{ "cells": [ { "cell_type": "markdown", "id": "d015209b-0d23-4c85-994c-79b3535c75a3", "metadata": {}, "source": [ "#### __Formation South Green 2022 - Structural Variants Detection by using short and long reads__ \n", "\n", "#
__DAY 2 : How to analyze vcf results ?__
\n", "\n", "Created by C. Tranchant (DIADE-IRD), J. Orjuela (DIADE-IRD), F. Sabot (DIADE-IRD) and A. Dereeper (PHIM-IRD)\n", "\n", "***\n", "\n", "# Table of contents\n", "\n", "\n", "[I - Get some basic stats from vcf files](#statvcf)\n", "\n", "[II - FILTERING VCF](#vcffiltering)\n", "\n", "\n", "\n", "***\n", "\n" ] }, { "cell_type": "markdown", "id": "18a5bfca-2b96-49b1-bdc0-4d174f9f7c3e", "metadata": {}, "source": [ "## __I. Get some basic stats from vcf files__ \n", "\n", "In this exercise, we are going to work with the whole vcf generated from the 20 clones.\n", "\n", "#### Firstly, download the directory that contains the vcf into the subdirectory `work` and decompress it\n", "link : https://itrop.ird.fr/sv-training/VCF_CLONES.tar.gz" ] }, { "cell_type": "code", "execution_count": null, "id": "d4120b59-c2e5-4c54-8787-a8f755fbbb70", "metadata": {}, "outputs": [], "source": [ "%%bash\n", "cd /home/jovyan/work/\n", "wget https://itrop.ird.fr/sv-training/VCF_CLONES.tar.gz\n", "tar zxvf VCF_CLONES.tar.gz\n", "rm VCF_CLONES.tar.gz" ] }, { "cell_type": "markdown", "id": "304d9ab0-294c-4307-8cb7-a14154fb10cb", "metadata": {}, "source": [ "### First go into the directory that containsthe vcf file to analyze \n", "\n", "* List the content of this directory\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "d0c0a6de-6fc7-48bf-ab3a-278590402d1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/jovyan/work/VCF_CLONES\n" ] } ], "source": [ "cd /home/jovyan/work/VCF_CLONES/" ] }, { "cell_type": "code", "execution_count": 3, "id": "33b3039d-ef19-4766-af89-8e831266fca1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genome.txt output.vcf output.vcf.idx rawSNP.vcf rawSNP.vcf.idx\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "id": "aa5e1b2b-82f7-4710-b84f-06545badbfa9", "metadata": {}, "source": [ "### Count the number of variants with `bcftools stat` \n", "\n", "* run the bcftools stats on the vcf file and save the result into the file `rice-CHR6.200000.vcf.stat`\n", "* check that the file have been correctly created and display the 35 first lines of this file\n", "* How many samples were used for this SNP analysis ?\n", "* How many SNPs were detected ?" ] }, { "cell_type": "code", "execution_count": 4, "id": "e005df0c-3ac1-4c29-930e-8c4cda3036a4", "metadata": {}, "outputs": [], "source": [ "%%bash\n", "bcftools stats output.vcf > output.vcf.stat" ] }, { "cell_type": "markdown", "id": "1d79f4a9-afaf-4b0a-9a4a-b3d46fcca1e0", "metadata": {}, "source": [ "#### Display the first lines of the stat file" ] }, { "cell_type": "code", "execution_count": 5, "id": "6350543e-0c09-42fa-a4ac-9bb93d03f711", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# This file was produced by bcftools stats (1.10.2+htslib-1.10.2-3) and can be plotted using plot-vcfstats.\n", "# The command line was:\tbcftools stats output.vcf\n", "#\n", "# Definition of sets:\n", "# ID\t[2]id\t[3]tab-separated file names\n", "ID\t0\toutput.vcf\n", "# SN, Summary numbers:\n", "# number of records .. number of data rows in the VCF\n", "# number of no-ALTs .. reference-only sites, ALT is either \".\" or identical to REF\n", "# number of SNPs .. number of rows with a SNP\n", "# number of MNPs .. number of rows with a MNP, such as CC>TT\n", "# number of indels .. number of rows with an indel\n", "# number of others .. number of rows with other type, for example a symbolic allele or\n", "# a complex substitution, such as ACT>TCGA\n", "# number of multiallelic sites .. number of rows with multiple alternate alleles\n", "# number of multiallelic SNP sites .. number of rows with multiple alternate alleles, all SNPs\n", "# \n", "# Note that rows containing multiple types will be counted multiple times, in each\n", "# counter. For example, a row with a SNP and an indel increments both the SNP and\n", "# the indel counter.\n", "# \n", "# SN\t[2]id\t[3]key\t[4]value\n", "SN\t0\tnumber of samples:\t20\n", "SN\t0\tnumber of records:\t568240\n", "SN\t0\tnumber of no-ALTs:\t0\n", "SN\t0\tnumber of SNPs:\t564851\n", "SN\t0\tnumber of MNPs:\t0\n", "SN\t0\tnumber of indels:\t8428\n", "SN\t0\tnumber of others:\t0\n", "SN\t0\tnumber of multiallelic sites:\t167022\n", "SN\t0\tnumber of multiallelic SNP sites:\t156797\n", "# TSTV, transitions/transversions:\n", "# TSTV\t[2]id\t[3]ts\t[4]tv\t[5]ts/tv\t[6]ts (1st ALT)\t[7]tv (1st ALT)\t[8]ts/tv (1st ALT)\n", "TSTV\t0\t247090\t493302\t0.50\t187661\t375044\t0.50\n", "# SiS, Singleton stats:\n" ] } ], "source": [ "%%bash\n", "head -n 35 output.vcf.stat" ] }, { "cell_type": "markdown", "id": "74b53089-03fd-4d0a-a710-712acb2c8268", "metadata": {}, "source": [ "### Generating statistics from a VCF to determine how to set filters on it `vcftools`\n", "\n", "We will generate more statistics from a VCF using vcftools (LINK MANUAL), a very useful and fast program for handling vcf files \n", "to easily calculate these statistics in order to better define filters we will apply and to get an idea of how to set such filtering thresholds. \n", "\n", "The main information we will consider are:\n", "* Depth: Usually, we filter SNP with a minimum and maximum depth. We use a minimum depth cutoffs to remove false positive calls and to keep higher quality calls too. \n", "A maximum cut off allow to remove regions with very, very high read depths such as repetitive regions.\n", "* Quality Genotype quality : With this filter, we should not trust any genotype with a Phred score below 20 which suggests a less than 99% accuracy.\n", "* Minor allele frequency MAF can cause big problems with SNP calls - and also inflate statistical estimates downstream. Ideally you want an idea of the distribution of your \n", "allelic frequencies but 0.05 to 0.10 is a reasonable cut-off. You should keep in mind however that some analyses, particularly demographic inference can be biased by MAF thresholds.\n", "* Missing data How much missing data are you willing to tolerate? It will depend on the study but typically any site with >25% missing data should be dropped.\n", "* biallelic, heterozygosity...\n", "\n", "In this training, we will just display quality and depth distribution... but you should do on each value filterd.\n", "\n", "#### Mean depth per individual and per site `--depth, --site-mean-depth`\n", "\n", "* run vcftool with the correct options\n", "* check that the files have been created and display the first lines" ] }, { "cell_type": "code", "execution_count": 7, "id": "f421cbdc-d195-4050-9b7e-a4dbb4b357ad", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "VCFtools - 0.1.16\n", "(C) Adam Auton and Anthony Marcketta 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf output.vcf\n", "\t--depth\n", "\t--out depthi\n", "\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PGT,Number=1,Type=String,Description=\"Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PID,Number=1,Type=String,Description=\"Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PL,Number=G,Type=Integer,Description=\"Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=RGQ,Number=1,Type=Integer,Description=\"Unconditional reference genotype confidence, encoded as a phred quality -10*log10 p(genotype call is wrong)\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "After filtering, kept 20 out of 20 Individuals\n", "Outputting Mean Depth by Individual\n", "After filtering, kept 568240 out of a possible 568240 Sites\n", "Run Time = 4.00 seconds\n", "\n", "VCFtools - 0.1.16\n", "(C) Adam Auton and Anthony Marcketta 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf output.vcf\n", "\t--out depths\n", "\t--site-mean-depth\n", "\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PGT,Number=1,Type=String,Description=\"Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PID,Number=1,Type=String,Description=\"Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PL,Number=G,Type=Integer,Description=\"Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=RGQ,Number=1,Type=Integer,Description=\"Unconditional reference genotype confidence, encoded as a phred quality -10*log10 p(genotype call is wrong)\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "After filtering, kept 20 out of 20 Individuals\n", "Outputting Depth for Each Site\n", "After filtering, kept 568240 out of a possible 568240 Sites\n", "Run Time = 7.00 seconds\n" ] } ], "source": [ "%%bash\n", "vcftools --vcf output.vcf --depth --out depthi\n", "vcftools --vcf output.vcf --site-mean-depth --out depths" ] }, { "cell_type": "markdown", "id": "1d5bd604-63a5-4577-93a2-0af7681bef12", "metadata": {}, "source": [ "#### Display the first lines of the files just created by vcftools" ] }, { "cell_type": "code", "execution_count": 8, "id": "fbaae264-89ce-4f6d-ac2c-d304521f9447", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 1277076\n", "-rw-r----- 1 jovyan users 151798 Jun 17 09:46 output.vcf.idx\n", "-rw-r----- 1 jovyan users 792485101 Jun 17 09:49 rawSNP.vcf\n", "-rw-r----- 1 jovyan users 19 Jun 17 09:49 genome.txt\n", "-rw-r----- 1 jovyan users 238921 Jun 17 09:49 rawSNP.vcf.idx\n", "-rw-r----- 1 jovyan users 497636158 Jun 17 09:51 output.vcf\n", "-rw-r--r-- 1 jovyan users 40211 Jun 20 20:35 output.vcf.stat\n", "-rw-r--r-- 1 jovyan users 473 Jun 20 20:39 depthi.idepth\n", "-rw-r--r-- 1 jovyan users 17146658 Jun 20 20:39 depths.ldepth.mean\n", "==> depthi.idepth <==\n", "INDV\tN_SITES\tMEAN_DEPTH\n", "Clone1\t568240\t36.0589\n", "Clone10\t568236\t30.6221\n", "Clone11\t568239\t31.2727\n", "Clone12\t568230\t26.0382\n", "Clone13\t568240\t32.018\n", "Clone14\t568225\t31.6534\n", "Clone15\t568233\t31.1039\n", "Clone16\t568227\t31.5335\n", "Clone17\t568235\t32.4415\n", "\n", "==> depths.ldepth.mean <==\n", "CHROM\tPOS\tMEAN_DEPTH\tVAR_DEPTH\n", "Reference\t30\t4.75\t4.30263\n", "Reference\t36\t5.7\t5.27368\n", "Reference\t38\t5.9\t5.67368\n", "Reference\t39\t5.95\t5.62895\n", "Reference\t42\t6.6\t4.56842\n", "Reference\t45\t6.95\t3.94474\n", "Reference\t54\t8.05\t3.41842\n", "Reference\t58\t8.6\t3.41053\n", "Reference\t61\t8.85\t2.97632\n" ] } ], "source": [ "%%bash\n", "ls -lrt\n", "head *depth*" ] }, { "cell_type": "markdown", "id": "a9641f84-af45-4137-8f60-6016edd56596", "metadata": {}, "source": [ "#### Extracting quality per site `--site-quality`\n", "\n", "* run vcftool with the correct options\n", "* check that the files have been created and display the first lines" ] }, { "cell_type": "code", "execution_count": 9, "id": "d16f68ef-956a-4385-917b-ad8285965021", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "VCFtools - 0.1.16\n", "(C) Adam Auton and Anthony Marcketta 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf output.vcf\n", "\t--out qual\n", "\t--site-quality\n", "\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PGT,Number=1,Type=String,Description=\"Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PID,Number=1,Type=String,Description=\"Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PL,Number=G,Type=Integer,Description=\"Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=RGQ,Number=1,Type=Integer,Description=\"Unconditional reference genotype confidence, encoded as a phred quality -10*log10 p(genotype call is wrong)\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "After filtering, kept 20 out of 20 Individuals\n", "Outputting Quality for Each Site\n", "After filtering, kept 568240 out of a possible 568240 Sites\n", "Run Time = 4.00 seconds\n" ] } ], "source": [ "%%bash\n", "vcftools --vcf output.vcf --site-quality --out qual" ] }, { "cell_type": "markdown", "id": "bf7e3619-df35-4f8a-a7f4-0c490d69a7b7", "metadata": {}, "source": [ "#### Display the first lines of the file just created by vcftools" ] }, { "cell_type": "code", "execution_count": 10, "id": "861692d3-23ed-4848-893a-77981a00fef2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 1290848\n", "-rw-r----- 1 jovyan users 151798 Jun 17 09:46 output.vcf.idx\n", "-rw-r----- 1 jovyan users 792485101 Jun 17 09:49 rawSNP.vcf\n", "-rw-r----- 1 jovyan users 19 Jun 17 09:49 genome.txt\n", "-rw-r----- 1 jovyan users 238921 Jun 17 09:49 rawSNP.vcf.idx\n", "-rw-r----- 1 jovyan users 497636158 Jun 17 09:51 output.vcf\n", "-rw-r--r-- 1 jovyan users 40211 Jun 20 20:35 output.vcf.stat\n", "-rw-r--r-- 1 jovyan users 473 Jun 20 20:39 depthi.idepth\n", "-rw-r--r-- 1 jovyan users 17146658 Jun 20 20:39 depths.ldepth.mean\n", "-rw-r--r-- 1 jovyan users 14102265 Jun 20 20:39 qual.lqual\n", "CHROM\tPOS\tQUAL\n", "Reference\t30\t66.89\n", "Reference\t36\t200.62\n", "Reference\t38\t114.16\n", "Reference\t39\t91.7\n", "Reference\t42\t318.58\n", "Reference\t45\t113.02\n", "Reference\t54\t333.4\n", "Reference\t58\t1001.62\n", "Reference\t61\t422.5\n" ] } ], "source": [ "%%bash\n", "ls -lrt\n", "head *qual" ] }, { "cell_type": "markdown", "id": "5d6e6f7d-37f0-48c7-994c-0f624d957a43", "metadata": {}, "source": [ "\n", "__Calculate allele frequency__\n", "\n", "* --freq2 : outputs the frequencies without information about the alleles\n", "* --freq would return their identity. \n", "* --max-alleles 2 to exclude sites that have more than two alleles." ] }, { "cell_type": "code", "execution_count": 11, "id": "c8d02f46-2fb0-462a-9077-49f1aa614f66", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "VCFtools - 0.1.16\n", "(C) Adam Auton and Anthony Marcketta 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf output.vcf\n", "\t--max-alleles 2\n", "\t--freq\n", "\t--out AF\n", "\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PGT,Number=1,Type=String,Description=\"Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PID,Number=1,Type=String,Description=\"Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PL,Number=G,Type=Integer,Description=\"Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=RGQ,Number=1,Type=Integer,Description=\"Unconditional reference genotype confidence, encoded as a phred quality -10*log10 p(genotype call is wrong)\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "After filtering, kept 20 out of 20 Individuals\n", "Outputting Frequency Statistics...