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From Protein Prediction 2 Winter Semester 2014
Revision as of 16:38, 30 October 2014 by Goldberg (talk | contribs) (Schedule & Notes)

Exercise 'Protein Prediction II' for Computer Scientists

Protein Interaction Network, Phylogenetic Tree, Microarray Scatterplot

Welcome to our main information exchange for the exercise 'Protein Prediction II' for Computer Scientists!

The organization, requirements and grading of the exercise are explained here by the mentors. All students will have one log in account and may add content to this wiki. The students are encouraged to add in their opinion useful links or other material to the wiki page and design the class together with us.


The exercise grade accounts for 60% of the final lecture grade. The grading will be based on your short 5 minutes bi-weekly presentation of your progress during the coding phase, the resulted visualization component that you will develop during the exercise, its clear and complete documentation, and the final 10 minutes presentation of the component to the group including Prof. Rost.


The mentors for the exercises will be:

Our exercises will heavily benefit from the expertise and enthusiasm of:

  • Sebastian Wilzbach - a Bioinformatics student at Rostlab and a former GSoC student. Enthusiast of NodeJS and CoffeeScript.
  • David Dao - an Informatics Student at TUM interested in interactive visualization, data mining and evolutionary Bioinformatics. Also a former GSoC student

Project description

One of the biggest breakthroughs in life sciences has been the ability to sequence the genetic code of life - the DNA. As the sequencing techniques become cheaper and faster, the amount of biological data stored in our databases is increasing every day. The data does not come from sequencing experiments only, but also from many other technologies that are used to understand organisms and diseases at the systems level. It a challenge nowadays to integrate the variety of data in a meaningful and research facilitating way.

The visualization plays a crucial role in the ability of interpreting the data and focusing on those details that shed light on specific hypotheses. The complexity of the data we are facing in modern life sciences requires more advances technologies than the usual static HTML pages when accessing them. They require dynamic visualization tools that allow real-time interactions. JavaScript is a language of modern browsers and thus is perfect for developing client-side browser-based tools. In the exercises of this semester we will build tools for the visualization of biological data on the web from scratch and share those with the community!

Schedule & Notes

Date Topic Links
16 Oct Introduction to BioJS (Guest lecturer: Dr. Manuel Corpas) & HTML Slides - Intro to the exercise
Slides - Intro to web development
BioJS Article Collection
Homework 1
23 Oct Technology Fundamentals (HTML, CSS, SVG, Git(Hub), JavaScript) Slides - JavaScript: The bad parts
Homework 2
30 Oct Introduction to Interactive Visualization with D3 Preview
Slides - Recap and Intro to D3
Homework 3
06 Nov Visualization Principles & Best Practice
13 Nov Project Proposals, Intro into BioJS (+Project submission) Preview
20 Nov Supervised Team Meeting
03 Dec Supervised Team Meeting
17 Dec Supervised Team Meeting
08 Jan Final Project Presentation (Prof. Rost)
15 Jan Deadline for Documentation and Submission to BioJS


We encourage the students to retain from contacting the mentors by email. Instead please use the opportunity to talk to us during the exercise or use the PP2 CS 2014 mailing list. Feel free to post any question you might have concerning organizational or technical aspects of the exercise. The mentors will keep an eye on the mailing list but would expect (where possible) the students to help each other reciprocally.

Recommended Readings


D3 - Interactive Data Visualization for the Web, Scott Murray, O’Reilly (2013) Free online version


We listed a tons of Resources you can use for your project! This list can be extended by you :)

Related courses

Online schools