Alignment & Sequence Variation 8: Variant Calling

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En provenance du cours de Johns Hopkins University

Command Line Tools for Genomic Data Science

191 notes

Johns Hopkins University

Command Line Tools for Genomic Data Science

191 notes

Cours 5 sur 8 dans 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.

À partir de la leçon

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

Rencontrer les enseignants

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

In this section, we're going to show how to put it all together and

to write the various simple application for doing variant calling.

Let's start with our sample, load file.

The first operation was to sort and index the file, and this file is already sorted.

So I'm going to use same tools index sample.ban.

Which creates an index file sample.ban.bak.

In the next step, I will use CentOS one with the empire op module,

in order to create a BCF file.

That has one line for every position in the genome that contains a line read.

And that also has genotypes likelihood that are going to be used by.

So that was a long sentence, let's make it short.

One, and I map.

We want to have genotype likelihood values -g, the output would be nbcf, or map.

The reference sequence is the one that we used earlier on the system.

Gino's a human HG19,

last name, Sample dot down.

And we want this to be output into a compressed BCF file.

Now we will be using BCF tools on the sample dot BCF file.

To call variance.

bcftools1, with the comment call,

-v to report variance only.

-m for the multiallelic core.

-O, we want the output to be specified in compressed VCF format.

-o, we want the output to go into VCF sample.vcf.gz.

And then finally,

the input file will be sample.bcf.

And this produces the sample.vcf.gz file.

Which, as we've shown, has a variance.

Now, next one can perform a number of field tests,

which I'm not going to demonstrate here.

And or one might want to visually inspect the quality and

number of alignments at one particular position.

So let's look at this variant here, which is a more complex variant.

And position 7579643.

And we can visualize that with same tools,

1 keyview.

Let's look at the command line options for these.

Okay.

We can give you the position.

So on chromosome 17, at the position

75796 and let's say it's 00.

So that we have a little bit of context.

And then Sample.ban.

-dT.

And with -dT we're selecting the display to be simple text,

and lastly we have to specify the fasta sequence for

the genome and that's again the long swing.

Ign/prev.

So the text file, we can save this into a file or we can pop it.

And you'll see, here, the location of completion.

We can view these, we see this as the HTML.

Or we can see them in the terminal, without deploying that.

There it is.

So the location of the 7579641 is here at this location and then as you might

recall, the variant started collision at 7579643 so, and each location here.

And so on, So

this concludes our demonstration on how we can use alignment tools,

and then the combination of SAMtools and

BCFtools in order to produce varying costs from sequencing data.

Or the shotgun data from.

Or exome sequencing beta.

And that concludes this section and this lecture.

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