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CSC8309 : Genome Scale Data Analytics

  • Offered for Year: 2021/22
  • Module Leader(s): Dr Simon Cockell
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semester 2 Credit Value: 10
ECTS Credits: 5.0


To provide an introduction to that analysis of genome-scale data arising from high-throughput technologies in the molecular analysis of prokaryotic and eukaryotic organisms.

To discuss, in a comparative way, the methods for analysing high-throughput data sets and their relative strengths and limitations. To provide practical experience of the analysis of actual data sets. To gain an understanding of the research data management issues in handling the large data sets these analyses produce.

This module is intended to bring the student up to date in the latest research methods for studying the molecular biology of organisms at a global level in a high throughput fashion. It is a highly practical module introducing the theory and reinforcing this theory using exercises in the analysis of previously generated datasets.

Outline Of Syllabus

Understanding of recent advances in appropriate high-throughput technologies, including the physics and/or chemistry behind these. An understanding of how these technologies can be used to address biological questions, and how the limitations of the technologies impacts on experimental design.

The use of common high-throughput analysis pipelines, either local or web-delivered, from community and single cell, the interpretation of results from these pipelines and the application of this interpretation to experimental questions.

An understanding of the data characteristics and standards, including Minimal Information standards and ontological description which can be used as part of this analysis. Meeting the requirements from these standards when releasing this data.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials240:3012:00Lectures split into 15-30 minute sessions, asynchronous online
Scheduled Learning And Teaching ActivitiesPractical28:0016:00Two practicals, detailed documentation provided, delivered Present in Person if available.
Guided Independent StudyDirected research and reading240:3012:00Lecture follow-up
Guided Independent StudyProject work161:0016:00Coursework
Scheduled Learning And Teaching ActivitiesDrop-in/surgery31:304:30Three 1h30m PiP (if available, otherwise synchronous online) questions on lectures.
Guided Independent StudyIndependent study351:0035:00Background reading
Scheduled Learning And Teaching ActivitiesScheduled on-line contact time31:304:30Three 1h30m PiP questions (if available, otherwise synchronous online) on practicals and assessment.
Teaching Rationale And Relationship

The lecture programme is designed to introduce students to the principles of the methods used to monitor the molelcular and cellular activities of organisms at a global level, from traditional "one-at-a-time" approaches to whole organism, high-throughput. The methods of analyses and the data generated have limitations, and understanding these are an important pre-requisite to understanding the analysis methods. The students will be introduced to the principles before having an opportunity to analyse, on an individual basis, actual raw data sets. The scheduled learning and teaching activities will be used in part for group-based problem solving and in part to allow the students to present the outcomes of their data analyses. Finally, the students will be assessed on a written report of their analytical approach and the resulting data.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report2M1006 pages max.
Assessment Rationale And Relationship

A single summative assessment is completed by the end of the module. The application of the analytical methods will determine the extent to which the students have achieved the learning outcomes. The written report is designed to ensure that the students understand the methods used to obtain the data and the limitations of this data.

Reading Lists