Bioinformatics Algorithms (Part 1)

开始时间: 待定 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: University of California, San Diego (加州大学圣地亚哥分校)

授课老师: Pavel Pevzner Phillip E. C. Compeau



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Sequencing the human genome in 2001 started a computational revolution in biology, which has arguably been an impetus for more new algorithms than any other fundamental realm of science.  The newly formed links between computer science and biology affect the way we teach computational ideas to biologists, as well as how applied algorithms are taught to computer scientists. 

Genome sequencing is just one of hundreds of biological problems that have become inextricable from the computational methods required to solve them. In this course, we will take a look at some of the algorithmic ideas that are fundamental to an understanding of modern biology.  Computational concepts like dynamic programming and network analysis will help us explore algorithms applied to a wide range of biological topics, from finding genes to reconstructing the tree of life.  Throughout the process, we will apply real bioinformatics tools and analyze real genetic data.

In order to streamline homework assignments and solidify the material covered in the course, we will employ Rosalind (, a fun new resource for learning bioinformatics that was founded by the instructors.  We hope that Rosalind will show you how fun solving problems in bioinformatics can be.


Note: the syllabus may undergo revisions throughout the course.

Each homework will consist of approximately 5 programming assignments and will utilize the Rosalind bioinformatics online education platform:

Week 1

  • Introduction
  • Algorithms Primer
  • Molecular Biology Primer
  • The Motif Finding Problem 
  • Genome Rearrangements 
  • Programming Assignment #1: Computing rearrangement distance between genomes/Finding regulatory motifs

Week 2

  • Edit Distance
  • Pairwise Sequence Alignment
  • Multiple Sequence Alignment  
  • Programming Assignment #2: Constructing Optimal Overlap Alignment/Constructing Chimeric Alignments  

Week 3

  • Gene Prediction and Spliced Alignment
  • Space-Efficient Sequence Alignment
  • Applications of Suffix Trees and Suffix Arrays in Bioinformatics
  • Programming Assignment #3: Exon Chaining Problem/Finding Longest Repeats

Week 4

  • DNA Sequencing
  • Burrows Wheeler Transform (BWA) for Read Mapping in DNA Sequencing
  • Programming Assignment #4: Constructing de Bruijn graphs and Fragment Assembly

Week 5

  • Peptide Sequencing and Protein Identification
  • Spectral Alignment
  • Gibbs Sampling and Random Projections for Motif Finding
  • Programming Assignment #5: Antisymmetric Path Problem for de novo Peptide Sequencing/Finding Motifs using Gibbs Sampling  

Week 6

  • DNA Arrays and Clustering Algorithms
  • Distance-Based Algorithms for  Evolutionary Tree Reconstruction
  • Character-Based Algorithms for  Evolutionary Tree Reconstruction
  • Profile Hidden Markov Models for Sequence Alignment
  • Programming Assignment #6: Solving Small Parsimony Problems/Finding a Hidden Path in a Profile HMM



曹单锋 2014-01-28 14:38 1 票支持; 0 票反对

导师是生物信息算法领域无人不知的大牛。课程涉及很多算法,贪心、图论、动态规划等等。课程有点难,需要一定的编程能力,论坛里经常有人发“I’m out”之类的帖子。总共10周的课程,作业都是编程题,每周都会有4-8个,总共64题,通过60%给证,通过80%优秀。我每周都花>15h的时间在这门课上,中途也有放弃的念头,后面几周的作业很多没写,最后60+飘过。统计了下,我一共写了4000+行的代码(C++)。。。收获非常大,寒假打算再复习一遍。几个月之后还会有part2,我肯定也是会修的。


xtliwen 2013-05-31 15:38 0 票支持; 0 票反对


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This course will cover some of the common algorithms underlying the following fundamental topics in bioinformatics: assembling genomes, comparing DNA and protein sequences, predicting genes, finding regulatory motifs, analyzing gene expression, constructing evolutionary trees, analyzing genome rearrangements, and identifying proteins.


生物 生物信息学 生物信息 生物信息算法 生物信息学算法 bioinformatics 加利福尼亚大学圣地亚哥分校 加利福尼亚大学圣迭戈分校



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