Tutorial: Low Pass Sequence Analysis - Revision history

Csidore: /* Example Dataset */

2019-09-17T07:59:52Z

Example Dataset

← Older revision		Revision as of 03:59, 17 September 2019
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	To conserve time and disk-space, our analysis will focus on a small region of chromosome 20, from 33,500,000 to 33,600,000 bp. We will first map reads for 3 individuals. We will then perform the variant calling by combining the results with mapped reads from the other 7 individuals to generate a list of polymorphic sites and estimate genotypes at each of these sites.		To conserve time and disk-space, our analysis will focus on a small region of chromosome 20, from 33,500,000 to 33,600,000 bp. We will first map reads for 3 individuals. We will then perform the variant calling by combining the results with mapped reads from the other 7 individuals to generate a list of polymorphic sites and estimate genotypes at each of these sites.
	We will compare the results of the variant calling on the low pass dataset with results from the exome sequencing of the same individual. Finally we will use the LD refinement to increase the accuracy of our genotypes.		We will compare the results of the variant calling on the low pass dataset with results from the exome sequencing of the same individual. Finally, we will use the LD refinement to increase the accuracy of our genotypes.

	The example dataset will be ~~availabe~~ in the folder ~~Workshops/Abecasis/Workshop_lowpass/~~, so let's move there		The example dataset will be available in the workshop folder , so let's move there

	> cd WS_Sidore/Workshop_low_pass		> cd WS_Sidore/Workshop_low_pass

Csidore: /* Requirements */

2019-09-17T07:59:03Z

Requirements

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	== Requirements ==		== Requirements ==


			If you are participating in the Sardinia Summer School, everything is already installed and you can move on.

	[http://genome.sph.umich.edu/wiki/GotCloud GotCloud] package		[http://genome.sph.umich.edu/wiki/GotCloud GotCloud] package

Csidore: /* Example Dataset */

2019-09-17T07:57:55Z

Example Dataset

← Older revision		Revision as of 03:57, 17 September 2019
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	The example dataset will be availabe in the folder Workshops/Abecasis/Workshop_lowpass/, so let's move there		The example dataset will be availabe in the folder Workshops/Abecasis/Workshop_lowpass/, so let's move there

	> cd ~~DAY2_Jul10/WS_CarloSidore~~/Workshop_low_pass		> cd WS_Sidore/Workshop_low_pass

	== Building an Index for Short Read Alignment ==		== Building an Index for Short Read Alignment ==

Csidore: /* Initial set of variant calls */

2018-07-05T14:21:18Z

Initial set of variant calls

← Older revision		Revision as of 10:21, 5 July 2018
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	Now run the Makefiles as gotcloud suggests and continue with the workshop while gotCloud executes (it will take 5-10 minutes):		Now run the Makefiles as gotcloud suggests and continue with the workshop while gotCloud executes (it will take 5-10 minutes):

	> make -f snpcall/umake.Makefile &> snpcall.log &		> make -f snpcall/umake.snpcall.Makefile &> snpcall.log &

	Note that, in this case we are using a single CPU to run the snp calling. If you have multiple CPUs you can run gotcloud in parallel using multiple CPUs by setting the parameter "-j".		Note that, in this case we are using a single CPU to run the snp calling. If you have multiple CPUs you can run gotcloud in parallel using multiple CPUs by setting the parameter "-j".

Csidore at 13:00, 5 July 2018

2018-07-05T13:00:38Z

← Older revision		Revision as of 09:00, 5 July 2018
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	The example dataset will be availabe in the folder Workshops/Abecasis/Workshop_lowpass/, so let's move there		The example dataset will be availabe in the folder Workshops/Abecasis/Workshop_lowpass/, so let's move there

	> cd ~~D2.WS03_NGS_variant_calling-CarloSidore~~/Workshop_low_pass/		> cd DAY2_Jul10/WS_CarloSidore/Workshop_low_pass

	== Building an Index for Short Read Alignment ==		== Building an Index for Short Read Alignment ==

Csidore: /* Genotype Refinement Using Linkage Disequilibrium Information */

2017-06-15T07:37:45Z

Genotype Refinement Using Linkage Disequilibrium Information

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	This command creates a .vcf.gz.tbi, the index file which contains position information and allows for quick access.		This command creates a .vcf.gz.tbi, the index file which contains position information and allows for quick access.

