{"_id":"564a046ee2efd717002afd11","category":{"_id":"56fbb83d8f21c817002af880","version":"55faf11ba62ba1170021a9aa","__v":0,"project":"55faf11ba62ba1170021a9a7","sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-03-30T11:27:57.862Z","from_sync":false,"order":1,"slug":"tutorials","title":"TUTORIALS"},"parentDoc":null,"project":"55faf11ba62ba1170021a9a7","__v":214,"version":{"_id":"55faf11ba62ba1170021a9aa","project":"55faf11ba62ba1170021a9a7","__v":46,"createdAt":"2015-09-17T16:58:03.490Z","releaseDate":"2015-09-17T16:58:03.490Z","categories":["55faf11ca62ba1170021a9ab","55faf8f4d0e22017005b8272","55faf91aa62ba1170021a9b5","55faf929a8a7770d00c2c0bd","55faf932a8a7770d00c2c0bf","55faf94b17b9d00d00969f47","55faf958d0e22017005b8274","55faf95fa8a7770d00c2c0c0","55faf96917b9d00d00969f48","55faf970a8a7770d00c2c0c1","55faf98c825d5f19001fa3a6","55faf99aa62ba1170021a9b8","55faf99fa62ba1170021a9b9","55faf9aa17b9d00d00969f49","55faf9b6a8a7770d00c2c0c3","55faf9bda62ba1170021a9ba","5604570090ee490d00440551","5637e8b2fbe1c50d008cb078","5649bb624fa1460d00780add","5671974d1b6b730d008b4823","5671979d60c8e70d006c9760","568e8eef70ca1f0d0035808e","56d0a2081ecc471500f1795e","56d4a0adde40c70b00823ea3","56d96b03dd90610b00270849","56fbb83d8f21c817002af880","573c811bee2b3b2200422be1","576bc92afb62dd20001cda85","5771811e27a5c20e00030dcd","5785191af3a10c0e009b75b0","57bdf84d5d48411900cd8dc0","57ff5c5dc135231700aed806","5804caf792398f0f00e77521","58458b4fba4f1c0f009692bb","586d3c287c6b5b2300c05055","58ef66d88646742f009a0216","58f5d52d7891630f00fe4e77","59a555bccdbd85001bfb1442","5a2a81f688574d001e9934f5","5b080c8d7833b20003ddbb6f","5c222bed4bc358002f21459a","5c22412594a2a5005cc9e919","5c41ae1c33592700190a291e","5c8a525e2ba7b2003f9b153c","5cbf14d58c79c700ef2b502e","5db6f03a6e187c006f667fa4"],"is_deprecated":false,"is_hidden":false,"is_beta":true,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"githubsync":"","user":"554290cd6592e60d00027d17","metadata":{"title":"","description":"","image":[]},"updates":[],"next":{"pages":[],"description":""},"createdAt":"2015-11-16T16:29:34.924Z","link_external":false,"link_url":"","sync_unique":"","hidden":false,"api":{"settings":"","results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":1,"body":"##Prerequisites\n\nIn order to be able to use all resources which are discussed in this QuickStart you need to have access to [TCGA Controlled Data](tcga-data-access#controlled-data) through dbGaP.\n\nIf you don’t have access to TCGA Controlled Data, you can still analyze the [Open Data from TCGA dataset](tcga-data-access#open-data) using the available apps without special permission from dbGaP.\n\n\n##Procedure\n\nWe'll start by creating a project and populating it with TCGA files. Then we'll use one of the CGC somatic calling workflows, \"Vardict Somatic Calling\", to carry out the analysis. Finally, we'll examine the results.\n[block:callout]\n{\n  \"type\": \"warning\",\n  \"title\": \"On this page:\",\n  \"body\": \"* [Create a project](#section--create-a-project-)\\n* [Add analysis data](#section-add-analysis-data)\\n  * [Find files associated with the case](#section-find-files-associated-with-the-case)\\n  * [Add TCGA files to your project](#section-add-tcga-files-to-your-project)\\n  * [Add the FASTA index file to your project](#section-add-the-fasta-index-file-to-your-project)\\n  * [Add the BED file to your project](#section-add-the-bed-file-to-your-project)\\n* [Choose the workflow](#section-choose-the-workflow)\\n* [Run the analysis](#section-run-the-analysis)\\n* [View the results](#section-view-the-results)\"\n}\n[/block]\n##**Create a project **\n \n The first step to running an analysis on the CGC is to create a project.\n\n1. Choose **Create a project** under **Projects** in the top navigation bar and the window for naming your project is shown.\n2. Enter \"Quickstart\" as the project name. \n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/03d3c6b-cgc-quickstart-16.png\",\n        \"cgc-quickstart-16.png\",\n        1231,\n        839,\n        \"#e5e8ea\"\n      ]\n    }\n  ]\n}\n[/block]\n3. Choose **Pilot Funds** as the billing group.