DM847: Introduction to Bioinformatics

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.

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

Exam element a)

Timing

Autumn and January

Tests

Portfolio

EKA

N340004102

Assessment

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card - Name

Language

English

Duration

Oral exam - 30 minutes

Examination aids

All common aids allowed

ECTS value

Additional information

Portfolio examination consisting of the following elements:

A written project assignment handed in during the course
Final oral exam during the exam period

To achieve a passing grade overall, both elements 1 and 2 must individually meet the learning objectives.

The assessment of element 1 takes place in conjunction with the completion of element 2.

The grade is primarily based on element 2, but element 1 can raise or lower the grade by one grade step.

Indicative number of lessons

86 hours per semester

Teaching Method

Planned lessons

Total number of planned lessons: 86

Common lessons in classromm/auditorium: 41 hours

Team lessons in classroom: 45 hours

In the lectures, concepts, algorithms, and models are introduced along with examples. During these sessions, there are also quizzes that students first solve individually, then discuss with their peers, and finally review with the teacher. During the exercise hours, students practice skills and delve deeper into the contents covered in previous lectures. These hours include both individual and group work activities.

Other planned teaching activities:

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

Odense

Show full time table

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Registration

NAT

Offered in

Odense

Recommended course of study

Profile	Education	Semester	Offer period

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.