Module Catalogue 2024/25

Pre Requisite Comment

None.

Co Requisite Comment

None.

Aims

To introduce the data that arises from studies in molecular biology.

To reinforce the theory underlying the concepts and techniques of sequence analysis and postgenomic bioinformatics.

An understanding of the distributed and available resources for bioinformatics analyses.

This module provides an understanding of the basic theory behind bioinformatics analyses, the computational and algorithmic approaches underpinning modern bioinformatics approaches, and experience in practically applying that theory. The module introduces basic concepts of molecule biology, sequence analysis and genomic era biology. It introduces a number of many different tools and their usage, as well as the analysis algorithms behind some of them including BLAST and dynamic programming. More advanced approaches to biological sequence analysis, assembly, comparison and annotation are also introduced, including comparative genomics. The basics of protein motifs, structure and families, and phylogenetics are introduced. Later parts of the module introduces the concepts behind modern postgenomic bioinformatics including material of biological pathways and networks.

Outline Of Syllabus

Basic concepts of molecular biology: genomes, transcripomes, proteomes.
Database searching tools.
Sequencing algorithms.
Sequence analysis: genome assembly and annotation, sequence alignment and comparison, multiple sequence alignment.
Phylogenetic analysis, genome comparison and molecular evolution.
Protein families.
Protein structure prediction.
Biological network analysis including gene networks and pathway analysis.
Transcriptomics and proteomics analysis including single-cell data analysis.
Analysis of other –omics data.

Intended Knowledge Outcomes

To be able to describe and discuss:
Genomes, genome sequencing, genomic structure and comparison.
- The data arising from such studies.
- The theoretical underpinnings of genome and post genomic analysis.
- The use and theory of networks for predictive biology.
- The application of computing and statistics to predictive biology.
- The technology for studies in modern post-genomic biology and the data that is generated by such studies.
- The basic algorithms underpinning tools for sequence and protein analysis.
- The advantages and shortcomings of various bioinformatics software tools.
- The appropriate application of a range of bioinformatics software.

Intended Skill Outcomes

To be able to:
- Operate a variety of bioinformatics software
- Analyse the biological significance of the output of a range of bioinformatics tools
- Demonstrate the use of much of the existing software for the analysis of genomic data.
- Apply knowledge of specific computational, mathematical and statistical techniques to the storage and
analysis of biological data.
- Select the most appropriate bioinformatics tools for a given analysis.
- Apply statistical and computational techniques to problems in predictive biology
- Assemble and annotate genomes
- Use networks for biological data analysis.

Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Rationale And Relationship

The low stakes summative assessment will assess the student’s growing knowledge of the field.

The formative assessment will assess the students growing knowledge of bioinformatics algorithm design applied to the problem set in the summative assessment, providing feedback.

The summative assessment will assess the students’ ability to understand and apply the concepts of a range of a bioinformatics analytical techniques, including those described in the formative piece, and also their ability to design new approaches.

The practical reports are not typical essays. Where the word count is a good proxy for effort, the report should collect the results of the practical experiments that the students have performed, using the techniques covered throughout the module, contain a description of design decisions, an experimental plan and a report of experimental results, hence the requested 4000 word limit.

General Notes

N/A