\n4. Select **This project will contain TCGA Controlled Data** since we will use TCGA Controlled Data.\n5. Click **Create**.\n\n##Add analysis data\n\nIn this Quickstart, we will use the TCGA data to analyze a Cervical Squamous Cell Carcinoma patient with TTN missense mutation. To add analysis data:\n\n1. Choose **Data Overview **from the **Data** menu.\nThe Data Overview page is displayed. The TCGA GRCh38 dataset is selected by default.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/98deed7-qs-2.png\",\n        \"qs-2.png\",\n        1215,\n        1025,\n        \"#95c0de\"\n      ],\n      \"border\": true\n    }\n  ]\n}\n[/block]\n \n2. Select **CESC** from the **Cases by Disease** section. The **Disease Details** section will show:\n  * The total number of cases.\n  * The gender distribution.\n  * Race.\n  * Age at diagnosis.\n  * The sample type.\n  * The next step is to filter these cases using the Case Explorer.\n\n3. Click **Case Explorer** in the upper right corner (see above) to open the Case Explorer.\n[block:callout]\n{\n  \"type\": \"info\",\n  \"body\": \"The [Case Explorer](doc:the-case-explorer) allows researchers to easily find a subset of TCGA data based on a disease and gene mutation.\"\n}\n[/block]\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/0fa579a-qs-3.png\",\n        \"qs-3.png\",\n        1211,\n        856,\n        \"#174a85\"\n      ]\n    }\n  ]\n}\n[/block]\n 4.Click **TTN** in the **Top mutated genes in CESC** table in the upper right corner, as shown above. All available cases will be displayed on the scatter plot.\n[block:callout]\n{\n  \"type\": \"info\",\n  \"body\": \"The scatter plot is populated to show the relation between copy number variation (CNV) on the **y-axis **and gene expression levels on the **x-axis** for the selected gene in patients with CESC. The colors of the circles represent different types of mutation (see the **Variant Classification** filter below the scatter plot).\",\n  \"title\": \"Circle colors on the scatter plot\"\n}\n[/block]\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/0d60382-qs-4.png\",\n        \"qs-4.png\",\n        1202,\n        990,\n        \"#1c5182\"\n      ]\n    }\n  ]\n}\n[/block]\n5. Select a case, as shown above. The case information will be displayed in the bottom of the page.\n6. Click **Continue to Data Browser** to copy the file for the case we selected. This will take us to the Data Browser where we can find the WXS aligned BAM files from this case.\n[block:callout]\n{\n  \"type\": \"info\",\n  \"title\": \"Selecting multiple Cases\",\n  \"body\": \"Copy multiple files at once by selecting them all before clicking the **Continue to Data Browser **button.\"\n}\n[/block]\n### Find files associated with the case\n\nUsing the Data Browser, we'll build a query to filter data from this case by combining metadata attributes. In the example below, we will choose WXS (Whole Exome Sequencing) as experimental strategy and BAM as data format.\n\nUpon opening it, the Data Browser will display the case we picked using the Case Explorer.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/4bbd270-qs-5.png\",\n        \"qs-5.png\",\n        795,\n        775,\n        \"#f0efef\"\n      ]\n    }\n  ]\n}\n[/block]\nTo find the matched tumor/normal aligned BAM files associated with this case:\n\n1. Choose the **WXS** as experimental strategy:\n    i Click **File**.\n    ii Search for \"Experimental strategy\" and select it.\n    iii Select **Experimental strategy.**\n    iv Next, choose the **WXS** (Whole Exome Sequencing) metadata filter.\n    v Click **Add property**. \n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/f9a8aea-qs-7.png\",\n        \"qs-7.png\",\n        543,\n        299,\n        \"#ea6c50\"\n      ]\n    }\n  ]\n}\n[/block]\n 2. Repeat this procedure to add **BAM format **as a property.\n   i. Click **Data format.**\n   ii. Choose **BAM** filter.\n   iii. Click **Add property**.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/729ccc8-qs-8.jpg\",\n        \"qs-8.jpg\",\n        716,\n        316,\n        \"#ddddde\"\n      ]\n    }\n  ]\n}\n[/block]\nThis will give you all files created as a result of the WXS experiment.