Hello, we are pleased to welcome you to Bioinformatics 1 - An introduction to Bioinformatics.
We will have lectures every Friday from 10:00-11:00 in Lecture Theatre 1 of Appleton Tower (AT), and practical computing labs in weeks 2, 4, 6, and 8 from 14:00-16:00 in AT 6.06.
We will be streaming lectures online, recording them, and making them available on this site so if you cannot make a lecture in-person or if for whatever reason you prefer to watch these asynchronously that should work fine for you. The course is supported by a discussion board on Piazza so that you can always ask questions there whenever you need. A similar approach is taken for practical labs. The labs are structured using computational notebooks which will be made available on this site and on GitHub for you to use. You can choose to use the University Notebook system "Noteable" to run these notebooks, or if you prefer, you can install free software on your own machines. We will explain how to do this for those who are interested, but Noteable works very well so there is no obligation to do so.
We will retain the emphasis on trying to make this course as engaging and enjoyable as possible with a strong emphasis on practical application of the skills that we will be teaching you.
Just to highlight some of the key features of the course this year:-
- The course is assessed by two pieces of project based coursework to make sure the quantity of assessment is consistent with a 10 credit course, they will be worth 50% each.
- We have very popular Discussion boards which we host on Piazza. These will be regularly monitored throughout the course to give you the opportunity to ask questions of us and of your colleagues. We also use this to make you aware of other resources, events and activites that we think you might find interesting.
- The core book for the course "Bioinformatics & Functional Genomics by Pevsner" is available electronically from the University Library and on the course Resrouce List.
- Computational Notebooks will accompany much of the material so that you can practice hands-on coding in a guided manner.
- We have an optional mini-course for those that want to catch up with Python coding fundamentals.
Although last year's course went smoothly it is always possible that there could be technical issues. Whenever these arise we will work hard to resolve them as quickly as possible and would like to encourage you to feedback to us throughout the course if these happen or indeed if there are things we can improve to make things easier and more understandable for you. We have created a dedicated channel in the discussion for course feedback that we will be monitoring so please do feel free to use it.
On successful completion of this course, you should be able to:
- Communicate between biological and computational domains to facilitate effective inter-disciplinary working.
- Use and/or implement Bioinformatics tools, services and software in practical research scenarios.
- Have sufficient background knowledge, skills and understanding to discover and apply additional bioinformatics techniques.
In this course, we will introduce key biological concepts including the main types of molecules we study (DNA, RNA, and protein) and the cell biological processes involved in the regulation and function of biological systems. The cornerstone of foundational Bioinformatics lies in the analysis of sequences; strings of characters that encode genetic information in organisms. We will describe the theory and put into practice how we work with and analyse these sequences using a range of databases, algorithms, and tools. You will undertake mini research projects using publicly available data to put your learning into practice. The course is taught using Python; students need to be comfortable with basic coding in Python as this is required to use the course notebooks each week and for the assessed coursework.
Topics change slightly each year, but typically include:
pairwise and multiple sequence alignment, biological databases, ontologies & functional enrichment analysis, network analysis, multi-omics analysis (transcriptomics, proteomics, methylomics), and biomedical text analytics.
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