Learn bioinformatics
by doing

Watch a DNA sequence get transcribed into mRNA and translated into a protein chain. Adjust codons and see how mutations change the output in real time.

Decompose a DNA sequence into k-mers and visualize their frequency distribution. Explore how k-mer size affects genome assembly and sequence comparison.

Dissect bioinformatics file formats byte by byte. Learn how headers, sequences, and quality scores are structured, and validate files against the specification.

Explore protein structures in 3D. Toggle between cartoon, ball-and-stick, and surface representations. Color by secondary structure, residue type, or B-factor.

Simulate experiments and watch p-value distributions form. Explore the effects of sample size, effect size, and multiple testing on statistical significance.

Step through Needleman-Wunsch and Smith-Waterman algorithms cell by cell. Adjust gap penalties and substitution matrices to see how alignment scores change.

Build phylogenetic trees from sequence data using distance-based methods. Compare neighbor-joining and UPGMA, and explore how distance metrics affect tree topology.

Draw or load small molecules, compute descriptors, and compare fingerprint similarity. Learn how cheminformatics methods underpin drug discovery pipelines.

Apply PCA, t-SNE, and UMAP to biological datasets. Adjust hyperparameters and see how clusters form, split, and merge in real time.

Visualize GPU compute patterns for bioinformatics workloads. Compare CPU vs. GPU execution for k-mer counting, pairwise alignment, and matrix operations.

Learn how DNA is transcribed to mRNA and translated to protein.

Understand DNA, RNA, and protein sequences — the foundational data types of bioinformatics.

Learn to read and write FASTA and FASTQ — the two most common file formats in bioinformatics.

Learn the fundamentals of pairwise sequence alignment — how and why we compare biological sequences.

Explore the universal features shared by all living cells — from DNA and the central dogma to minimal genomes — and learn the computational foundations for working with biological sequences.

Compare genomes across the three domains of life — Bacteria, Archaea, and Eukarya — and discover how sequence similarity, gene duplication, and horizontal transfer shape the diversity of genomes.

Discover how eukaryotic genomes are structured — from endosymbiosis and hybrid genomes to introns, regulatory DNA, repetitive elements, and the computational methods used to assemble and annotate them.

Understand the chemical foundations of life — from water and noncovalent interactions to enzymes, free energy, ATP, and the metabolic pathways that power cells — along with the databases and computational tools for studying metabolism.

Explore Michaelis-Menten kinetics, enzyme inhibition mechanisms, and structure-based drug design — from Lipinski's rules and molecular similarity to HIV protease inhibitors, kinase inhibitors, and metabolic regulation.

Learn how amino acid sequences fold into three-dimensional protein structures — from primary sequence through α helices, β sheets, and domains to the computational tools for predicting and classifying protein folds.

Understand how proteins bind ligands, catalyze reactions, and are regulated by phosphorylation, allostery, and GTP switches — plus the computational tools for predicting protein function and interaction networks.

Explore the double helix in depth — from nucleotide chemistry and base pairing to Chargaff's rules, DNA topology, and the computational methods for analyzing DNA sequence composition.

Learn how DNA is packaged into chromatin, how histone modifications and DNA methylation regulate gene expression, how chromosomes are organized in 3D, and the computational tools for analyzing the epigenome.

Learn how mutations, duplications, rearrangements, and selection drive genome evolution — and the computational methods for detecting these forces through whole-genome comparison.

Understand how cells faithfully copy their DNA — from the replication fork machinery and error correction to replication origins, telomeres, and the computational tools for analyzing replication dynamics.

Learn how cells detect and fix DNA damage through multiple repair pathways, how homologous recombination shuffles genetic information, how transposable elements reshape genomes, and the computational methods for analyzing these processes.

Learn how RNA polymerase reads DNA to produce RNA, how eukaryotic pre-mRNAs are capped, spliced, and polyadenylated, and the computational methods for analyzing transcriptomes at single-cell and spatial resolution.

Understand how ribosomes decode mRNA into proteins — from tRNA adaptors and the genetic code to polyribosomes, protein folding, quality control, and the computational tools for proteomics and translation analysis.

Explore the evidence for an RNA world before DNA and protein — from RNA's catalytic abilities and self-replicating molecules to computational methods for predicting RNA structure and searching for ribozymes in genomes.

