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From Protein Prediction 1 Summer Semester 2016 For Informaticians

This is the main information exchange for the exercise 'Protein Prediction I Beginners' (for Computer Scientists). The exercise is not mandatory, which means you are free to decide whether you take part and how much time you devote to this exercise. The organisation of the exercise is explained and the tasks are given here.


While there are many ways to learn, the most effective way of committing new information to memory is to actively use it. Therefore, this exercise is designed to help you digest the information presented to you in the lectures:

  • The exercise shall help you to understand and revise basic biological concepts, mainly high school level. You need this understanding to follow the lecture and to do well in the exam.
  • Further, the exercise shall help you to understand how biological data is used and analysed in bioinformatics or computational biology, how prediction methods are developed to support experimental efforts to understand "life".

Exercise location and time

Thursdays, 12:30 - 2:00 pm, Hörsaal 3 (MI 00.06.011)

Grading and Requirements

  • The exercise will not be graded.
  • Attendance of the exercise is not mandatory.
  • However, if you actively take part in the exercise, this will help you to follow the lecture and to learn for the exam.
  • Bonus: There is way to earn a Bonus of 0.3 if you volunteer to prepare and give a mini-talk in the course of the exercises. If you succeed you earn the bonus. The bonus can only be applied, if pass the final exam with at least 4.0

Exercise procedures

  • During the exercise session we will be available to discuss open questions with regard to the lecture and exercise.
  • Important: We will not respond to emails and we will only be available to answer your questions during the exercise and lecture time. Do not come to our offices without appointment.
  • By doing the tasks given here in this wiki and by attending the exercise sessions, you will be able to better follow the lecture and learn valuable information for the exam. It will help considerably to learn for the exam.


Below (under Topics and schedule) is the schedule of topics we will cover in the exercises. Click on a topic to get to the corresponding task page.

  • For each topic we first list Keywords. These are terms that you need to understand to follow the lecture. To test your knowledge, try to define and explain these keywords (in a few sentences). If you cannot think of anything to say about a keyword, read up on that topic.
  • Next (Sources) we list suggested literature (textbooks, web pages, articles) that will help you to understand the topic. You can use this as a resource to complete your knowledge of the keywords and to help you answer the questions and solve the tasks. You are not required to read and study any of these, but they provide more detailed knowledge on the topic and are a good complement to the lecture. Of course you are free to use any other source you like.
  • Finally, in the section Exercise we provide Questions and Hands-on tasks. You can test and further your knowledge of a given topic by answering the questions and doing the hands-on tasks. The questions and hand-on tasks will be discussed in the exercise sessions.

Exercise session

  • Before each exercise session, prepare the announced topics and exercises.
  • You can take the opportunity to ask questions pertaining to the topics and given exercises.
  • During each session, we will ask you to discuss your answers to the questions / tasks specified in this wiki. If nobody found an answer, we will show how to find one, but we will not publish master solutions.
  • In addition to this and dependent on your contribution we will provide a talk covering the absolute minimum ideas. But please be aware that this not necessarily covers all relevant content and exam questions might be possible which go beyond the material of the slides.

Topics and schedule (updated)


This material was largely prepared and written by Dr. Edda Kloppmann and Dr. Andrea Schafferhans