\n", "After filtering, kept 401218 out of a possible 568240 Sites\n", "Run Time = 5.00 seconds\n", "\n", "VCFtools - 0.1.16\n", "(C) Adam Auton and Anthony Marcketta 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf output.vcf\n", "\t--max-alleles 2\n", "\t--freq2\n", "\t--out AF2\n", "\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PGT,Number=1,Type=String,Description=\"Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PID,Number=1,Type=String,Description=\"Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=PL,Number=G,Type=Integer,Description=\"Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification\">\n", "Warning: Expected at least 2 parts in FORMAT entry: ID=RGQ,Number=1,Type=Integer,Description=\"Unconditional reference genotype confidence, encoded as a phred quality -10*log10 p(genotype call is wrong)\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=AF,Number=A,Type=Float,Description=\"Allele Frequency, for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAC,Number=A,Type=Integer,Description=\"Maximum likelihood expectation (MLE) for the allele counts (not necessarily the same as the AC), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "Warning: Expected at least 2 parts in INFO entry: ID=MLEAF,Number=A,Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT allele, in the same order as listed\">\n", "After filtering, kept 20 out of 20 Individuals\n", "Outputting Frequency Statistics...\n", "After filtering, kept 401218 out of a possible 568240 Sites\n", "Run Time = 4.00 seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total 1323152\n", "-rw-r----- 1 jovyan users 151798 Jun 17 09:46 output.vcf.idx\n", "-rw-r----- 1 jovyan users 792485101 Jun 17 09:49 rawSNP.vcf\n", "-rw-r----- 1 jovyan users 19 Jun 17 09:49 genome.txt\n", "-rw-r----- 1 jovyan users 238921 Jun 17 09:49 rawSNP.vcf.idx\n", "-rw-r----- 1 jovyan users 497636158 Jun 17 09:51 output.vcf\n", "-rw-r--r-- 1 jovyan users 40211 Jun 20 20:35 output.vcf.stat\n", "-rw-r--r-- 1 jovyan users 473 Jun 20 20:39 depthi.idepth\n", "-rw-r--r-- 1 jovyan users 17146658 Jun 20 20:39 depths.ldepth.mean\n", "-rw-r--r-- 1 jovyan users 14102265 Jun 20 20:39 qual.lqual\n", "-rw-r--r-- 1 jovyan users 17346823 Jun 20 20:40 AF.frq\n", "-rw-r--r-- 1 jovyan users 15727473 Jun 20 20:40 AF2.frq\n", "==> AF2.frq <==\n", "CHROM\tPOS\tN_ALLELES\tN_CHR\t{FREQ}\n", "Reference\t30\t2\t36\t0.944444\t0.0555556\n", "Reference\t36\t2\t36\t0.944444\t0.0555556\n", "Reference\t38\t2\t36\t0.944444\t0.0555556\n", "Reference\t39\t2\t38\t0.947368\t0.0526316\n", "Reference\t42\t2\t38\t0.947368\t0.0526316\n", "Reference\t45\t2\t40\t0.95\t0.05\n", "Reference\t54\t2\t38\t0.947368\t0.0526316\n", "Reference\t58\t2\t38\t0.842105\t0.157895\n", "Reference\t61\t2\t40\t0.95\t0.05\n", "\n", "==> AF.frq <==\n", "CHROM\tPOS\tN_ALLELES\tN_CHR\t{ALLELE:FREQ}\n", "Reference\t30\t2\t36\tT:0.944444\tG:0.0555556\n", "Reference\t36\t2\t36\tC:0.944444\tA:0.0555556\n", "Reference\t38\t2\t36\tT:0.944444\tG:0.0555556\n", "Reference\t39\t2\t38\tC:0.947368\tA:0.0526316\n", "Reference\t42\t2\t38\tT:0.947368\tG:0.0526316\n", "Reference\t45\t2\t40\tC:0.95\tA:0.05\n", "Reference\t54\t2\t38\tC:0.947368\tG:0.0526316\n", "Reference\t58\t2\t38\tT:0.842105\tA:0.157895\n", "Reference\t61\t2\t40\tC:0.95\tA:0.05\n" ] } ], "source": [ "%%bash\n", "vcftools --vcf output.vcf --freq --out AF --max-alleles 2\n", "vcftools --vcf output.vcf --freq2 --out AF2 --max-alleles 2\n", "ls -lrt\n", "head *.frq" ] }, { "cell_type": "markdown", "id": "2de89286-b5c4-4c32-964e-45aca7ae12bc", "metadata": {}, "source": [ "### Generating density plot QUAL & DEPTH \n", "\n", "#### Plotting quality per site " ] }, { "cell_type": "code", "execution_count": 13, "id": "307877db-c85c-47de-a16f-6c38ab9e3e70", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " CHROM POS QUAL\n", "0 Reference 30 66.89\n", "1 Reference 36 200.62\n", "2 Reference 38 114.16\n", "3 Reference 39 91.70\n", "4 Reference 42 318.58\n", "... ... ... ...\n", "568235 Reference 999983 166.58\n", "568236 Reference 999985 84.98\n", "568237 Reference 999987 135.96\n", "568238 Reference 999990 97.25\n", "568239 Reference 999993 142.96\n", "\n", "[568240 rows x 3 columns]\n" ] }, { "data": { "text/plain": [ "count 568240.000000\n", "mean 2581.014661\n", "std 1376.167200\n", "min 30.030000\n", "25% 1639.830000\n", "50% 1997.840000\n", "75% 3434.790000\n", "max 14705.700000\n", "Name: QUAL, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import pandas as pd\n", "\n", "qual_file=\"qual.lqual\"\n", "df_qual = pd.read_csv(qual_file, index_col=False, sep=\"\\t\")\n", "print(df_qual)\n", "df_qual['QUAL'].describe()" ] }, { "cell_type": "code", "execution_count": 14, "id": "c3dc4b15-7c35-4b40-acb7-30aeeece268b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot with seaborn\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.kdeplot(x=\"QUAL\", data=df_qual)" ] }, { "cell_type": "code", "execution_count": 122, "id": "2fed282d-9cc3-403b-b627-1f73e6047d4c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'PUT A Y-AXIS LABEL')" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot with