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 Genomic Data Science
Algorithms for DNA Sequencing
proposé par
À propos de ce cours
4.8
351 notes
Compétences que vous acquerrez
Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1DNA 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: Why study this?4 min
Lecture: DNA sequencing past and present3 min
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
2Preprocessing, 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: Boyer-Moore basics8 min
Lecture: Boyer-Moore: putting it all together6 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
3Edit 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
Lecture: Solving the edit distance problem12 min
Lecture: Using dynamic programming for edit distance12 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
4Algorithms 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: The shortest common superstring problem8 min
Practical: Implementing shortest common superstring 4 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
86 avis33%
a commencé une nouvelle carrière après avoir terminé ces cours
25%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
par VK•Aug 8th 2017
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.
par MD•Nov 10th 2016
This was really fun. Really enjoyed the a-ha of the algorithms and the fun of solving the alignment and assembly problems. Feel mildly powerful after assembling a virus genome.
Enseignants
Ben Langmead, PhD
Assistant ProfessorComputer Science
Jacob Pritt
Department of Computer Science
À propos de Johns Hopkins University
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 Genomic Data Science
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, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics.
To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
Coursera propose un accès universel à la meilleure formation au monde,
en partenariat avec des universités et des organisations du plus haut niveau, pour proposer des cours en ligne.
© 2018 Coursera Inc. Tous droits réservés.
