Teaching EnardHellmann lab

Wolfgang Enard

Content

This lecture builds on knowledge obtained in molecular biology and genetics on the Bachelor's level. It aims to deepen an understanding how the human genome was sequenced and annotated and how it is currently used to study human biology in health and disease. The following topics are addressed: The human genome project, high throughput sequencing technologies, basics in sequence analysis, gene annotation, gene expression analysis.

Learning Outcome

The students will be able to describe and understand fundamental principles of human genomic research. They will acquire the basic background knowledge to apply genomic technologies.

Wolfgang Enard

Content:

This lecture covers genetic, paleoanthropological, archaeological, cultural, psychological, and epistemological aspects of human evolution. It begins with the relatedness of humans to other human and non-human individuals, and how ancient DNA has revolutionized our understanding of this ancestry.It then explores the fossil and archaeological record of humans since the split from chimpanzees, including the role of brain size, bipdealism and other adaptations, as well as the methods and assumptions underlying these inferences. We will also discuss findings from developmental and comparative psychology that shed light on how humans may have adapted to an ultrasocial ecological niche, and how this has led to the emergence of cumulative culture as a second system of inheritance. Finally, we will examine how these processes have shaped aggression, cooperation, morality, and other behaviors. We will consider how the ecology of humans has drastically changed with the emergence of agriculture, and what implications this may—and may not—have for shaping future societies.

Learning outcomes:

Students will gain an overview of the major topics, data, and methods relevant to the study of human evolution. They will develop an understanding of how cognitive biases can shape interpretations, particularly when examining our own evolutionary history. Furthermore, they will be able to critically evaluate what evolutionary evidence can—and cannot—tell us in informing individual and societal decisions.

Ines Hellmann

Content:

1. Literature search (Web of science, PubMed, Google Scholar and journal websites), literature managment (Endnote and Mendely).
   In addition, we will discuss citation formats and rules and what counts as plagiarism.
2. In silico cloning using CLC (primer design, in silco digests, construct design)
3. Protein function prediction (homology based searches, protein localization prediction, post-translational modification prediction, structural predictions)
4. Protein modelling using chimera
5. Image preparation and quantification (using Fiji)
6. Basics of R (Datacamp - selflearning)
7. Phylogenetic analysis (basic concepts for evolution, the basics of building and interpreting phylologenies, building phyolgenetic trees)
8. Plotting and basics of the statistical programming language R and ggplot2

Skills:

1. Safe usage of basic computational biology tools essential for molecular biologists
2. Basic understanding of the underlaying computational principles (e.g. algorithms)
3. Understanding of the evaluation criteria used for these bioinformatic tools
4. Understanding the relevance of computational biology for wet lab experimental design and analysis.

Wolfgang Enard & Ines Hellmann

Content:

In the seminar, the students critically present and discuss current publications related to genomic analyses that are relevant in the context of current Research of the AG Enard and AG Hellmann. This includes papers related to experimental and computational aspects of single-cell RNA-sequencing, evolutionary genomics, (primate) iPS cells or gene regulation and its evolution.

Learning outcomes:

The students will be able to extract and judge relevant information also from complex literature and to exchange information and ideas on a scientific level.

Wolfgang Enard & Ines Hellmann

Ines Hellmann

Content:

Whole transcriptome analysis by RNA-seq is on the verge of becoming a standard analysis in many molecular biology laboratories. As it is the case for many next generation sequencing (NGS) based methods, the analysis of the data is often more complex than the generation of the data and biologists often (wrongly) believe that the analysis falls in the domain of bioinformaticians. This course aims to set this record straight by enabling students to analyse RNA-seq data by executing and most importantly understanding the following steps: 1. Basic handling skills of NGS data accessing a unix server via the shell commandline. 2. Normalisation and outlier removal of RNA-seq data. 3. Differential expression analysis. 4. Gene-set enrichment analysis. 5. Gene expression network analysis.

Learning outcomes:

This course enables students to analyse RNA-seq data starting from raw sequence files ending with expression network analysis.

Ines Hellmann

Ines Hellmann

Content:

Handling, visualizing and interpreting statistical data is the key to success in many fields of biology. In this practical we will repeat some basic concepts and apply them to interpret published figures and statistics. Most importantly, the course teaches how to generate plots using R and ggplot2. Qualification goals: The students will be able to handle and plot data using the statistical scripting language R. This is a key qualification for modern, quantitative biology and will provide the necessary basics to apply and extend these skills when handling and plotting data in scientific projects.

Learning outcomes:

The students will be able to handle and plot data using the statistical scripting language R. This is a key qualification for modern, quantitative biology and will provide the necessary basics to apply and extend these skills when handling and plotting data in scientific projects.