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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/dna-mutations
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课程名称：寻找DNA和蛋白质中的突变（生物信息学 VI） 课程概述：在该专业序列的前几门课程中，我们讨论了如何对基因组进行测序和比较。本课程将涵盖寻找潜藏在DNA和蛋白质中的突变的高级主题。 在课程的前半部分，我们将探讨个体基因组如何与物种的“参考基因组”不同。我们的目标是将个体的小片段DNA“映射”到参考基因组。我们将看到，解决此问题的组合模式匹配算法既优雅又极为高效，只需相对较少的运行时间和内存。 在课程的后半部分，我们将学习如何识别即使经过多次突变而几乎失去可识别性的蛋白质的功能。这种情况在HIV研究中尤为突出，因为病毒的快速突变使研究人员分析它时面临挑战。我们将采用一种基于强大机器学习工具隐马尔可夫模型的方法。 最后，您将学习如何应用流行的生物信息学软件工具，利用隐马尔可夫模型将蛋白质与相关蛋白质家族进行比较。 课程大纲： - 第一周：阅读映射简介 - 主要讨论两个生物学问题：如何确定导致疾病的突变及为什么生物学家尚未开发出HIV疫苗。 - 第二周：Burrows-Wheeler变换 - 介绍Burrows-Wheeler变换及其在字符串压缩中的应用，同时展示它作为现代阅读映射算法的基础。 - 第三周：加速Burrows-Wheeler阅读映射 - 提高我们算法的速度，并将其概括到模式出现错误的情况，模拟将具有错误的读取映射到参考基因组的生物学问题。 - 第四周：隐马尔可夫模型简介 - 探讨转录组之间高度变化的序列对齐问题，引入隐马尔可夫模型以改善我们的算法。 - 第五周：用于序列对齐的Profile HMM - 学习如何将隐马尔可夫模型应用于使用Profile HMM进行序列对齐，并考虑一些复杂主题。 - 第六周：生物信息学应用挑战 - 在课程的最后一周，进行应用挑战，应用我们开发的HMM序列对齐算法。
Name:Week 1: Introduction to Read Mapping
Description:
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):
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.
Name:Week 2: The Burrows-Wheeler Transform
Description:
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.
Name:Week 3: Speeding Up Burrows-Wheeler Read Mapping
Description:
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.
Name:Week 4: Introduction to Hidden Markov Models
Description:
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.
Name:Week 5: Profile HMMs for Sequence Alignment
Description:
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.
Name:Week 6: Bioinformatics Application Challenge
Description:
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.
In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.
