This repository is a central hub for bioinformatics tutorials, project guides, tools, and articles I've created. It houses a growing collection of resources I've developed to make bioinformatics more approachable and accessible. Whether you’re a beginner learning the basics or a researcher exploring more complex analyses, these resources are designed to help you navigate the world of bioinformatics, computational biology, and systems biology.
This repository is broken into six sections sections:
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Programming For Bioinformatics: This section provides tutorials and articles on essential skills to help biologists build strong programming foundations and effectively apply computational methods to solve biological problems.
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Bioinformatics Analysis Tutorials: This section contains detailed guides and DIY tutorials for performing a range of bioinformatics analyses, from functional enrichment analysis to PPI network analysis, enabling researchers to derive meaningful insights from biological data.
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Bioinformatics Project Walkthroughs: This section showcases in-depth project walkthroughs, demonstrating how bioinformatics techniques can be applied to real-world research questions.
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Bioinformatics Tools and Packages: This section showcases bioinformatics analysis tools and packages for streamlining data processing, analysis, and visualization.
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Systems Biology & Metabolic Engineering: This section provides tools and tutorials to model dynamic systems and simulate biological processes.
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Machine Learning / AIxBIO: This section provides resources to evaluate models, optimize performance, and compare algorithms for bioinformatics applications.
Programming is at the heart of bioinformatics. This section provides tutorials, and articles, on essential skills to help biologists build strong programming foundations and effectively apply computational methods to solve biological problems.
- Python Fundamentals For Biologists: This extended guide offers a hands-on approach to learning Python, focusing on topics such as control flow, functions, data handling, and data visualization. It's tailored for biologists who want to use Python for data analysis and visualization.
- Bash Fundamentals For Bioinformatics: Learn how to navigate the command line, automate repetitive tasks, and execute bioinformatics workflows with this interactive introduction to Bash.
- R Fundamentals for Biologists: This guide offers a hands-on approach to learning R, focusing on topics such as control flow, functions, data handling, and data visualization. It's a companion piece to Python Fundamentals For Biologists, helping coders familiar with Python pick up R basics in 60-minutes or less.
- Math For Computational Biology: This repository provides tutorials that cover mathematical concepts foundational to bioinformatics and computational biology (including calculus, ordinary differential equations, and linear algebra), offering a solid preparation for graduate level coursework.
- Catching Silent Errors in Bioinformatics Code with Assert Statements: Discover how to use assert statements to debug and validate your code, preventing silent errors that can lead to incorrect biological interpretations.
- Unit Testing in Bioinformatics: Why It Matters and When to Use It: LEarn to write modular, well-defind, pieces of code that can be verfied and tested before being integrating it into the larger system.
- Looping Through Repetitive Tasks: Automating workflows is a critical skill in bioinformatics. This beginner's guide introduces command-line loops to streamline repetitive bioinformatics tasks.
- Go On, Git!: An introduction to version control with Git for bioinformatics.
This section contains detailed guides and DIY tutorials for performing a range of bioinformatics analyses, from functional enrichment analysis to PPI network analysis, enabling researchers to derive meaningful insights from biological data.
- Introduction to Functional Enrichment Analysis: A beginner-friendly guide to gene ontology, pathway, and gene set enrichment analysis.
- From Genes to Networks: Uncovering Biological Insights through Protein-Protein Interactions: This tutorial walks you through mapping DEGs to PPI networks, identifying hubs, and simulating genetic circuits to uncover biological insights.
- Unraveling Gene Co-Expression Networks: Insights into Transcriptional Regulation: Learn how to construct and interpret gene co-expression networks to investigate transcriptional regulation in this practical guide.
- BLAST - The Biological Search Engine: This resource covers BLAST tools for sequence alignment, offering practical examples for identifying homologous sequences.
- Identifying and Visualizing Marker Genes with ScanPy: A step-by-step guide to analyzing single-cell RNA-seq data and identifying marker genes using ScanPy.
