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Guided learning journeys that build knowledge step by step.
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A structured path for biology and evolution students to master phylogenetic analysis, covering molecular evolution concepts, tree-building methods (distance, maximum likelihood, Bayesian), and practical implementation with programming. The path builds from foundational genetics and programming to advanced inference techniques, ensuring a systematic learning experience.
This learning path provides a comprehensive introduction to systems biology, covering foundational concepts in molecular biology and mathematics, then progressing to network modeling, pathway analysis, dynamical systems, and omics integration. It emphasizes computational methods and practical applications, culminating in a capstone project where learners integrate multi-omics data to model a biological system.
A systematic learning path for computational biology students to understand and apply computational methods for analyzing protein structures. It covers protein structure fundamentals, the Protein Data Bank, visualization, structural alignment, and structure prediction, with practical applications.
A comprehensive learning path for advanced students to master computational methods in proteomics, covering mass spectrometry data analysis, protein identification, quantification, and post-translational modification analysis. The path integrates essential biochemistry and programming skills to build a solid foundation for tackling real-world proteomics challenges.
A comprehensive learning path for biology and computational biology students to master computational transcriptomics. It covers the full pipeline from RNA-seq data generation and processing to advanced analyses of differential expression, transcript assembly, alternative splicing, and non-coding RNAs, with a foundation in genomics and programming.
A comprehensive learning path for advanced students to understand and apply computational methods in genomics, covering genome assembly, annotation, variant calling, GWAS, and comparative genomics. It builds from foundational sequence analysis and programming to advanced analytical techniques.
A systematic path for computational biology students to master core methods in biological sequence analysis, including scoring matrices, pairwise alignment algorithms, BLAST, and multiple sequence alignment. Builds from fundamental biology and programming prerequisites to practical application.
This learning path introduces high school biology students with no programming background to Python for biological applications. Starting with basic programming concepts, it progresses through biological data structures, file parsing, and the Biopython library, culminating in practical data handling skills relevant to genomics and bioinformatics.
This path provides a foundational understanding of biochemistry, covering biomolecules, enzymes, metabolism, and thermodynamics, tailored for students entering computational biology. It bridges basic chemistry and biology to the molecular concepts needed for modeling and analysis.
A structured learning path for high school biology students to understand core concepts in genetics and genomics, covering inheritance, genomes, genetic variation, gene regulation, and population genetics. The path builds from basic cell biology to advanced topics, ensuring a solid foundation.