Alignment & Sequence Variation 5: BWA

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Из курса от партнера Johns Hopkins University

Command Line Tools for Genomic Data Science

219 оценки

Johns Hopkins University

Command Line Tools for Genomic Data Science

219 оценки

Курс 5 из 8 — Specialization Genomic Data Science

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Из урока

Week Three

In this module, we'll be going over Alignment and Sequence Variation in another sequence of 8 presentations.

Alignment & Sequence Variation 1: Overview4:08

Alignment & Sequence Variation 2: Alignment & Variant Detection Tools5:06

Alignment & Sequence Variation 3: VCF11:39

Alignment & Sequence Variation 4: Bowtie9:02

Alignment & Sequence Variation 5: BWA 4:37

Alignment & Sequence Variation 6: SAMtools (mpileup)6:35

Alignment & Sequence Variation 7: BCFtools8:13

Alignment & Sequence Variation 8: Variant Calling5:26

Познакомьтесь с преподавателями

Liliana Florea, PhD
Assistant Professor
McKusick-Nathans Institute of Genetic Medicine

In this section, we will illustrate how we can use BWA to create alignments.

So, the same as before, if we have a new genome, for

which we need to create an index.

We can do so with one of the BWA commands.

So lets try BWA, and to see which are the options.

So it has a number of commands that can be used,

index, mem, aln, bwasw and so on.

And there's some information at the bottom that tells us that you at first,

need to index the genome.

Then you can use one of the three possible alignment algorithms and so on.

So let's go back to our HPV or the file which contains a collection

of human papillomavirus genomes, and let's try to create an index for that.

We would do so with BWA index,

and followed by HPV on.

And it's that simple.

And now we have a number of files that were created that contain the index.

Now lets try to map the reads and

there are three options for alignment but

the one that is the most applicable is BWA mem.

To see what the command line options are for BWA mem,

we simply have to type this BWA mem and again that is fairly long.

So I'm going to save the output into bwa.log.

So I can look at it with my text editor.

So we're looking at this.

So the format, the usage is bwa, followed by mem for the command line.

A number of options listed below.

The index base which is the reference past this sequence.

And lastly, the first two reads.

So if you're looking at the algorithm option, just as before,

we have the number of threads with -d.

Some information about the alignment that can be changed minimum seed length,

look for internal seeds, and so on.

Some scoring option for substitutions in Dells, and so on.

Some information about input, output, and so on.

But in the vast majority of the cases,

just using BWA with the default parameter is going to do the job.

So now, to map the exome reads to the human genome,

I'm simply going to type, bwa mem to do the alignment.

I'm going to use four threads to make this faster,

I have an index on my system which I shall be using.

Okay, and then the fastq.

And within a safety output,

which would be in same format, in exome.bwa.san.

So now that we have the san file,

we can look within more exome.bwa.san,

just like we've done before.

And we have the genome sequences here in one alignment for

each read, or line for each read.

And just as before, we can very simply descend towards

view with the fastest sequence of the reference genome.

To transform this, to convert this file into a bam file for further analysis.

And now santools view, And

this will show us the information.

And that concludes this section.

Next we're going to be looking at tools for varying points.

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