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COMP40400

Academic Year 2024/2025

Bioinformatics (COMP40400)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Gianluca Pollastri
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

-Proteins. DNA. RNA. Public molecular biology repositories. -Dynamic programming algorithms for sequence comparison: global sequence alignment (Needleman-Wunsch); local sequence alignment (Smith-Waterman); semiglobal comparison. Variations to the basic alignment algorithms: variable gab penalties; KBand; substitution matrices. BLAST. -Multiple sequence alignments. ClustalW. -Introduction to molecular phylogenetics. Methods for tree reconstruction: distance-based methods (UPGMA, Neighbour-Joining); maximum parsimony; maximum likelihood and full bayesian; bootstrapping. -Introduction to protein structure prediction. Introduction to artificial neural networks. Protein structure prediction by machine learning: 1D structural features; 2D structural features; 3D reconstruction.

About this Module

Learning Outcomes:

At the end of the course students should: -be aware of most of the main goals and problems in computational biology; -understand dynamic programming techniques and be able to replicate at least manually the main dynamic programming techniques for biological sequences; -be familiar with the main public molecular biology databases ; -understand the goals of molecular phylogeny, and be aware of the main techniques proposed to compute phylogenies; -understand the basic concepts of protein structure prediction

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

56
Lectures

12
Practical

32
Total

100

Approaches to Teaching and Learning:
Lectures.
Individual completion of practical bioinformatics tasks.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Assignment 1, on dynamic programming Week 3 Alternative linear conversion grade scale 40% No
10
 No
Assignment(Including Essay): Assignment 2, on data discovery on public repositories Week 5 Alternative linear conversion grade scale 40% No
10
 No
Assignment(Including Essay): Assignment 3, on phylogenetics Week 8 Alternative linear conversion grade scale 40% No
10
 No
Assignment(Including Essay): Assignment 4, on protein structure prediction Week 10 Alternative linear conversion grade scale 40% No
10
 No
Exam (In-person): Standard final exam with multiple questions about the course. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
60
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 14:00 - 14:50
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