Enroll Course: https://www.coursera.org/learn/comparing-genomes
Coursera’s ‘Comparing Genes, Proteins, and Genomes (Bioinformatics III)’ course is an essential next step for anyone who delved into genome sequencing in its predecessor. This course masterfully transitions from assembling sequences to understanding the intricate dance of evolution through comparison.
The first half of the course is a brilliant introduction to sequence alignment, focusing on comparing short biological sequences like genes and proteins. The highlight here is the introduction to dynamic programming, a powerful algorithmic tool that allows us to quantify evolutionary divergence by calculating the number of mutations separating two sequences. Week 1 sets the stage with an engaging overview of sequence alignment and its importance, even introducing a charming bioinformatics cartoon. Week 2 builds upon this by demonstrating how finding the longest path in a directed acyclic graph is fundamental to aligning DNA strings, irrespective of the specific alignment assumptions. Week 3 then tackles advanced topics, addressing memory efficiency for long sequences and the complexities of aligning multiple strings simultaneously.
The second half of the course shifts focus to the broader picture: comparing entire genomes. It introduces the concept of genome rearrangements, which are more dramatic evolutionary events than simple insertions or deletions. This section explores how to model these rearrangements to understand species evolution and, intriguingly, to identify potentially ‘fragile regions’ within a genome where chromosomal breakage might be more frequent. Week 4 introduces this concept with an engaging analogy involving earthquakes and pancakes. Week 5 delves deeper into applying genome rearrangement analysis to pinpoint genome fragility, revealing a surprising connection between distance calculation and genomic stability. The course culminates in Week 6 with a practical bioinformatics application challenge, applying the learned sequence alignment algorithms to infer the non-ribosomal code.
Overall, ‘Comparing Genes, Proteins, and Genomes’ is a well-structured, intellectually stimulating course. It provides a robust understanding of bioinformatics algorithms and their application in evolutionary biology. The blend of theoretical concepts, algorithmic approaches, and practical challenges makes it highly recommendable for students and researchers in biology, computer science, and bioinformatics.
Enroll Course: https://www.coursera.org/learn/comparing-genomes