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 forma parte de Programa Especializado - Genomic Data Science
Algorithms for DNA Sequencing
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Acerca de este Curso
22,772 vistas recientes
Habilidades que obtendrás
Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Programa - Qué aprenderás en este curso
Semana
14 horas para completar
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 (Total 112 minutos), 7 readings, 2 quizzes
19 videos
Lecture: Genomes as strings, reads as substrings5m
Lecture: String definitions and Python examples3m
Practical: String basics 7m
Practical: Manipulating DNA strings 7m
Practical: Downloading and parsing a genome 6m
Lecture: How DNA gets copied3m
Optional lecture: How second-generation sequencers work 7m
Optional lecture: Sequencing errors and base qualities 6m
Lecture: Sequencing reads in FASTQ format4m
Practical: Working with sequencing reads 11m
Practical: Analyzing reads by position 6m
Lecture: Sequencers give pieces to genomic puzzles5m
Lecture: Read alignment and why it's hard3m
Lecture: Naive exact matching10m
Practical: Matching artificial reads 6m
Practical: Matching real reads 7m
7 lecturas
Welcome to Algorithms for DNA Sequencing10m
Pre Course Survey10m
Syllabus10m
Setting up Python (and Jupyter)10m
Getting slides and notebooks10m
Using data files with Python programs10m
Programming Homework 1 Instructions (Read First)10m
2 ejercicios de práctica
Module 120m
Programming Homework 114m
Semana
23 horas para completar
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 (Total 114 minutos), 1 reading, 2 quizzes
15 videos
Lecture: Diversion: Repetitive elements5m
Practical: Implementing Boyer-Moore 10m
Lecture: Preprocessing7m
Lecture: Indexing and the k-mer index10m
Lecture: Ordered structures for indexing8m
Lecture: Hash tables for indexing7m
Practical: Implementing a k-mer index 7m
Lecture: Variations on k-mer indexes9m
Lecture: Genome indexes used in research9m
Lecture: Approximate matching, Hamming and edit distance6m
Lecture: Pigeonhole principle6m
Practical: Implementing the pigeonhole principle 9m
1 lectura
Programming Homework 2 Instructions (Read First)10m
2 ejercicios de práctica
Module 220m
Programming Homework 212m
Semana
32 horas para completar
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 (Total 92 minutos), 1 reading, 2 quizzes
13 videos
Practical: Implementing dynamic programming for edit distance 6m
Lecture: A new solution to approximate matching9m
Lecture: Meet the family: global and local alignment10m
Practical: Implementing global alignment 8m
Lecture: Read alignment in the field4m
Lecture: Assembly: working from scratch2m
Lecture: First and second laws of assembly8m
Lecture: Overlap graphs8m
Practical: Overlaps between pairs of reads 4m
Practical: Finding and representing all overlaps 3m
1 lectura
Programming Homework 3 Instructions (Read First)10m
2 ejercicios de práctica
Module 320m
Programming Homework 38m
Semana
42 horas para completar
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 (Total 83 minutos), 1 reading, 2 quizzes
13 videos
Lecture: Greedy shortest common superstring7m
Practical: Implementing greedy shortest common superstring 7m
Lecture: Third law of assembly: repeats are bad5m
Lecture: De Bruijn graphs and Eulerian walks8m
Practical: Building a De Bruijn graph 4m
Lecture: When Eulerian walks go wrong9m
Lecture: Assemblers in practice8m
Lecture: The future is long?9m
Lecture: Computer science and life science5m
Lecture: Thank yous 43s
1 lectura
Post Course Survey10m
2 ejercicios de práctica
Programming Homework 48m
Module 414m
40%
comenzó una nueva carrera después de completar estos cursos
33%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones sobre 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!
Instructores
Ben Langmead, PhD
Assistant ProfessorComputer Science
Jacob Pritt
Department of Computer Science
Acerca de Universidad 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.
Acerca del programa especializado Genomic Data Science
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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