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 ScienceEnseigné 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 |
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
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
This is an excellent course. Lectures are very well prepared, practicals provide step-by-step explanations of the scripts (which is especially useful for people with little coding experience) and homeworks are well thought through, so that they force students to use the knowledge gained in the module. Some of the homeworks are challenging, but all the information needed to do the exercises is provided in lectures and practicals. All the notebooks containing scripts are provided, which makes it easy to take notes and better understand the scripts by running some examples. The way the concepts are explained in the lectures (the computational problem is described in details and then the ways of dealing with it are carefully explained in order of increasing complexity) provides insight into not only how these algorithms work but also why (what is the purpose/cause/reason behind these solutions). I can imagine how much work and thought went into preparation of these lectures and I honestly admire the teachers for their efforts. Taking this course was a great experience: I learned a lot and enjoyed it a lot. A big thank you! Please, keep up the good work.
this is just so good. i did take a lot of courses online and in my university on bioinformatics and this is the best course design i saw so far. i had to take pauses while watching the lectures to appreciate how much effort the creators of the course put to make it this connected and comprehensive. thank you!
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 :)
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YC
This is a super nice course.