This course will provide hands-on experience of an integrative omics approach to investigations into the mechanisms by which a human parasite, Leishmania, develops resistance to drugs used to treat it. The course will cover next generation sequence analysis, including the analysis of whole genome sequence data and sequence read assembly and data mining; proteomics data analysis, including informatics approaches to the problem of large-scale protein identification; and informatics approaches to metabolite identification from metabolomics data. It will also cover the use of protein structural analysis techniques to investigate the molecular mechanisms underlying drug resistance. The course will show how integrative analysis across these omics levels can be utilised to reveal more about the biology of this system than each level can alone. The critical importance of statistics and modelling of biological systems will also be emphasised across the different parts of the project.

The course will run in one of the three designated timetable blocks in Semester 2. Contact teaching for this course will take place over 5 or 6 weeks. Lecture/Tutorial sessions of 1-3 hours duration several times per week; computer practical sessions of 1-3 hours several times per week.

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The course will be assessed entirely by coursework assessment (100%).

The assessment will comprise 2 components:

■ a group presentation (20%) [ILO 6]

■ a report of 3000 words (80%) [ILOs 1,2,3,4,5]

Reassessments are normally available for all courses, except those that contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below:

Group oral presentations cannot be re-assessed unless the entire group are eligible for re-assessment and have chosen to be re-assessed in that item of assessment.

Computer lab report coursework assessments for this course can be reassessed. Reassessment will not involve re-doing the lab report for reasons of fairness, but will instead involve doing an alternative piece of work addressing the same learning objectives.

This course aims to equip students with extensive, critical and integrative understanding of how integrated 'polyomics' approaches enrich biological inference from multiple omics datasets. Students will have the chance to put these analysis concepts into practice during extensive computer lab practicals and will develop analytical skills and a thorough appreciation of the value of statistics in omics analyses, practical computing skills and the ability to assess critically, and in the appropriate biological context, procedures for integrating the results of multiple omics analyses.

By the end of this course, students will be able to:

■ Creatively apply and critically discuss the principles underlying:

- the use of next generation sequencing to infer the functional attributes of sequences within
a genome;

- the use of proteomic analysis to identify proteins in biological samples, to identify
differences in abundance of individual proteins between samples, and to show how
identified proteins can be related to functional regions within a genome sequence;

- the use of metabolomic analysis to infer the components and connectivity of metabolic
networks within cells;

- the use of protein structure predictions to investigate how mutations affect protein function;

■ Critically evaluate the utility of studying genomes, proteomes and metabolomes simultaneously in deriving novel biological inferences that cannot be made using each dataset alone;

■ Critically discuss the importance of statistical rigour in generating meaningful inference from omics outputs;

■ Synthesise polyomics data to derive meaningful biological inferences;

■ Demonstrate, through the creative use of computing approaches, the principal skills involved in analysing omics datasets in the areas of genomics, proteomics, metabolomics and the analysis of protein structure and biophysical properties of proteins.

■ Work in teams to design critically and construct creatively a group presentation on a topic related to the work carried out in this course using electronic aids (e.g.Powerpoint), and deliver the presentation to an audience with clarity.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.