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Variant Calling Workshop Chris Fields Variant Calling Workshop v2 | Chris Fields 1 Powerpoint by Casey Hanson

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Page 1: Variant Calling Workshop Chris Fields Variant Calling Workshop v2 | Chris Fields1 Powerpoint by Casey Hanson

Variant Calling Workshop v2 | Chris Fields 1

Variant Calling Workshop

Chris Fields

Powerpoint by Casey Hanson

Page 2: Variant Calling Workshop Chris Fields Variant Calling Workshop v2 | Chris Fields1 Powerpoint by Casey Hanson

Variant Calling Workshop v2 | Chris Fields 2

Introduction

In this lab, we will do the following:

1. Perform variant calling analysis on the IGB biocluster.

2. Visualize our results on the desktop using the Integrative Genomics Viewer (IGV) tool.

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Step 0A: Accessing the IGB BioclusterOpen Putty.exe

In the hostname textbox type:

biocluster.igb.Illinois.edu

Click Open

If popup appears, Click Yes

Enter login credentials assigned to you; example, user class45.

Now you are all set!

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Step 0B: Lab SetupThe lab is located in the following directory:

~/mayo/fields

This directory contains the finished version of the lab (i.e. the version of the lab after the tutorial). Consult it if you unsure about your runs. You don’t have write permissions to the lab directory.

Create a working directory of this lab in your home directory for your output to be stored. Note ~ is a symbol in Unix paths referring to your home directory. Copy the necessary shell files (.sh) files from ~/mayo/fields to your working directory.

Note: in this lab, we will NOT login to a node on the biocluster. Instead, we will submit jobs to the biocluster.

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Step 0C: Lab Setup

$ mkdir ~/fields

# Make working directory in your home

directory

$ cp ~/mayo/fields/*.sh ~/fields

# Copy shell files to your working

directory.

Create a working directory called ~/fields in your home directory.

Copy all shell files (.sh) from ~/mayo/fields to your working directory.

Copied Files

annotate_snpeff.sh

call_variants_ug.sh

hard_filtering.sh

post_annotate.sh

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Step 0D: Shared Desktop Directory

For viewing and manipulating files on the classroom computers, we provide a shared directory in the following folder on the desktop:

classes/mayo

In today’s lab, we will be using the following folder in the shared directory:

classes/mayo/fields

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Variant Calling SetupIn this exercise, we will use data from the 1000 Genomes project (WGS, 60x coverage) to call variants, in particular single nucleotide polymorphisms.

The initial part of the GATK pipeline (alignment, local realignment, base quality score recalibration) has been done, and the BAM file has been reduced for a portion of human chromosome 20. This is the data we will be working with in this exercise.

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Step 1A: Running a Variant Calling Job In this step, we will start a variant calling job using the qsub command.

Additionally, we will gather statistics about our job using the qstat command.

$ cd ~/fields

# Change directory to your working directory.

$ qsub -q classroom –l ncpus=4 call_variants_ug.sh

# This will execute call_variants_ug.sh on the

biocluster.

$ qstat –u <INSERT YOUR USERNAME>

# Get statistics on your submitted job

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Step 1B: Output of Variant Calling JobPeriodically, call qstat to see if your job has finished.

You should have 4 files when it has completed.

Discussion

What did we just do?

We ran the GATK UnifiedGenotyper to call variants.

Look at file structure.

Files

raw_indels.sh

raw_indels.vcf.idx

raw_snps.vcf

raw_snps.vcf.idx

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Step 1C: SNP and Indel Counting

In this step, we will count the # of SNPS and Indels identified in the raw_snps.vcf and raw_indels.vcf files.

We will use the program grep, which is a text matching program.$ grep –c –v ‘^#’ raw_snps.vcf # Get the number of

SNPs.

# -v Tells grep to show all lines not beginning with # in

raw_snps.vcf.

# -c Tells grep to return the total number of returned lines.

# Output should be 13612.

$ grep –c –v ‘^#’ raw_indels.vcf # Get the number of

indels.

# Output should be 1070.

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Step 1D: SNP and Indel Counting in dbSNPIn this step, we will count the number of SNPs and Indels in dbSNP.

dbSNP SNPs and Indels have the rs# identifier where # is a number.

Example: rs1000$ grep –c ‘rs[0-9]*’ raw_snps.vcf # Get the number of dbSNP

SNPs.

# Return all lines in raw_snps.vcf containing rs followed by a

number.

