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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23

Department: Biosciences

BIOL40715: Bioinformatics and Data Science

Type Tied Level 4 Credits 15 Availability Available in 2021/22
Tied to C2K009 Plant Biotechnology and Enterprise [First intake in 2022/23]

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To provide students with a broad understanding of bioinformatics.
  • To provide students with the knowledge and skills of R environment for data analysis and visualization.
  • To provide students with the knowledge and skills to analyse genomic and transcriptomic data using open source software.
  • To provide students with the knowledge and skills to analyse DNA and protein sequence data.
  • To provide students with the knowledge and skills to analyse Next Generation Sequencing data.
  • To provide students with the knowledge and skills to use public bioinformatics databases.

Content

  • Introduction of bioinformatics.
  • R environment for data analysis and visualization.
  • Linux and high-performance computing
  • Analysis of RNA-seq data using open source software.
  • Analysis of small-scale mutations in genome sequencing data using open source software.
  • Analysis of DNA and protein sequence data.
  • Public bioinformatics databases.

Learning Outcomes

Subject-specific Knowledge:
  • Essential knowledge of the R environment for data analysis and visualization.
  • Essential knowledge of Linux and high-performance computing.
  • Essential knowledge of RNAseq data analysis.
  • Essential knowledge of genome sequencing data analysis.
  • Essential knowledge of DNA and protein sequence data analysis.
  • Familiar with major public bioinformatics databases.
Subject-specific Skills:
  • Ability to use R environment for data analysis and visualization.
  • Ability to use Linux and high-performance computing.
  • Ability to analyse RNAseq data using open source software.
  • Ability to analyse small-scale mutations in genome sequencing data using open source software.
  • Ability to analyse DNA and protein sequence data.
  • Ability to use major public bioinformatics databases.
Key Skills:
  • Data analysis, visualization and interpretation
  • Linux and high-performance computing
  • Hypothesis building
  • Problem solving

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Sixteen 3 hour workshops over spring term will be delivered.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Workshops 16 Twice per week in one term 3 hours 48
Preparation and reading 102
Total 150

Summative Assessment

Component: Coursework Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Basic Bioinformatics Report 40%
Mini project report 60%

Formative Assessment:

Formative verbal feedback on submitted work report


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University