Introduction to Microbial Genomics & Metagenomics
This course will provide an introduction to microbial genomics. The content is intended to provide a basic hands-on training on data analysis, tools and resources for microbial bioinformatics and metagenomics.
Who are expected to attend?
Microbiologist, faculty, post doctorates, graduate students, research staff, and industry researchers interested in microbial genomics.
Format of the course
Topics will be delivered using a mixture of lectures (20%), practical sessions (60%) and open discussions (20%).
Practical work during the course will use small example datasets, but it is possible to work on your personal data.
This course will be held in a computer lab, which has desktop computers running Microsoft Windows OS with high speed internet connection. However, you can use your own laptop, only be sure you have Chrome or Firefox and have a recent version of Java installed.
The course will highlight key web-based resources, approaches and methodologies for DNA sequence data analysis for microbial genomics.
The major topics to be covered will include:
16S/18S metagenomics and species identification.
Multi locus sequence typing.
SNPs and plasmids profiling.
SNPs based phylogeny.
Prediction of antibiotics resistant mechanism.
This course has been created for people who want to understand the basics of structural bioinformatics including protein-ligand docking and virtual screening, i.e.:
Researchers whose main area or expertise is not a structural bioinformatics, but they want to broaden their knowledge in this field
Computer programmers who are going to deal with structural data (crystal structures, PDB format files, modeling pipelines) or with docking/virtual screening software
Biology, biotechnology and bioinformatics students who want to expand their knowledge into structural bioinformatics and molecular interactions
This course is more like a workshop. The trainer introduces theoretical bases as well as basic pipeline for structure modeling and molecular interactions.
Structure and function of DNA, RNA and proteins
Information provided by 3D structures
General methods of solving protein structures
Structural databases and formats
Protein Data Bank
PubChem and ZINC libraries
Structural file formats
Representation of macromolecules and ligands
Search methods and flexibility of objects
Useful data in docking
DOCK6 approach of protein-ligand docking
Grundlagen der Bioinformatik
This is a practical workshop, which will introduce basics of bioinformatics. It is aimed at people with basic biological background and knowledge in molecular biology, who want to learn how to process and work with biological data using computational tools. It has been designed especially for:
Researchers working in experimental laboratories on biological macromolecules proteins, RNA and DNA.
Scientists whose main area of expertise is not biology, but they need to cope with biological data.
Managers introducing new products (e.g. drugs), who want to broaden their knowledge about nowadays bioinformatics.
Computer programmers who are going to write programs dealing with biological molecules.
The course is divided into two parts: theoretical and practical. The lecture is interwoven with simple practical exercises, quizzes and tests to make sure that attendees understand the concepts.
During the training we will take you along a guided tour that covers all the basic aspects of biological data analysis. This workshop is an ideal opportunity to get familiar with bioinformatics and its many faces! At the end, you should be able to tackle any basic bioinformatics problem: you will know where to get the required data from, how to obtain relevant information and how basic software and algorithms work. Our aim is to facilitate your understanding of biological data.
By the end of the course you will be able to work with biological data using bioinformatics tools.
Biological data formats
PSI-BLAST / Multiple Sequence Alignments (MSA)
Modellierung von biologischen Strukturen
This is a practical workshop, which will introduce basics of molecular modelling of proteins, RNA and DNA. It is well suited for people with biological background, with knowledge in molecular biology, who want to learn how to predict 3D structures of proteins, RNA and DNA molecules.
It is especially suitable for:
Researchers working in experimental laboratories on structure determination or analysis
Scientists whose main area of expertise is not biology, but they need to cope with the results of structural biology research
Managers introducing new products (e.g. drugs) who want to broaden their knowledge about novel techniques and possibilities in the field of drug design, structural biology and bioinformatics
Computer programmers who are going to write programs dealing with biomolecules.
The course is divided into theoretical and practical parts. The lecture is interwoven with simple practical exercises, quizzes and tests to make sure attendees understand the concepts.
During the training you will be taught how to use software dedicated for building 3D models. Furthermore, we will show how to improve and assess models.
During the course we will present various software and different approaches used in novel structure modelling techniques. Our aim is to facilitate your understanding of modelling principles, reasoning and structure-based conclusion making process.
By the end of the course you will be able to prepare a 3D structure model of protein, RNA or DNA molecules of reasonable standard.
Two main approaches in 3D structure modelling de novo, homology
Homology modelling basics background, techniques, software
Choosing a template
Preparing a model
Types of modelling de novo, homology
Homology modelling basics background, techniques, software
Choosing a template
Preparing a model
Programmieren in Python für Biologen
Dieser Kurs richtet sich an:
Wissenschaftler, die mit biologischen Daten arbeiten.
Forscher, die Routineaufgaben automatisieren möchten.
Biologen, die Ihre Arbeit mit einfachen Programmen verstärken möchten ohne gleich Vollzeitprogrammierer zu werden.
Manager, die ein Grundverständnis für die Arbeit von Programmierern erlangen möchten.
Am Ende des Kurses werden die Teilnehmer in der Lage sein kurze Programme selbständig zu schreiben, um biologische Daten zu analysieren und zu manipulieren.
