À propos de ce cours : 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.

Créé par : Johns Hopkins University

Enseigné par : Ben Langmead, PhD, Assistant Professor
Computer Science
Enseigné par : Jacob Pritt
Department of Computer Science

Informations de base	Cours 4 de 8 dans Genomic Data Science Specialization
Langue	English
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.8 étoiles Note moyenne des utilisateurs 4.8Voir ce que disent les étudiants

Programme de cours

SEMAINE 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 vidéos, 7 lectures

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
Vidéo: Lecture: Why study this?
Vidéo: Lecture: DNA sequencing past and present
Vidéo: Lecture: Genomes as strings, reads as substrings
Vidéo: Lecture: String definitions and Python examples
Vidéo: Practical: String basics
Vidéo: Practical: Manipulating DNA strings
Vidéo: Practical: Downloading and parsing a genome
Vidéo: Lecture: How DNA gets copied
Vidéo: Optional lecture: How second-generation sequencers work
Vidéo: Optional lecture: Sequencing errors and base qualities
Vidéo: Lecture: Sequencing reads in FASTQ format
Vidéo: Practical: Working with sequencing reads
Vidéo: Practical: Analyzing reads by position
Vidéo: Lecture: Sequencers give pieces to genomic puzzles
Vidéo: Lecture: Read alignment and why it's hard
Vidéo: Lecture: Naive exact matching
Vidéo: Practical: Matching artificial reads
Vidéo: Practical: Matching real reads
Reading: Programming Homework 1 Instructions (Read First)

Noté: Module 1

Noté: Programming Homework 1

SEMAINE 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 vidéos, 1 lecture

Vidéo: Lecture: Boyer-Moore basics
Vidéo: Lecture: Boyer-Moore: putting it all together
Vidéo: Lecture: Diversion: Repetitive elements
Vidéo: Practical: Implementing Boyer-Moore
Vidéo: Lecture: Preprocessing
Vidéo: Lecture: Indexing and the k-mer index
Vidéo: Lecture: Ordered structures for indexing
Vidéo: Lecture: Hash tables for indexing
Vidéo: Practical: Implementing a k-mer index
Vidéo: Lecture: Variations on k-mer indexes
Vidéo: Lecture: Genome indexes used in research
Vidéo: Lecture: Approximate matching, Hamming and edit distance
Vidéo: Lecture: Pigeonhole principle
Vidéo: Practical: Implementing the pigeonhole principle
Reading: Programming Homework 2 Instructions (Read First)

Noté: Module 2

Noté: Programming Homework 2

SEMAINE 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 vidéos, 1 lecture

Vidéo: Lecture: Solving the edit distance problem
Vidéo: Lecture: Using dynamic programming for edit distance
Vidéo: Practical: Implementing dynamic programming for edit distance
Vidéo: Lecture: A new solution to approximate matching
Vidéo: Lecture: Meet the family: global and local alignment
Vidéo: Practical: Implementing global alignment
Vidéo: Lecture: Read alignment in the field
Vidéo: Lecture: Assembly: working from scratch
Vidéo: Lecture: First and second laws of assembly
Vidéo: Lecture: Overlap graphs
Vidéo: Practical: Overlaps between pairs of reads
Vidéo: Practical: Finding and representing all overlaps
Reading: Programming Homework 3 Instructions (Read First)

Noté: Module 3

Noté: Programming Homework 3

SEMAINE 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 vidéos, 1 lecture

Vidéo: Lecture: The shortest common superstring problem
Vidéo: Practical: Implementing shortest common superstring
Vidéo: Lecture: Greedy shortest common superstring
Vidéo: Practical: Implementing greedy shortest common superstring
Vidéo: Lecture: Third law of assembly: repeats are bad
Vidéo: Lecture: De Bruijn graphs and Eulerian walks
Vidéo: Practical: Building a De Bruijn graph
Vidéo: Lecture: When Eulerian walks go wrong
Vidéo: Lecture: Assemblers in practice
Vidéo: Lecture: The future is long?
Vidéo: Lecture: Computer science and life science
Vidéo: Lecture: Thank yous
Reading: Post Course Survey

Noté: Programming Homework 4

Noté: Module 4

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Créateurs

Johns Hopkins University

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Notation et examens

Note moyenne 4.8 sur 5 sur 313 notes

This is more of my area... just starting the first videos and I'm already excited.

It is well explained. More space to programming practical exercise is suggested.Very intriguing Programming Homework! You have to apply all the python knowledge you have learned so far and also the one you have still to learn :)