MVE510 Introduction to bioinformatics Autumn 25

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On this page and in the course PM, you will find all the information about the course! All relevant files are collected under Files and linked below, and the reports for the computer labs are submitted under Assignments. Questions are primarily answered during classes, but you can also post questions on the course website under Discussions. Specific coding questions about the computer exercises are answered by the teaching assistants during the scheduled computer labs. The information on the page will be updated continuously during the course, so please activate notifications for Announcements.

If you find any errors on the course website, please notify Anna.

 

Syllabus, Schedule & Course PM

 

Teachers

Lecturer & course administrator: Anna Johnning
Examiner: Erik Kristiansson
Teaching assistants: Laleh Vargahaei & Sophia Axillus

 

Course material

The course is based on the following material that is available to download from the Canvas course page,

  • Lecture notes
  • Eight academic papers
  • Exercises on sequence alignment

There is also an optional course book, where the course covers chapters 2–5, 7–9, and 13. A few copies are available from the Chalmers library.

  • Next-Generation Sequencing Data Analysis by Xinkun Wang. 
    • Hardback: ISBN 9780367349899
    • Ebook: ISBN 9781000897234

 

Lecture Notes Content Additional Material

Lecture 1
(Tue, w 1*)
  • A first introduction to bioinformatics
  • Course overview
  • Introduction to the statistical programming language R

Lecture 2
(Fri, w 1)
  • Next-generation DNA sequencing (NGS)

Lecture 3
(Tue, w 2)
  • Sequencing errors
  • Pre-processing of NGS data
  • (Wang, p. 73–78)

Lecture 4
(Fri, w 2)
  • Genome sequencing

Lecture 5
(Tue, w 3)
  • Sequence alignment
  • The Needleman-Wunsch and Smith-Waterman algorithms
  • Suffix trees and arrays

Lecture 6
(Fri, w 3)

Lecture 7
(Tue, w 4)
  • Transcriptome sequencing (RNA-seq)
  • Linear models

Lecture 8
(Fri, w 4)

Lecture 9
(Tue, w 5)
  • Multiple testing

Deadline for submitting the report to Computer Exercise 2

Lecture 10
(Fri, w 5)
  • Unsupervised data exploration

Lecture 11
(Tue, w 6)
  • Metagenomics
  • (Wang p. 175–188)

Lecture 12
(Fri, w 6 &
Tue, w 7)

Lecture 13
(Fri, w 7)
  • Repetition
  • Old exam questions
  • Repetition_2020, Repetition_2021, Repetition_2022, Repetition_2024

Guest lectures
  • December 12 (Fri, w 6): Sambio Core Facilities at University of Gothenburg
  • December 16 (Tue, w7): AstraZeneca

Deadline for submitting the report to Computer Exercise 3

Deadline for submitting the report to Computer Exercise 4

Written exam

(* As this is the first time I'm lecturing in this course, these lecture times should be seen as a rough approximation. For examples, some material from "Lecture 4" might spill over into the fifth scheduled lecture. Thank you for your understanding. /Anna)

Examination

This course is examined based on,

  • Written exam (5 hp, grade U–5)
  • Four compulsory computer exercises (2.5 hp, U/G) 

The course has no elements that generate bonus points for the written exam.

 

Written Exam

A passed exam is worth 5 credits and determines your grade in the course. The maximum score is 40 points, a pass requires at least 18 points, a grade of 4 requires 26 points, and a grade of 5 requires 34 points. No aids are allowed during the exam. Shortly after the exam, suggested solutions will be posted on Canvas. Old exams, along with some answers/solutions, can be found here.

This year's exams will be given on,

  • Thursday morning, January 15th
  • Friday afternoon, April 10th,
  • Wednesday morning, August 26th. 

 

Computer exercises

The course contains four compulsory computer exercises. The purpose of the computer exercises is to,

  • Gain hands-on experience in bioinformatic analyses,
  • Apply concepts covered by the lectures in realistic situations,
  • Interpret and critically evaluate the results, and
  • Practice programming.

Computer Exercise 1 is examined during the scheduled computer labs by the teaching assistants. Computer Exercise 2–4 are examined through written reports. Each exercise should be done in groups of a maximum of two students. 

Instructions & Deadlines

It is perfectly fine to submit the reports earlier! Make sure you plan your work so that you can utilse the help from the teaching assistants during the scheduled computer labs. Here is a suggested time plan where "X" marks the deadlines and the scheduled computer labs are marked at the buttom (high bar = 4 h lab, low bar =  2 h lab),

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Software
If you want to complete the exercises on your own computer, you can either access the relavant software through https://apps.chalmers.se/ or install the following:

All software should be available on the computers in computer rooms HB105, HB110, E-Studion, and ED2480, as well as in all computer rooms at the Chemistry, Physics, and Mathematics buildings.

Reports
One report per group is submitted through the Canvas course page under Assignments. The report should be either in PDF or Word format. All of the code necessary to generate the reported results should be included at the end of the report as an appendix. Please double-check that the submitted code works as intended. Remember to add the names and Swedish social security numbers of all students in the group on the front page. 

The reports should contain answers to all the questions posed in the instructions. The answers should almost always have some motivation, meaning that even though the question could be answered with a yes or a no, we require a motivation for why. Example question: "Do you see any differences?" If the answer is "yes", describe the observed differences and interpret them. When you have been asked to generate a figure, this figure should be included in the report. The reports do not need an Introduction, Method, Results, or Discussion section, but should include some information about what you have done. Example: “When doing the quality assessment using fastqc, we saw that…”. If you want to, you can use this optional report template. 

 

Use of AI tools

During the studies, the students are free to use AI tools to support their learning, but it is not permitted to use generative AI to generate all code or write report text for computer labs. No AI tools are permitted on the exam. The purpose of the computer exercises is to increase the knowledge and understanding of bioinformatics, which in turn increases the chance of passing the exam. It is, therefore, strongly recommended to write and debug your programming code without AI help. For those who still want to use generative AI, e.g. the following examples are allowed, but the student should be aware that they cannot blindly trust the answers generated by AI.
  • Help with debugging your code
  • Interpreting error messages when coding
  • Correct grammatical errors in your report text

 

Course evaluation

Please feel free to discuss with us, teachers, about ways we can improve the course during the course! It is also possible to provide feedback via the student representatives listed below, especially before the mid-course meeting on Monday, November 24, at 12:00 (study week 4), and the course board meeting on TBD. At the end of the course, we would be grateful if you would fill out the course survey, so that we know what should be kept or improved in the course for the coming year.

Eva María Hönnudóttir Sigurþórsdóttir (MPENM)
Emma Nilsson (MPBIO)
Tyra Strand (MPBIO)
Hélène Vazaloukas (MPBIO)
Yizhen Wen (MPMED)

Course summary:

Course Summary
Date Details Due