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Module

BMS3025 : Bioinformatics

  • Offered for Year: 2020/21
  • Module Leader(s): Dr Phillip Aldridge
  • Lecturer: Dr Sarra Ryan, Dr Simon Cockell, Dr Daniel Rico Rodriguez, Professor Robert Hirt, Dr Lisa Russell, Professor Caroline Austin
  • Owning School: Biomedical, Nutritional and Sports Scien
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

Science is an ebb and flow of discovery and innovation. The scientific research community is experiencing a rapid increase in the size of the data sets our experiments generate. This has meant we have had to alter our data analysis skill-set to analyse, draw conclusions and present our studies in an understandable format.

A good example of where we as scientists are learning and continually improving our skill set is within the field of Bioinformatics. In this module we aim to examine the uses of various tools to analyse the large datasets we are now generating. We will also provide you with several case studies where these tools have been or are being exploited in research.

A key aspect of the underpinning skills required for bioinformatics is the ability to have confidence in exploiting a PC or MAC for more than our usual activities of document generation in MS Word, analysis of data using software such as MS excel and social media access.

• train students in using one analysis platform that is used across the bioinformatics sector
• explore the use of health informatics to improve the quality and safety of patient care
• examine the uses of various tools to analyse the large datasets
• generate an understanding, and an appreciation, of databases, workflows, and basic coding
• give the students the theory behind, and the practice in using, modern bioinformatic analysis tools

Outline Of Syllabus

The following topic and themes will be covered in this module:

• An introduction to the analysis platform R
• Impact of Next Generation Sequence Technologies on Science
• Impact of CHiP-seq and RNA-seq on experimental design
• Phylogenetics
• Statistical analysis of large datasets as a mean to identify experimental robustness
• Variant calling
• the use of computer code in data analysis
• the use of health informatics to improve the quality and safety of patient car

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion130:0030:00Assessment
Structured Guided LearningLecture materials61:006:00Core conceptual material relating to module outcomes - Non-synchronous online
Scheduled Learning And Teaching ActivitiesPractical22:004:00PIP
Scheduled Learning And Teaching ActivitiesWorkshops41:004:00Teaching to develop students familiarity with the software platform R - Synchronous online
Guided Independent StudyIndependent study155:0055:00Writing up lecture notes, revision and general reading
Scheduled Learning And Teaching ActivitiesModule talk11:001:00Introduction to the module - Synchronous online
Total100:00
Teaching Rationale And Relationship

In this module for 2020/2021 we will exploit the learning environment of the PC-clusters and synchronous teaching to deliver interactive sessions based on previous lecture material that will act to introduce the students to the data analysis platform R.

Six non-synchronous sessions of material will be used to impart information in a concise manner, to highlight areas of importance and to interrelate with directed reading and self-directed study. These materials will be used to introduce a range of analysis strategies.

The two present-in-person sessions will provide the students with structured sessions in which they can explore and develop their understanding of R and in a structured environment, continue to analyse the data set provided for the assessment and to gain valuable direct learning to explore to module content.

Private study will be used for self-directed learning, including further reading.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

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

Other Assessment
Description Semester When Set Percentage Comment
Case study1M100Report on the analysis of large sequence data-set - 1500 words
Formative Assessments
Description Semester When Set Comment
PC Examination1MPost-lecture MCQ questions available via Canvas (online remote)
PC Examination1MPractical based MCQs embedded within practical so students can assess understanding and progress.
Assessment Rationale And Relationship

Summative Assessment: In the summative assessment the students will be presented with a large dataset which they have to analyse using the tools, ideas and approaches covered in the module. The assessment will explore their understanding of the material taught, as well as push their critical thinking and data-analysis skills. Their results will be presented as 1,500 word report, which will have to contain appropriate diagrams, tables and figures.

Reading Lists

Timetable