An algorithm requiring the interpretation and manipulation of networks, or graphs. Rosalind

A tree modeling the evolutionary scenario deriving a collection of taxa from their proposed ancestors. Rosalind

Hidden Markov models. Rosalind

Solve the exercises of the HMM track on Rosalind. We will use the matrix visualization developed during the project to add the remaining exercises to Dodona with newly generated test cases and sometimes with some slight modifications to the problem statement. Solutions to these exercises will be presented by the students in class. You might find some inspiration about using HMM in the following Jupyter Notebook demos:

• the fair bet casino
• the occasionally dishonest casino
• lambda phage demo

• read chapter How do we compare DNA sequences? in the book by Compeau & Pevzner
• check the Eyeless demo (Jupyter Notebook)

Find or design a (non-classic) dynamic programming problem that can be added to Dodona. No problems related to alignment / edit distance. Bonuses for problems that are applications in computational biology. Submit a ZIP-file to Indianio containing:

• problem description (introduction - assignment - examples)
• README file containing notes and references
  • where did you find the assignment
  • generating test cases
  • non-trivial testing of solutions
• your sample solution

• read chapter 02 in the textbook: All the sequences’ men
• check the Mycoplasma demo (Jupyter Notebook)

An algorithm involving the manipulation and properties of chains of symbols.

The mathematical study of the chance of occurrence of random events, or the chance with which a specific event will occur. In preparation:

• read chapter 01 in the textbook: The first look at a genome
• check the Haemophilus demo (Jupyter Notebook)

If you are an accomplished coder, then you can write a separate program for every new task you encounter. In practice, these programs only need to be written once and packaged into modules, where those of us who are not great coders can use them quickly and efficiently. In this first series of exercises, we will familiarize ourselves with a sampling of some of the more popular bioinformatics tools taken from “out of the box” software. In preparation:

• read chapter 00 in the textbook: Prologue
• get yourself familiar with the BioPython module