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Lecture: Preprocessing

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Algoritmos Aplicado ao Sequenciamento de DNA

Universidade Johns Hopkins

4.8 (414 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 (414 classificações)

5 stars
337 ratings
4 stars
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3 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.

Na lição

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

Week 2 Introduction 1:34

Lecture: Boyer-Moore basics8:50

Lecture: Boyer-Moore: putting it all together6:15

Lecture: Diversion: Repetitive elements5:02

Practical: Implementing Boyer-Moore 10:01

Lecture: Preprocessing7:02

Lecture: Indexing and the k-mer index10:43

Lecture: Ordered structures for indexing8:06

Lecture: Hash tables for indexing7:13

Practical: Implementing a k-mer index 7:23

Lecture: Variations on k-mer indexes9:25

Lecture: Genome indexes used in research9:34

Lecture: Approximate matching, Hamming and edit distance6:53

Lecture: Pigeonhole principle6:37

Practical: Implementing the pigeonhole principle 9:30

Ministrado por

Ben Langmead, PhD
Assistant Professor
Jacob Pritt

Transcrição

We just learned that Boyer-Moore preprocesses the pattern P in order to

build lookup tables that help it use the bad character and good suffix rules.

Let's focus on this idea of preprocessing in some more detail because

preprocessing is an idea that's going to send us down a somewhat

different path in the coming lectures.

So, the naive exact matching algorithm did not use preprocessing.

Right? If you give me a pattern P and a text T,

I can run the exact matching algorithm, but if you give me a pattern P today but

no text, you say I'm going to give you the text tomorrow, there's nothing for

me to do until you eventually do give me the text.

With Boyer-Moore on the other hand, we can preprocess the pattern P.

Boyer-Moore is an algorithm that preprocesses the pattern P,

so if I'm Boyer-Moore and

you give me the pattern P but not the text T, there's something I can do.

I can build the lookup tables for the bad character and

the good suffix rules, then later on, when you give me the text T,

I can use those lookup tables to execute Boyer-Moore.

In fact, if we're in a situation where we're going to be matching the same

pattern against many different texts, so the P is going to stay the same but

the T is going to change, then I can reuse my preprocessing for the pattern.

I can reuse the lookup tables for the bad character and

the good suffix rules over and over again for each new text that you give me.

I don't have to re-preprocess the pattern each time.

So the cost of preprocessing is something that can be amortized over many problems.

It might be costly to do once, but because we use it again and

again, we essentially make up for that cost over time.

So, what about the idea of preprocessing the text T?

So at first glance,

this might not seem like such a good idea because the pattern is usually short

whereas the text can be very very long, maybe billions of letters long.

So the task of preprocessing the text sounds like it's an awful lot of work, but

we also just discussed how the cost of preprocsssing

can be amortized over many problems.

So if we're in a situation where we have to solve many matching problems where

the text T is the same but the pattern P can vary from problem to problem,

then it might be worthwhile to preprocess the text T.

In other words, we might be able to amortize away much of the cost or

all of the cost of preprocessing.

So, an algorithm that uses a preprocessed version of the text T

goes by a special name.

It's called an offline algorithm,

whereas an algorithm that does not preprocess the text is called online.

So, notice that

this definition is the same regardless of whether we preprocess P or not.

Right? So an online algorithm may or

may not preprocess P, and an offline algorithm may or may not preprocess P.

The distinction is whether or not we are preprocessing the text T.

So, let me outline a few scenarios, and I'd like you to think about for

each of these scenarios whether it's more online or offline.

Is it an offline situation or an online situation?

And I'd suggest that you pause the video after I ask each of these questions so

you can think about what your answer would be.

So first of all, the naive exact matching algorithm.

Was that an online or an offline algorithm?

This was online.

The naive algorithm did no preprocessing at all, so

it definitely didn't preprocess the text.

So it's an online algorithm.

Okay, second question.

Is the Boyer-Moore algorithm online or offline?

It's online.

Boyer-Moore does require some preprocessing, but

it preprocesses only the pattern P.

So it's online.

Okay, third.

This one's a little trickier.

How about a search engine,

like the kind of search engine you would use to search the world wide web,

when you type a query into a search engine and then press search.

Do you think that the algorithm it uses to return those results to you is online or

offline?

It pretty much has to be offline.

In this scenario the text T is enormous.

It's the entire World Wide Web.

So if the search engine didn't preprocess the World Wide Web in some way,

then every time you entered a new search term and hit search,

it would have to go basically scan the entire World Wide Web.

And there's no way you would get a quick answer if that's what it was doing.

But when we search the web, we know that we get a quick answer.

We have an answer in less than a second.

So it must be that the search engine has preprocessed the World Wide Web into some

form that allows it to jump very quickly to the search results that you asked for.

So it is offline.

Next question.

How about our problem, the read alignment problem?

Is it online or offline?

So, if we have many sequencing reads, many snippets of DNA, and we'd like

to figure out where they all originate with respect to the same reference genome,

this seems like a place where an offline algorithm would be more appropriate.

It would be even clearer if I told you that I'm actually going to give you one

human DNA sequencing data set this week, and then I'm going to give you another one

next week and another one the week after that.

In each case, every time we analyze a new data set, and then within a data set,

each time we analyze a new read, the pattern P is changing.

But the text T is not changing.

The genome does not change.

It's actually the case that genomes,

like the human reference genome do change a little bit over time.

They get updated, they correct errors, and they fill in gaps and things like this.

But in general, they stay static.

They change maybe once a year or something like that.

So this is a situation where an offline algorithm seems more appropriate.

So far though we've learned about the naive exact matching algorithm and

about Boyer- Moore, and these are both online algorithms.

Boyer-Moore is very fast, and it's a very widely used online algorithm.

But for read alignment, now would be a good time for

us to think of some offline algorithms.

Algorithms that involve taking the reference genome and preprocessing it

somehow in a way that allows us to match many reads very quickly.

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