We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.
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課程信息
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Learner Career Outcomes
43%
完成這些課程後已開始新的職業生涯
33%
通過此課程獲得實實在在的工作福利
您將獲得的技能
Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Learner Career Outcomes
43%
完成這些課程後已開始新的職業生涯
33%
通過此課程獲得實實在在的工作福利
100% 在線
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第 4 門課程（共 8 門）
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完成時間大約為22 小時
英語（English）
字幕：英語（English）
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
DNA sequencing, strings and matching
This module we begin our exploration of algorithms for analyzing DNA sequencing data. We'll discuss DNA sequencing technology, its past and present, and how it works.
完成時間為 4 小時
19 個視頻 （總計 112 分鐘）, 7 個閱讀材料, 2 個測驗
19 個視頻
Lecture: Why study this?4分鐘
Lecture: DNA sequencing past and present3分鐘
Lecture: Genomes as strings, reads as substrings5分鐘
Lecture: String definitions and Python examples3分鐘
Practical: String basics 7分鐘
Practical: Manipulating DNA strings 7分鐘
Practical: Downloading and parsing a genome 6分鐘
Lecture: How DNA gets copied3分鐘
Optional lecture: How second-generation sequencers work 7分鐘
Optional lecture: Sequencing errors and base qualities 6分鐘
Lecture: Sequencing reads in FASTQ format4分鐘
Practical: Working with sequencing reads 11分鐘
Practical: Analyzing reads by position 6分鐘
Lecture: Sequencers give pieces to genomic puzzles5分鐘
Lecture: Read alignment and why it's hard3分鐘
Lecture: Naive exact matching10分鐘
Practical: Matching artificial reads 6分鐘
Practical: Matching real reads 7分鐘
7 個閱讀材料
Welcome to Algorithms for DNA Sequencing10分鐘
Pre Course Survey10分鐘
Syllabus10分鐘
Setting up Python (and Jupyter)10分鐘
Getting slides and notebooks10分鐘
Using data files with Python programs10分鐘
Programming Homework 1 Instructions (Read First)10分鐘
2 個練習
Module 120分鐘
Programming Homework 114分鐘
完成時間為 3 小時
Preprocessing, indexing and approximate matching
In this module, we learn useful and flexible new algorithms for solving the exact and approximate matching problems. We'll start by learning Boyer-Moore, a fast and very widely used algorithm for exact matching
完成時間為 3 小時
15 個視頻 （總計 114 分鐘）, 1 個閱讀材料, 2 個測驗
15 個視頻
Lecture: Boyer-Moore basics8分鐘
Lecture: Boyer-Moore: putting it all together6分鐘
Lecture: Diversion: Repetitive elements5分鐘
Practical: Implementing Boyer-Moore 10分鐘
Lecture: Preprocessing7分鐘
Lecture: Indexing and the k-mer index10分鐘
Lecture: Ordered structures for indexing8分鐘
Lecture: Hash tables for indexing7分鐘
Practical: Implementing a k-mer index 7分鐘
Lecture: Variations on k-mer indexes9分鐘
Lecture: Genome indexes used in research9分鐘
Lecture: Approximate matching, Hamming and edit distance6分鐘
Lecture: Pigeonhole principle6分鐘
Practical: Implementing the pigeonhole principle 9分鐘
1 個閱讀材料
Programming Homework 2 Instructions (Read First)10分鐘
2 個練習
Module 220分鐘
Programming Homework 212分鐘
完成時間為 2 小時
Edit distance, assembly, overlaps
This week we finish our discussion of read alignment by learning about algorithms that solve both the edit distance problem and related biosequence analysis problems, like global and local alignment.
完成時間為 2 小時
13 個視頻 （總計 92 分鐘）, 1 個閱讀材料, 2 個測驗
13 個視頻
Lecture: Solving the edit distance problem12分鐘
Lecture: Using dynamic programming for edit distance12分鐘
Practical: Implementing dynamic programming for edit distance 6分鐘
Lecture: A new solution to approximate matching9分鐘
Lecture: Meet the family: global and local alignment10分鐘
Practical: Implementing global alignment 8分鐘
Lecture: Read alignment in the field4分鐘
Lecture: Assembly: working from scratch2分鐘
Lecture: First and second laws of assembly8分鐘
Lecture: Overlap graphs8分鐘
Practical: Overlaps between pairs of reads 4分鐘
Practical: Finding and representing all overlaps 3分鐘
1 個閱讀材料
Programming Homework 3 Instructions (Read First)10分鐘
2 個練習
Module 320分鐘
Programming Homework 38分鐘
完成時間為 2 小時
Algorithms for assembly
In the last module we began our discussion of the assembly problem and we saw a couple basic principles behind it. In this module, we'll learn a few ways to solve the alignment problem.
完成時間為 2 小時
13 個視頻 （總計 83 分鐘）, 1 個閱讀材料, 2 個測驗
13 個視頻
Lecture: The shortest common superstring problem8分鐘
Practical: Implementing shortest common superstring 4分鐘
Lecture: Greedy shortest common superstring7分鐘
Practical: Implementing greedy shortest common superstring 7分鐘
Lecture: Third law of assembly: repeats are bad5分鐘
Lecture: De Bruijn graphs and Eulerian walks8分鐘
Practical: Building a De Bruijn graph 4分鐘
Lecture: When Eulerian walks go wrong9分鐘
Lecture: Assemblers in practice8分鐘
Lecture: The future is long?9分鐘
Lecture: Computer science and life science5分鐘
Lecture: Thank yous 43
1 個閱讀材料
Post Course Survey10分鐘
2 個練習
Programming Homework 48分鐘
Module 414分鐘
4.8
109 條評論
來自DNA测序算法的熱門評論
This course provided me a very quick overview of all the core concepts pertaining to DNA sequencing. It is very well organized, crystal clear demonstration of concepts and I really enjoyed the course.
This is the best course so far in this specialization. I enjoyed the way both instructors explained the concepts using simple analogies. It was a really productive month for me!
講師
Ben Langmead, PhD
Assistant ProfessorComputer Science
Jacob Pritt
Department of Computer Science
關於 约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
關於 基因组数据科学 專項課程
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.
This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy.
This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.
To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
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