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Biocondutor para ciência de dados genômicos

Universidade Johns Hopkins

4 (190 classificações) | 10K alunos inscritos

Curso 6 de 8 no Análise de Dados GenômicosAnálise Genômica Programa de cursos integrados

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.

Visualizar o programa do curso

Habilidades que você aprenderá

Bioinformatics, Bioconductor, Genomics, R Programming

Avaliações

4 (190 classificações)

5 stars
87 ratings
4 stars
52 ratings
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1 star
16 ratings

AB

Apr 12, 2018

Amazing course, motivated me to use bio conductor for descriptive seq studies of DNA.

CZ

Feb 10, 2016

I learned a lot from this class and the instructor is good.

Na lição

Week Four

In this week, we will cover Getting data in Bioconductor, Rsamtools, oligo, limma, and minfi

Getting data into Bioconductor6:32

Count-based RNA-seq analysis15:34

Ministrado por

Kasper Daniel Hansen, PhD
Assistant Professor, Biostatistics and Genetic Medicine

Transcrição

In this session we shall discuss the sort read package.

The ShortRead package contains two different functionalities for

reading in raw sequencing reads.

Typically in the form of a so called FastqFile.

It also contains a functionality for reading in aligned reads.

Although a lot of that functionality is now outdated and

you should be using the Rsam 2 packets instead.

Finally it has some old functionalities for examining base level calls

by which I mean intensities before you do the base coding.

So, let's start by loading the packets and getting a path to a factual file

that snips within the package itself for justification purposes.

So, this is just pointed down to a fastq file.

And we read it just by the function called readFastq which returns an object or

class ShortReadQ which we will examine in a little bit.

We can see in this case there are 20 thousand reads and

each read is 72 pages long.

You can also read the file by starting but

by it's then saying a fastQ file I check which we do like this.

Which really is just a point of doubt your FastQ file.

And why do we need this?

Well, we need this sometimes in order to read in smaller chunks of the file.

Then we can read it like this.

We will, we're going to see some more compelling examples of this when we

discuss band files in another session.

So how, do we access the information inside of FastqFile.

Well FastqFile is conceptually pretty simple, it contains reads, [INAUDIBLE].

And, we have access to function sread gives us a recent form of a DNA

string set.

Quality returns a quality values in some weird

letters things that we're going to discuss in a little bit.

And then, finally, id gives us the read names.

So the quality values as we saw before, is a BStringSet.

It contains all kinds of weird characters, and

they encode qualities on something called the fret scale.

And before I go into what the fret scale represent,

let's just say that quality values into 12 between 0 and 40.

And what happens is that different characters there

corresponds to different values.

This is done for compression and in order to make the file there

no basically in order to make the file size smaller.

So in order to do any real computation on qualities

you have to convert them into integers.

You can actually see what happens, and

you can do that by coalescing the quality scores into a matrix.

So here I coalesce a matrix, and I show the first two reads and

the first ten pages of the first two reads.

So we can see here by comparing to the thing upstairs that 8 gets translated

to 39 and I get's translated to 40.

So first scale qualities are numbers between 0 and 40 and

they're supposed to have a probabilistic interpretation

giving you some idea of the chance that the reported base is correct.

Or in other words the chance of getting a sequencing arrow.

How you actually get to this probabilistic interpretation is that it

requires some methods and some thinking.

Sometimes, you control about having qualities caught before and

after something that is known as qualitative remapping.

And I'm just mentioning this in order to tell you that you shouldn't always believe

in the probabilistic interpretations of these quality values.

So, this is really all you need to know about show read packets for handling

line reads, we will turn to the essentials packets that we will discuss later.

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