Welcome

This is the homepage for the AU course Bioinformatics and programming (Bioinformatik og programmering). You will find all course content here. The Brightspace course page is only used for communication, and assignments.

Schedule

Reading:

Lecture notes: Chapter 2, Chapter 3, Chapter 4, Chapter 5, Chapter 6, Chapter 7

Make sure you have installed Python and VScode for the first lecture.

Lectures:

In the first lecture, I will outline how the course is organized and how you will get the most out of your efforts in learning programming.
In the first lecture, I will also talk about how a Python program works and about values, math, and logic.
The second lecture is replace by the video below:

Exercises:

If you have yet to do so at home, you will install Python and the text editor. To do this, follow the instructions in Chapter 3. Then, start doing the exercises in Chapter 4, Chapter 5, and Chapter 6. You will also have time to do these exercises in the TA session of week two, so go slow. It is important to properly absorb the basic concepts at the beginning of the course; otherwise, it will become too difficult later on. Have a look

And make sure to familiarize yourself with the Myiagi game and the pysteps tool described in Chapter 7. These are useful companions in the course.

Reading:

Lecture notes: Chapter 8, Chapter 9

I will cover chapters 8-9 in the lecture notes.

Lectures:

In the first lecture, I will talk about how to use logic to control which statements in your program that get executed.
In the first lecture, I will also talk about how you can efficiently organize your code using functions.
In the second lecture, I will talk more about functions.

Exercises:

The topics for this week’s exercises are statements, variables, operators, expressions, substitution, reduction, and logic. You will work on the rest of the exercises in Chapter 4, Chapter 5, Chapter 6, and Chapter 7. Do what you can at home and do the rest at the TA session.

Reading:

Lecture notes: Chapter 10, Chapter 11, Chapter 12, Chapter 13

Lectures:

In the first lecture, I will talk about objects and methods.
In the first lecture, I will also talk about lists.
In the second lecture, I will talk about dictionaries and a bit about tuples.

Exercises:

The topics for this week’s exercises are if, else, logic, and functions. You are meant to complete all the exercises in Chapter 8 and Chapter 9. Do what you can at home and do the rest at the TA session.

Week 4

Reading:

Lecture notes: Chapter 14, Chapter 15

Lectures:

In the first lecture, I will talk about iteration and lists.
In the second lecture, I will talk about how your code can interact with files on your computer.

Exercises:

Note

Only MO and Bio classes attend the exercises this week. MM classes do not. The exercise is repeated next week for the MM classes to attend*

The topics for this week are objects, methods, strings, lists, tuples, and dictionaries. You are meant to complete all the exercises in chapters Chapter 10, Chapter 11, Chapter 12, and Chapter 13. Do what you can at home and do the rest at the TA session.

Week 5

Reading:

Lecture notes: Chapter 16, Chapter 17, Chapter 18
Chapter 11: Genomewide Association Studies
Benefits and limitations of genomewide association studies

Lectures:

In the first lecture, I will talk about databases, genotyping arrays, and genomewide association studies (GWAS).
In the first lecture, I will also talk about building simple data strutures in Python.
In the second lecture, I will talk about how to use recursion in Python.

Exercises:

Note

Only MM classes attend the exercises this week. MO and Bio classes do not. The exercise is repeated from last week*.

The topics for this week are iteration and data structures. You are meant to complete all the exercises in Chapter 14 and Chapter 15.

Week 6

Reading:

Lecture notes: Chapter 20
Understanding Bioinformatics 127-141

Lectures:

In the first lecture, I will talk about global pairwise alignment In the first lecture
In the first lecture, I will also talk about the weekly programming project.
In the second lecture, I will talk about local pairwise alignment and more realistic gap scoring.

Exercises:

The programming project described in Chapter 20.

From the project files page, you can download the files you need for programming projects. There will be lots of work, so do what you can at home and do the rest at the TA session.

