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Lecture: DNA sequencing past and present

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DNA测序算法

约翰霍普金斯大学

4.8（415 个评分） | 16K 名学生已注册

课程 4（共 8 门，基因组数据科学专项课程）

我们将学会对 DNA 测序数据进行分析的计算方法——算法和数据结构。我们将学习一点DNA、基因组、以及 DNA 测序的应用的相关知识。我们将使用 Python 实现关键算法和数据结构，并分析实际的基因组和 DNA 测序数据集。

查看授课大纲

您将学习的技能

Bioinformatics Algorithms, Algorithms, Python Programming, Algorithms On Strings

审阅

4.8（415 个评分）

5 stars
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MD

Nov 10, 2016

This was really fun. Really enjoyed the a-ha of the algorithms and the fun of solving the alignment and assembly problems. Feel mildly powerful after assembling a virus genome.

LK

Mar 12, 2016

Excellent intro to the computational challenges in analyzing genomic sequences.\n\nLectures and programming exercises explain algorithms very clearly even for beginners.

从本节课中

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.

Module 1 Introduction1:32

Lecture: Why study this?4:29

Lecture: DNA sequencing past and present3:08

Lecture: Genomes as strings, reads as substrings5:02

Lecture: String definitions and Python examples3:33

Practical: String basics 7:14

Practical: Manipulating DNA strings 7:00

Practical: Downloading and parsing a genome 6:29

Lecture: How DNA gets copied3:37

Optional lecture: How second-generation sequencers work 7:10

Optional lecture: Sequencing errors and base qualities 6:37

Lecture: Sequencing reads in FASTQ format4:39

Practical: Working with sequencing reads 11:11

Practical: Analyzing reads by position 6:58

Lecture: Sequencers give pieces to genomic puzzles5:41

Lecture: Read alignment and why it's hard3:09

Lecture: Naive exact matching10:18

Practical: Matching artificial reads 6:53

Practical: Matching real reads 7:24

教学方

Ben Langmead, PhD
Assistant Professor
Jacob Pritt

脚本

DNA sequencing became a practical tool with the invention of the chain

termination method by Fred Sanger and colleagues in the 1970's.

This method is often called Sanger sequencing, or

sometimes it's called first generation sequencing.

And though the method, at first, was rather labor intensive,

requiring manual intervention by a human, over the years,

the method was also improved and automated.

And it was packaged in to commercial products,

like these machines that are illustrated in the picture on the right.

It remained the dominant method for

DNA sequencing all the way through the Human Genome Project, and

Fred Sanger won a Nobel Prize for chemistry for this invention.

The Human Genome Project used hundreds of first generation DNA sequencers.

The story of the Human Genome Project and

how it used DNA sequencing technology is really an amazing one.

The technology was an important part of what made that project possible, and

it was also an important part of what drove the competition between the two

competing teams.

But if the story of the Human Genome Project is really amazing,

another amazing story is what's happened to DNA sequencing technology since the end

of the Human Genome Project.

And that story is summarized in this plot here.

So here, the horizontal axis shows years since the year 2001,

which is around the end of the Human Genome Project.

And the vertical axis shows the cost of sequencing

one human genome's worth of DNA, or so.

And the cost is shown on a logarithmic scale, so

each tic mark on this vertical axis is another factor of ten.

So, one thing that I bet you notice when you look at this

plot is that something very important seems to happen around the year 2007.

That's the year that a new kind of sequencing technology started to be

used in life science labs around the world.

This new technology goes by a few different names.

It's sometimes called next-generation sequencing, or

it's sometimes called second-generation sequencing,

to distinguish it from first-generation sequencing, which was Sanger Sequencing.

But another good name for this technology is massively parallel sequencing,

because the reason the technology is so inexpensive is because it's able to

sequence many millions, or even billions of molecules of DNA all in parallel.

All at the same time.

We'll see later how it's actually able to do this.

This plot is showing cost, but cost is only part of the story.

Besides improvements in cost, there were also improvements in speed,

in accuracy, and how easy it is to actually use these machines.

And so over time, as a result of all these different improvements, DNA sequencers

have really become the premiere tool for studying nucleic acids like DNA and

RNA, which are crucial to many different phenomena in life science.

There are thousands of these second generation sequencers deployed

across the world.

And all together, they're generating many,

many petabytes of sequencing data per year.

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