seaborn\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "\n", "plt.figure(figsize = (15,8))\n", "ax=sns.kdeplot(x=\"QUAL\", data=df_qual[df_qual.QUAL<10000])\n", "ax.set_title(\"PUT A TITLE\")\n", "ax.set_xlabel(\"PUT A X-AXIS LABEL\")\n", "ax.set_ylabel(\"PUT A Y-AXIS LABEL\")" ] }, { "cell_type": "markdown", "id": "ad84b027-b453-44d8-8f26-22bf372e1e53", "metadata": {}, "source": [ "#### Plotting Mean depth per site " ] }, { "cell_type": "code", "execution_count": 15, "id": "f4c5181c-f1fb-42b3-86a2-ddfc93218cbe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " CHROM POS MEAN_DEPTH VAR_DEPTH\n", "0 Reference 30 4.75 4.302630\n", "1 Reference 36 5.70 5.273680\n", "2 Reference 38 5.90 5.673680\n", "3 Reference 39 5.95 5.628950\n", "4 Reference 42 6.60 4.568420\n", "... ... ... ... ...\n", "568235 Reference 999983 1.85 3.923680\n", "568236 Reference 999985 1.35 3.713160\n", "568237 Reference 999987 0.35 0.765789\n", "568238 Reference 999990 0.20 0.378947\n", "568239 Reference 999993 0.15 0.450000\n", "\n", "[568240 rows x 4 columns]\n" ] }, { "data": { "text/html": [ "
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POSMEAN_DEPTHVAR_DEPTH
count568240.000000568240.000000568240.000000
mean508603.85501933.484068183.429022
std297048.5144794.305675128.256867
min30.0000000.1000000.094737
25%227415.75000031.700000108.095000
50%534353.00000033.900000170.029000
75%765228.25000036.050000227.945000
max999993.00000049.0500002491.250000
\n", "
" ], "text/plain": [ " POS MEAN_DEPTH VAR_DEPTH\n", "count 568240.000000 568240.000000 568240.000000\n", "mean 508603.855019 33.484068 183.429022\n", "std 297048.514479 4.305675 128.256867\n", "min 30.000000 0.100000 0.094737\n", "25% 227415.750000 31.700000 108.095000\n", "50% 534353.000000 33.900000 170.029000\n", "75% 765228.250000 36.050000 227.945000\n", "max 999993.000000 49.050000 2491.250000" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "depth_file=\"depths.ldepth.mean\"\n", "df_depth = pd.read_csv(depth_file, index_col=False, sep=\"\\t\")\n", "print(df_depth)\n", "df_depth.describe()" ] }, { "cell_type": "code", "execution_count": 125, "id": "2aacd4ac-01d3-4d99-b64e-c9a68754211c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'PUT A Y-AXIS LABEL')" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot with seaborn\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "ax=sns.kdeplot(x=\"MEAN_DEPTH\", data=df_depth)\n", "ax.set_xlim(1, 70)\n", "ax.set_title(\"PUT A TITLE\")\n", "ax.set_xlabel(\"PUT A X-AXIS LABEL\")\n", "ax.set_ylabel(\"PUT A Y-AXIS LABEL\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "5ddfc616-e990-4fce-a0bb-2d534445e8e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'PUT A Y-AXIS LABEL')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot with seaborn\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "ax=sns.kdeplot(x=\"MEAN_DEPTH\", data=df_depth[df_depth.MEAN_DEPTH<50])\n", "ax.set_xlim(1, 50)\n", "ax.set_title(\"PUT A TITLE\")\n", "ax.set_xlabel(\"PUT A X-AXIS LABEL\")\n", "ax.set_ylabel(\"PUT A Y-AXIS LABEL\")" ] }, { "cell_type": "markdown", "id": "788df901-4e83-4102-95c5-c097e6d72171", "metadata": {}, "source": [ "### III - FILTERING VCF \n" ] }, { "cell_type": "markdown", "id": "0ab0fde3-23e2-46c9-a54a-cd84c6960fc5", "metadata": {}, "source": [ "#### Which filters ?\n", "\n", "Here, we will apply on the vcf the following filters :\n", "\n", "* QUAL > 300\n", "* DP > 30 and DP < 400\n", "* Less than 3 SNPs into a window of 10pb\n", "\n", "The threshodl of each filter depends on the SNP analysis (sample number, sequencing depth).\n", "\n", "In a second step, according the following analysys (eg: population genomics), usually, we will apply other filters such as :\n", "* removing missing data\n", "* keeping only biallellic\n", "* heterozygosity\n", " \n", "#### Select only the SNPs (and remove the INDELs) - `gatk variantFiltration`\n", "* Filter vcf to keep only SNPs\n", "* Check that the new vcf has been created\n", "* Get the number of polymorphisms in the new vcf file " ] }, { "cell_type": "code", "execution_count": 18, "id": "e0c297d1-12f7-425a-8f80-6d36f823924d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using GATK jar /opt/conda/share/gatk4-4.2.6.1-1/gatk-package-4.2.6.1-local.jar\n", "Running:\n", " java -Dsamjdk.use_async_io_read_samtools=false -Dsamjdk.use_async_io_write_samtools=true -Dsamjdk.use_async_io_write_tribble=false -Dsamjdk.compression_level=2 -Xmx8G -Xms8G -jar /opt/conda/share/gatk4-4.2.6.1-1/gatk-package-4.2.6.1-local.jar SelectVariants -R /home/jovyan/work/SV_DATA/REF/reference.fasta -V output.vcf -select-type SNP -O output.onlySNP.vcf\n", "20:46:06.685 INFO NativeLibraryLoader - Loading libgkl_compression.so from jar:file:/opt/conda/share/gatk4-4.2.6.1-1/gatk-package-4.2.6.1-local.jar!