Algorithms for DNA Sequencing

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About this course: 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.

Created by:  Johns Hopkins University
  • Taught by:  Ben Langmead, PhD, Assistant Professor

    Computer Science
  • Taught by:  Jacob Pritt

    Department of Computer Science
Basic Info Course 4 of 8 in the Genomic Data Science Specialization Language English How To Pass Pass all graded assignments to complete the course. User Rating…

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Nog niet gevonden wat je zocht? Bekijk deze onderwerpen: Biologie, Natuurkunde, Archeologie, Biomedische en Software Development / Ontwikkeling.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  Johns Hopkins University
  • Taught by:  Ben Langmead, PhD, Assistant Professor

    Computer Science
  • Taught by:  Jacob Pritt

    Department of Computer Science
Basic Info Course 4 of 8 in the Genomic Data Science Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.8 stars Average User Rating 4.8See what learners said Coursework

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Syllabus


WEEK 1


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.


19 videos, 7 readings expand


  1. Reading: Welcome to Algorithms for DNA Sequencing
  2. Reading: Pre Course Survey
  3. Reading: Syllabus
  4. Reading: Setting up Python (and Jupyter)
  5. Reading: Getting slides and notebooks
  6. Reading: Using data files with Python programs
  7. Video: Module 1 Introduction
  8. Video: Lecture: Why study this?
  9. Video: Lecture: DNA sequencing past and present
  10. Video: Lecture: Genomes as strings, reads as substrings
  11. Video: Lecture: String definitions and Python examples
  12. Video: Practical: String basics
  13. Video: Practical: Manipulating DNA strings
  14. Video: Practical: Downloading and parsing a genome
  15. Video: Lecture: How DNA gets copied
  16. Video: Optional lecture: How second-generation sequencers work
  17. Video: Optional lecture: Sequencing errors and base qualities
  18. Video: Lecture: Sequencing reads in FASTQ format
  19. Video: Practical: Working with sequencing reads
  20. Video: Practical: Analyzing reads by position
  21. Video: Lecture: Sequencers give pieces to genomic puzzles
  22. Video: Lecture: Read alignment and why it's hard
  23. Video: Lecture: Naive exact matching
  24. Video: Practical: Matching artificial reads
  25. Video: Practical: Matching real reads
  26. Reading: Programming Homework 1 Instructions (Read First)

Graded: Module 1
Graded: Programming Homework 1

WEEK 2


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


15 videos, 1 reading expand


  1. Video: Week 2 Introduction
  2. Video: Lecture: Boyer-Moore basics
  3. Video: Lecture: Boyer-Moore: putting it all together
  4. Video: Lecture: Diversion: Repetitive elements
  5. Video: Practical: Implementing Boyer-Moore
  6. Video: Lecture: Preprocessing
  7. Video: Lecture: Indexing and the k-mer index
  8. Video: Lecture: Ordered structures for indexing
  9. Video: Lecture: Hash tables for indexing
  10. Video: Practical: Implementing a k-mer index
  11. Video: Lecture: Variations on k-mer indexes
  12. Video: Lecture: Genome indexes used in research
  13. Video: Lecture: Approximate matching, Hamming and edit distance
  14. Video: Lecture: Pigeonhole principle
  15. Video: Practical: Implementing the pigeonhole principle
  16. Reading: Programming Homework 2 Instructions (Read First)

Graded: Module 2
Graded: Programming Homework 2

WEEK 3


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.


13 videos, 1 reading expand


  1. Video: Module 3 Introduction
  2. Video: Lecture: Solving the edit distance problem
  3. Video: Lecture: Using dynamic programming for edit distance
  4. Video: Practical: Implementing dynamic programming for edit distance
  5. Video: Lecture: A new solution to approximate matching
  6. Video: Lecture: Meet the family: global and local alignment
  7. Video: Practical: Implementing global alignment
  8. Video: Lecture: Read alignment in the field
  9. Video: Lecture: Assembly: working from scratch
  10. Video: Lecture: First and second laws of assembly
  11. Video: Lecture: Overlap graphs
  12. Video: Practical: Overlaps between pairs of reads
  13. Video: Practical: Finding and representing all overlaps
  14. Reading: Programming Homework 3 Instructions (Read First)

Graded: Module 3
Graded: Programming Homework 3

WEEK 4


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.


13 videos, 1 reading expand


  1. Video: Module 4 introduction
  2. Video: Lecture: The shortest common superstring problem
  3. Video: Practical: Implementing shortest common superstring
  4. Video: Lecture: Greedy shortest common superstring
  5. Video: Practical: Implementing greedy shortest common superstring
  6. Video: Lecture: Third law of assembly: repeats are bad
  7. Video: Lecture: De Bruijn graphs and Eulerian walks
  8. Video: Practical: Building a De Bruijn graph
  9. Video: Lecture: When Eulerian walks go wrong
  10. Video: Lecture: Assemblers in practice
  11. Video: Lecture: The future is long?
  12. Video: Lecture: Computer science and life science
  13. Video: Lecture: Thank yous
  14. Reading: Post Course Survey

Graded: Programming Homework 4
Graded: Module 4

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