2 Prerequisites
2.0.1 Don’t know where to start?
Checkout PATOQ Wiki for the Bioinformatics and Computational Biology Learning Roadmap!
2.1 Biology Fundamentals
2.1.1 Key Concepts
- Molecular Biology: DNA, RNA, proteins, gene expression
- Cell Biology: Cell structure, organelles, and functions
- Genetics: Inheritance, genetic variation, genomics
- Biochemistry: Metabolic pathways, enzyme kinetics
- Biophysics: Physical principles in biological systems
2.1.2 Recommended Resources
- Books:
- Molecular Biology of the Cell by Bruce Alberts
- Lewin’s Genes by Jocelyn E. Krebs et al.
- Lehninger Principles of Biochemistry by Nelson and Cox
- Physical Biology of the Cell by Rob Phillips
- Online Courses:
2.2 Programming Fundamentals
2.2.1 Key Concepts
- Python Programming: Syntax, data structures, scripting
- R Programming: Statistical computing and data analysis
- Bash Scripting: Command-line basics in Unix/Linux
- Version Control: Git and GitHub
- Best Practices in Coding
2.2.2 Recommended Resources
- Books:
- Python for Biologists by Martin Jones
- R for Data Science by Hadley Wickham
- Bioinformatics Data Skills by Vince Buffalo
- Online Courses:
- Python for Data Science and AI on Coursera
- R Programming on Coursera
- Introduction to Git and GitHub on Coursera
2.3 Mathematics Fundamentals
2.3.1 Key Concepts
- Linear Algebra: Vectors, matrices, and their applications
- Calculus: Differential and integral calculus
- Discrete Mathematics: Combinatorics, graph theory
- Algorithms and Data Structures
- Numerical Methods
2.3.2 Recommended Resources
- Books:
- Introduction to Linear Algebra by Gilbert Strang
- Discrete Mathematics and Its Applications by Kenneth H. Rosen
- Numerical Recipes by William H. Press et al.
- Online Courses:
- Linear Algebra on edX
- Calculus 1A: Differentiation on edX
2.4 Statistics and Probability Fundamentals
2.4.1 Key Concepts
- Probability Theory: Distributions, random variables
- Statistical Inference: Hypothesis testing, confidence intervals
- Regression Analysis: Linear and non-linear models
- Multivariate Statistics
- Machine Learning Basics
2.4.2 Recommended Resources
- Books:
- The Elements of Statistical Learning by Trevor Hastie et al.
- Applied Multivariate Statistical Analysis by Richard A. Johnson
- Statistical Inference by George Casella and Roger L. Berger
- Online Courses:
- Statistics and Data Science on edX
- Probability and Statistics on Coursera
2.5 Project Management and Communication Skills
2.5.1 Key Concepts
- Scientific Writing and Communication
- Project Planning and Management
- Collaboration with Multidisciplinary Teams
- Data Management and Documentation
- Research Ethics and Reproducibility
2.5.2 Recommended Resources
- Books:
- The Craft of Scientific Writing by Michael Alley
- Project Management for Scientists by Christopher L. Cummings
- Online Courses:
- Scientific Writing and Communication on Coursera
- Project Management Principles and Practices on Coursera
2.6 Case Studies and Practical Applications
2.6.1 Key Concepts
- Real-World Bioinformatics Projects
- Data Analysis Workflows
- Best Practices in Computational Biology
- Reproducible Research
- Open Science Principles
2.6.2 Recommended Resources
- Hands-on Projects:
- Galaxy Project - Web-based platform for data analysis
- Rosalind - Platform for learning bioinformatics through problem solving
- NCBI Datasets - Public datasets for practice
- Case Studies:
- Bioinformatics.ca - Workshops and case studies
- Harvard Chan Bioinformatics Core - Training materials and case studies