Comparing Genes, Proteins, and Genomes (Bioinformatics III)

Go to Course: https://www.coursera.org/learn/comparing-genomes

Introduction

### Course Review: Comparing Genes, Proteins, and Genomes (Bioinformatics III) #### Overview "Comparing Genes, Proteins, and Genomes" is the third course in the Bioinformatics Specialization offered on Coursera. This course is designed for students who have completed the previous courses that cover genome assembly and sequencing. Building on those foundations, the focus shifts to comparing biological sequences to uncover insights into evolutionary processes and the genetic differences that define various species. #### Course Structure and Syllabus The course is structured into six weeks, each with a specific focus that builds on the knowledge acquired in the prior weeks: 1. **Week 1: Introduction to Sequence Alignment** - The course kicks off with an introduction to sequence alignment, discussing how to compare DNA and protein sequences and setting the stage for understanding the importance of dynamic programming in this context. 2. **Week 2: From Finding a Longest Path to Aligning DNA Strings** - This week delves deeper into aligning DNA strings, utilizing graph theory to find the highest scoring alignments. The concepts are illustrated with relatable examples, making the material easier to grasp. 3. **Week 3: Advanced Topics in Sequence Alignment** - As the course progresses, learners are introduced to memory-efficient algorithms for aligning long sequences and managing multiple-string alignments, which are critical for more complex bioinformatics applications. 4. **Week 4: Genome Rearrangements and Fragility** - This week covers the more complex topic of genome rearrangements, introducing concepts such as chromosome breakage and the notion of "fragile regions." This sets the stage for understanding evolutionary dynamics at a genome-wide level. 5. **Week 5: Applying Genome Rearrangement Analysis to Find Genome Fragility** - Here, students continue exploring the relationship between genome distance and fragility, enhancing their understanding of how these concepts interconnect. 6. **Week 6: Bioinformatics Application Challenge** - The course culminates in a challenge that allows students to apply their newly acquired skills to real-world bioinformatics problems, particularly in inferring the non-ribosomal code. #### Learning Experience One of the standout features of this course is the engaging teaching style, complemented by creative illustrations from bioinformatics artist Randall Christopher. His cartoons add a humorous touch and serve to reinforce the concepts being taught. Additionally, learners will benefit from a mix of theoretical knowledge and practical applications. The course also includes quizzes and programming assignments, which are crucial for understanding the practical aspects of bioinformatics. These assessments are well-designed, allowing students to apply their knowledge and receive immediate feedback. #### Recommendations I highly recommend "Comparing Genes, Proteins, and Genomes (Bioinformatics III)" to anyone interested in diving deeper into the field of bioinformatics and genomic analysis. Here are a few reasons why it's a great choice: - **Prerequisite Knowledge:** This course assumes a foundation in bioinformatics, making it suitable for those who have completed the earlier courses in the specialization. If you've enjoyed those, this course is a natural next step. - **Interdisciplinary Skills:** The course beautifully integrates concepts from biology, computer science, and mathematics, making it perfect for learners seeking to strengthen their interdisciplinary skills. - **Practical Applications:** The focus on real-world applications, especially in the final project, will equip you with valuable experience that is directly applicable to ongoing research and industry challenges in genomics. - **Engaging Content:** The incorporation of humor and creative elements makes this technically rigorous course more approachable and enjoyable. In summary, if you're eager to explore the complexities of genetic comparison and deepen your understanding of bioinformatics, this course is a worthy investment in your academic and professional journey. Enroll today and embark on an exciting exploration of the molecular foundations of life!

Syllabus

Week 1: Introduction to Sequence Alignment

Welcome to class!

If you joined us in the previous course in this Specialization, then you became an expert at assembling genomes and sequencing antibiotics. The next natural question to ask is how to compare DNA and amino acid sequences. This question will motivate this week's discussion of sequence alignment, which is the first of two questions that we will ask in this class (the algorithmic methods used to answer them are shown in parentheses):

1. How Do We Compare DNA Sequences? (Dynamic Programming)
2. Are There Fragile Regions in the Human Genome? (Combinatorial Algorithms)

As in previous courses, each of these two chapters is accompanied by a Bioinformatics Cartoon created by talented artist Randall Christopher and serving as a chapter header in the Specialization's bestselling print companion. You can find the first chapter's cartoon at the bottom of this message. Why have taxis suddenly become free of charge in Manhattan? Where did Pavel get so much spare change? And how should you get dressed in the morning so that you aren't late to your job as a crime-stopping superhero? Answers to these questions, and many more, in this week's installment of the course.

Welcome to Week 2 of the class!

Last week, we saw how touring around Manhattan and making change in a Roman shop help us find a longest common subsequence of two DNA or protein strings.

This week, we will study how to find a highest scoring alignment of two strings. We will see that regardless of the underlying assumptions that we make regarding how the strings should be aligned, we will be able to phrase our alignment problem as an instance of finding the longest path in a directed acyclic graph.

Welcome to Week 3 of the class!

Last week, we saw how a variety of different applications of sequence alignment can all be reduced to finding the longest path in a Manhattan-like graph.

This week, we will conclude the current chapter by considering a few advanced topics in sequence alignment. For example, if we need to align long strings, our current algorithm will consume a huge amount of memory. Can we find a more memory-efficient approach? And what should we do when we move from aligning just two strings at a time to aligning many strings?

Welcome to Week 4 of the class!

You now know how to compare two DNA (or protein) strings.  But what if we wanted to compare entire genomes? When we "zoom out" to the genome level, we find that substitutions, insertions, and deletions don't tell the whole story of evolution: we need to model more dramatic evolutionary events known as genome rearrangements, which wrench apart chromosomes and put them back together in a new order. A natural question to ask is whether there are "fragile regions" hidden in your genome where chromosome breakage has occurred more often over millions of years. This week, we will begin addressing this question by asking how we can compute the number of rearrangements on the evolutionary path connecting two species.

You can find this week's Bioinformatics Cartoon from Randall Christopher at the bottom of this E-mail. What do earthquakes and a stack of pancakes have to do with species evolution? Keep learning to find out!

Last week, we asked whether there are fragile regions in the human genome. Then, we took a lengthy detour to see how to compute a distance between species genomes, a discussion that we will continue this week.

It is probably unclear how computing the distance between two genomes can help us understand whether fragile regions exist. If so, please stay tuned -- we will see that the connection between these two concepts will yield a surprising conclusion to the class.

In the sixth and final week of the course, we will apply sequence alignment algorithms to infer the non-ribosomal code.