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Optional lecture: Sequencing errors and base qualities

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Algoritmos Aplicado ao Sequenciamento de DNA

Universidade Johns Hopkins

4.8 (415 classificações) | 16K alunos inscritos

Curso 4 de 8 no Análise de Dados GenômicosAnálise Genômica Programa de cursos integrados

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.

Visualizar o programa do curso

Habilidades que você aprenderá

Bioinformatics Algorithms, Algorithms, Python Programming, Algorithms On Strings

Avaliações

4.8 (415 classificações)

5 stars
337 ratings
4 stars
62 ratings
3 stars
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2 stars
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1 star
1 ratings

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.

Na lição

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

Ministrado por

Ben Langmead, PhD
Assistant Professor
Jacob Pritt

Transcrição

We talked about how sequencing by synthesis works.

But before we look at some real sequencing reads,

we need to understand a little bit more about these sequencers,

and how they can make mistakes, and then how they convey uncertainty.

So, here's our cartoon again showing the template strands attached to the slide.

This cartoon helped us to understand second generation sequencing, but

it leaves out an important detail which is that before we add any bases or

any polymerases, we first take these template strands and amplify them.

That is, we make many copies of them,

where all the copies are clustered around the original strand.

So as you can see here a cluster of clones are now surrounding

each of the template strands.

So, we need these clusters rather than the individual templates

because otherwise when we go to take our photograph of the glowing

terminator bases, there wouldn't be enough light coming from a single template.

So we need the light coming from all of the clones that are all close to each

other on this slide in order to get enough light for our photograph.

However, there's a problem that can occur.

So let's concentrate on just a single cluster.

And let's say that we're in the middle of the sequencing process, and

in one of the steps, in one of the sequencing cycles, just by accident,

we end up adding a base that isn't terminated.

So, in this diagram, one of the bases that we added was spuriously not terminated.

So, for example, it could be this blue one right here.

Because it's not terminated, it will not block the polymerase, and

because it doesn't block the polymerase the polymerase will keep going.

And we're going to get another base added on top of that blue base, so

this red base that you see here.

Now that red base is terminated, so then the preliminary stops going, and

everything's okay after that.

But there's already some damage that has been done because

one of the templates is now out of sync with the others in the same cluster so

that when we go and snap our photograph and

we see light coming from all of the various templates that are in this same

cluster, we see not just one color of light, but two different colors of light.

We see the blue light coming from all the templates that are where they should be,

and then we see a little speck of red light coming from the template that's

ahead of schedule,

that's had an extra base incorporated into that template strand.

So, let's say we keep going.

The sequencing reaction keeps going, and

we go through a few more sequencing cycles.

And we end up in a situation that looks like this, where we have some templates

that are on schedule that are showing up as orange in our photograph here,

and then we also have some that are ahead of schedule.

All right? So we had a few more spuriously

unterminated bases that were integrated, and so now three of the template strands

are out of sync with the rest of the template strands.

So as you can see that as we move from one sequencing cycle to the next,

the number of strands that will fall out of sync will tend to grow.

So, there's a piece of software that analyzes these images and attempts to

figure out what all the bases are, and this software is called a base caller.

And the base caller has to deal with ambiguity, like we can see here.

So sometimes the base caller will be very confident, like if all the light coming

from the cluster is exactly the same color, or sometimes it won't be so

confident like if the light coming from a cluster is an even mix of two or

more colors.

If the base color is not so confident we would like to know that, so

that when we go to analyze these reads we'd like to know which are the bases that

we shouldn't be quite so sure about that could have been some other base instead.

So for each base, for each base call,

the base caller reports an important value, which is called the base quality.

The base quality is simply the base caller's estimate of the probability

that the base was called incorrectly.

In this equation, the Q is the base quality, and

the p is the probability that the base call is incorrect.

Now of course we don't know what p is for sure, but

it's something that the sequencing software can estimate.

So why do we use this particular expression?

Why don't we just report p?

Well, the scale that Q is on, this expression that you see here,

actually makes for some easier interpretation.

So, for example, if the base quality is 10,

that corresponds to a 1 in 10 chance that the base call is incorrect.

If the quality is 20, that corresponds to a 1 in a 100 chance, and

if the quality is 30, that corresponds to a 1 in a 1000 chance.

So, as we add factors of ten to Q, we're multiplying by factors of ten over here,

and all these base qualities are nice, small numbers.

So how does the base color get a value for Q, given a picture that looks like this?

So in this case we can see that the majority of the light coming from

the cluster is orange, so I'm going to say I think

the nucleotide of this position is probably a C, orange means C.

But there's some uncertainty, so

we want to report a base quality that conveys this uncertainty.

So that when we go to analyze the read we can keep this in mind that

the base isn't totally clearly a C, it's actually a C with a little uncertainty.

So let's estimate the probability that we're wrong.

And simple way that we can do this is to take all of the light that is not

orange and quantify it somehow, add it up somehow and

then divide by the total amount of light that we see coming from the cluster.

So, in other words, what fraction of the light that we see contradicts

our hypothesis that this is a C?

So, in this case, we count three green bits of light,

and we count nine points of light total.

So that comes out to a fraction of around 1/3.

So in reality, base callers do something much more complicated than this, but this,

you can see, is a not unreasonable way to estimate that probability p.

And so if we let p be 1/3, like we estimated here, and

then we plug it into our equation, we get a value of q that's about 4.77.

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