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/567f57e-qs-9.png\",\n        \"qs-9.png\",\n        1137,\n        678,\n        \"#11548a\"\n      ]\n    }\n  ]\n}\n[/block]\nClick the refresh icon next to the count cards below the Data Browser to display the number of cases and results returned by the query, which is one case and two files. The next step is adding TCGA files to your project.\n\n### Add TCGA files to your project\n\nTo add TCGA files to your project after finding them using the Data Browser:\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/1ba8308-qs-10.jpg\",\n        \"qs-10.jpg\",\n        738,\n        192,\n        \"#eaedee\"\n      ]\n    }\n  ]\n}\n[/block]\n1. Click **Copy files to project **in the upper right corner.\n2. Choose your **Quickstart** project.\nThe confirmation window is displayed.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/18369e4-qs-11.jpg\",\n        \"qs-11.jpg\",\n        478,\n        305,\n        \"#e5eef0\"\n      ]\n    }\n  ]\n}\n[/block]\n3. Click **Copy selected files**.\n\nThis concludes the procedure of adding TCGA files to your project. The next step is adding a FASTA index file to your project.\n\n###Add the FASTA index file to your project\n\nFor your task to execute properly, you will need to add a FASTA index file to your project:\n\n1. Open your \"Quickstart\" project.\n2. Click the **Files** tab.\n3. Click **Add files**.\n4. Use the search field to look for Homo_sapiens_assembly38.fasta.fai.\n5. Select the file.\n6. Click **Copy to Project**.\n\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/dad138e-qs-12.jpg\",\n        \"qs-12.jpg\",\n        1069,\n        321,\n        \"#e2ebef\"\n      ]\n    }\n  ]\n}\n[/block]\n 7. Click **Copy** to confirm.\nThis concludes the procedure of adding a FASTA index to your project. The next step is adding a BED file to your project.\n\n### Add the BED file to your project\n\nThe procedure for adding a BED file is the same as adding a FASTA file. Please follow the procedure above again and copy the \"Homo_sapiens_primary_assembly38_80_intervals.bed\" file to your project. \n\nThe next step after that is choosing the workflow for your analysis.\n\n## Choose the workflow\n\nWith the analysis data now prepared, we need to choose the workflow for performing the analysis. We'll use public workflow **Vardict Somatic Calling**, a somatic caller that employs a heuristic approach to call variants that meet desired thresholds for read depth, base quality, variant allele frequency, and statistical significance.\n\nTo select the workflow:\n\n1. Click **Public Apps** in the top bar navigation.\n2. Search for \"Vardict Somatic Calling\".\n3. Click **Copy** below the workflow.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/e3a0e11-qs-13.jpg\",\n        \"qs-13.jpg\",\n        739,\n        499,\n        \"#f2f3f3\"\n      ]\n    }\n  ]\n}\n[/block]\nThe screen is refreshed.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/b25103e-qs-14.jpg\",\n        \"qs-14.jpg\",\n        703,\n        400,\n        \"#f1f2f2\"\n      ]\n    }\n  ]\n}\n[/block]\n4. Choose your \"Quickstart\" project.\n5. Click **Copy**.\n\nThis will copy the workflow to your project apps. The next step is running the analysis.\n\n## Run the analysis\n\nNow that the analysis data and the workflow are ready, it's time to run the analysis.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/bbb44fb-qs-15.jpg\",\n        \"qs-15.jpg\",\n        1076,\n        273,\n        \"#e5e9ec\"\n      ]\n    }\n  ]\n}\n[/block]\nTo run the analysis:\n1. Click the **Apps** tab in your Quickstart project.\n2. Click **Run** next to the **Vardict Somatic Calling **workflow.\n3. Next, click **Select file(s)** next to each of the inputs choose the files:\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/e75fbd0-cgc-quickstart-16.png\",\n        \"cgc-quickstart-16.png\",\n        1231,\n        839,\n        \"#e5e8ea\"\n      ]\n    }\n  ]\n}\n[/block]\n  * **BED File** - choose \"Homo_sapiens_primary_assembly38_80_intervals.bed\".