The module aims to develop in students the ability to:
● Demonstrate a critical understanding of the origin, value and limitations of the types of data generated by different genomic, transcriptomic and proteomic technologies and of appropriate tools for analysis of the data.
● Demonstrate how bioinformatics is used to solve real-world research questions at post-graduate level, with reference to postgraduate research projects and beyond.
On successful completion of this module students will be able to:
● Apply bioinformatics tools to address a wide range of real-world analytical tasks involving biological information.
● Analyse critically the principles underlying the current generation of research tools.
● Exploit the information available in a wide variety of biological databases to address current research questions.
● Self-directed learning.
● Tutorials: feedback sessions.
The module is divided into a compulsory, core block and six optional blocks. All students should take the core block and choose another three blocks. Learning will take place in timetabled lectures and workshops, and by self-directed learning. Feedback sessions (tutorial) will be provided after assessments, supporting student learning.
• Advanced topics in Genome Bioinformatics.
• Advanced topics in Proteome Bioinformatics.
• Sequence Alignment: programs and algorithms.
• Protein secondary sequence databases. How they are made and searched.
• Linux platform.
• Genomic: application and analyses of genomic datasets at postgraduate level.
• Proteomic: application and analyses of proteome datasets at postgraduate level.
• Protein multiple sequence alignment: evaluating alternative algorithms.
• Searching secondary databases of protein sequences.
• Advanced modelling of proteins.
• Secondary databases of protein structure and their roles.
• Structure based function predictions: identification of binding and catalytic sites.
• Secondary databases of protein structure.
• Structure-informed functional annotation.
• Next-generation genomic methods.
• de novo assembly.
• Sequence mapping and identification of genetic variation.
• Assembling sequence reads and assessing assemblies.
• Mapping reads and identifying variants.
• Proteomics and bioinformatics for identifying proteins by mass spectrometry.
• Label-based quantification strategies including SILAC and iTRAQ labelling strategies.
• Label free quantification strategies including spectral counting and intensity-based LC-MS.
• Identifying proteins using Mascot search engine.
• iTRAQ quantification using OpenMS.