- Connecting the Dots: A Guide to Multi-Omics Data: Understanding the interrelationships between genomic, epigenomic, transcriptomic, proteomic, metabolomic, phenomic, and stimulus data
- Understanding Mutual Information in Bioinformatics: A Deep Dive into Entropy, Mutual Information, and Their Application in Gene Expression Analysis
- Multi-Omics Analysis Unveils the Hidden Patterns of Gene Expression Control: Exploring cell's regulatory logic, revealing how organisms achieve precise control over their molecular machinery.
This section showcases in-depth project walkthroughs, demonstrating how bioinformatics techniques can be applied to real-world research questions.
- Decoding The Viral Proteome: Analyze the herpes virus proteome to uncover patterns in amino acid frequencies (beginner friendly).
- Uncovering Amiloride's Mechanisms of Action in Multiple Myeloma: A Bioinformatics Approach: This project investigates the mechanisms by which amiloride induces cell death in multiple myeloma cells through comprehensive bioinformatics analyses.
- Investigating Transcriptional Changes in Alzheimer's Disease Using a 3xTg-AD Mouse Model: Uncover transcriptional changes and molecular mechanisms driving Alzheimer’s disease progression in the insular cortex of 3xTG-AD mice using RNA-seq data.
- Exploring mutual features between the 5xFAD, 3xTG-AD, and PS3O1S Alzheimer's Disease Mouse Models: Explore the similarities, differences, and overlapping features—including differentially expressed genes (DEGs), Gene Ontology (GO) terms, biological pathways, and protein-protein interaction (PPI) networks—between three mutant mouse models of Alzheimer’s disease (AD).
This section showcases bioinformatics analysis tools and packages for streamlining data processing, analysis, and visualization.
- SmallSeqFlow: A streamlined, notebook-based RNA-seq analysis pipeline optimized for small-sample studies.
- PhysioNexus: PhysioNexus maps complex cause-and-effect relationships between time-series physiological variables using Granger causality to transform data into interpretable networks of system interactions.
- BiophysicsExpert: BiophysicsExpert is an educational tool that provides users with a searchable database of over 150 biophysics formulas, allowing them to quickly find equations based on known variables or desired unknowns.
Systems biology focuses on understanding complex biological networks. This section provides tools and tutorials to model dynamic systems and simulate biological processes.
- Numerical Methods & Systems Biology Toolkit: A collection of numerical methods and tools for solving common systems biology problems, from ordinary differential equations to parameter optimization.
- An Introduction To Dynamical Systems & Markov Models: A primer on modeling biological systems using dynamical systems and Markov models, with practical examples.
- Understanding the SIR Model: A Tool for Modeling Infectious Diseases: Learn how to use the SIR model to simulate and understand the spread of infectious diseases.
- The Biology Network: Learn how static and dynamic network models can be used simulate and analyze biological systems, from metabolic pathways to regulatory circuits.
- Leveraging Network Analysis For Drug Target Discovery: Explore the role of network analysis in identifying potential drug targets.
- Modeling Predator-Prey Dynamics: Learn how to use the Lotka-Volterra model to understanding the dynamic interactions between species within an ecosystem.
- Bistability and Hysteresis in Biological Systems: Learn about the concepts of bistability and hysteresis, and their practical relevance in modeling cellular behavior.
Machine learning offers powerful tools for analyzing and interpreting biological data. This section provides resources to evaluate models, optimize performance, and compare algorithms for bioinformatics applications.
- Evaluating Machine Learning Models In Biology: A biotechnologist's guide to evaluating machine learning algorithms and using resampling methods like cross-validation to assess model performance (with practical examples).
- Evaluation Metrics For Classification Problems: A comprehensive guide to metrics for evaluating classification models.
- Evaluation Metrics For Regression Problems: A comprehensive guide to metrics for evaluating regression models.
- Comparing Machine Learning Algorithms: Spot Checking For Regression and Classificaiton Problems: Learn how to spot-check regression and classification models to identify the best-performing algorithm for your data.
- Introduction To Hyperparameter Optimization: Master hyperparameter tuning to optimize model performance and achieve better results.