specimen, acquisition of tomograms
Wednesday: Image processing I (Analysis of tomographic data)
Thursday: Image processing II (Single particle analysis); Particle selection, basic image processing, alignment, multivariate statistics
Friday: Image processing III (Validation and visualization of macromolecular complexes); Fourier Shell Correlation, introduction into UCSF chimera
Dietmar Riedel (Electron Microscopy Group): Introduction to electron microscopy: staining procedures, fixation procedures, principles of different room temperature, low temperature and cryo embedding methods (all applied to cells, membranes and organelles). Ultrathin sectioning of embedded PC12 cells and acquisition of angular series in a transmission electron microscope. Processing of the serial sections for tomographic reconstruction and 3D modelling.
Holger Stark, Niels Fischer (3D Cryo Electron Microscopy Group): Preparation of an electron microscopically grid by negative stain. Sample: 70 S ribosome. Image acquisition using different electron microscopes for negative stain and cryo samples. Extraction of individual molecular images. Multivariate statistical analysis, classification and averaging. Determination of projection angles. Calculation of the 3D structure of the complex by backprojection algorithms. Visualization and interpretation of the 3D structure (movies and animations).
Hands-on work will be done on the following steps of the non-targeted metabolomics approach:
The MSc class will be divided into four course groups and each group will take one of the listed DNA and one of the listed Protein courses, respectively.
Module M.MolBio.21: Proteins, Nucleic Acids, Cell Biology and Genetics (2-day courses)
DNA (Faesen)
DNA (Oudelaar)
DNA (Jakobs / Stoldt)
DNA (Heide)
Proteins (Lorenz)
Proteins (Stein)
Proteins (Rodnina / Peske)
Proteins (Rehling / Dennerlein)
The MSc class will be divided into four course groups. Each group will take one of each of the 6 different course topics, which are listed below.
Module M.MolBio.22: Bioinformatics and Statistics
Protein-Nucleic Acid Interaction (Rodnina / Poulis)
RNA Analysis (M. Bohnsack / K. Bohnsack)
RNA Analysis (Cramer / Zumer)
Light Microscopy (Rizzoli)
Light Microscopy (Lenart)
Cellular Compartments (Görlich)
Cellular Compartments (Kovtun)
Cell Culture (M. Bohnsack / K. Bohnsack)
Cell Culture (Wienands)
Expression Analysis (Schuh)
Expression Analysis (Günesdogan)
Introduction to Statistics (Liepe)
Programming in R (Beißbarth / Lidschreiber / Leha)
NGS Analysis with R (Lidschreiber)
Protein Bioinformatics (Söding)
Molecular Evolution, Phylogeny & Comparative Genomics (de Vries)
Single-Cell & Spatial Transcriptomics (Altenbuchiger)
Machine Learning in Python (Hauschild / Pape)
Integrated Structural Biology (Cramer / Dienemann)
(3D-Cryo) Electron Microscopy (Stark / Riedel)
NMR Spectroscopy (Griesinger)
Mass Spectrometry and Proteomics (Urlaub)
Mass Spectrometry and Metabolomics (K. Feußner / I. Feußner)