Finding Mutations in DNA and Proteins (Bioinformatics VI)

Go to Course: https://www.coursera.org/learn/dna-mutations

Introduction

# Course Review: Finding Mutations in DNA and Proteins (Bioinformatics VI) In recent years, bioinformatics has emerged as a crucial interdisciplinary field, merging biology, computer science, and mathematics. One standout course offered on Coursera, “Finding Mutations in DNA and Proteins (Bioinformatics VI),” dives deep into the complexities of detecting mutations within DNA and proteins. This course is part of a larger specialization in bioinformatics and builds upon knowledge acquired in previous courses while introducing advanced computational techniques. ## Overview “Finding Mutations in DNA and Proteins” appeals to students eager to explore genomic variations and their implications for diseases. It addresses pressing questions in modern biology: How do individual genomes differ from the reference genome? What advanced computational methods can we employ to uncover disease-causing mutations? Over the span of six weeks, students will progress from foundational concepts of read mapping to complex algorithms like the Burrows-Wheeler Transform and Hidden Markov Models. This progression equips learners with the skills necessary to tackle real-world bioinformatics challenges, culminating in a practical application challenge where students can showcase their newly acquired knowledge. ## Course Syllabus Breakdown ### Week 1: Introduction to Read Mapping The course kicks off by introducing two pivotal questions in bioinformatics: locating disease-causing mutations and understanding the ongoing challenges in developing an HIV vaccine. This foundational week sets the stage for the computational techniques to be explored, such as Combinatorial Pattern Matching and Hidden Markov Models. Each chapter is complemented by engaging illustrations from cartoonist Randall Christopher, adding an enjoyable visual layer to complex topics. ### Week 2: The Burrows-Wheeler Transform Learners are introduced to the Burrows-Wheeler Transform, a crucial concept for string compression that serves as a cornerstone for read-mapping algorithms. This week not only covers theoretical aspects but also discusses practical applications, making foundational concepts relatable and useful in a genomic context. ### Week 3: Speeding Up Burrows-Wheeler Read Mapping This week enhances the algorithm introduced previously, focusing on handling patterns with potential errors – a common occurrence in biological sequences. The emphasis on speeding up algorithms reflects a real-world concern in bioinformatics, where efficiency can significantly impact research outcomes. ### Week 4: Introduction to Hidden Markov Models As the course progresses, students delve deeper into more complex job requirements in bioinformatics by learning about Hidden Markov Models. This week's exploration tackles the alignment of sequences with numerous mutations, such as those related to various strains of HIV, showcasing the real-world implications of computational biology. ### Week 5: Profile HMMs for Sequence Alignment Building upon the understanding of Hidden Markov Models, this week applies this knowledge to sequence alignment. Students will grasp how these models can adapt to complex biological questions and learn to implement advanced clustering methods, further expanding their skill set in bioinformatics. ### Week 6: Bioinformatics Application Challenge The course culminates with an Application Challenge, allowing students to apply all of the algorithms they’ve learned throughout the course in practical scenarios. This hands-on component is an excellent way for learners to consolidate their knowledge and showcase their skills. ## Learning Experience and Recommendation This course is well-structured, progressively building from fundamental concepts to complex algorithms, with a good mix of theory and practical application. The engaging format, combined with useful coursework and real-world implications, makes it accessible to both new learners and those with some background in bioinformatics. ### Who Should Take This Course? - **Biologists**: Individuals looking to understand the computational tools that can aid in genomics. - **Computer Scientists**: Those who want to branch out into bioinformatics and learn how their skills can be applied to solve biological problems. - **Healthcare Professionals**: Anyone involved in genetic research or personalized medicine will find the insights into mutations particularly valuable. In conclusion, I highly recommend “Finding Mutations in DNA and Proteins” for anyone interested in the intersection of biology and technology. By the end of this course, you'll not only have a toolkit of advanced computational methods but also a deeper understanding of how these methods impact health, disease, and genetic diversity. Whether you're a student, a researcher, or a professional in the field, you'll find this course enriching and essential for your educational journey in bioinformatics.

Syllabus

Week 1: Introduction to Read Mapping

Welcome to our class! We are glad that you decided to join us.

In this class, we will consider the following two central biological questions (the computational approaches needed to solve them are shown in parentheses):

1. How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)
2. Why Have Biologists Still Not Developed an HIV Vaccine? (Hidden Markov Models)

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.

Welcome to week 2 of the class!

This week, we will introduce a paradigm called the Burrows-Wheeler transform; after seeing how it can be used in string compression, we will demonstrate that it is also the foundation of modern read-mapping algorithms.

Welcome to week 3 of class!

Last week, we saw how the Burrows-Wheeler transform could be applied to multiple pattern matching. This week, we will speed up our algorithm and generalize it to the case that patterns have errors, which models the biological problem of mapping reads with errors to a reference genome.

Welcome to week 4 of class!

This week, we will start examining the case of aligning sequences with many mutations -- such as related genes from different HIV strains -- and see that our problem formulation for sequence alignment is not adequate for highly diverged sequences.

To improve our algorithms, we will introduce a machine-learning paradigm called a hidden Markov model and see how dynamic programming helps us answer questions about these models.

Welcome to week 5 of class!

Last week, we introduced hidden Markov models. This week, we will see how hidden Markov models can be applied to sequence alignment with a profile HMM. We will then consider some advanced topics in this area, which are related to advanced methods that we considered in a previous course for clustering.

Welcome to the sixth and final week of class!

This week brings our Application Challenge, in which we apply the HMM sequence alignment algorithms that we have developed.