{"id":4623,"date":"2026-04-09T02:03:14","date_gmt":"2026-04-08T17:03:14","guid":{"rendered":"https:\/\/bdsl.jbnu.ac.kr\/blog\/?p=4623"},"modified":"2026-04-09T02:31:01","modified_gmt":"2026-04-08T17:31:01","slug":"variant-calling-using-gatk","status":"publish","type":"post","link":"https:\/\/bdsl.jbnu.ac.kr\/blog\/variant-calling-using-gatk\/","title":{"rendered":"Variant calling using GATK"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Introduction <\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Germline and somatic variants can reveal the genetic factors underlying a disease or phenotype. These variants are identified by mapping sequencing reads to a reference genome and detecting differences. However, it is necessary to distinguish disease-associated variants from natural genetic variation, such as single nucleotide polymorphisms (SNPs), which are widely distributed across population rather than enriched in specific disease groups.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Therefore, accurate identification of phenotype-associated variants requires not only read mapping but also the incorporation of external knowledge and filtering strategies. Mapping to a reference genome followed by systematic filtering enables the detection of candidate variants. Because these procedures are well-defined, they can be standardized to improve reproducibility and analytical convenience.<br><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this exercise, we will use a GATK workflow to perform variant discovery. This material is intended for a graduate-level class in the Department of Molecular Biology at Jeonbuk National University and is provided for educational purposes only. The code examples are designed specifically for the exercise server and are not recommended for direct use in real research settings.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Installation of gatk using conda <\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Create a local directory on the exercise server to install <code>gatk<\/code>. For manual execution, specify the path to the <code>gatk<\/code> executable, for example <code>~\/local\/gatk-4.3.0.0\/gatk<\/code>.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">cd ~\nmkdir \u2013p local\/gatk \ncd local\/gatk\nwget https:\/\/github.com\/broadinstitute\/gatk\/releases\/download\/4.3.0.0\/gatk-4.3.0.0.zip\nunzip gatk-4.3.0.0.zip\ncd gatk-4.3.0.0\nconda env create \u2013n gatk \u2013f gatkcondaenv.yml<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Copy the exercise directory <\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In this activity, we will use a GATK workflow, which requires several input files. First, inspect the file system in the <code>gatk-workflow<\/code> directory. You will find FASTQ files that are part of a publicly available dataset.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">cd ~\ncp \/home\/shared\/gatk-workflows\/ . -r<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The source of the FASTQ input files \n<ul class=\"wp-block-list\">\n<li>\u00a0\u00a0\u00a0\u00a0\u00a0 https:\/\/zenodo.org\/record\/2582555\/files\/SLGFSK-N_231335_r1_chr5_12_17.fastq.gz<br>\u00a0\u00a0\u00a0\u00a0\u00a0 https:\/\/zenodo.org\/record\/2582555\/files\/SLGFSK-N_231335_r2_chr5_12_17.fastq.gz<br>\u00a0\u00a0\u00a0\u00a0\u00a0 https:\/\/zenodo.org\/record\/2582555\/files\/SLGFSK-T_231336_r1_chr5_12_17.fastq.gz<br>\u00a0\u00a0\u00a0\u00a0\u00a0 https:\/\/zenodo.org\/record\/2582555\/files\/SLGFSK-T_231336_r2_chr5_12_17.fastq.gz<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Step 1. Convert FASTQ to unmapped SAM file <\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Paired short read data files (FASTQ) can be merged into a single SAM file. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The script (<code>1_fastq2usam.sh<\/code>) can be found in the <code>gatk-workflow<\/code> directory. You do not need to execute the code as the <code>bam<\/code> file was created in advance. <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Script: <code>1_fastq2usam.sh<\/code><\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">~\/local\/gatk-4.3.0.0\/gatk FastqToSam -F1 inputs\/1M\/normal_1M_r1.fastq -F2 inputs\/1M\/normal_1M_r2.fastq -SM n1 -RG HT33CBBXX -PL ILLUMINA -O inputs\/normal.1M.unmapped.bam\n~\/local\/gatk-4.3.0.0\/gatk FastqToSam -F1 inputs\/1M\/tumor_1M_r1.fastq -F2 inputs\/1M\/tumor_1M_r2.fastq -SM t1 -RG HT33CBBXX -PL ILLUMINA -O inputs\/tumor.1M.unmapped.bam<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Step 2. Align to the reference genome (normal tissue)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The sequencing reads will be mapped to the human reference genome. This process internally uses BWA, and therefore the corresponding BWT index files for the reference genome must be provided. The input data configuration is defined in the file <code>gatk-workflows\/gatk-workflows\/gatk4-data-processing\/processing-for-variant-discovery-gatk4.hg38.wgs.normal.inputs.json<\/code>.