Informações sobre o curso
4.8
331 ratings
83 reviews
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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Sugerido: 7 hours/week

Aprox. 20 horas restantes
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English

Legendas: English

Habilidades que você terá

Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Clock

Sugerido: 7 hours/week

Aprox. 20 horas restantes
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English

Legendas: English

Programa - O que você aprenderá com este curso

1

Seção
Clock
4 horas para concluir

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. ...
Reading
19 vídeos (Total de 112 min), 7 leituras, 2 testes
Video19 videos
Lecture: Why study this?4min
Lecture: DNA sequencing past and present3min
Lecture: Genomes as strings, reads as substrings5min
Lecture: String definitions and Python examples3min
Practical: String basics 7min
Practical: Manipulating DNA strings 7min
Practical: Downloading and parsing a genome 6min
Lecture: How DNA gets copied3min
Optional lecture: How second-generation sequencers work 7min
Optional lecture: Sequencing errors and base qualities 6min
Lecture: Sequencing reads in FASTQ format4min
Practical: Working with sequencing reads 11min
Practical: Analyzing reads by position 6min
Lecture: Sequencers give pieces to genomic puzzles5min
Lecture: Read alignment and why it's hard3min
Lecture: Naive exact matching10min
Practical: Matching artificial reads 6min
Practical: Matching real reads 7min
Reading7 leituras
Welcome to Algorithms for DNA Sequencing10min
Pre Course Survey10min
Syllabus10min
Setting up Python (and Jupyter)10min
Getting slides and notebooks10min
Using data files with Python programs10min
Programming Homework 1 Instructions (Read First)10min
Quiz2 exercícios práticos
Module 120min
Programming Homework 114min

2

Seção
Clock
3 horas para concluir

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...
Reading
15 vídeos (Total de 114 min), 1 leitura, 2 testes
Video15 videos
Lecture: Boyer-Moore basics8min
Lecture: Boyer-Moore: putting it all together6min
Lecture: Diversion: Repetitive elements5min
Practical: Implementing Boyer-Moore 10min
Lecture: Preprocessing7min
Lecture: Indexing and the k-mer index10min
Lecture: Ordered structures for indexing8min
Lecture: Hash tables for indexing7min
Practical: Implementing a k-mer index 7min
Lecture: Variations on k-mer indexes9min
Lecture: Genome indexes used in research9min
Lecture: Approximate matching, Hamming and edit distance6min
Lecture: Pigeonhole principle6min
Practical: Implementing the pigeonhole principle 9min
Reading1 leituras
Programming Homework 2 Instructions (Read First)10min
Quiz2 exercícios práticos
Module 220min
Programming Homework 212min

3

Seção
Clock
2 horas para concluir

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....
Reading
13 vídeos (Total de 92 min), 1 leitura, 2 testes
Video13 videos
Lecture: Solving the edit distance problem12min
Lecture: Using dynamic programming for edit distance12min
Practical: Implementing dynamic programming for edit distance 6min
Lecture: A new solution to approximate matching9min
Lecture: Meet the family: global and local alignment10min
Practical: Implementing global alignment 8min
Lecture: Read alignment in the field4min
Lecture: Assembly: working from scratch2min
Lecture: First and second laws of assembly8min
Lecture: Overlap graphs8min
Practical: Overlaps between pairs of reads 4min
Practical: Finding and representing all overlaps 3min
Reading1 leituras
Programming Homework 3 Instructions (Read First)10min
Quiz2 exercícios práticos
Module 320min
Programming Homework 38min

4

Seção
Clock
2 horas para concluir

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....
Reading
13 vídeos (Total de 83 min), 1 leitura, 2 testes
Video13 videos
Lecture: The shortest common superstring problem8min
Practical: Implementing shortest common superstring 4min
Lecture: Greedy shortest common superstring7min
Practical: Implementing greedy shortest common superstring 7min
Lecture: Third law of assembly: repeats are bad5min
Lecture: De Bruijn graphs and Eulerian walks8min
Practical: Building a De Bruijn graph 4min
Lecture: When Eulerian walks go wrong9min
Lecture: Assemblers in practice8min
Lecture: The future is long?9min
Lecture: Computer science and life science5min
Lecture: Thank yous min
Reading1 leituras
Post Course Survey10min
Quiz2 exercícios práticos
Programming Homework 48min
Module 414min
4.8
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Melhores avaliações

por VKAug 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.

por AZMar 11th 2016

Awesome, you will learn a lot about how DNA assemblers work, but very challenging and time demand in, especially if your background is in life science and not computer science.

Instrutores

Ben Langmead, PhD

Assistant Professor
Computer Science

Jacob Pritt

Department of Computer Science

Sobre 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....

Sobre o Programa de cursos integrados 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....
Genomic Data Science

Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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