/com/intel/gkl/native/libgkl_compression.so\n", "20:46:07.263 INFO SelectVariants - ------------------------------------------------------------\n", "20:46:07.264 INFO SelectVariants - The Genome Analysis Toolkit (GATK) v4.2.6.1\n", "20:46:07.264 INFO SelectVariants - For support and documentation go to https://software.broadinstitute.org/gatk/\n", "20:46:11.547 INFO SelectVariants - Initializing engine\n", "20:46:12.050 INFO FeatureManager - Using codec VCFCodec to read file file:///home/jovyan/work/VCF_CLONES/output.vcf\n", "20:46:12.116 INFO SelectVariants - Done initializing engine\n", "20:46:12.154 INFO ProgressMeter - Starting traversal\n", "20:46:12.154 INFO ProgressMeter - Current Locus Elapsed Minutes Variants Processed Variants/Minute\n", "20:46:22.156 INFO ProgressMeter - Reference:760148 0.2 417000 2501499.7\n", "20:46:24.180 INFO ProgressMeter - Reference:998645 0.2 559812 2793008.5\n", "20:46:24.180 INFO ProgressMeter - Traversal complete. Processed 559812 total variants in 0.2 minutes.\n", "20:46:24.196 INFO SelectVariants - Shutting down engine\n", "[June 20, 2022 8:46:24 PM UTC] org.broadinstitute.hellbender.tools.walkers.variantutils.SelectVariants done. Elapsed time: 0.29 minutes.\n", "Runtime.totalMemory()=8565817344\n" ] } ], "source": [ "%%bash\n", "gatk SelectVariants --java-options \"-Xmx8G -Xms8G\" -R /home/jovyan/work/SV_DATA/REF/reference.fasta -V output.vcf -select-type SNP -O output.onlySNP.vcf" ] }, { "cell_type": "markdown", "id": "f0e51c8f-ab8a-4f86-88a7-404d663552d5", "metadata": {}, "source": [ "#### Get the SNP count of this new vcf file" ] }, { "cell_type": "code", "execution_count": 19, "id": "a91a9c9e-6069-4543-afff-df4fdb07a0a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 1.8G\n", "-rw-r----- 1 jovyan users 149K Jun 17 09:46 output.vcf.idx\n", "-rw-r----- 1 jovyan users 756M Jun 17 09:49 rawSNP.vcf\n", "-rw-r----- 1 jovyan users 19 Jun 17 09:49 genome.txt\n", "-rw-r----- 1 jovyan users 234K Jun 17 09:49 rawSNP.vcf.idx\n", "-rw-r----- 1 jovyan users 475M Jun 17 09:51 output.vcf\n", "-rw-r--r-- 1 jovyan users 40K Jun 20 20:35 output.vcf.stat\n", "-rw-r--r-- 1 jovyan users 473 Jun 20 20:39 depthi.idepth\n", "-rw-r--r-- 1 jovyan users 17M Jun 20 20:39 depths.ldepth.mean\n", "-rw-r--r-- 1 jovyan users 14M Jun 20 20:39 qual.lqual\n", "-rw-r--r-- 1 jovyan users 17M Jun 20 20:40 AF.frq\n", "-rw-r--r-- 1 jovyan users 15M Jun 20 20:40 AF2.frq\n", "-rw-r--r-- 1 jovyan users 467M Jun 20 20:46 output.onlySNP.vcf\n", "-rw-r--r-- 1 jovyan users 147K Jun 20 20:46 output.onlySNP.vcf.idx\n", "# This file was produced by bcftools stats (1.10.2+htslib-1.10.2-3) and can be plotted using plot-vcfstats.\n", "# The command line was:\tbcftools stats output.onlySNP.vcf\n", "#\n", "# Definition of sets:\n", "# ID\t[2]id\t[3]tab-separated file names\n", "ID\t0\toutput.onlySNP.vcf\n", "# SN, Summary numbers:\n", "# number of records .. number of data rows in the VCF\n", "# number of no-ALTs .. reference-only sites, ALT is either \".\" or identical to REF\n", "# number of SNPs .. number of rows with a SNP\n", "# number of MNPs .. number of rows with a MNP, such as CC>TT\n", "# number of indels .. number of rows with an indel\n", "# number of others .. number of rows with other type, for example a symbolic allele or\n", "# a complex substitution, such as ACT>TCGA\n", "# number of multiallelic sites .. number of rows with multiple alternate alleles\n", "# number of multiallelic SNP sites .. number of rows with multiple alternate alleles, all SNPs\n", "# \n", "# Note that rows containing multiple types will be counted multiple times, in each\n", "# counter. For example, a row with a SNP and an indel increments both the SNP and\n", "# the indel counter.\n", "# \n", "# SN\t[2]id\t[3]key\t[4]value\n", "SN\t0\tnumber of samples:\t20\n", "SN\t0\tnumber of records:\t559812\n", "SN\t0\tnumber of no-ALTs:\t0\n", "SN\t0\tnumber of SNPs:\t559812\n", "SN\t0\tnumber of MNPs:\t0\n", "SN\t0\tnumber of indels:\t0\n", "SN\t0\tnumber of others:\t0\n", "SN\t0\tnumber of multiallelic sites:\t161928\n", "SN\t0\tnumber of multiallelic SNP sites:\t156797\n", "# TSTV, transitions/transversions:\n", "# TSTV\t[2]id\t[3]ts\t[4]tv\t[5]ts/tv\t[6]ts (1st ALT)\t[7]tv (1st ALT)\t[8]ts/tv (1st ALT)\n", "TSTV\t0\t244929\t488971\t0.50\t186662\t373108\t0.50\n", "# SiS, Singleton stats:\n" ] } ], "source": [ "%%bash\n", "ls -lrth\n", "bcftools stats output.onlySNP.vcf | head -n35" ] }, { "cell_type": "markdown", "id": "90e77c6a-3d4c-4d6e-bf30-60bfbe237765", "metadata": {}, "source": [ "#### Compress the vcf and generate the index of the compressed vcf - `tabix -p vcf vcf_file`" ] }, { "cell_type": "code", "execution_count": 8, "id": "a2af6a39-3eac-40cc-bc0d-cff8cfa5f35f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[bgzip] No such file or directory: output.onlySNP.vcf\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total 1.4G\n", "-rw-r----- 1 