Methods in Bioinformatics

High-throughput experimental assays generate large amounts of data that must be handled and processed appropriately in order to extract meaningful biological knowledge. Bioinformatics provides methods and tools to perform complex and elaborate analyses of large scale (BIG) biological data, prompting novel testable hypotheses and allowing their verification. Proficiency in data handling and processing, and the ability to unravel and highlight complex relationships in biological data using adequate tools and methods constitute a crucial skill for the modern biotechnology researcher.
The aims of this course are (i) to introduce the basic principles of procedural and object-oriented programming, (ii) to present the R programming language and software environment as an effective instrument for the analysis of large scale biological data, (iii) to provide a primer on methods for the analysis of gene expression (RNA-Seq) data and their statistical foundations.
The course is ideally linked to those dealing with genomics and bioinformatics.

After following this course, the students are expected to:
(1)Understand the basic principles of programming and be able to map those concepts to R programming language specificities and features.
(2)Know the syntax of the R programming language and its basic data types, data structures, and functions.
(3)Become proficient in splitting simple data analysis procedures into elementary logical steps and translate them to R functions and scripts.
(4)Know how to import data into the R environment.
(5)Be able to represent data and their relationships using basic R plotting functions.
(6)Know how to manage R software packages and libraries.
(7)Produce impactful reports of an analysis workflow, by integrating text, R code, and plots.
(8)Perform and interpret preliminary RNA-seq data analysis: normalization, Principal Component Analysis (PCA), and quality control.
(9)Know how to execute differential expression analysis.
(10)Be able to perform post-processing and functional enrichment analysis of differentially expressed genes.