Practical Computing for Biologists

Organizers and lecturers

• Prof. Dr. Per Palsboll (GREEN, RUG) - Also coordinator

• Prof. Dr. T. Hackl (GREEN, RUG)

• Prof. Dr. S.J.J. Lequime (GREEN, RUG)

Aim of the course

At the end of the course, the student is able to:

1. Explain and summarize the origin(s) and properties of big data; evaluate and solve challenges in analyses of big data in biology.
2. Use high performance computing clusters in a Linux/Unix environment: examine directory structure & permissions, create and modify files & directories as well as install and execute software.
3. Evaluate, extract and explore information contained in large, complex data files employing basic functions and options included in standard 'nix analysis software.
4. Analyse large data sets and visualize results in Python; integrate external Python libraries in own analyses
5. Design, implement own custom pipelines using scripting languages (e.g., BASH, R and/or Python) to explore, analyse, and visualize data; defend the specific programmatic choice made, critically evaluate the performance and areas of improvement in developed pipeline
   

Contents & Structure

Practical Computing for Biologists (PCfB) introduces students to general computational tools in order to enable to design and execute efficient computations. PCfB presents a broad range of open-source, free and flexible computational tools applicable to geneticists, molecular biologists, ecologists, oceanographers, physiologists, or anyone with an interest or need for efficient means to handle and analyse large data sets in their research. PCfB emphasizes the practical application of computational tools and methods toward real-life analyses.

PCfB covers data­-centered computing in a Unix/Linux environment. PCfB introduces the basics of a 'nix environment, such as; remote installation and execution of software. Students will be familiar with command line tools to explore and analyze data as well as the use of scripting languages such as Python to (a) code custom analyses, (b) design effective pipelines with existing software, (c) data visualisation, (d) versioning with git and (e) reproducible analyses with markdown.

Topics addressed in PCfB will employ practical example from different research fields, e.g., Next Generation Sequencing (NGS) data in genetics and molecular biology, as well as remote sensing and oceanographic data widely used in spatial ecological and evolutionary biology.

The course comprises of short lectures in a Q & A format addressing the new concepts introduced in daily text book readings along with discussion and execution of relevant examples. Afternoons are aimed at practical computer exercises. The two first weeks include individual graded assignments. During the last week, students will conduct a project assignment individually or in small groups implementing aimed the skills acquired during the course towards, solving real-life analyses. Students will present their pipeline, results and reflect on the project in an oral presentation to the entire class during the last days of the course.

General Information