Genomics
Computational analysis of genomes, sequence variation, genome assembly, annotation, and comparative genomics.
Explore Genomics →
COMPBIOLOGY.ORG
Computational Biology, Reimagined.
Exploring the intersection of biology, computation, data science, genomics, artificial intelligence, and molecular research.
01 / FOUNDATIONS
Turning Biological Data Into Computational Insight
Modern biology generates enormous amounts of information. Computational biology provides the methods needed to transform that information into meaningful scientific insight.
From DNA and RNA sequences to protein structures, single-cell datasets, biological networks, and large-scale omics experiments, computational approaches allow researchers to analyze biological systems at a scale that would be impossible manually.
CompBiology.org brings together computational concepts, analytical methods, biological databases, programming, mathematical modeling, and emerging technologies in one scientific resource.
01
Biology
Genomes, proteins, cells, pathways, organisms, and biological systems generate the data we seek to understand.
02
Computation
Algorithms, statistics, programming, modeling, and machine learning transform complex datasets into analyzable information.
02 / EXPLORE
Explore Computational Biology
Discover the major disciplines shaping modern computational and biological data science.
Bioinformatics
Algorithms, databases, sequence analysis, biological annotation, data visualization, and reproducible workflows.
Explore Bioinformatics →Transcriptomics
Computational analysis of RNA expression, transcriptomes, differential expression, and regulatory patterns.
Explore Transcriptomics →Proteomics
Computational investigation of protein sequences, structures, abundance, interactions, and functions.
Explore Proteomics →Structural Biology
Explore molecular structures through computational modeling, structural alignment, docking, and simulation.
Explore Structures →Systems Biology
Investigate biological systems as interconnected networks of genes, proteins, pathways, and molecular interactions.
Explore Systems Biology →Single-Cell Biology
Analyze individual-cell datasets to investigate cellular populations, states, trajectories, and interactions.
Explore Single-Cell →Spatial Biology
Combine molecular measurements with spatial information to understand biological organization within tissues.
Explore Spatial Biology →
03 / INTELLIGENCE
Artificial Intelligence Meets Biology
Artificial intelligence and machine learning are transforming computational approaches to biological research. Complex biological datasets can be analyzed using models capable of identifying patterns, relationships, and predictive signals.
Sequence Analysis
Classification and prediction from biological sequences.
Protein Structures
Computational prediction and structural analysis.
Biological Imaging
Automated analysis of complex biological images.
Multi-Omics
Integration of multiple molecular data layers.
AI
04 / WORKFLOW
The Computational Biology Workflow
From a biological question to interpretable scientific results.
01
Question
Define a biological problem and determine what information is needed to investigate it.
02
Data
Collect biological datasets from experiments, databases, literature, or computational simulations.
03
Processing
Assess quality, filter information, normalize datasets, and prepare data for analysis.
04
Analysis
Apply algorithms, statistics, modeling, machine learning, or other computational methods.
05
Interpretation
Translate computational results into meaningful biological observations and hypotheses.
05 / DATA
One Biological System. Multiple Data Layers.
Modern biological research increasingly combines multiple types of molecular information. Computational methods make it possible to connect these layers and investigate biological systems from different perspectives.
DNA
GENOMICS
Sequence & variation
RNA
TRANSCRIPTOMICS
Expression & regulation
PROTEINS
PROTEOMICS
Structure & function
MOLECULES
METABOLOMICS
Metabolic information
06 / TOOLKIT
The Computational Toolkit
Languages, databases, algorithms, and platforms form the foundation of computational biological research.
PY
Python
Data processing, automation, visualization, machine learning, and custom biological analysis.
R
R Programming
Statistics, genomics, transcriptomics, visualization, and biological data analysis.
CLI
Command Line
Essential for large datasets, computational pipelines, servers, and high-performance computing.
DB
Databases
Access sequence, genome, protein, structure, pathway, expression, and literature resources.
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07 / SEQUENCES
Understanding Biological Sequences
Sequence analysis is one of the foundations of computational biology. Researchers compare DNA, RNA, and protein sequences to identify similarities, conserved regions, motifs, mutations, domains, and evolutionary relationships.
Alignment
Compare biological sequences.
Similarity
Identify related sequences.
Motifs
Find recurring sequence patterns.
Phylogenetics
Explore evolutionary relationships.
08 / LEARNING
Learn Computational Biology Step by Step
LEVEL 01
Beginner
Build a strong foundation in biology, programming, statistics, and computational thinking.
- Molecular Biology
- Genetics
- Python Fundamentals
- Basic Statistics
- Biological Databases
LEVEL 02
Intermediate
Develop practical skills for analyzing real biological datasets and designing computational workflows.
- Sequence Alignment
- Genome Analysis
- RNA-seq
- Data Visualization
- Biological Annotation
LEVEL 03
Advanced
Explore advanced methods used in modern computational biology research.
- Machine Learning
- Single-Cell Analysis
- Spatial Omics
- Network Biology
- Multi-Omics Integration
09 / RESEARCH
Built for the Next Generation of Biological Research
Computational biology is becoming an essential component of modern biological research. As datasets become larger and more complex, researchers need computational frameworks capable of connecting information across scales.
DNA
Genome-scale analysis
RNA
Expression profiling
PROTEINS
Molecular structure
CELLS
Single-cell analysis
10 / FUTURE
The Future of Computational Biology
Biology is becoming increasingly data-driven, interconnected, and computational.
01
AI-Assisted Biology
Machine learning models designed to understand biological sequences, structures, images, and datasets.
02
Multi-Omics Integration
Connecting multiple molecular layers to build richer representations of biological systems.
03
Single-Cell Mapping
Increasingly detailed computational representations of individual cells and their molecular states.
04
Biological Modeling
Computational models capable of representing increasingly complex biological processes.
Explore. Analyze. Understand Biology.
Discover the computational concepts, methods, resources, and technologies shaping modern biological research.