When navigating to a Workspace, users are presented with multiple workspace options.

The Gen3 platform offers two workspace environments: Jupyter Notebooks and R Studio.

There are six workspaces:

Virtual machines (VM):

• Small Jupyter Notebook VM

• Large Jupyter Notebook Power VM

• R Studio VM

Pre-made workflow workspaces:

• Autoencoder Demo

• CIP Demo

• Tensorflow-Pytorch.

To start a workspace, select Launch. You will see the following launch loading screen.

Once the VM is ready, the initial screen for the workspace will appear. For scripts and output that need to be saved when the workspace is terminated, store those files in the pd/ directory.

This workspace will persist once the user has logged out of the BDC-Gen3 system. If the workspace is no longer being used, terminate the workspace by selecting Terminate Workspace at the bottom of the window. You will be returned to the Workspace page with all of the workspace options.

For more information about the Gen3 Workspace, refer to .

What is a Portable Format for Bioinformatics?

A Portable Format for Bioinformatics (PFB) allows users to transfer both the metadata from the the Data Dictionary as well as the Data Dictionary itself. As a result, data can be transferred while keeping the structure from the original source. Specifically, a PFB consists of three parts:

• A schema

• Metadata

• Data

For more information and an in-depth review that includes Python tools for PFB creation and exploration, refer to the and install the newest version.

Schema

A schema is a JSON formatted Data Dictionary containing information about the properties, such as value types, descriptions, and so on.

To view the PFB schema, use the following command:

Example Output

NOTE: To make the outputs more human-readable, the above information was then piped through the program . Example: pfb show -i PFB_file.avro schema | jq

Metadata

The metadata in a PFB contains all of the information explaining the linkage between nodes and external references for each of the properties.

To view the PFB metadata, use the following command:

Example Output

Data

The data in the PFB are the values for the properties in the format of the Data Dictionary.

To view the data within the PFB, use the following command:

To view at a certain number of entries in the PFB file, use the flag -n to designate a number. For example, to view the first 10 data entries within the PFB, use the following command:

Example Output

1. Navigate to https://biodatacatalyst.nhlbi.nih.gov/use-bdc/explore-data/dug/ to access Dug Semantic Search.

2. Semantic search is a concept-based search engine designed for users to search biomedical concepts, such as “asthma,” “lung,” or “fever,” and the variables related to and/or used to measure them. For example, a search for “chronic pain acceptance” will return a list of related biomedical concepts, such as chronic pain, headaches, neuralgia, or fibromyalgia, each of which can be expanded to display related variables and CDEs. Semantic search can also find variable names and descriptions directly, using synonyms from its knowledge graphs to find search-related variables.

3. Enter a search term and press “Enter,” or click on the Search button. This will take you to the Semantic Search interface.

Requirements

To obtain access to BDC-PIC-SURE, you must have an NIH eRA Commons account. For instructions and to register an account, refer to the eRA website.

Login

After you have created an eRA Commons account, you can log in to BDC-PIC-SURE by navigating to and selecting to log in with eRA Commons. You will be directed to the NIH website to log in with your eRA Commons credentials. After signing in and accepting the terms of the agreement on the NIH RAS Information Sharing Consent page, allow the BDC-Gen3 service to manage your authorization.

Upon login, you will be directed to the Data Access Dashboard. This page provides a summary of PIC-SURE Authorized Access, PIC-SURE Open Access, and the studies you are authorized to access.

Overview

Dug Semantic Search is a tool that allows users to deep dive into BDC studies and biomedical topics, research, and publications to identify related studies, datasets, and variables. If you are interested in how Dug connects study variables to biomedical concepts, read the Dug paper or visit the Help Portal.

This tool applies semantic web and knowledge graph techniques to improve BDC research data Findability, Access, Interoperability, and Reusability (FAIR). Through this process, semantic search helps users identify novel relations, build unique research questions, and identify potential collaborations.

The Patient Information Commons: Standard Unification of Research Elements (PIC-SURE) integrates clinical and genomic data to allow users to search, query, and export data at the variable and variant levels. This allows users to create analysis-ready data frames without manually mapping and merging files.

BDC Powered by PIC-SURE (BDC-PIC-SURE) functions as part of the BDC ecosystem, allowing researchers to explore studies funded by the National Heart, Lung, and Blood Institute (NHLBI), whether they have been granted access to the participant level data or not.