jovyan users 149K Jun 17 09:46 output.vcf.idx\n", "-rw-r----- 1 jovyan users 756M Jun 17 09:49 rawSNP.vcf\n", "-rw-r----- 1 jovyan users 19 Jun 17 09:49 genome.txt\n", "-rw-r----- 1 jovyan users 234K Jun 17 09:49 rawSNP.vcf.idx\n", "-rw-r----- 1 jovyan users 475M Jun 17 09:51 output.vcf\n", "-rw-r--r-- 1 jovyan users 40K Jun 20 20:35 output.vcf.stat\n", "-rw-r--r-- 1 jovyan users 473 Jun 20 20:39 depthi.idepth\n", "-rw-r--r-- 1 jovyan users 17M Jun 20 20:39 depths.ldepth.mean\n", "-rw-r--r-- 1 jovyan users 14M Jun 20 20:39 qual.lqual\n", "-rw-r--r-- 1 jovyan users 17M Jun 20 20:40 AF.frq\n", "-rw-r--r-- 1 jovyan users 15M Jun 20 20:40 AF2.frq\n", "-rw-r--r-- 1 jovyan users 147K Jun 20 20:46 output.onlySNP.vcf.idx\n", "-rw-r--r-- 1 jovyan users 50M Jun 20 20:46 output.onlySNP.vcf.gz\n", "-rw-r--r-- 1 jovyan users 830 Jun 20 20:47 output.onlySNP.vcf.gz.tbi\n", "-rw-r--r-- 1 jovyan users 52M Jun 20 21:23 output.filteredSNP.vcf\n" ] } ], "source": [ "%%bash\n", "bgzip output.onlySNP.vcf\n", "ls -lrth" ] }, { "cell_type": "code", "execution_count": 9, "id": "57232bd3-aa17-44d5-839f-380cc1e653b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 1.4G\n", "-rw-r----- 1 jovyan users 149K Jun 17 09:46 output.vcf.idx\n", "-rw-r----- 1 jovyan users 756M Jun 17 09:49 rawSNP.vcf\n", "-rw-r----- 1 jovyan users 19 Jun 17 09:49 genome.txt\n", "-rw-r----- 1 jovyan users 234K Jun 17 09:49 rawSNP.vcf.idx\n", "-rw-r----- 1 jovyan users 475M Jun 17 09:51 output.vcf\n", "-rw-r--r-- 1 jovyan users 40K Jun 20 20:35 output.vcf.stat\n", "-rw-r--r-- 1 jovyan users 473 Jun 20 20:39 depthi.idepth\n", "-rw-r--r-- 1 jovyan users 17M Jun 20 20:39 depths.ldepth.mean\n", "-rw-r--r-- 1 jovyan users 14M Jun 20 20:39 qual.lqual\n", "-rw-r--r-- 1 jovyan users 17M Jun 20 20:40 AF.frq\n", "-rw-r--r-- 1 jovyan users 15M Jun 20 20:40 AF2.frq\n", "-rw-r--r-- 1 jovyan users 147K Jun 20 20:46 output.onlySNP.vcf.idx\n", "-rw-r--r-- 1 jovyan users 50M Jun 20 20:46 output.onlySNP.vcf.gz\n", "-rw-r--r-- 1 jovyan users 53M Jun 20 21:23 output.filteredSNP.vcf\n", "-rw-r--r-- 1 jovyan users 830 Jun 20 21:23 output.onlySNP.vcf.gz.tbi\n" ] } ], "source": [ "%%bash\n", "tabix -f -p vcf output.onlySNP.vcf.gz\n", "ls -lrth" ] }, { "cell_type": "markdown", "id": "67d62c00-4c7d-47d2-8334-69c78d78bbb3", "metadata": {}, "source": [ "#### Applying filters on QUAL, DEPTH and CLUSTER SNPsSelect only the SNPs (and remove the INDELs) - `gatk variantFiltration`\n", "\n", "Be careful, it miss one filter in the following command" ] }, { "cell_type": "code", "execution_count": null, "id": "c360b424-da46-4580-8e06-d5dccf988025", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using GATK jar /opt/conda/share/gatk4-4.2.6.1-1/gatk-package-4.2.6.1-local.jar\n", "Running:\n", " java -Dsamjdk.use_async_io_read_samtools=false -Dsamjdk.use_async_io_write_samtools=true -Dsamjdk.use_async_io_write_tribble=false -Dsamjdk.compression_level=2 -Xmx12G -Xms12G -jar /opt/conda/share/gatk4-4.2.6.1-1/gatk-package-4.2.6.1-local.jar VariantFiltration -R /home/jovyan/work/SV_DATA/REF/reference.fasta -V output.onlySNP.vcf.gz --filter-expression QUAL<300 --filter-name LOW_QUAL --filter-expression DP<30 --filter-name LOW_DP --filter-expression DP>40 --filter-name HIGH_DP --cluster-size 3 --cluster-window-size 10 -O output.filteredSNP.vcf\n", "21:23:38.386 INFO NativeLibraryLoader - Loading libgkl_compression.so from jar:file:/opt/conda/share/gatk4-4.2.6.1-1/gatk-package-4.2.6.1-local.jar!/com/intel/gkl/native/libgkl_compression.so\n", "21:23:38.579 INFO VariantFiltration - ------------------------------------------------------------\n", "21:23:38.579 INFO VariantFiltration - The Genome Analysis Toolkit (GATK) v4.2.6.1\n", "21:23:38.580 INFO VariantFiltration - For support and documentation go to https://software.broadinstitute.org/gatk/\n", "21:23:38.588 INFO VariantFiltration - Initializing engine\n", "21:23:39.109 INFO FeatureManager - Using codec VCFCodec to read file file:///home/jovyan/work/VCF_CLONES/output.onlySNP.vcf.gz\n", "21:23:39.143 INFO VariantFiltration - Done initializing engine\n", "21:23:39.239 INFO ProgressMeter - Starting traversal\n", "21:23:39.239 INFO ProgressMeter - Current Locus Elapsed Minutes Variants Processed Variants/Minute\n", "21:23:59.187 INFO ProgressMeter - Reference:3154 0.3 2000 6015.6\n", "21:24:20.901 INFO ProgressMeter - Reference:4765 0.7 3000 4320.5\n", "21:24:50.043 INFO ProgressMeter - Reference:6326 1.2 4000 3389.6\n", "21:25:26.606 INFO ProgressMeter - Reference:7868 1.8 5000 2794.2\n", "21:26:11.363 INFO ProgressMeter - Reference:9421 2.5 6000 2366.5\n", "21:27:06.877 INFO ProgressMeter - Reference:11021 3.5 7000 2022.8\n", "21:28:06.188 INFO ProgressMeter - Reference:12722 4.4 8000 1798.1\n", "21:29:13.009 INFO ProgressMeter - Reference:14441 5.6 9000 1617.9\n", "21:30:27.406 INFO ProgressMeter - Reference:16221 