	We can now pick the lines that we want with the notation "CHR:FIRSTPOS-LASTPOS"~~, since~~ we are interested in a single position, FIRSTPOS and LASTPOS are the same		We can now pick the lines that we want with the notation "CHR:FIRSTPOS-LASTPOS". Since we are interested in a single position, FIRSTPOS and LASTPOS are the same

	> tabix -h exome/vcfs/chr20/chr20.filtered.vcf.gz 20:33514465-33514465\| tail -n2\| cut -f 2,10		> tabix -h exome/vcfs/chr20/chr20.filtered.vcf.gz 20:33514465-33514465\| tail -n2\| cut -f 2,10

Csidore: /* Genotype Refinement Using Linkage Disequilibrium Information */

2017-06-15T07:37:02Z

Genotype Refinement Using Linkage Disequilibrium Information

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	For instance , let's check the genotype of HG00111 at position 33514465, extracting the information from a vcf generated with gotCloud and exome sequencing on the sample HG00111.		For instance , let's check the genotype of HG00111 at position 33514465, extracting the information from a vcf generated with gotCloud and exome sequencing on the sample HG00111.
	This time rather than using "zgrep", that could be very slow for large dataset, we will use "tabix" which provides quick access to every line of the vcf.		This time rather than using "zgrep", that could be very slow for large dataset, we will use "tabix" which provides quick access to every line of the vcf.

	We first need to index the .vcf file		We first need to index the .vcf file

Csidore: /* Genotype Refinement Using Linkage Disequilibrium Information */

2017-06-15T07:36:37Z

Genotype Refinement Using Linkage Disequilibrium Information

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	The initial set of genotype calls is generated examining a single individual at a time. These calls are typically quite good for deep sequencing data, but much less accurate for low pass sequence data.		The initial set of genotype calls is generated examining a single individual at a time. These calls are typically quite good for deep sequencing data, but much less accurate for low pass sequence data.

	For instance , let's check the genotype of HG00111 at position 33514465, extracting the information from a vcf generated with gotCloud and exome sequencing on the sample HG00111		For instance , let's check the genotype of HG00111 at position 33514465, extracting the information from a vcf generated with gotCloud and exome sequencing on the sample HG00111.
			This time rather than using "zgrep", that could be very slow for large dataset, we will use "tabix" which provides quick access to every line of the vcf.
			We first need to index the .vcf file

			> tabix exome/vcfs/chr20/chr20.filtered.vcf.gz

			This command creates a .vcf.gz.tbi, the index file which contains position information and allows for quick access.

			We can now pick the lines that we want with the notation "CHR:FIRSTPOS-LASTPOS", since we are interested in a single position, FIRSTPOS and LASTPOS are the same

	> tabix -h exome/vcfs/chr20/chr20.filtered.vcf.gz 20:33514465-33514465\| tail -n2\| cut -f 2,10		> tabix -h exome/vcfs/chr20/chr20.filtered.vcf.gz 20:33514465-33514465\| tail -n2\| cut -f 2,10

Csidore: /* Initial set of variant calls */

2017-06-15T07:32:11Z

Initial set of variant calls

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	Let's consider a sample genotyping at the position 33514465 (if needed, check the FORMAT fields in the meta-information section in the vcf to understand the data format)		Let's consider a sample genotyping at the position 33514465 (if needed, check the FORMAT fields in the meta-information section in the vcf to understand the data format)

	> ~~tabix~~ -h snpcall/vcfs/chr20/chr20.filtered.vcf.gz ~~20:33514465-33514465 \| tail -n2~~ \|cut -f 2,4,5,9,14		> zgrep -E "CHROM\|33514465" snpcall/vcfs/chr20/chr20.filtered.vcf.gz \| cut -f 2,4,5,9,14

	POS REF ALT FORMAT HG00111		POS REF ALT FORMAT HG00111

Csidore: /* Genotype Refinement Using Linkage Disequilibrium Information */

2017-06-15T07:30:04Z

Genotype Refinement Using Linkage Disequilibrium Information

← Older revision		Revision as of 03:30, 15 June 2017
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	For instance , let's check the genotype of HG00111 at position 33514465, extracting the information from a vcf generated with gotCloud and exome sequencing on the sample HG00111		For instance , let's check the genotype of HG00111 at position 33514465, extracting the information from a vcf generated with gotCloud and exome sequencing on the sample HG00111

	> ~~zgrep~~ -~~E "CHROM\|33514465"~~ exome/vcfs/chr20/chr20.filtered.vcf.gz \| cut -f 2,10		> tabix -h exome/vcfs/chr20/chr20.filtered.vcf.gz 20:33514465-33514465\| tail -n2\| cut -f 2,10

	POS HG00111		POS HG00111