Bioinformatics for Beginners
ApplyAt a glance
Qualification:
Certificate of attendance "Bioinformatics for Beginners" (2 ECTS)
Start:
on request
Duration:
Costs:
CHF 1'150.00
Comment on costs:
There is a discount for students. Contact us if you are interested.
Language of instruction:
English
Dates:
Objectives and content
Target audience
A two-day course for students and other life scientists who are just starting with bioinformatics.
Prerequisites for this course: Although not required, familiarity with python or a coding language is recommended. Participants should bring laptops that have a working terminal.
Objectives
We’ll start by exploring the most used bioinformatics resources for variety of different data sets, continue with a genomics practice on mutations. We’ll cover the best practices and common challenges in this practice as a foundation to the understanding of bioinformatics pipelines. Rest of the course, you’ll be working in smaller project groups. You’ll be able to choose projects on phylogenetics, immunobiology, cancer genomics and machine learning. As you can see, majority of the course will be hands-on allowing you to practice your acquired knowledge.
At the end students will
- realize the wealth of bioinformatics database structures and the -omic tools
- understand how to download and use data from bioinformatics databases
- learn best practices and common challenges in genomics
- explore downstream analyses approaches for bioinformatics datasets
- work on a project to practice acquired knowledge on a topic of interest
- work in groups on datasets provided by real life science researchers on phylogenetics, immunobiology, molecular biology, cancer genomics and machine learning
- present their findings
Content
Day 1:
09:00 - 10:00 introductions, warm-up
10:00 - 12:00 introduction to bioinformatics and genomics
12:00 - 13:00 lunch break
13:00 - 14:00 bioinformatics databases (UCSC Genome browser, biomart, oma, string and others from SIB)
14:00 - 15:00 genomics practice on a human population dataset
15:00 - 15:30 coffee break
15:30 - 16:30 practice on variant and downstream analysis
16:30 - 18:00 project selection and project work
Day 2:
09:00 - 10:00 Q&A, discussions
10:00 - 12:00 work on project in groups
12:00 - 13:00 lunch break
13:00 - 15:30 work on project in groups
15:30 - 16:00 coffee break
16:00 - 17:30 presentations
17:30 - 18:00 wrap-up, conclusions, feedback
Methodology
The module will consist of lectures and practical exercises. In addition to lectures, students will be required to self-study selected topics. Students will work in groups on a data challenge and present their results at the end of the course.
- Exercises during the course: 50%
- Data challenge: 50%
The course is taught in English. We’ll be working in python. You may also use bash, R etc. depending on the projects. No prior knowledge in coding is required although familiarity with a coding language will be helpful. Please bring laptops that have a terminal. If you have questions, feel free to email Tugce Bilgin.
More details about the implementation
There are two days of classes on the last two Fridays of May. Some preliminary reading and preparation materials are provided. First day includes theory introduction and hands-on exercises. The second day includes practical guided work on the group projects, discussions and final presentations.
Enquiries and contact
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Maria Anisimova is Professor of Computational Genomics, Head of Bioinformatics center at the Institute of Computational Life Sciences ZHAW and research group leader at the Swiss Institute of Bioinformatics. She received her PhD in 2001 from University College London (UCL), United Kingdom. Her research group develops methods for analysing genomic sequences, including modelling the molecular evolution and adaptive change. Most recently her group focused on modelling the evolution of insertions, deletions and tandem repeats in genomic sequences, as well as using semantic web technologies for literature-based discovery in biomedical sciences.
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Tugce Bilgin is a lecturer in Columbia University, USA. She has taught three coding courses at City University of New York and is teaching evolution and coding courses at Pratt Institute for Arts and Design. She holds a PhD in computational evolutionary biology from University of Zurich and has worked as a postdoctoral researcher in Switzerland for four years before moving to US in 2018 to focus on teaching. She is the founder and co-head of an annually organized Evolutionary Genomics school, a one-week workshop that hosted more than 200 students so far. She is passionate about making science more accessible for students and leading two pedagogy research projects. She received a Diversity Matters reward and was an invited panelist for Anti-racist Pedagogy Discussion in Columbia University. She is organizing a symposium on Education in this year’s congress of Society of Molecular biology and Evolution.
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Feifei Xia is a second-year PhD student in professor Anisimova’s lab. She received her Master degree in data science from the University of Zurich in 2021 and worked as an intern at Empa St. Gallen for half a year before she joined the applied computational genomic group. Her research focuses on refining cancer classification and understanding cancer evolution with short tandem repeat variations in colorectal cancer. She built a dashboard to visualize and analyze the associations between short tandem repeat variations and gene expression data across different cancer subtypes.
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Max Verbiest holds a BSc. in biomedical sciences from the University of Amsterdam and a joint MSc. degree in bioinformatics and systems biology from the University of Amsterdam and the Vrije Universiteit Amsterdam. He joined professor Anisimova's lab in 2020 as a PhD student. In his research, he investigates how variations at short tandem repeats - a specific type of repetitive genetic element - influence colorectal cancer. To do this, he integrates DNA and gene expression data to determine the functional effects of short tandem repeat mutations. This can be combined with clinical patient information to link mutations to specific phenotypes in cancer.
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| Start | Application deadline | Registration link |
|---|---|---|
| on request | Application |