发现 DNA 和蛋白质的突变（生物信息学 VI）

<p>Welcome to our class! We are glad that you decided to join us.</p><p>In this class, we will consider the following two central biological&nbsp;questions (the computational approaches needed to solve them are shown in parentheses):</p><ol><li>How Do We Locate Disease-Causing Mutations? (<em>Combinatorial Pattern Matching</em>)</li><li>Why Have Biologists Still Not Developed an HIV Vaccine?&nbsp;(<em>Hidden Markov Models</em>)</li></ol><p>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 <a href="http://bioinformaticsalgorithms.com" target="_blank">print companion</a>. You can find the first chapter's cartoon at the bottom of this message. </p><p><img src="https://stepic.org/media/attachments/lessons/292/chapter7_cropped.jpg" title="Image: https://stepic.org/media/attachments/lessons/292/chapter7_cropped.jpg" width="528"></p>

<p>Welcome to week 2 of the class!</p>

<p>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.</p>

<p>Welcome to week 3 of class!</p>

<p>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.</p>

<p>Welcome to week 4 of class!</p>

<p>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.</p> <p>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.</p>