Algorithms for Bioinformatics (COMP90014)

Technological advances in DNA sequencing, RNA sequencing and proteomics have provided a wealth of data from which biological insight can be obtained.  Refining this data is a non-trivial matter due to the increased input sizes seen in modern high-throughput bioinformatics.  This subject provides algorithmic strategies and data structures capable of meeting the challenge.  While focused on bioinformatic data, the concepts herein apply to big data analysis as a whole.

This subject covers key algorithms and data structures used in bioinformatics and assumes you have experience in programming. Strategies which frequently appear in modern software are explored so that bioinformatics tools may be appropriately selected, executed, and interpreted.  This exploration yields a toolkit from which new computational methods can be created. Indicative topics include sequence operations for comparison, alignment and indexing, graph data structures in the context of genome assembly, phylogenetics and network analysis, and both supervised and unsupervised machine learning within the fields of optimisation, dimensionality reduction, clustering and classification.

Intended learning outcomes

On completion of this subject the student is expected to be able to:

• Understand bioinformatics data representations
• Describe the computational challenges posed by common bioinformatics analyses
• Apply important algorithms used in solving bioinformatics problems
• Design and implement algorithms and data structures to address key questions in bioinformatics
• Design and implement a toolkit of algorithmic problem-solving techniques that can be applied to a diverse range of bioinformatics tasks
• Understand the feasibility constraints imposed by computational complexity of algorithms
• Learn techniques for extracting information from data and visualising the results of analyses

Generic skills

• Practical mindset to solving problems.
• Greater creativity and mental flexibility.
• Improved code literacy and software skills.
• Ability to measure success of tasks.