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.
Ce cours fait partie de la Spécialisation Science des données génomiques
Algorithms for DNA Sequencing
proposé par
À propos de ce cours
23,101 consultations récentes
Compétences que vous acquerrez
Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
14 heures pour terminer
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 vidéos (Total 112 min), 7 lectures, 2 quiz
19 vidéos
Lecture: Genomes as strings, reads as substrings5 min
Lecture: String definitions and Python examples3 min
Practical: String basics 7 min
Practical: Manipulating DNA strings 7 min
Practical: Downloading and parsing a genome 6 min
Lecture: How DNA gets copied3 min
Optional lecture: How second-generation sequencers work 7 min
Optional lecture: Sequencing errors and base qualities 6 min
Lecture: Sequencing reads in FASTQ format4 min
Practical: Working with sequencing reads 11 min
Practical: Analyzing reads by position 6 min
Lecture: Sequencers give pieces to genomic puzzles5 min
Lecture: Read alignment and why it's hard3 min
Lecture: Naive exact matching10 min
Practical: Matching artificial reads 6 min
Practical: Matching real reads 7 min
7 lectures
Welcome to Algorithms for DNA Sequencing10 min
Pre Course Survey10 min
Syllabus10 min
Setting up Python (and Jupyter)10 min
Getting slides and notebooks10 min
Using data files with Python programs10 min
Programming Homework 1 Instructions (Read First)10 min
2 exercices pour s'entraîner
Module 120 min
Programming Homework 114 min
Semaine
23 heures pour terminer
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 vidéos (Total 114 min), 1 lecture, 2 quiz
15 vidéos
Week 2 Introduction 1 min
Lecture: Diversion: Repetitive elements5 min
Practical: Implementing Boyer-Moore 10 min
Lecture: Preprocessing7 min
Lecture: Indexing and the k-mer index10 min
Lecture: Ordered structures for indexing8 min
Lecture: Hash tables for indexing7 min
Practical: Implementing a k-mer index 7 min
Lecture: Variations on k-mer indexes9 min
Lecture: Genome indexes used in research9 min
Lecture: Approximate matching, Hamming and edit distance6 min
Lecture: Pigeonhole principle6 min
Practical: Implementing the pigeonhole principle 9 min
1 lecture
Programming Homework 2 Instructions (Read First)10 min
2 exercices pour s'entraîner
Module 220 min
Programming Homework 212 min
Semaine
32 heures pour terminer
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 vidéos (Total 92 min), 1 lecture, 2 quiz
13 vidéos
Module 3 Introduction 1 min
Practical: Implementing dynamic programming for edit distance 6 min
Lecture: A new solution to approximate matching9 min
Lecture: Meet the family: global and local alignment10 min
Practical: Implementing global alignment 8 min
Lecture: Read alignment in the field4 min
Lecture: Assembly: working from scratch2 min
Lecture: First and second laws of assembly8 min
Lecture: Overlap graphs8 min
Practical: Overlaps between pairs of reads 4 min
Practical: Finding and representing all overlaps 3 min
1 lecture
Programming Homework 3 Instructions (Read First)10 min
2 exercices pour s'entraîner
Module 320 min
Programming Homework 38 min
Semaine
42 heures pour terminer
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 vidéos (Total 83 min), 1 lecture, 2 quiz
13 vidéos
Module 4 introduction 1 min
Lecture: Greedy shortest common superstring7 min
Practical: Implementing greedy shortest common superstring 7 min
Lecture: Third law of assembly: repeats are bad5 min
Lecture: De Bruijn graphs and Eulerian walks8 min
Practical: Building a De Bruijn graph 4 min
Lecture: When Eulerian walks go wrong9 min
Lecture: Assemblers in practice8 min
Lecture: The future is long?9 min
Lecture: Computer science and life science5 min
Lecture: Thank yous 43s
1 lecture
Post Course Survey10 min
2 exercices pour s'entraîner
Programming Homework 48 min
Module 414 min
4.8
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Principaux examens pour Algorithms for DNA Sequencing
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!
Enseignants
Ben Langmead, PhD
Assistant ProfessorComputer Science
Jacob Pritt
Department of Computer Science
À propos de Université Johns-Hopkins
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.
À propos de la Spécialisation Science des données génomiques
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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