6.8 10000 1470.0\n", "21:30:39.230 INFO ProgressMeter - Reference:17920 7.0 11000 1571.5\n", "21:30:49.835 INFO ProgressMeter - Reference:19543 7.2 12000 1672.1\n", "21:31:09.679 INFO ProgressMeter - Reference:21162 7.5 13000 1731.6\n", "21:31:35.211 INFO ProgressMeter - Reference:22899 7.9 14000 1764.8\n", "21:32:10.691 INFO ProgressMeter - Reference:24588 8.5 15000 1759.7\n", "21:32:51.273 INFO ProgressMeter - Reference:26124 9.2 16000 1739.0\n", "21:33:40.141 INFO ProgressMeter - Reference:27709 10.0 17000 1697.4\n", "21:34:38.023 INFO ProgressMeter - Reference:29388 11.0 18000 1639.4\n", "21:35:43.062 INFO ProgressMeter - Reference:31046 12.1 19000 1575.0\n", "21:36:55.339 INFO ProgressMeter - Reference:32846 13.3 20000 1507.3\n", "21:37:12.255 INFO ProgressMeter - Reference:36466 13.6 22000 1623.6\n", "21:37:32.557 INFO ProgressMeter - Reference:38174 13.9 23000 1656.0\n", "21:38:00.440 INFO ProgressMeter - Reference:39964 14.4 24000 1672.1\n" ] } ], "source": [ "%%bash\n", "gatk VariantFiltration --java-options \"-Xmx12G -Xms12G\" -R /home/jovyan/work/SV_DATA/REF/reference.fasta -V output.onlySNP.vcf.gz --filter-expression \"QUAL<300\" --filter-name \"LOW_QUAL\" --filter-expression \"DP<30\" --filter-name \"LOW_DP\" --filter-expression \"DP>40\" --filter-name \"HIGH_DP\" --cluster-size 3 --cluster-window-size 10 -O output.filteredSNP.vcf" ] }, { "cell_type": "code", "execution_count": null, "id": "1f399782-0233-4222-92f3-88eaef3c1ade", "metadata": {}, "outputs": [], "source": [ "ls -lrtht" ] }, { "cell_type": "code", "execution_count": 2, "id": "631e487c-9397-46ec-8022-f5a8cca2518a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[bgzip] No such file or directory: output.filteredSNP.vcf\n", "tbx_index_build failed: output.filteredSNP.vcf.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total 876K\n", "-rw-r--r-- 1 jovyan users 1.3K Jun 20 14:44 README.md\n", "-rw-r--r-- 1 jovyan users 14K Jun 20 14:44 Day4_SV_genome.ipynb\n", "-rw-r--r-- 1 jovyan users 12K Jun 20 14:44 Day3b_Variants_structuraux_CORR.ipynb\n", "-rw-r--r-- 1 jovyan users 19K Jun 20 17:03 Day1_Mapping_Practice_EMPTY.ipynb\n", "-rw-r--r-- 1 jovyan users 61K Jun 20 17:04 Day1_Mapping_Practice_CORR.ipynb\n", "-rw-r--r-- 1 jovyan users 13K Jun 20 17:35 Day2b_Appel_variants_SNP_EMPTY.ipynb\n", "-rw-r--r-- 1 jovyan users 72K Jun 20 20:21 Day2a_Mapping_analysis_CORR.ipynb\n", "-rw-r--r-- 1 jovyan users 11K Jun 20 20:23 Day2a_Mapping_analysis-EMPTY.ipynb\n", "-rw-r--r-- 1 jovyan users 302K Jun 20 21:02 Day2b_Appel_variants_SNP_CORR.ipynb\n", "-rw-r--r-- 1 jovyan users 21K Jun 20 21:19 Day2c_SNP_analysis_EMPTY.ipynb\n", "-rw-r--r-- 1 jovyan users 329K Jun 20 21:20 Day2c_SNP_analysis_CORR.ipynb\n" ] } ], "source": [ "%%bash\n", "bgzip output.filteredSNP.vcf\n", "tabix -p vcf output.filteredSNP.vcf.gz\n", "ls -lrtht" ] }, { "cell_type": "code", "execution_count": null, "id": "6d3ce9eb-10ee-4f88-a402-4289f137c98c", "metadata": {}, "outputs": [], "source": [ "%%bash\n", "bcftools stats output.filteredSNP.vcf.gz | head -n 35" ] }, { "cell_type": "markdown", "id": "850f66c5-a78a-448d-bbc2-12a485c3d1bc", "metadata": {}, "source": [ "#### _Commands to apply other filters_\n", "\n", "##### remove missing data if more than 5% of missing data at a site (12 samples over 228 samples) `vcftools`\n", "\n", "`vcftools --vcf input.vcf --max-missing-count 12 --remove-filtered-all --recode --recode-INFO-all --out ouput.onlySNP.12na`\n", "\n", "##### keep SNPs with only 90% homzygous (222 samples)\n", "\n", "`cat input.vcf | java -jar SnpSift.jar filter \" (countHom( )> 222) \" > output.onlySNP.12na.recode.222homoz.vcf`\n", "\n", "##### keep only biallelic sites\n", "`vcftools --vcf input.vcf --min-alleles 2 --max-alleles 2 --remove-filtered-all --recode --recode-INFO-all --out output.onlySNP.filteredPASS.na.225.Homoz.biallelic`" ] }, { "cell_type": "markdown", "id": "32a7a1f6-3537-4866-8bb6-8b9175dae0cd", "metadata": {}, "source": [ "### IV - VCF ANNOTATION " ] }, { "cell_type": "markdown", "id": "b95ddbb3-0a59-42cc-9882-ade334409ad1", "metadata": { "tags": [] }, "source": [ "java -jar snpEff.jar -v -o statschr6.onlySNP.html rice6.1 input.vcf > output.snpeff.vcf" ] }, { "cell_type": "markdown", "id": "c318abee-7dcd-4ad4-9981-7ebac9eb164f", "metadata": { "tags": [] }, "source": [ "## V - Compute the SNP density along the chromosomes " ] }, { "cell_type": "code", "execution_count": null, "id": "471c9e7d-48fc-4c83-a212-9ecc6d9cfa8a", "metadata": {}, "outputs": [], "source": [ "echo -e \"Reference\\t1000000\\n\" > genome.txt\n", "bedtools genomecov -bga -split -i output.vcf -g genome.txt > density.csv" ] }, { "cell_type": "code", "execution_count": null, "id": "5780d9da-0550-48bc-a34d-8d16b324ceca", "metadata": { "tags": [] }, "outputs": [], "source": [ "head density.csv" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }