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'''Note:''' the latest version of this practical is available at: [[SeqShop: Variant Calling and Filtering for SNPs Practical]]
* The ones here is the original one from the June workshop (updated to be run from elsewhere)
See the [[Media:SeqShop - GotCloud snpcall.pdf|introductory slides]] for an intro to this tutorial.
** How to examine the variants at particular genomic positions
** How to evaluate the quality of SNP calls
== Setup in person at the SeqShop Workshop ==
''This section is specifically for the SeqShop Workshop computers.''
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''If you are not running during the SeqShop Workshop, please skip this section.''
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=== Setup your run environment===This is the same setup you did for the previous tutorial, but you need to redo it each time you log in.
This is the same setup you did for the previous tutorial, but you need to redo it each time you log in. It will setup some environment variables to point you to:* [[GotCloud ]] program
* Tutorial input files
* Setup an output directory
* You won't see any output after running <code>source</code>
** It silently sets up your environment
** If you want to view the detail of the set upsetup, type
less /home/mktrost/seqshop/setup.txt
and press 'q' to finish.
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View setup.txt
== Setup when running on your own outside of the SeqShop Workshop ==
''This section is specifically for running on your own outside of the SeqShop Workshop.''
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''If you are running during the SeqShop Workshop, please skip this section.''
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This tutorial builds on the alignment tutorial, if you have not already, please first run that tutorial: [[SeqShop:_Sequence_Mapping_and_Assembly_Practical, June 2014|Alignment Tutorial]]
== Examining GotCloud SnpCall Input files ==
Per sample BAM files contain sequence reads that are mapped to positions in the genome.
For a reminder on how to look at/read BAM files, see: [[SeqShop:_Sequence_Mapping_and_Assembly_Practical, June 2014#BAM_Files|SeqShop Aligment: BAM Files]]
For this tutorial, we will use the 4 BAMs produced in the [[SeqShop: Sequence Mapping and Assembly Practical, June 2014]] as well as with 58 BAMs that were pre-aligned to that 1MB region of chromosome 22.
=== Reference Files ===
We looked at them yesterday, but you can take another look at the chromosome 22 reference files included for this tutorial:
ls ${REFSS}/ref22
<li>View Screenshot</li>
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The alignment pipeline only processed 4 samples, but for snpcall, we want to run on 62 samples.
* The other 58 samples were already aligned:
ls ${INSS}/bams
Look at the BAM index for those BAMs:
less ${INSS}/bams/bam.index
Remember, use <code>'q'</code> to exit out of <code>less</code>
;Do you notice a difference between this index and yours?
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<li>It doesn't have a full path to the BAM file, while your index has /home/...</li>
<li>That's ok, we will use the <code>--base_prefix ${SS}</code> command-line option to prefix the BAM paths</li><li>Alternatively, we could have set BAM_INDEX in <code>gotcloud.conf</code> contains to the path to those the BAMs<pre>BAM_INDEX = /home/username/seqshop/example</pre> </li>[[File<ul><li>NOTE:BamindexConfthe conf file can't interpret ${SS} environment variables or '~', so you would have to specify the full path</li><li>We just used the command-line option for this tutorial since this path will vary by user.png|300px]]</li></ul>
We need to add these BAMs to our index
* Append the bam.index from the pre-aligned BAMs to the one you generated from the alignment pipeline
** '''Be sure to do this command just once''' cat ${INSS}/bams/bam.index >> ${OUT}/bam.index
* ">>" will append to the file that follows it
** Check that your BAM index is the correct size
**:<pre>wc -l ${OUT}/bam.index</pre>
*** <code>wc -l</code> counts the number of lines in the file
*** Should be 62
Verify your BAM index contains the additional BAMs
We will use the same configuration file as we used yesterday in GotCloud Align.
See [[SeqShop:_Sequence_Mapping_and_Assembly_Practical_Sequence Mapping and Assembly Practical, June 2014#GotCloud Configuration File|SeqShop: Alignment: GotCloud Configuration File]] for more details
* Note we want to limit snpcall to just chr22 so the configuration already has <code>CHRS = 22</code> (default was 1-22 & X).
Now that we have all of our input files, we need just a simple command to run:
${GC}/gotcloud snpcall --conf ${INSS}/gotcloud.conf --numjobs 4 --region 22:36000000-37000000--base_prefix ${SS} --outdir ${OUT}* <code>${GC}/gotcloud</code> runs GotCloud* <code>align</code> tells GotCloud you want to run the alignment pipeline.* <code>--conf</code> tells GotCloud the name of the configuration file to use.** The configuration for this test was downloaded with the seqshop input files.
* --numjobs tells GotCloud how many jobs to run in parallel
** Depends on your system
* --region 22:36000000-37000000
** The sample files are just a small region of chromosome 22, so to save time, we tell GotCloud to ignore the other regions
* <code>--base_prefix</code> tells GotCloud the prefix to append to relative paths.
** The Configuration file cannot read environment variables, so we need to tell GotCloud the path to the input files, ${SS}
** Alternatively, gotcloud.conf could be updated to specify the full paths
* <code>--out_dir</code> tells GotCloud where to write the output.
** This could be specified in gotcloud.conf, but to allow you to use the ${OUT} to change the output location, it is specified on the command-line
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** Use down arrow to move down one line
* Scroll right until you should see per sample genotype information
Remember, use <code>'q'</code> to exit out of <code>less</code>
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* Scroll down: they all look like they <code>PASS</code>
Remember, use 'q' to exit out of less
Let's check if they are all PASS.
=== Genotype Refinement Input ===
The GotCloud genotype refinement pipeline takes as input ${OUT}/split/chr22/chr22.filtered.PASS.vcf.gz (the VCF file of PASS'ing SNPs from snpcall).
The bam index and the configuration file we used for GotCloud snpcall will tell GotCloud genotype refinement everything it needs to know, so no new input files need to be prepared.
=== Running GotCloud Genotype Refinement ===
Since everything is setup, just run the following command (very similar to snpcall).
${GC}/gotcloud ldrefine --conf ${INSS}/gotcloud.conf --numjobs 2 --region 22:36000000-37000000--base_prefix ${SS} --outdir ${OUT}
* Beagle will take about 2-3 minutes to complete
* Thunder will automatically run and will take another 3-4 minutes
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When completed, it should look like this:
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=== Genotype Refinement Output ===
; What's new in the output directory?
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:<pre>ls ${OUT}</pre>
<li><code>beagle</code> directory : Beagle output</li>
<li><code>thunder</code> directory : Thunder output</li>
<li><code>umake.beagle.conf*</code> : Configuration values Contain the configuration & steps used for GotCloud beagle</li><li><code>umake.beagle.Makefile</code> : GNU makefile for commands run as part of GotCloud beagle</li><li><code>umake.beagle.Makefile.log</code> : Log of the in GotCloud beagle run</li>
<li><code>umake.thunder.*</code> files : Contain the configuration & steps used in GotCloud thunder</li>
Let's take a look at that interesting location we found in the [[SeqShop:_Sequence_Mapping_and_Assembly_Practical, June 2014#Accessing_BAMs_by_Position|alignment tutorial]] : chromosome 22, positions 36907000-36907100
Use tabix to extract that from the VCFs:
The region we selected contains ''APOL1'' gene, which is known to play an important role in kidney diseases such as nephrotic syndrome. One of the non-synonymous risk allele, <code>rs73885139</code> located at position <code>22:36661906</code> increases the risk of nephrotic syndrome by >2-folds. Let's see if we found the interesting variant by looking at the VCF file by position.
${GC}/bin/tabix ${OUT}/vcfs/chr22/chr22.filtered.vcf.gz 22:36661906 | head -1
Did you see a variant at the position?
${GC}/bin/tabix ${OUT}/vcfs/chr22/chr22.filtered.vcf.gz 22:36661906 | head -1
22 36661906 . A G 18 PASS DP=409;MQ=59;NS=62;AN=124;AC=2;AF=0.013827;AB=0.4065;AZ=-0.5287;
Let's check the sequence data to confirm that the variant really exists
${GC}/bin/samtools tview $IN{SS}/bams/HG01242.recal.bam $REF{SS}/ref22/human.g1k.v37.chr22.fa
* Type 'g' to go to a specific position
=== Improvements === Let's get some information on the BEAGLE VCF:  perl ${SS}/ext/bed-diff .pl --vcf1 ${SS}/ref22/1kg.omni.chr22.36Mb.vcf.gz --vcf2 ${OUT}/beagle/chr22/chr22.filtered.PASS.beagled.ALL.vcf.gz --gcRoot ${GC} --out ${OUT}/bedDiff.beagle  Look at the results: more ${OUT}/bedDiff.beagle.summary <div class="mw-collapsible mw-collapsed" style="width:400px">*Results<div class="mw-collapsible-content"> OVERALL: 43601 44293 0.9844 NREF-EITHER: 19667 20359 0.9660 NMAJ-EITHER: 14585 15277 0.9547  HOMREF: 23934 100 1 0.9958 HET: 329 11959 175 0.9596 HOMALT: 4 83 7708 0.9888  HOMMAJ: 29016 126 2 0.9956 HET: 364 11959 140 0.9596 HOMMIN: 3 57 2626 0.9777</div></div> Now, let's see if it improved after running Thunder VCF: perl ${EXTSS}/ext/ --vcf1 ${REFSS}/ref22/1kg.omni.chr22.36Mb.vcf.gz --vcf2 ${OUT}/thunder/chr22/ALL/thunder/chr22.filtered.PASS.beagled.ALL.thunder.vcf.gz --gcRoot ${GC} --out ${OUT}/bedDiff.outthunder Look at the results: more ${OUT}/bedDiff.thunder.summary <div class="mw-collapsible mw-collapsed" style="width:400px">*Results<div class="mw-collapsible-content"> OVERALL: 43685 44293 0.9863 NREF-EITHER: 19758 20366 0.9701 NMAJ-EITHER: 14688 15296 0.9603 HOMREF: 23927 106 2 0.9955 HET: 286 12057 120 0.9674 HOMALT: 6 88 7701 0.9879 HOMMAJ: 28997 144 3 0.9950 HET: 286 12057 120 0.9674 HOMMIN: 5 50 2631 0.9795</div></div> There is an improvement. == What is GotCloud snpcall doing? ==To run GotCloud, you really just needed a single command. Well, that one command runs many steps. Here is a diagram of all the steps. [[File:SnpcallPipeline.jpg|1200px]] Aren't you glad you didn't have to configure & run each one yourself?

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