Postgraduate Course: Next Generation Genomics (BILG11004)

Course Outline
School	School of Biological Sciences	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	This course will introduce and explore the novel algorithms and applications required for next-generation genomics analysis. The explosion in second and third generation sequencing technologies has led to a matching revolution in the bioinformatics methods of analysis of the vast quantities of raw data produced, and the new challenges of mining these data for biological insight. This course will introduce next-generation genomics technologies, and guide students through the bioinformatics analyses of data across a wide range of applications. There will be a particular focus on building understanding of the algorithms used in the bioinformatics analyses, and of the interplays between genetic variability, genome complexity and experimental and statistical noise. The course emphasizes the need for appropriate bioinformatic tools for analyzing complex genomes, and uses a series of practical sessions to demonstrate the utility of these methods for studying non-model systems.
Course description	The course will cover the following topic areas, through a combination of delivered lectures, class discussions, and hands-on practicals: - Introduction to the diversity of next generation sequencing technologies, including recent innovations in long-read sequencing. -The structure of next generation sequencing data, including technical specifications of different sequencing technologies, and sequence file formats. - Bioinformatic approaches for de novo genome assembly, and the characterisation of non-model animal and plant genomes. - Identifying genetic variants from sequence data aligned to a reference genome.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites	It is RECOMMENDED that students have passed Bioinformatics Programming and System Management (PGBI11095)	Co-requisites
Prohibited Combinations		Other requirements	(a) for biological science students undertaking a Biology MSc, have completed the 'Bioinformatics Programming & System Management' course or other core programming course, or (b) for computer science or informatics students undertaking an Informatics (Bioinformatics) MSc, have completed the Bioinformatics 1 course.

Course Delivery Information

Academic year 2022/23, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 2
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 30, Summative Assessment Hours 3, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 65 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment)	The course will be assessed by: (a) an in-course assessment essay, assessing the quality of a newly-sequenced eukaryotic genome, which will account for 50% of the overall course mark. (b) an essay-based exam paper, which will also account for 50% of the course mark.
Feedback	Formative / summative feedback will be provided for all assessments.
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)	Next Generation Genomics		2:00

Learning Outcomes
On completion of this course, the student will be able to: Demonstrate an understanding of the molecular and technical underpinnings of next-generation sequencing technologies, and critically evaluate these technologies in terms of their utility for a specific biological research question. Be conversant with the core algorithms used in data analyses and be able to choose in an informed way the best analytical solutions for particular problem. Understand how genome assembly algorithms achieve their task. Use popular quality control methods such as FastQC and emerging genome assembly softwares such as Wtdbg2/Redbean can be used to analyse genomic sequencing data. Understand how sequence data of individuals and populations can elucidate underlying biological processes.