While biological data continues to grow exponentially in size and quality, many of biology PhD students are not trained adequately in the computing skills necessary for leveraging this information deluge. In the course "Computing Skills for Biologists: Building a Toolbox", Dr. Stefano Allesina will provide a hands on introduction to a valuable toolbox for the effective analysis of biological data.

Based on the author's experience teaching scientific computing at the University of Chicago and his textbook on the same subject, he will emphasise the automation of repetitive tasks and the construction of pipelines for data organization, analysis, visualization, and publication. Stressing practice rather than theory, he will draw from actual biological data and solve cogent problems spanning the entire breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. 

Beginners will benefit from an overview of the many tools that compose the computational toolbox of the practicing computational biologist, while the more seasoned researchers will learn how to combine these tools to make the analysis of biological data automated, robust and reproducible.

The course encompasses a variety of programming languages (Bash, Python, R) and tools (UNIX shell, Git, Regular Expressions, LaTeX, Databases). For each lecture, a different topic is discussed, a few exercises are solved together in class; longer exercises are assigned as group work. 

Because each topic is treated quite briefly, students are encouraged to read more about the tools presented in class (a reading list is provided for each tool). Rigor and reproducibility of data analysis, as well as good coding style and organization are emphasized.

Course Objectives:

1. Showcase a variety of computational tools.

2. Illustrate the use of the tools using real biological data from published papers.

3. Integrate these tools to produce efficient computational pipelines, automating the analysis, visualization and reporting of biological data.

4. Use appropriate tools for data organization and sharing, debugging, profiling.

Course length and ECTS

The course includes 35 hours on-site, including breaks. The course is equivalent to 3,5 ECTS (European Credit Transfer System), assuming the rule of 10:1, which means that 1 course lesson (=1 hour) gives 0.1 ECTS credits, taking a preparation factor of 1.5. Participants who have completed the course will receive a certificate of participation at the end of it.

Requirements

All participants must bring their own personal laptop.

Instructions on how to install the required software and data can be found here.

Language

English

Participation

The course accepts a maximum of 20 students.

Fees and funding

The fees include full participation in the course and course material, provision of a certificate of attendance, half-board accommodation at Casa Morgado Esporão for up to 5 nights including breakfast, lunch and coffee breaks (offering assortment of coffees, teas, fruit juices and cookies).

Unfortunately we have no scholarships on offer. We do offer early bird discounts and shared accommodation is provided for those with more limited budgets.

Cancelation Policy

If you wish to cancel your participation in this course, cancellations up to 60 days before the course start date will incur a 40% cancellation fee. For later cancellations, or non-attendance, the full course fee will be charged.

In the unlikely event that the course is cancelled due to unforeseen circumstances, accepted candidates will either be entitled to a full refund of the course fee, or the fee can be credited toward a future course. The organisation is not responsible for travel costs, or any other expenses incurred by the candidate as a result of such cancellation. Every effort will be made to avoid the cancellation of the planned course.

Contacts

For any additional informations about the course, please contact us at "scienceretreats" "at" "gmail" "dot" "com".

Course Tutors 

Stefano Allesina is a Professor in the Department of Ecology and Evolution at the University of Chicago, where he directs a laboratory devoted to the development of mathematical, statistical and computational methods for the analysis of ecological systems, with an emphasis on ecological networks and dynamics. He is a Deputy Editor for PLoS Computational Biology, the author (with M. Wilmes of “Computing Skills for Biologists” (Princeton UP 2019), and the Director of the Graduate Program in Ecology & Evolution at the University of Chicago.