DM847: Introduction to Bioinformatics

The Study Board for Science

Teaching language: English
EKA: N340004112, N340004102
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340004101
ECTS value: 10

Date of Approval: 15-05-2023


Duration: 1 semester

Version: Approved - active

Entry requirements

The course cannot be followed by students who have passed MM552.

Academic preconditions

Students taking the course are expected to:

  • Have basic knowledge in probability theory
  • Have basic knowledge in algorithmics
  • Have proficiency in programming

Course introduction

The purpose of this course is to give an introduction to bioinformatics research. In each class, we will start with a concrete biological and/or medical question, transform it into a computational problem formulation, design a mathematical model, solve it, and finally derive and evaluate real-world answers from within the model. The course aims at providing the basic insights in modern bioinformatics research. The course provides knowledge in computer science models and methods designed for application in biology and medicine.

The course provides an academic basis for solving bioinformatics problems by modelling and implementing computer programs. The course also provides a scientific basis for analyzing the advantages and disadvantages of different computational methods in bioinformatics, develop new variants of the methods if required by the specific problem, and communicate research-based knowledge and discuss professional and scientific problems with both, specialists and non- specialists.

In relation to the learning outcomes of the degree the course has explicit focus on:

  • giving the competence to plan and execute fundamental bioinformatics tasks
  • knowledge of common supervised and unsupervised data mining methods
  • application of common network enrichment and next-generation sequencing data analysis methods
  • developing skills in the development of new OMICS data mining platforms and software
  • planning and carrying out scientific projects at a high professional level including skills to design work packages in different situations that are complex, unpredictable and require novel solutions
  • developing competences to start and implement disciplinary and interdisciplinary cooperations 

Expected learning outcome

The learning objectives of the course are that the student demonstrates the ability to:
  • Explain and understand the central dogma of molecular biology, central aspects of gene regulation, the basic principle of epigenetic DNA modifications, and specialties w.r.t. bacteria & phage genetics
  • Model ontologies for biomedical data dependencies
  • Design of systems biology databases
  • Explain and implement DNA & amino acid sequence analysis methods (HMMs, scoring matrices, and efficient statistics with them on data structures like suffix arrays)
  • Explain and implement statistical learning methods on biological networks (network enrichment)
  • Explain the specialties of bacterial genetics (the operon prediction trick).
  • Explain and implement methods for suffix trees, suffix arrays, and the Burrows-Wheeler transformation
  • Explain de novo sequence pattern screening with EM algorithm and entropy models.
  • Explain and implement basic methods for supervised and unsupervised data mining, as well as their application to biomedical OMICS data sets

Content

The following main topics are contained in the course:
  • Central dogma of molecular genetics, epigenetics, and bacterial and phage genetics
  • Design of online databases for molecular biology content (ontologies, and example databases: NCBI, CoryneRegNet, ONDEX)
  • DNA and amino acid sequence pattern models (HMMS, scoring matrices, mixed models, efficient statistics with them on big data sets)
  • Specialities in bacterial genetics (sequence models and functional models for operons prediction)
  • De novo identification of transcription factor binding motifs (recursive expectation maximization, entropy-based models)
  • Analysis of next-generation DNA sequencing data sets (memory-aware short sequence read mapping data with Burrows Wheeler transformation and suffix arrays, bi-modal peak calling)
  • Visualization of biological networks (graph layouting: small but highly variable graphs vs. huge but rather static graphs)
  • Systems biology and statistics on networks (network enrichment with CUSP, jActiveModules and KeyPathwayMiner)
  • Basic supervised and unsupervised classification methods for OMICS data analysis

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)

Timing

Autumn

Tests

Mandatory assignments

EKA

N340004112

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

English

Examination aids

 To be announced during the course

ECTS value

0

Additional information

The prerequisite examination is a prerequisite for participation in exam element a)

Exam element a)

Timing

January

Tests

Oral exam

EKA

N340004102

Assessment

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card - Name

Language

English

Examination aids

To be announced during the course

ECTS value

10

Indicative number of lessons

86 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
These teaching activities result in an estimated indicative distribution of the work effort of an average student in the following way:

  • Intro phase (lectures) - Number of hours: 41
  • Training phase: Number of hours: 45 of these; tutorial: 41 hours and excursion: 4 hours

Activities during the study phase:

  • Solve assignments
  • Read the assigned literature
  • Practice to apply the acquired knowledge

Teacher responsible

Name E-mail Department
Afsaneh M. Nejad afsane@imada.sdu.dk Institut for Sundhedstjenesteforskning

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Registration

NAT

Offered in

Odense

Recommended course of study

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.