life-sciences

Bioinformatics & Computational Biology

Computational methods for analyzing biological data — sequence alignment with dynamic programming, phylogenetic tree construction via UPGMA, protein folding energy landscapes, differential gene expression analysis from RNA-seq data, and genome assembly using de Bruijn graphs.

bioinformaticssequence alignmentphylogeneticsprotein foldinggene expressiongenome assemblyRNA-seqcomputational biologygenomics

Bioinformatics sits at the intersection of biology, computer science, and statistics, providing the computational tools needed to decode the vast datasets generated by modern genomics and proteomics. From aligning DNA sequences to reconstructing evolutionary trees, these algorithms transform raw biological data into actionable biological insight.

These simulations let you perform Needleman-Wunsch global sequence alignment, build UPGMA phylogenetic trees from distance matrices, explore protein folding energy landscapes, analyze differential gene expression from RNA-seq count data, and assemble genomes from short reads using de Bruijn graph methods — all with interactive, real-time visualizations.

5 interactive simulations

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RNA-seq Differential Gene Expression

Simulate RNA-seq differential expression analysis — explore how fold change, sample size, and read depth affect statistical power to detect differentially expressed genes

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De Bruijn Graph Genome Assembly

Simulate genome assembly using de Bruijn graphs — explore how k-mer size, read length, and coverage depth affect assembly contiguity and correctness

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UPGMA Phylogenetic Tree Construction

Simulate UPGMA hierarchical clustering to build phylogenetic trees from distance matrices — explore how sequence divergence maps to evolutionary relationships

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Protein Folding Energy Landscape

Simulate a protein folding energy landscape — explore how temperature, hydrophobic strength, and chain length affect the folding funnel and native state stability

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Needleman-Wunsch Global Sequence Alignment

Simulate the Needleman-Wunsch dynamic programming algorithm for global sequence alignment — explore how scoring parameters affect optimal alignments