The BDC ecosystem hosts several datasets from the NIH NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). To access the BioLINCC studies, you must request access through dbGaP even if you have authorization from BioLINCC.

1. Search bar: Enter any phenotypic variable, study or table keyword into the search bar to search across studies. Users can also search specific variables by accession number, if known (phs/pht/phv).

2. Study Tags: Users can filter the results found through their search by limiting to studies of interest or excluding studies.

3. Variable Tags: Users can filter the results found through their search by limiting to keywords of interest or excluding keywords that are out of scope. For example, a user could filter to categorical variables, variables containing the term ‘blood’, and/or exclude variables containing the term ‘pressure’.

   How are variable tags generated? Each variable has a set of associated tags, which are generated during the PIC-SURE data loading process. These tags are generated based on information associated with the variable, including the name of the study, study description, dataset name, PIC-SURE data type (continuous or categorical), and variable description. For a search in PIC-SURE, tags associated with a variable are displayed. Note that tags applicable to less than 5% or more than 95% of the search results are not displayed since these are not useful for filtering results.

4. Search Results table: View all variables associated with your search term and/or study & variable tags.

5. Results Panel: Panel with content boxes that describe the cohort based on the variable filters applied to the query.

6. Data Summary: Displays the total number of participants in the filtered cohort which meet the query criteria. When first opening the Open or Authorized Access page, the number will be the total number of participants that you can access.

7. Added Variable Filters summary: View all filters which have been applied to the cohort.

8. Filter Action: Click on the filter icon to filter cohort participants by specific variable values.

9. Reset button: Allows users to start a new search and query by removing all added filters and clearing all active study and variable tags.

PIC-SURE Open Access allows you to search any clinical variable available in PIC-SURE. Your queries will return obfuscated aggregate counts per study and consent. There are some features specific to PIC-SURE Open Access, which are outlined below.

A. Stigmatizing Variables Removal: PIC-SURE Open Access data excludes clinical variables that contain potentially sensitive information. These variables are known as stigmatizing variables, which fall into the following categories:

• Mental health diagnoses, history, and treatment

• Illicit drug use history

• Sexually transmitted disease diagnoses, history, and treatment

• Sexual history

• Intellectual achievement, ability, and educational attainment

• Direct or surrogate identifiers of legal status

For more information about stigmatizing variables and the identification process, please refer to the documentation and code on the .

B. Data Obfuscation: Because participant-level data are not available in PIC-SURE Open Access, the aggregate counts are obfuscated to further anonymize the data. This means that:

• If the consent group, study, and/or total participants of the query is between one and nine, the results will be shown as < 10.\

• If the consent group results are between one and nine and the study and/or total participants of the query is greater than 10, the results will be obfuscated by ± 3.

• Query results that are zero participants will display 0.

C. View Filtered Results by Study: The filtered number of participants which match the query criteria is shown broken down by study and consent group. Users can see if they do or do not have access to specific studies.

Use Case: Using PIC-SURE Open Access to Investigate Asthma in Healthy and Obese Adult Populations

In this section, the functionalities of PIC-SURE Open Access will be described in the context of a scientific use case. Specifically, let’s say I am interested in investigating asthma in relation to obesity in adults.

I’m interested in two cohorts: obese adults with a body mass index (BMI) greater than 30 and healthy adults with a BMI between 18.5 and 24.9. However, I have not yet submitted a Data Access Request and therefore am not authorized to access any datasets.

First, let’s explore cohort A: Healthy adults with a BMI between 18.5 and 24.9 in Framingham Heart Study (FHS).

1. Search for ‘age’.

2. Apply ‘FHS’ study tag to view only ‘age’ variables within the Framingham Heart Study (phs000007).

3. Select the variable of interest. You may notice many variables that appear similar. These variables may be located in different datasets, or tables, but contain similar information. Open up the variable information modal by clicking on the row containing the variable of interest to learn more.

We can easily modify our filters to explore cohort B: Obese adults with a body mass index (BMI) greater than 30 in Framingham Heart Study.

1. Edit the BMI filter by clicking the edit icon in the Added Variable Filters section. Change the range to have a minimum of 30 and no maximum.