Learn how cells control which genes are turned on or off — from transcription factor binding and combinatorial logic to enhancers, genetic switches, and the computational methods for analyzing regulatory networks.

Explore how cells maintain gene expression patterns through epigenetic inheritance, regulate mRNAs via miRNAs and RNA-binding proteins, and how genomes evolve new regulatory programs — with the computational tools for each.

Learn how DNA is manipulated, sequenced, and analyzed — from restriction enzymes, cloning, and PCR to next-generation sequencing technologies, genome assembly, variant calling, and CRISPR-based functional genomics.

Learn how proteins are purified, identified by mass spectrometry, and studied by structural methods — from chromatography and SDS-PAGE to shotgun proteomics, cross-linking MS, and structural bioinformatics.

Explore how microscopy and structural biology techniques reveal the architecture of cells and macromolecules — from phase-contrast and fluorescence to super-resolution, cryo-EM, and the computational tools for bioimage analysis and structural bioinformatics.

Learn how computational tools extract quantitative data from microscopy images — from cell segmentation and morphological profiling to high-content screening, spatial transcriptomics, and AI-driven image analysis.

Explore the lipid bilayer and membrane proteins in depth — from phospholipid chemistry and membrane fluidity to transmembrane protein architecture, lipid rafts, and the computational tools for membrane modeling and topology prediction.

Explore how cells move molecules across membranes — from the physics of selective permeability and carrier-mediated transport to the electrophysiology of ion channels, action potentials, and synaptic signaling, with bioinformatics tools for transporter classification and channelopathy analysis.

Discover how eukaryotic cells direct thousands of different proteins to the correct compartments — from nuclear pore complexes and mitochondrial import machines to the ER translocon, signal sequences, and the computational tools for predicting protein localization.

Learn how vesicles transport cargo between organelles, from the ER to the Golgi to lysosomes and the cell surface, and explore bioinformatics approaches to studying trafficking pathways.

Understand how mitochondria and chloroplasts convert energy through electron transport chains, chemiosmosis, and ATP synthesis — and how their genomes reveal an endosymbiotic past.

Understand how cells communicate through extracellular signals, receptors, intracellular cascades, and second messengers — from general signaling logic to GPCR pathways.

Explore the key signaling pathways — from RTK-Ras-MAPK and PI3K-Akt to Wnt, Notch, Hedgehog, and plant signaling — that control cell growth and fate.

Learn about the three filament systems — actin, microtubules, and intermediate filaments — that give cells their shape, strength, and ability to move, plus the motor proteins that drive intracellular transport.

Explore how pathogens hijack the cytoskeleton, the molecular basis of ciliopathies and laminopathies, tauopathies and neurodegeneration, evolutionary diversification of cytoskeletal proteins, and cytoskeletal drugs in clinical medicine.

Understand how cells grow and divide through the regulated phases of the cell cycle — from cyclin-Cdk control and checkpoints to mitosis, cytokinesis, and meiosis.

Learn about the multiple pathways of programmed cell death — from apoptosis and its intrinsic and extrinsic pathways to necroptosis, pyroptosis, ferroptosis, and their roles in health and disease.

Learn how cells adhere to each other and to the extracellular matrix through cadherins, integrins, tight junctions, gap junctions, and the complex ECM network.

Understand how mutations in oncogenes and tumor suppressors drive cancer development through clonal evolution — from the multi-hit model to cancer genomics.

Explore how genomic profiling reveals cancer mechanisms and guides precision treatment — from oncogenic pathway analysis and targeted therapy to immunotherapy and liquid biopsy.

Learn how a single fertilized egg develops into a complex organism through morphogen gradients, Hox genes, pattern formation, organogenesis, and guided cell movements.

Learn how stem cells maintain and repair tissues through self-renewal, how hematopoiesis produces all blood cell types, and how iPSC reprogramming enables regenerative medicine.

Explore how pathogens infect cells, how the microbiome shapes health, and the computational tools for metagenomics, pathogen genomics, and viral surveillance.

Learn how the innate and adaptive immune systems detect and eliminate pathogens — from pattern recognition and complement to B/T cell development, immune memory, and vaccination.

Dive deeper into antibody structure and diversity, MHC peptide presentation, T cell recognition, and the computational tools for immune repertoire analysis and epitope prediction.