Enroll Course: https://www.coursera.org/learn/bioinformatics-methods-2
The explosion of biological data, particularly from large-scale projects like genome sequencing and RNA-seq, presents a significant challenge for modern biologists. Accessing and analyzing this data to derive meaningful insights requires specialized skills and tools. Coursera’s ‘Bioinformatic Methods II’ course directly addresses this need, offering a comprehensive dive into essential web-based resources and programs for biological data analysis.
This course is structured into several key modules, each focusing on a critical area of bioinformatics. The ‘Protein Motifs’ module introduces the concept of conserved regions within protein families, explaining how these can be identified using various methods from simple regular expressions to more complex profile hidden Markov models (HMMs). Understanding these motifs is crucial for deciphering protein function and identifying homologs.
The ‘Protein-Protein Interactions’ module delves into the complex world of how proteins interact. It covers methods for determining these interactions and discusses their strengths and weaknesses. The practical labs in this section are particularly valuable, guiding learners through tools and databases to analyze the interaction partners of specific proteins, like BRCA2, and introducing the foundational concept of Gene Ontology (GO) term enrichment analysis.
‘Protein Structure’ is another vital component, highlighting the importance of a protein’s three-dimensional structure for understanding its biological role. The course covers methods for structure determination and introduces the Protein Data Bank (PDB), the primary database for structural data. Learners get hands-on experience with tools like VAST for structural similarity searches and PyMOL for detailed structure exploration.
The latter half of the course focuses on ‘Gene Expression Analysis’. Module I introduces RNA-seq data processing, guiding students through databases like the Sequence Read Archive (SRA) and using the BioConductor suite in R to analyze expression levels, identify differentially expressed genes, and visualize results with heatmaps. Module II builds on this by exploring hierarchical clustering, identifying co-expressed genes using similarity metrics, and conducting Gene Ontology enrichment analysis.
Finally, the ‘Cis Regulatory Systems’ module explores the sequences that control gene expression, focusing on cis-elements and transcription factor binding. The course concludes with a review of the covered topics and a final assignment, integrating the learned skills.
Overall, ‘Bioinformatic Methods II’ is an excellent course for anyone looking to gain practical skills in analyzing biological data. The combination of theoretical explanations and hands-on labs using real-world tools makes it highly effective. It’s particularly well-suited for graduate students, postdocs, and researchers in biology, genetics, and related fields who need to navigate and interpret the vast datasets generated by modern biological research. The structured approach and focus on accessible web-based tools make complex bioinformatics concepts understandable and actionable.
Enroll Course: https://www.coursera.org/learn/bioinformatics-methods-2