\n  * **Normal BAM** - choose \"7ee5a028a6bc0812b1b10aec200b57ac_gdc_realn.bam\", which contains the analysis data that we have previously added to the project using the Data Browser and Case Explorer.\n  * **Reference FASTA** - choose \"Homo_sapiens_assembly38.fasta\".\n  * **Tumor BAM** - choose \"d403f4842fb79683464b18379bfa09b3_gdc_realn.bam\". \n\nNow that all the required input files for the workflow are set, click **Run** to start the analysis.\nWhen you start the task, a new page opens displaying the task's properties.\n\nThe status will be a progress bar (if the task is still running) or a label detailing whether the task has completed, been aborted or failed.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/47cb36f-qs-17.jpg\",\n        \"qs-17.jpg\",\n        1176,\n        682,\n        \"#12457a\"\n      ]\n    }\n  ]\n}\n[/block]\nFor additional information, including how to check the status of the task or how to troubleshoot in case of the failed task, check the [task statistics](doc:view-task-stats). Also, you will receive an email notification once the task is completed.\n\n## View the results\n\nTo see the results of your task\n\n1. Open the task page.\n2. Click on any of the files in the **Outputs** column.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/4e0d8f0-qs-18.jpg\",\n        \"qs-18.jpg\",\n        1119,\n        662,\n        \"#e3e9eb\"\n      ]\n    }\n  ]\n}\n[/block]","excerpt":"To introduce you to the major features of the CGC, this QuickStart will walk through a simple somatic calling analysis using Vardict Somatic Calling.","slug":"quickstart","type":"basic","title":"QuickStart"}

QuickStart

To introduce you to the major features of the CGC, this QuickStart will walk through a simple somatic calling analysis using Vardict Somatic Calling.

##Prerequisites In order to be able to use all resources which are discussed in this QuickStart you need to have access to [TCGA Controlled Data](tcga-data-access#controlled-data) through dbGaP. If you don’t have access to TCGA Controlled Data, you can still analyze the [Open Data from TCGA dataset](tcga-data-access#open-data) using the available apps without special permission from dbGaP. ##Procedure We'll start by creating a project and populating it with TCGA files. Then we'll use one of the CGC somatic calling workflows, "Vardict Somatic Calling", to carry out the analysis. Finally, we'll examine the results. [block:callout] { "type": "warning", "title": "On this page:", "body": "* [Create a project](#section--create-a-project-)\n* [Add analysis data](#section-add-analysis-data)\n * [Find files associated with the case](#section-find-files-associated-with-the-case)\n * [Add TCGA files to your project](#section-add-tcga-files-to-your-project)\n * [Add the FASTA index file to your project](#section-add-the-fasta-index-file-to-your-project)\n * [Add the BED file to your project](#section-add-the-bed-file-to-your-project)\n* [Choose the workflow](#section-choose-the-workflow)\n* [Run the analysis](#section-run-the-analysis)\n* [View the results](#section-view-the-results)" } [/block] ##**Create a project ** The first step to running an analysis on the CGC is to create a project. 1. Choose **Create a project** under **Projects** in the top navigation bar and the window for naming your project is shown. 2. Enter "Quickstart" as the project name. [block:image] { "images": [ { "image": [ "https://files.readme.io/03d3c6b-cgc-quickstart-16.png", "cgc-quickstart-16.png", 1231, 839, "#e5e8ea" ] } ] } [/block] 3. Choose **Pilot Funds** as the billing group. 4. Select **This project will contain TCGA Controlled Data** since we will use TCGA Controlled Data. 5. Click **Create**. ##Add analysis data In this Quickstart, we will use the TCGA data to analyze a Cervical Squamous Cell Carcinoma patient with TTN missense mutation. To add analysis data: 1. Choose **Data Overview **from the **Data** menu. The Data Overview page is displayed. The TCGA GRCh38 dataset is selected by default. [block:image] { "images": [ { "image": [ "https://files.readme.io/98deed7-qs-2.png", "qs-2.png", 1215, 1025, "#95c0de" ], "border": true } ] } [/block] 2. Select **CESC** from the **Cases by Disease** section. The **Disease Details** section will show: * The total number of cases. * The gender distribution. * Race. * Age at diagnosis. * The sample type. * The next step is to filter these cases using the Case Explorer. 