DM847: Introduction to Bioinformatics

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340004112, N340004102
Censorship: 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: 25-04-2019


Duration: 1 semester

Version: Archive

Comment

15017301 (former UVA) is identical with this course description. 
This course is co-read with MM552

Entry requirements

None

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 Blackboard for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)

Timing

Autumn

Tests

Mandatory assignments

EKA

N340004112

Censorship

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

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

Prerequisites

Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N340004101, DM847: Introduction to Bioinformatics

Tests

Oral exam

EKA

N340004102

Censorship

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

10

Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

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.

Activities during the study phase:

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

Teacher responsible

Name E-mail Department
Richard Röttger roettger@imada.sdu.dk

Timetable

40
Monday
28-09-2020
Tuesday
29-09-2020
Wednesday
30-09-2020
Thursday
01-10-2020
Friday
02-10-2020
08 - 09
09 - 10
10 - 11
Class f
Forelæsning
Online
11 - 12
Class f
Forelæsning
Online
12 - 13
Class h1e
Undervisning
Online
13 - 14
Class h1e
Undervisning
Online
14 - 15
Class f
Forelæsning
Online
15 - 16
Class f
Forelæsning
Online
Show full time table

Administrative Unit

Institut for Matematik og Datalogi (datalogi, fiktiv)

Recommended course of study

Profile Programme Semester Period