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2011/12 Taught Postgraduate Module Catalogue

MATH5841M Hidden Markov Models and their Application in Bioinformatics

15 creditsClass Size: 30

Module manager: Professor Walter R Gilks
Email: wally.gilks@maths.leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2011/12

Pre-requisite qualifications

MATH2715 or MATH2735 or MATH2750

This module is mutually exclusive with

MATH3841	Introduction to Hidden Markov Models
MATH5840M	Hidden Markov Models for Biological Sequence Analysis

Module replaces

MATH5840M

This module is approved as an Elective

Module summary

Markov models underlie many real-world processes. Hidden Markov models are used when these processes can be observed only indirectly. In bioinformatics, HMMs are widely used to understand biological sequences such as DNA or proteins, to dissect and categorise them, and to make predictions from them. This module will provide an understanding of biological sequence data and how they may be analysed using HMMs. A project will provide an opportunity to develop practical skills in applying HMMs to real biological sequence data.

Objectives

To develop an understanding of hidden Markov models (HMMs) and their use in biological sequence analysis.

Learning outcomes
On completion of this module, students should have:
(a) an understanding of Hidden Markov Models (HMMs);
(b) an understanding of dynamic programming algorithms;
(c) an elementary understanding of biological sequence data;
(d) experience in the application of HMMs;
(e) be familiar with some applications of HMMs in bioinformatics;
(f) be able to use selected online bioinformatic resources to analyse biological sequence data;
(g) improved statistical programming skills.

Syllabus

(a) Markov modelling and applications
(b) Dynamic programming algorithms
(c) Biological sequence motifs
(d) Hidden Markov models
(e) Bioinformatic applications of HMMs
(f) Software and web resources for HMMs

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	33	1.00	33.00
Practical	1	2.00	2.00
Private study hours			115.00
Total Contact hours			35.00
Total hours (100hr per 10 credits)			150.00

Private study

Reviewing lecture notes and wider reading: 49 hours;
Completing exercise sheets: 30 hours;
Completing assessed practical: 30 hours.

Opportunities for Formative Feedback

Regular exercise sheets.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Practical	Report on data analysis	20.00
Total percentage (Assessment Coursework)		20.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr 30 mins	80.00
Total percentage (Assessment Exams)		80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 27/02/2012

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