Introductory Bioinformatics

This course provides an in-depth introduction to bioinformatics, focusing on the integration of computational tools and biological data to address complex research challenges. Through a combination of theoretical concepts and hands-on practice, students explore key areas of bioinformatics using real-world datasets and examples. The course is designed to provide a unified, tripartite foundation in three core domains: (1) Biological Data, including the generation, structure, and storage of modern biological data such as sequences, structures, and omics datasets; (2) Computational Algorithms, covering the fundamental algorithms and probabilistic models that underpin bioinformatics analyses; and (3) Statistical Reasoning, emphasizing statistical thinking, data analysis, and the development of reproducible workflows. This integrated approach aims to cultivate "T-shaped" researchers who possess both a broad understanding of the bioinformatics landscape and the deep, transferable technical skills required to analyze, interpret, and derive meaningful insights from complex biological data.

This course introduces students to the interdisciplinary field of bioinformatics by integrating biological data, computational algorithms, and statistical reasoning. Students learn how biological information is generated, stored, analysed, and interpreted while gaining hands-on experience with bioinformatics databases, sequence analysis, structural bioinformatics, statistical analysis, and high-throughput 'omics' data. The course concludes with advanced topics in functional enrichment analysis and AI integration, equipping students with the skills to build reproducible bioinformatics workflows.

By the end of this course, students will be able to:

• Understand and articulate the Central Dogma of molecular biology as a data flow and information science problem.
• Navigate, retrieve, and assess data from primary biological databases such as NCBI, UniProt, and PDB.
• Explain the algorithmic and probabilistic foundations of bioinformatics, including dynamic programming, BLAST, and Hidden Markov Models.
• Connect biological sequences to protein structure and function using visualization and prediction tools.
• Acquire, clean, and visualize complex biological datasets.
• Perform end-to-end 'omics' data analysis using R and Bioconductor.
• Design and execute reproducible bioinformatics analysis workflows.

 Upon successful completion of this course, students will be able to:

• Describe the scope, history, and applications of bioinformatics and navigate biological databases and sequence file formats.
• Perform complete analyses of sequencing datasets, including variant identification, gene expression analysis, and biological pattern discovery.
• Perform sequence alignments, phylogenetic analyses, and genome annotation while understanding the underlying algorithms.
• Develop reproducible, script-based bioinformatics workflows that promote scientific transparency and reproducibility.

• Quickstart Molecular Biology – Philip N. Benfey.
• Bioinformatics: Sequence and Genome Analysis – David W. Mount.
• Bioinformatics and Functional Genomics – Jonathan Pevsner.
• An Introduction to Bioinformatics Algorithms – Neil C. Jones and Pavel Pevzner.
• Introduction to Protein Science: Architecture, Function and Genomics – Arthur Lesk.
• Essential Bioinformatics – Jin Xiong.
• Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins – Andreas D. Baxevanis and B. F. Francis Ouellette.
• R for Data Science – Hadley Wickham.

•  An Introduction to Statistical Learning: With Applications in R – Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
• Introduction to Protein Science: Architecture, Function and Genomics – Arthur Lesk.
• Quickstart Molecular Biology – Philip N. Benfey.