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Script: <code>2_gatk4_data_processing_normal.sh<\/code><\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">java -jar gatk-workflows\/cromwell-47.jar run gatk-workflows\/gatk4-data-processing\/processing-for-variant-discovery-gatk4.wdl --inputs gatk-workflows\/gatk4-data-processing\/processing-for-variant-discovery-gatk4.hg38.wgs.normal.inputs.json\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Step 3. Align to the reference genome (tumor tissue)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Same procedure but for the tumor samples.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Script: <code>3_gatk4_data_processing_tumor.sh<\/code><\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">java -jar gatk-workflows\/cromwell-47.jar run gatk-workflows\/gatk4-data-processing\/processing-for-variant-discovery-gatk4.wdl --inputs gatk-workflows\/gatk4-data-processing\/processing-for-variant-discovery-gatk4.hg38.wgs.tumor.inputs.json\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Step 4. Collect the results<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In the GATK workflow, results are generated under the <code>cromwell-executions<\/code> directory. Each workflow run is stored in a separate directory identified by a unique UUID (e.g., <code>2dbed345-2ae8-4eb2-837d-28c98825685e<\/code>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The alignment step outputs this UUID, and any scripts used to collect or process results should be updated accordingly to reference the correct workflow directory.<br><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Script: <code>4_collect_results.sh<\/code><\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\"># uid=2dbed345-2ae8-4eb2-837d-28c98825685e\nuid=f01518a9-caba-4f77-ad7e-189b179b05cc\ncp cromwell-executions\/PreProcessingForVariantDiscovery_GATK4\/$uid\/call-GatherBamFiles\/execution\/normal.hg38.bam.md5 intermediates\ncp cromwell-executions\/PreProcessingForVariantDiscovery_GATK4\/$uid\/call-GatherBamFiles\/execution\/normal.hg38.bam intermediates\ncp cromwell-executions\/PreProcessingForVariantDiscovery_GATK4\/$uid\/call-GatherBamFiles\/execution\/normal.hg38.bai intermediates\n\n# uid=e05ce202-2903-47fa-8d0e-883bff8877d3\nuid=28a5b370-cf8f-44e7-af8f-391cdcd4b326\ncp cromwell-executions\/PreProcessingForVariantDiscovery_GATK4\/$uid\/call-GatherBamFiles\/execution\/tumor.hg38.bam.md5 intermediates\ncp cromwell-executions\/PreProcessingForVariantDiscovery_GATK4\/$uid\/call-GatherBamFiles\/execution\/tumor.hg38.bam intermediates\ncp cromwell-executions\/PreProcessingForVariantDiscovery_GATK4\/$uid\/call-GatherBamFiles\/execution\/tumor.hg38.bai intermediates\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Step 5. Variant calling <\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Variants can be identified by comparing two BAM files representing the control (normal) and case (tumor) samples. Mutect2 is used to detect somatic variants specific to cancer. To accurately identify tumor-specific variants, the sample name corresponding to the normal sample must be provided.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Script: <code>5_variant_call_pair.sh<\/code><\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">mkdir outputs\n~\/local\/gatk-4.3.0.0\/gatk Mutect2 \\\n-R \/home\/shared\/GATK\/Homo_sapiens_assembly38.fasta \\\n-I .\/intermediates\/tumor.hg38.bam \\\n-I .\/intermediates\/normal.hg38.bam \\\n-normal n1 \\\n-O .\/outputs\/somatic_pair.vcf.gz<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">6. Function annotation (Funcotator)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Finally, the functions including genes are annotated to each variant using <code>Funcotator<\/code>. <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Script: <code>6_funcotator.sh<\/code><\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">~\/local\/gatk-4.3.0.0\/gatk Funcotator \\\n  --variant .\/outputs\/somatic_pair.vcf.gz \\\n  --reference \/home\/shared\/GATK\/Homo_sapiens_assembly38.fasta \\\n  --ref-version hg38 \\\n  --data-sources-path \/home\/shared\/funcotator_dataSources.v1.7.20200521s \\\n  -O tumor_somatic_variants.funcotated.vcf \\\n  --output-file-format VCF<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Inspect the final VCF file to identify tumor-specific variants. You may need to use dedicated software or tools for VCF analysis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Germline and somatic variants can reveal the genetic factors underlying a disease or phenotype. These variants are identified by mapping sequencing reads to a reference genome and detecting differences. However, it is necessary to distinguish disease-associated variants from natural genetic variation, such as single nucleotide polymorphisms (SNPs), which are widely distributed across population rather [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[21],"tags":[],"class_list":["post-4623","post","type-post","status-publish","format-standard","hentry","category-classes"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Variant calling using GATK - Biomedical Data Science Laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bdsl.jbnu.ac.kr\/blog\/variant-calling-using-gatk\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Variant calling using GATK - Biomedical Data Science Laboratory\" \/>\n<meta property=\"og:description\" content=\"Introduction Germline and somatic variants can reveal the genetic factors underlying a disease or phenotype. 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