On completion of this module students will have explored best practices in applied bioinformatics as they move forward in their research careers. Students will participate in tasks highlighting the importance of biological data analysis and interpretation, communicating their findings in an accurate and appropriate manner to the wider scientific community. Specifically, students will:

• Organise and manipulate biological data using a variety of different tools

• Familiarise themselves with a Unix environment

• Navigate and explore biological databases

• Apply Genomic and transcriptomics methods to real life data

• Apply statistical methods and hypothesis testing

• Apply basic coding to automate analysis

• Communicate data using a variety of graphs and tables

• Answer questions pertaining to applied bioinformatics.

Skills Developed

In this module, the students will be introduced to the following skills, which will be developed throughout its duration:

• The use of various computational tools for biological data and interpretation

• Using Python/Perl programming for easy data manipulation

• Apply the R statistical language to analyse biological datasets

• Effective presentation and writing of technical and scientific information

1. Biological data and databases, genome assembly, QC and annotation

2. Genomics methods including alignments, trees and selection test

3. Identifying evolutionary change in genomic sequences

4. Using genomics for human disease analysis

5. Transcriptome QC, short read mapping and transcript assembly

6. RNA-Seq data analyses including differential gene expression and functional enrichment

7. Applied transcriptomic analysis for human disease analysis

8. Basic statistical analysis for data science

9. Hypothesis testing in statistics

10. Introduction to Perl/Python

11. Using R for statistical analysis and data representation

Not applicable to this module.

• Feedback individually to students, post-assessment

Not yet recorded.