3. Click **Case Explorer** in the upper right corner (see above) to open the Case Explorer. [block:callout] { "type": "info", "body": "The [Case Explorer](doc:the-case-explorer) allows researchers to easily find a subset of TCGA data based on a disease and gene mutation." } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/0fa579a-qs-3.png", "qs-3.png", 1211, 856, "#174a85" ] } ] } [/block] 4.Click **TTN** in the **Top mutated genes in CESC** table in the upper right corner, as shown above. All available cases will be displayed on the scatter plot. [block:callout] { "type": "info", "body": "The scatter plot is populated to show the relation between copy number variation (CNV) on the **y-axis **and gene expression levels on the **x-axis** for the selected gene in patients with CESC. The colors of the circles represent different types of mutation (see the **Variant Classification** filter below the scatter plot).", "title": "Circle colors on the scatter plot" } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/0d60382-qs-4.png", "qs-4.png", 1202, 990, "#1c5182" ] } ] } [/block] 5. Select a case, as shown above. The case information will be displayed in the bottom of the page. 6. Click **Continue to Data Browser** to copy the file for the case we selected. This will take us to the Data Browser where we can find the WXS aligned BAM files from this case. [block:callout] { "type": "info", "title": "Selecting multiple Cases", "body": "Copy multiple files at once by selecting them all before clicking the **Continue to Data Browser **button." } [/block] ### Find files associated with the case Using the Data Browser, we'll build a query to filter data from this case by combining metadata attributes. In the example below, we will choose WXS (Whole Exome Sequencing) as experimental strategy and BAM as data format. Upon opening it, the Data Browser will display the case we picked using the Case Explorer. [block:image] { "images": [ { "image": [ "https://files.readme.io/4bbd270-qs-5.png", "qs-5.png", 795, 775, "#f0efef" ] } ] } [/block] To find the matched tumor/normal aligned BAM files associated with this case: 1. Choose the **WXS** as experimental strategy:     i Click **File**.     ii Search for "Experimental strategy" and select it.     iii Select **Experimental strategy.**     iv Next, choose the **WXS** (Whole Exome Sequencing) metadata filter.     v Click **Add property**. [block:image] { "images": [ { "image": [ "https://files.readme.io/f9a8aea-qs-7.png", "qs-7.png", 543, 299, "#ea6c50" ] } ] } [/block] 2. Repeat this procedure to add **BAM format **as a property.    i. Click **Data format.**    ii. Choose **BAM** filter.    iii. Click **Add property**. [block:image] { "images": [ { "image": [ "https://files.readme.io/729ccc8-qs-8.jpg", "qs-8.jpg", 716, 316, "#ddddde" ] } ] } [/block] This will give you all files created as a result of the WXS experiment. [block:image] { "images": [ { "image": [ "https://files.readme.io/567f57e-qs-9.png", "qs-9.png", 1137, 678, "#11548a" ] } ] } [/block] Click the refresh icon next to the count cards below the Data Browser to display the number of cases and results returned by the query, which is one case and two files. The next step is adding TCGA files to your project. ### Add TCGA files to your project To add TCGA files to your project after finding them using the Data Browser: [block:image] { "images": [ { "image": [ "https://files.readme.io/1ba8308-qs-10.jpg", "qs-10.jpg", 738, 192, "#eaedee" ] } ] } [/block] 1. Click **Copy files to project **in the upper right corner. 