Algorithms for Bioinformatics

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.

• 1Apply techniques to extract information from data and visualise results.
• 2Implement common bioinformatics algorithms, strategies, and data structures.
• 3Design and implement algorithms to solve new problems.
• 4Interpret outputs to diagnose, communicate, and remedy issues.
• 5Recognise which bioinformatics algorithms, data structures and strategies are best suited to a given task.
• 6Identify and mitigate the feasibility constraints imposed by computational complexity.