Research Projects

MSci students will not complete a research project during Year 3. Instead, you will take the below units that will prepare you for your research project during Year 4. These units will provide you with research skills that are essential for a modern biological science researcher.  Depending on your programme you will be required to do one, two or three of MSci Bioinformatics Tools and Resources, or Computational Approaches to Biology, or Reproducible Data Science.

  • MSci Research Project Proposal (10 credits).

This unit will introduce you to the critical reading and writing skills required to assess and author research papers and grants.  You will be split by your MSci degree programme into groups of up to 10 students.

The unit will start with a week-long workshop during Welcome Week. Within this workshop, you will be given guidance on how to critically read research papers and determine whether the experiments presented within a research paper address: the proposed hypothesis; are appropriately designed with suitable controls; and support the stated conclusions.  You will also be given guidance on how to critically read and review grant applications to determine: whether the stated hypothesis is timely and interesting; and whether the proposed experiments will address the stated hypothesis and are not overly reliant on each other.  Your acquisition of these skills will be assessed during the workshop by writing a 250 word abstract for a paper (5%), a 2 page critical review of a paper similar to those written when a paper is reviewed for a journal (10%), a 2 page grant review (10%), and through an oral grant pitch to a grant panel (5%).

Through the remainder of the academic year, you will be expected to draw up 2×5 page grant proposals (35% each) for your Year 4 project in consultation with potential supervisors, one in semester 5 and one in semester 6.  You will be expected to identify 2 members of academic staff whose work you find interesting, and then liaise with them to identify appropriate projects and discuss suitable experimental approaches.  One of these grant proposals will form the basis of your Year 4 project and should be selected in consultation with your Programme Director.  You should hand in a completed project preference form by the deadline indicated in the section on Key Dates and Deadlines.

 

  • MSci Bioinformatics Tools and Resources (10 credits).

This unit will introduce you to a wide range of bioinformatics tools and resources, including online databases, search algorithms, and basic scripting techniques.  The unit will be delivered through a series of eLearning modules, with supporting lectures and weekly computer lab sessions.

  • 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.
  • Command line basics: Introduction to Unix systems; the Unix/Linux/Mac OSX command line; directories and files; manipulating text files.
  • Scripting for bioinformatics: Introduction to the Perl programming language; scalars, arrays and hashes; operators, functions and loops; reading and writing files.
  • 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.

Completion of each eLearning module will be assessed by a MCQ quiz (2% each), delivered through Blackboard.  After all chapters have been delivered, you will be given a gene or protein sequence tailored to your MSci degree programme, and asked to find out all you can about it using the taught range of tools and resources.  The outcome of this research will be written up as a 7 page report (80%).

 

  • MSci Experimental Skills Module (20 credits).

You will complete a group research project within this unit.  You will be placed in groups of up to eight students and will be given an experimental problem that is appropriate to your MSci degree programme.  You will be expected, with the rest of the group and the support of a member of academic staff, to design the appropriate experiments to test the problem. You will be expected to explore the range of different experimental approaches available, select the most appropriate approach and plan the suitable controls; these experimental approaches will include state of the art techniques that are supported within the core facilities within the Faculty.  Then you will be responsible for executing one part of the plan and to produce data for your part of the project.  Your experimental planning and findings will be assessed through the following:

  • a 2 page write-up describing the technique, experimental design and statistics to be used to complete your component of the eoverall experimental plan (10%).
  • laboratory performance (10%)..
  • a 5 page write-up of your results, presenting data in an appropriate style for publication along with a short introduction and conclusion (30%). This component can also include deposition of data into an appropriate database.
  • preparation of a group A1 poster that is suitable for an international scientific conference (30%).
  • a 15 minute presentation of the poster as a group at a poster session for all MSci students (20%).

 

  • Computational Approaches to Biology (10 credits)

This unit will introduce you to essential mathematical concepts used in biological modelling. You will alsol be introduced to the Jupyter Notebook system, a widely used online application allowing the development of code for data analysis and numerical simulation based on the Python language.

The core of the unit will be structured along four main sections, each covering a particular set of techniques and applications:

  • Modelling of intracellular signalling and transcription pathways. This section will introduce you to the mathematical approaches used to model cellular signalling pathways and biological noise.
  • Techniques for modelling of large cellular systems. This section will introduce you to the mathematical approaches used to model protein-protein interaction networks in both normal and disease situations and to model metabolic systems.
  • Ecological and evolutionary modelling. This section will introduce you to the mathematical approaches used to model population dynamics and evolution.
  • Probabilistic modelling and machine learning. This section will introduce you to the mathematical approaches to model sequence data and expression data

The unit will be assessed by completing four online modules, one for each of the main sections of the course. These modules will consist of a series of multiple choice questions and short questions, some of which will require a short piece of code to be written.

  • Reproducible Data Science (10 credits)

This unit will provide students with the skills needed to engage in reproducible data science. Students will learn how to wrangle data, build data visualisations, and model their data using the open source data science software, R. Each of the sessions will be run as a combined seminar and hands-on coding workshop. Students will learn how to use a reproducible workflow to generate reproducible analysis. They will also learn about general computational skills such as using git and GitHub for version control, and Binder for building reproducible computational environments.  Graduates with data science skills are in high demand, with skills in using R particularly desirable to employers across the academic, industrial, and business sectors. This unit will provide students with a grounding in data science using R and the knowledge to build on this foundation for the development of more focused skills (such as machine learning using R).

The unit will be assessed by a single R-based assignment using R markdown worth 100%