Finding Mutations in DNA and Proteins (Bioinformatics VI)

开始时间: 04/22/2022 持续时间: Unknown

所在平台: CourseraArchive 课程类别: 其他类别 大学或机构: University of California, San Diego （加州大学圣地亚哥分校）

课程主页: https://www.coursera.org/course/mutations

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课程详情

Although biologists assembled a reference human genomes in 2001, your own genome differs from this reference genome by millions of mutations. In this chapter, we will learn combinatorial pattern matching algorithms for finding these mutations, something that future doctors will be able to do within minutes in order to find disease genes.

We will then examine the problem of detecting subtle similarities that evade the traditional sequence comparison algorithms that we studied in "Comparing Genes, Genomes, and Proteins". Having efficient algorithms for comparing highly diverged sequences is important, since a single misaligned nucleotide can misclassify a protein and prevent biologists from inferring its function.  We will consider a popular approach to sequence comparison based on an abstract machine called a Hidden Markov Model.

课程大纲

How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)

• What Causes Ohdo Syndrome?
• Introduction to Multiple Pattern Matching
• Herding Patterns into a Trie
• Preprocessing the Genome Instead
• Suffix Trees
• Suffix Arrays
  
• The Burrows-Wheeler Transform
• Inverting the Burrows-Wheeler Transform
• Pattern Matching with the Burrows-Wheeler Transform
• Speeding Up Burrows-Wheeler Pattern Matching
• Where are the Matched Patterns?
  
• Burrows and Wheeler Set Up Checkpoints
  
• Epilogue: Mismatch-Tolerant Read Mapping
  

Why Have Biologists Still Not Developed an HIV Vaccine? (Hidden Markov Models)

• Classifying the HIV Phenotype
• Gambling with Yakuza
  
• Two Coins up the Dealer's Sleeve
  
• Finding CG-Islands
  
• Hidden Markov Models
  
• The Viterbi Algorithm
  
• Finding the Most Likely Outcome of an HMM
  
• Profile HMMs for Sequence Alignment
  
• Classifying proteins with profile HMMs
  
• Learning the Parameters of an HMM
  
• Soft Decisions in Parameter Estimation
  
• The Many Faces of HMMs
  
• Epilogue: Nature is a Tinkerer and not an Inventor
  

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