Об этом курсе: 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.
Автор: Johns Hopkins University
Преподаватели: Ben Langmead, PhD, Assistant Professor
Computer Science
Преподаватели: Jacob Pritt
Department of Computer Science
| Основные сведения | Course 4 of 8 in the Genomic Data Science Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
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 видео, 7 материалов для самостоятельного изучения
- Reading: Welcome to Algorithms for DNA Sequencing
- Reading: Pre Course Survey
- Reading: Syllabus
- Reading: Setting up Python (and Jupyter)
- Reading: Getting slides and notebooks
- Reading: Using data files with Python programs
- Видео: Module 1 Introduction
- Видео: Lecture: Why study this?
- Видео: Lecture: DNA sequencing past and present
- Видео: Lecture: Genomes as strings, reads as substrings
- Видео: Lecture: String definitions and Python examples
- Видео: Practical: String basics
- Видео: Practical: Manipulating DNA strings
- Видео: Practical: Downloading and parsing a genome
- Видео: Lecture: How DNA gets copied
- Видео: Optional lecture: How second-generation sequencers work
- Видео: Optional lecture: Sequencing errors and base qualities
- Видео: Lecture: Sequencing reads in FASTQ format
- Видео: Practical: Working with sequencing reads
- Видео: Practical: Analyzing reads by position
- Видео: Lecture: Sequencers give pieces to genomic puzzles
- Видео: Lecture: Read alignment and why it's hard
- Видео: Lecture: Naive exact matching
- Видео: Practical: Matching artificial reads
- Видео: Practical: Matching real reads
- Reading: Programming Homework 1 Instructions (Read First)
Оцениваемый: Module 1
Оцениваемый: Programming Homework 1
НЕДЕЛЯ 2
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 видео, 1 материал для самостоятельного изучения
- Видео: Week 2 Introduction
- Видео: Lecture: Boyer-Moore basics
- Видео: Lecture: Boyer-Moore: putting it all together
- Видео: Lecture: Diversion: Repetitive elements
- Видео: Practical: Implementing Boyer-Moore
- Видео: Lecture: Preprocessing
- Видео: Lecture: Indexing and the k-mer index
- Видео: Lecture: Ordered structures for indexing
- Видео: Lecture: Hash tables for indexing
- Видео: Practical: Implementing a k-mer index
- Видео: Lecture: Variations on k-mer indexes
- Видео: Lecture: Genome indexes used in research
- Видео: Lecture: Approximate matching, Hamming and edit distance
- Видео: Lecture: Pigeonhole principle
- Видео: Practical: Implementing the pigeonhole principle
- Reading: Programming Homework 2 Instructions (Read First)
Оцениваемый: Module 2
Оцениваемый: Programming Homework 2
НЕДЕЛЯ 3
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 видео, 1 материал для самостоятельного изучения
- Видео: Module 3 Introduction
- Видео: Lecture: Solving the edit distance problem
- Видео: Lecture: Using dynamic programming for edit distance
- Видео: Practical: Implementing dynamic programming for edit distance
- Видео: Lecture: A new solution to approximate matching
- Видео: Lecture: Meet the family: global and local alignment
- Видео: Practical: Implementing global alignment
- Видео: Lecture: Read alignment in the field
- Видео: Lecture: Assembly: working from scratch
- Видео: Lecture: First and second laws of assembly
- Видео: Lecture: Overlap graphs
- Видео: Practical: Overlaps between pairs of reads
- Видео: Practical: Finding and representing all overlaps
- Reading: Programming Homework 3 Instructions (Read First)
Оцениваемый: Module 3
Оцениваемый: Programming Homework 3
НЕДЕЛЯ 4
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 видео, 1 материал для самостоятельного изучения
- Видео: Module 4 introduction
- Видео: Lecture: The shortest common superstring problem
- Видео: Practical: Implementing shortest common superstring
- Видео: Lecture: Greedy shortest common superstring
- Видео: Practical: Implementing greedy shortest common superstring
- Видео: Lecture: Third law of assembly: repeats are bad
- Видео: Lecture: De Bruijn graphs and Eulerian walks
- Видео: Practical: Building a De Bruijn graph
- Видео: Lecture: When Eulerian walks go wrong
- Видео: Lecture: Assemblers in practice
- Видео: Lecture: The future is long?
- Видео: Lecture: Computer science and life science
- Видео: Lecture: Thank yous
- Reading: Post Course Survey
Оцениваемый: Programming Homework 4
Оцениваемый: Module 4
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Авторы
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.
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Рейтинги и отзывы
I loved this course. The material was really interesting and it was well taught. The assignments were excellent as well.
Very good lectures. Some of the homework assignments were kind of hard though (and no help in the discussion forums).
Helpful material for DNA Sequencing and number crunching.
Well taught, I suggest a background in Python to make things more enjoyable.
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OS
I enjoyed the introduction to some of the algorithms used in genomics... and hope to explore these further in my career.