Important

Chapter 20 is a mandatory assignment. The deadline for handing it in (on Brightspace) is the night before your exercise class in week 41.

Week 7

Reading:

Lecture notes: Chapter 21
Understanding Bioinformatics: 117-127
Alignment methods: strategies, challenges, benchmarking, and comparative overview (don’t do the exercises).

Lectures:

In the first lecture, I will talk about protein substitution matrices and how to score protein alignments. - In the first lecture, I will also talk Python classes and the weekly programming project.
In the second lecture, I will talk about multiple alignment.

Exercises:

The web exercise: GWAS candidates
The programming project described in Chapter 21 (not a mandatory assignment).

From the project files page, you can download the files you need for both programming projects and web exercises. There will be lots of work, so do what you can at home and do the rest at the TA session.

Fall Vacation

Reading:

Lecture notes: Chapter 22
Bioinformatics Explained: BLAST
Biological Sequence Analysis pp. 192-197

Lectures:

In the first lecture, I will talk about how to search for matches in a sequence database and how to asses alignment significance.
In the first lecture, I will also talk about programming topics and the weekly programming project.
In the second lecture, I will talk about models of DNA evolution and how to measure evolutionary distance between sequences.

Exercises:

The web exercise: CCR5-delta32
The programming project described in Chapter 22 (not a mandatory assignment).

Reading:

Lecture notes: Chapter 23
Biological Sequence Analysis pp. 165-179

Lectures:

In the first lecture, I will talk about methods for sequence clustering.
In the first lecture, I will also talk about the programming project.
In the second lecture, I will talk about bioinformatics code libraries for Python, such as BioPython, and the Master in Bioinformatics that we offer at the Bioinformatics Centre.

Exercises:

The web exercise: MRSA
The programming project described in Chapter 23 (not a mandatory assignment).

Reading:

Lecture notes: Chapter 24
Biological Sequence Analysis pp. 192-202

Lectures:

In the first lecture, I will talk about phylogenetic trees.
In the first lecture, I will also talk about Python topics and the weekly programming project.
In the second lecture, I will talk about gene prediction in prokaryotes.

Exercises:

The web exercise: Aardvark?
The programming project described in Chapter 24.

Important

Chapter 24 is a mandatory assignment. The deadline for handing it in is the night before your exercise class in week 46.

Reading:

Lecture notes: Chapter 25
Biological Sequence Analysis pp. 46-66

Lectures:

In the first lecture, I will talk about hidden Markov models (HMMs).
In the first lecture, I will also talk about python topics and the weekly programming project.
In the second lecture, I will talk about more about HMMs

Exercises:

The programming project described in Chapter 25 (not a mandatory assignment).

Reading:

Lecture notes: Chapter 26
Understanding Bioinformatics pp. 438-448
Automatic generation of gene finders for Eukaryotic species
The Theory and Practice of Genome Sequence Assembly

0.0.0.0.1 Lectures

In the first lecture, I will talk about applications of hidden Markov models gene finding and protein annotation.
In the first lecture, I will also talk about Python topics and the weekly programming project.
In the second lecture, I will talk genome assembly.

Exercises:

The web exercise: Plasmid ORFs
The programming project described in Chapter 26 (not a mandatory assignment).

Reading:

Lecture notes: Chapter 27
Understanding Bioinformatics pp. 494-496
~~Exploring Bioinformatics pp. 242-248~~

Lectures:

In the first lecture, I will talk about neural networks
In the first lecture, I will also talk about the programming project.
In the second lecture, I will talk about applications of HMMs ~~RNA secondary structure prediction~~.

Exercises:

The web exercise: Read mapping
The programming project described in Chapter 27 (not a mandatory assignment).

Reading:

None this week.

Lectures:

In the first lecture, I will talk about python and algorithms. You will also hear a guest talk by about bioinformatics outside the class room.
In the last lecture, we will evaluate the course and review the exam’s practicalities.

Exercises:

The web exercise: Neural networks