# -c Tells grep to return the total number of returned lines.

# Output should be 12248.

$ grep –c ‘rs[0-9]*’ raw_indels.vcf # Get the number of dbSNP

indels.

# Output should be 1019.

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Step 2A: Hard Filtering Variant Calls

We need to filter these variant calls in some way.

In general, we would filter on quality scores. However, since we have a very small set of variants, we will use hard filtering.

$ qsub -q classroom –l ncpus=4

hard_filtering.sh

# This will execute hard_filtering.sh on the

biocluster.

$ qstat –u <INSERT YOUR USERNAME>

Output Files

hard_filtered_snps.vcf

hard_filtered_indels.vcf

Periodically, call qstat to see if your job has finished.

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Step 2B: Hard Filtering Variants CallsIn this step, we will count the # of filtered SNPs and Indels.

$ grep –c ‘PASS’ hard_filtered_snps.vcf # Count # of passes

# Output 8270

$ grep –c ‘PASS’ hard_filtered_indels.vcf # Count # of PASSES

# Output 1041

Discussion

1. Did we lose any variants?2. How many PASSED the filter?3. What is the difference in the filtered and raw input?

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Step 3A: Annotating Variants With SnpEffWit our filtered variants, we now need to annotate them with SnpEff.

SnpEff adds information about where variants are in relation to specific genes.

$ qsub -q classroom –l ncpus=4

annotate_snpeff.sh

# This will execute snpeff.sh on the

biocluster.

$ qstat –u <INSERT YOUR USERNAME>

Output Files

hard_filtered_snps_annotated.vcf

hard_filtered_indels_annotated.vcf

Periodically, call qstat to see if your job has finished.

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Step 3B: Annotating Variants With SnpEffThe IDs for the human assembly version we us are from Ensemble. The Ensemble format is ENSGXXXXXXXXXXX.

Example: FOXA2’s Ensemble ID is ENSG00000125798.

In this step, we would like to see if there are any variants of FOXA2.

$ grep –c ‘ENSG00000125798’ hard_filtered_snps_annotated.vcf

# Get the number of SNPS in FOXA2, ENSG00000125798.

# Output should be 3.

$ grep –c ‘ENSG00000125798’ hard_filtered_indels_annotated.vcf

# Get the number of Indels in FOXA2, ENSG00000125798.

# Output should be 0.

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Step 4: GATK Variant Annotator

SnpEff adds a lot of information to the VCF.

GATK Variant Annotator helps remove a lot of the extraneous information.

$ qsub -q classroom –l ncpus=4 post_annotate.sh

# This will execute post_annotate.sh on the biocluster.

$ qstat –u <INSERT YOUR USERNAME>

Periodically, call qstat to see if your job has finished.

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Step 5A: Visualization With IGV

Switch the genome to Human (b37).

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Step 5B: Viewing Results in IGV

Load the VCF files marked ‘final’ and Click on the ‘20’ (chr 20).

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Step 5C: Viewing Results in IGV

Click and drag from the ‘20 mb’ mark to roughly the centromeric region on the chromosome (~2.6 mb)

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Step 5D: Viewing Results in IGV

The result should look similar to the screenshot below:

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Step 5E: Viewing Results in IGV

Right click on the track to bring up a menu, and then select Set Feature Visibility Window.

Set to ‘10000000’ (10 million, or 10 Mb)

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Step 5F: Viewing Results in IGV

In the search box, enter in ‘FOXA2’.

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Checkpoint: Viewing Results in IGV

1. How many SNPs are here?

2. How many Indels are here?

3. How many SNPs are ‘hets’?

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Step 6A: Viewing Results in IGV

Now we’ll load in a ‘small’ BAM file.This is the same BAM file you analyzed on the cluster

Load the following BAM file in classes/mayo/fields:

NA12878.HiSeq.WGS.bwa.cleaned.recal.b37.20_arm1.bam

What is happening here?

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Step 6B: Viewing Results in IGVRight click on BAM track and select ‘Show coverage track’.

Note colors in coverage track.

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Step 6C: Viewing Results in IGVZoom in to regions with SNPs

Mouse-over the SNP regions in the VCF tracks to get more information (including annotation from SnpEff and VariantAnnotator)

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Browse around…

How are filtered SNPs are displayed?

How about low-quality bases?

Can you change the display to sort BAM reads by strand?

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Step 7: SnpEff Results

SnpEff gives a nice summary HTML file that should have been downloaded with your result (one for SNP, one for indel results)

Open those up in a browser.