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.
Este curso faz parte do Programa de cursos integrados Análise de Dados GenômicosAnálise Genômica
Algoritmos Aplicado ao Sequenciamento de DNA
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Informações sobre o curso
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Habilidades que você terá
Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Programa - O que você aprenderá com este curso
Semana
14 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.
19 vídeos (Total 112 mín.), 7 leituras, 2 testes
19 videos
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
7 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
2 exercícios práticos
Module 120min
Programming Homework 114min
Semana
23 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
15 vídeos (Total 114 mín.), 1 leitura, 2 testes
15 videos
Week 2 Introduction 1min
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
1 leituras
Programming Homework 2 Instructions (Read First)10min
2 exercícios práticos
Module 220min
Programming Homework 212min
Semana
32 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.
13 vídeos (Total 92 mín.), 1 leitura, 2 testes
13 videos
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
1 leituras
Programming Homework 3 Instructions (Read First)10min
2 exercícios práticos
Module 320min
Programming Homework 38min
Semana
42 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.
13 vídeos (Total 83 mín.), 1 leitura, 2 testes
13 videos
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 43s
1 leituras
Post Course Survey10min
2 exercícios práticos
Programming Homework 48min
Module 414min
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comecei uma nova carreira após concluir estes cursos
33%
consegui um benefício significativo de carreira com este curso
Principais avaliações do Algoritmos Aplicado ao Sequenciamento de DNA
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!
Instrutores
Ben Langmead, PhD
Assistant ProfessorComputer Science
Jacob Pritt
Department of Computer Science
Sobre Universidade 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.
Sobre Programa de cursos integrados Análise de Dados GenômicosAnálise Genômica
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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