2. Choose your **Quickstart** project. The confirmation window is displayed. [block:image] { "images": [ { "image": [ "https://files.readme.io/18369e4-qs-11.jpg", "qs-11.jpg", 478, 305, "#e5eef0" ] } ] } [/block] 3. Click **Copy selected files**. This concludes the procedure of adding TCGA files to your project. The next step is adding a FASTA index file to your project. ###Add the FASTA index file to your project For your task to execute properly, you will need to add a FASTA index file to your project: 1. Open your "Quickstart" project. 2. Click the **Files** tab. 3. Click **Add files**. 4. Use the search field to look for Homo_sapiens_assembly38.fasta.fai. 5. Select the file. 6. Click **Copy to Project**. [block:image] { "images": [ { "image": [ "https://files.readme.io/dad138e-qs-12.jpg", "qs-12.jpg", 1069, 321, "#e2ebef" ] } ] } [/block] 7. Click **Copy** to confirm. This concludes the procedure of adding a FASTA index to your project. The next step is adding a BED file to your project. ### Add the BED file to your project The procedure for adding a BED file is the same as adding a FASTA file. Please follow the procedure above again and copy the "Homo_sapiens_primary_assembly38_80_intervals.bed" file to your project. The next step after that is choosing the workflow for your analysis. ## Choose the workflow With the analysis data now prepared, we need to choose the workflow for performing the analysis. We'll use public workflow **Vardict Somatic Calling**, a somatic caller that employs a heuristic approach to call variants that meet desired thresholds for read depth, base quality, variant allele frequency, and statistical significance. To select the workflow: 1. Click **Public Apps** in the top bar navigation. 2. Search for "Vardict Somatic Calling". 3. Click **Copy** below the workflow. [block:image] { "images": [ { "image": [ "https://files.readme.io/e3a0e11-qs-13.jpg", "qs-13.jpg", 739, 499, "#f2f3f3" ] } ] } [/block] The screen is refreshed. [block:image] { "images": [ { "image": [ "https://files.readme.io/b25103e-qs-14.jpg", "qs-14.jpg", 703, 400, "#f1f2f2" ] } ] } [/block] 4. Choose your "Quickstart" project. 5. Click **Copy**. This will copy the workflow to your project apps. The next step is running the analysis. ## Run the analysis Now that the analysis data and the workflow are ready, it's time to run the analysis. [block:image] { "images": [ { "image": [ "https://files.readme.io/bbb44fb-qs-15.jpg", "qs-15.jpg", 1076, 273, "#e5e9ec" ] } ] } [/block] To run the analysis: 1. Click the **Apps** tab in your Quickstart project. 2. Click **Run** next to the **Vardict Somatic Calling **workflow. 3. Next, click **Select file(s)** next to each of the inputs choose the files: [block:image] { "images": [ { "image": [ "https://files.readme.io/e75fbd0-cgc-quickstart-16.png", "cgc-quickstart-16.png", 1231, 839, "#e5e8ea" ] } ] } [/block] * **BED File** - choose "Homo_sapiens_primary_assembly38_80_intervals.bed". * **Normal BAM** - choose "7ee5a028a6bc0812b1b10aec200b57ac_gdc_realn.bam", which contains the analysis data that we have previously added to the project using the Data Browser and Case Explorer. * **Reference FASTA** - choose "Homo_sapiens_assembly38.fasta". * **Tumor BAM** - choose "d403f4842fb79683464b18379bfa09b3_gdc_realn.bam". Now that all the required input files for the workflow are set, click **Run** to start the analysis. When you start the task, a new page opens displaying the task's properties. The status will be a progress bar (if the task is still running) or a label detailing whether the task has completed, been aborted or failed. [block:image] { "images": [ { "image": [ "https://files.readme.io/47cb36f-qs-17.jpg", "qs-17.jpg", 1176, 682, "#12457a" ] } ] } [/block] For additional information, including how to check the status of the task or how to troubleshoot in case of the failed task, check the [task statistics](doc:view-task-stats). Also, you will receive an email notification once the task is completed. ## View the results To see the results of your task 1. Open the task page. 2. Click on any of the files in the **Outputs** column. [block:image] { "images": [ { "image": [ "https://files.readme.io/4e0d8f0-qs-18.jpg", "qs-18.jpg", 1119, 662, "#e3e9eb" ] } ] } [/block]