Einführung in die Programmiersprache Python
Python als Werkzeug für Biologen
Die Kommandozeile iPython
Ihr erstes Programm
Skripte in Python
Arbeiten mit Sequenzen von DNA, RNA und Proteinen
Muster in Sequenzen finden
Transkription und Translation
Biologische Datenformate lesen
Bioinformatische Analysetools verwenden
Lokale Programme starten
Web Services verwenden
Automatische Pipelines erstellen
Tabellen lesen und schreiben
Daten aus MS Excel / OpenOffice importieren
Sortieren nach mehreren Kriterien
Suchen in grossen Dateien
Filtern von Duplikaten
Durchschnitt, Standardabweichung und Median berechnen
Die Schnittstelle von Python zu R
Säulen-, Balken-, und Kuchendiagramme erstellen
Die Fläche unter einer Kurve berechnen (Area Under Curve, AUC)
In diesem Kurs lernen die Teilnehmer, wissenschaftliche Ergebnisse in Form von Präsentationen, Fachartikeln und Postern zu verbreiten. Der theoretische Teil des Kurses ist eng mit praktischen Übungen verknüpft, mit denen wissenschaftliche Arbeit übersichtlich, verständlich und interessant dargestellt werden kann. Am Ende werden die Teilnehmer einen Kurzvortrag vorbereiten, der im Gedächtnis bleibt.
Fachleute und Laien
Qualitativ hochwertige Präsentationen
Zielsetzung und Zielpublikum
Struktur und Inhalt
Umgang mit Lampenfieber
Wissenschaftliche Texte strukturieren
Präzision und Überprüfbarkeit
Einführung in Genetik
This course has been created for people who want to make reasonable decisions related to genetics, e.g.:
managers introducing new products using genetically modified plants
researchers whose main area or expertise is not genetics but they need to feed the results of genetic research into their work
computer programmers who are going to deal with data related to DNA
forensic experts, judges, lawyers, who need to make decisions based on DNA profiling (aka DNA testing or DNA matching)
It can be helpful for people who simply want to understand how genetics works.
It is a more narrative course in terms of the story, starting from the very origin of life and explaining how our genetic code has evolved over time and how can we trace the ancestry of human evolution.
Using the story of evolution, the trainer introduces you into scientific lingo and explains the workings of genes, DNA and RNA, heredity and natural selection.
The lecture is intertwined with simple exercises, quizzes and tests to make sure you understood the concepts and facts.
This course does not cover moral, political or religious point of view of genetics, but presents confirmed facts, not theories or conjectures.
History of Earth
History of Life
How can we be so sure about "LUCA", the last universal common ancestor.
How to read molecular sequences
Why evolution is an established scientific fact, not a theory
The "Cauldron" and appearance of RNA
How primitive forms of life exchange their genetic code
What can we read from fossil vestiges
Two groups of bacteria
How organism became multicellular
From bacteria to humans
How an organism work
How energy is extracted and used
Mutations and evolution
Is natural selection a theory or a fact?
Brief history of homo sapiens
How do we know that we come from Africa?
Prehumans (2mln years ago)
Human brain and neurons
The price for big brain
Genes and race
Modern evidence of evolution
Genome, genetics, DNA, RNA and chromosomes explained
Tracing maternal ancestors, mitochondrial DNA (mtDNA Tests)
Tracing male ancestors (Y Line Tests, Y chromosomal DNA test)
How cloning works?
DNA matching, how a genetic code of a single cell related to a person DNA
The Gnome project
What causes Cancer
HIV and AIDS
Genetics and the Industry
How can we change the DNA to improve food production
The history of penicillin, we do not have to do it at random
Genetics and live stock, current state and the future
Genetics and drug production
Statistik für Forscher
This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines.
It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs.
In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...)
In the case of public courses, mixed examples are used.
Though various software is used during this course (Microsoft Excel to SPSS, Statgraphs, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion.
This course can be delivered as a blended course i.e. with homework and assignments.
Scientific Method, Probability & Statistics
Very short history of statistics
Why can be "confident" about the conclusions
Probability and decision making
Preparation for research (deciding "what" and "how")
The big picture: research is a part of a process with inputs and outputs
Questioners and measurement
What to measure
Design of Experiments
Analysis of Data and Graphical Methods
Research Skills and Techniques
Describing Bivariate Data
Introduction to Bivariate Data
Values of the Pearson Correlation
Guessing Correlations Simulation
Properties of Pearson's r
Computing Pearson's r
Restriction of Range Demo
Variance Sum Law II
Conditional Probability Demo
Gamblers Fallacy Simulation
Bayes' Theorem Demonstration
Monty Hall Problem Demonstration
Areas of Normal Distributions
Varieties of Normal Distribution Demo
Normal Approximation to the Binomial
Normal Approximation Demo
Sample Size Demo
Central Limit Theorem Demo
Sampling Distribution of the Mean
Sampling Distribution of Difference Between Means
Sampling Distribution of Pearson's r
Sampling Distribution of a Proportion
Degrees of Freedom
Characteristics of Estimators
Bias and Variability Simulation
Logic of Hypothesis Testing
Type I and Type II Errors
One- and Two-Tailed Tests
Interpreting Significant Results
Interpreting Non-Significant Results
Steps in Hypothesis Testing
Signficance Testing and Confidence Intervals
t Distribution Demo
Difference between Two Means (Independent Groups)
All Pairwise Comparisons Among Means
Difference between Two Means (Correlated Pairs)
Correlated t Simulation
Specific Comparisons (Correlated Observations)
Pairwise Comparisons (Correlated Observations)
Factors Affecting Power
Introduction to Simple Linear Regression
Linear Fit Demo
Partitioning Sums of Squares
Standard Error of the Estimate
Prediction Line Demo
Inferential Statistics for b and r
One-Factor ANOVA (Between-Subjects)
Multi-Factor ANOVA (Between-Subjects)
Unequal Sample Sizes
Tests Supplementing ANOVA
Power of Within-Subjects Designs Demo
Chi Square Distribution
Testing Distributions Demo
2 x 2 Table Simulation
Analysis of selected case studies