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MSci Genetics

Year of entry: 2020

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Course unit details:
MSci Bioinformatics Tools and Resources

Unit code BIOL33011
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 1
Offered by School of Biological Sciences
Available as a free choice unit? No

Overview

This unit will introduce students to a wide range of online bioinformatics tools and resources, including for sequence analysis, data processing, genomics, transcriptomics, protein structure analysis, and functional genomics.  The unit material will be mostly delivered online, with computer lab surgeries to support the students’ independent learning.  The skills learnt during the unit will be put to use in the analysis of the mystery gene, which will form the basis of a written report.

Aims

Introduce students to a wide range of bioinformatics tools and resources, including online databases and search algorithms.

 

Syllabus

Each chapter in the unit will introduce a bioinformatics tool/resource/technique or set of resources. 

Introduction to Bioinformatics: The importance of bioinformatics and computers in modern biology; generating and analysing large datasets; range of tools and resources covered in the unit.

Sequence searches: Manipulating sequence data; BLAST searches and variants; other tools for protein sequence searches.

Protein databases: Protein domains and databases; Interpro, Pfam, PRINTS, PROSITE; domain searches; structure databases.

Genome analysis: The UCSC genome browser; comparative genomics; Galaxy tools and workflows.

RNAseq and differential expression: RNAseq for transcriptomics; mapping and counting reads; estimating transcript relative expression; Tophat, Cufflinks, Cuffdiff pipeline in Galaxy.

Functional pathway analysis: Gene Ontology; KEGG pathways; assessing functional enrichment of gene lists in DAVID.

Structural bioinformatics: Manipulating protein structure information; predicting the effects of mutations on protein structure and function.

Phylogenetics: Understanding phylogenetic trees; multiple sequence alignment; inferring and visualising trees; distance, parsimony, and maximum likelihood methods.

Disease and variation databases: Understand how to analyse disease-causing and non-disease variants.

Teaching staff: Sam Griffiths-Jones, David Talavera, Simon Lovell, Chris Knight.

Teaching and learning methods

The majority of the material will be delivered online, in the form of eLearning tutorials on a tool/resource/technique or set of resources. A chapter will be delivered and completed by the students each week. Completion of each chapter will be supported by a weekly computer lab session run as a drop-in surgery for students with problems. Completion and understanding of each chapter will be assessed by a short (10 questions per test) MCQ quiz, delivered through Blackboard. After all chapters have been delivered, the students will be given a gene or protein sequence, and asked to find out all they can about it using the range of tools and resources. The gene or protein will be tailored to different MSci degree programmes. The students will produce a write-up on this exercise for assessment.

 

Knowledge and understanding

  • Understand a range of bioinformatics tools, resources and databases
  • Understand proteomic, genomic and transcriptomic data-types
  • Understand and apply methods of genome and transcriptome analysis
  • Understand the fundamentals of molecular evolution and phylogeny
  • Understand how protein sequence and structure determine function
  • Understand pattern-recognition concepts underpinning commonly used analysis tools
  • Understand the limitations of current databases and analysis tools

Intellectual skills

  • Develop problem-solving skills

Practical skills

  • Use a wide range of bioinformatics tools, databases and resources
  • Manipulate massive genome-scale datasets
  • Analyse and annotate unknown protein and gene sequences and structures
  • Use the UCSC genome browser and Galaxy to study the conservation of sequence elements in multiple genomes
  • Map and analyse RNAseq dataset to discover differentially expressed transcripts
  • Assess lists of genes for enrichment in functional pathways
  • Assess the effects of protein sequence mutations on structure and function
  • Build and analyse multiple sequence alignments and trees
  • Gain experience in communicating research findings in a short research paper

Transferable skills and personal qualities

  • Develop general computational and problem-solving skills that can be applied in a wide range of environments and circumstances

Assessment methods

Method Weight
Other 20%
Written assignment (inc essay) 80%

7-page practical write-up (80%)

Other - short MCQ tests after each weekly session (20%)

Feedback methods

Correct answers for the MCQ tests will be provided each week. Feedback for the write-up will be through Grademark.

Recommended reading

Online tutorial/practical exercise material provided for each chapter.

Study hours

Scheduled activity hours
Lectures 5
Practical classes & workshops 20
Independent study hours
Independent study 75

Teaching staff

Staff member Role
Sam Griffiths-Jones Unit coordinator

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