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Lecture: DNA sequencing past and present

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Lecture: DNA sequencing past and present

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.

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.

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您将学习的技能

Bioinformatics Algorithms, Algorithms, Python Programming, Algorithms On Strings

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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