Algorithms in Computational Biology
( 30 Modules )

Module #1
Introduction to Computational Biology
Overview of the field, importance, and applications of computational biology

Module #2
Biological Sequences and Data Structures
Representation of biological sequences, data structures for storing and manipulating sequences

Module #3
Sequence Alignment Algorithms
Global and local alignment algorithms, dynamic programming, and scoring matrices

Module #4
Multiple Sequence Alignment
Challenges and algorithms for aligning multiple biological sequences

Module #5
Phylogenetics and Tree Reconstruction
Introduction to phylogenetics, tree reconstruction methods, and distance-based algorithms

Module #6
Markov Models and Hidden Markov Models
Introduction to Markov models, hidden Markov models, and their applications in bioinformatics

Module #7
Gene Finding and Annotation
Algorithms for identifying genes, promoters, and other functional elements in genomes

Module #8
Genome Assembly and Fragment Assembly
Challenges and algorithms for assembling genomes and fragments

Module #9
High-Throughput Sequencing Technologies
Overview of next-generation sequencing technologies and their applications

Module #10
Read Mapping and Variant Calling
Algorithms for mapping reads to a reference genome and identifying variants

Module #11
RNA-Seq Analysis
Algorithms for quantifying gene expression, differential expression, and novel transcript discovery

Module #12
Protein Structure Prediction
Algorithms for predicting protein structure from sequence data

Module #13
Protein-Protein Interactions and Network Analysis
Algorithms for predicting protein-protein interactions and analyzing biological networks

Module #14
Computational Epigenetics
Algorithms for analyzing epigenetic data, including ChIP-seq and DNA methylation analysis

Module #15
Cancer Genomics and Tumor Evolution
Algorithms for analyzing cancer genomes, identifying driver mutations, and reconstructing tumor evolution

Module #16
Machine Learning and Deep Learning in Bioinformatics
Introduction to machine learning and deep learning techniques and their applications in bioinformatics

Module #17
Visualization and Interpretation of Biological Data
Methods for visualizing and interpreting large-scale biological data

Module #18
Computational Systems Biology
Algorithms for modeling and analyzing biological systems, including metabolic networks and signaling pathways

Module #19
Synthetic Biology and Design of Biological Systems
Algorithms for designing and optimizing biological systems, including genetic circuits and metabolic pathways

Module #20
Case Studies in Computational Biology
Real-world examples of computational biology applications in medicine, agriculture, and biotechnology

Module #21
Big Data in Bioinformatics
Challenges and opportunities of big data in bioinformatics, including data storage, processing, and analysis

Module #22
Computational Tools and Resources for Bioinformatics
Overview of popular computational tools and resources for bioinformatics, including NCBI, UCSC, and Ensembl

Module #23
Special Topics in Computational Biology
Advanced topics in computational biology, including single-cell analysis and microbiome analysis

Module #24
Ethics and Responsible Conduct in Computational Biology
Ethical considerations in computational biology, including data sharing, privacy, and reproducibility

Module #25
Project Development and Presentation
Guided project development and presentation on a selected topic in computational biology

Module #26
Computational Biology in the Cloud
Cloud-based infrastructures for computational biology, including AWS, Google Cloud, and cloud-based workflows

Module #27
Collaborative Bioinformatics Research
Collaborative research projects in bioinformatics, including team science and interdisciplinary research

Module #28
Grant Writing and Research Proposals
Guidance on writing research proposals and grants in computational biology

Module #29
Communication and Dissemination of Computational Biology Results
Effective communication of computational biology results, including scientific writing, visualizations, and oral presentations

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Algorithms in Computational Biology career

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Algorithms in Computational Biology ( 30 Modules )

Algorithms in Computational Biology
( 30 Modules )