2. Note the total participant count in the Data Summary.

We can easily repeat these steps for other studies, such as the Genetic Epidemiology of COPD (COPDGene) study, and create a table like the one below. By comparing these two studies, I can see that COPDGene may be more promising for my research since it contains many more participants in my cohorts of interest than FHS does.

I can then use the Request Access button to go directly to the study’s dbGaP page and begin submitting a DAR.

PIC-SURE provides two ways to search: PIC-SURE Open Access and PIC-SURE Authorized Access. PIC-SURE Open Access enables the user to explore aggregate-level data without any dbGaP data authorizations. PIC-SURE Authorized Access feature allows the user to explore participant-level data and requires authorization to access at least one study through an active dbGaP Data Access Request (DAR).

Table Comparison of PIC-SURE Open and Authorized Access

What is the PIC-SURE API?

Databases exposed through the PIC-SURE API encompass a wide heterogeneity of architectures and data organizations underneath. PIC-SURE hides this complexity and exposes the different databases in the same format, allowing researchers to focus on the analysis and medical insights, thus easing the process of reproducible sciences. The API is available in two different programming languages, python and R, allowing investigators to query databases in the same way using either of those languages. The PIC-SURE API tutorial notebooks can be directly accessed on GitHub.

PIC-SURE Access Token

To access the PIC-SURE API, a user-specific token is needed. This is the way the API grants access to individual users to protected-access data. The user token is strictly personal; do not share it with anyone. You can copy your personalized access token by selecting the User Profile tab at the top of the screen.

Here, you can Copy your personalized access token, Reveal your token, and Refresh your token to retrieve a new token and deactivate the old token.

Analysis in the BDC Ecosystem

The PIC-SURE API can be accessed via tutorial notebooks on either BDC- or BDC-.

To launch one of the analysis platforms, go to the . From the Resources menu, select Services. A list of platforms and services on the BDC ecosystem will be displayed.

From the Analyze Data in Cloud-based Shared Workspaces section, select Launch for your preferred analysis platform.

BDC-Seven Bridges

Jupyter notebook examples in R and python can be found under the Public projects tab by selecting PIC-SURE API.

From the Data Studio tab, select an example that fits your research needs. Here, we will select PIC-SURE JupyterLab examples.

This will take you to the PIC-SURE API analysis workspace, where you can view the examples in python. Copy this workspace to your own project to edit or run the code yourself.

BDC-Terra

To access the Jupyter notebook examples in R and python for the PIC-SURE API, select View Workspaces from the .

Select the Public tab and search for “PIC-SURE”. Workspaces for both the python and R examples will be displayed. You must clone the workspaces to edit or run the code within them.

PIC-SURE integrates clinical and genomic datasets across BDC, including TOPMed and TOPMed related studies, COVID-19 studies, and BioLINCC studies. Each variable is organized as a concept path that contains information about the study, variable group, and variable. Though the specifics of the concept paths are dependent on the type of study, the overall information included is the same.

For more information about additional dbGaP, TOPMed, and PIC-SURE concept paths, refer to Appendix 1.

Table of Data Fields in PIC-SURE

Note that there are two data types in PIC-SURE: categorical and continuous data. Categorical variables refers to any variables that have categorized values. For example, “Have you ever had asthma?” with values “Yes” and “No” is a categorical variable. Continuous variables refer to any variables that have a numeric range of values. For example, “Age” with a value range from 10 to 90 is a continuous variable. The internal PIC-SURE data load process determines the type of each variable based on the data.

Video Walkthroughs

Playlist

BioData Catalyst Powered by PIC-SURE

Videos

The BDC ecosystem hosts several datasets from the NIH NHLBI Collaborating Network of Networks for Evaluating COVID-19 and Therapeutic Strategies (CONNECTS) program. These COVID-19 related studies follow the guidelines for implementing common data elements (CDEs) and for de-identifying dates, ages, and free text fields. For more information about these efforts, you can view the CDE Manual and De-Identification Guidance documents on the CONNECTS COVID-19 Therapeutic Trial Common Data Elements webpage.

Table of COVID-19 Studies Included in the CONNECTS Program Available in PIC-SURE

BDC-PIC-SURE has integrated clinical and genomic data from a variety of heart, lung, blood, and sleep related datasets. These include NHLBI Trans-Omics for Precision Medicine (TOPMed) and TOPMed related studies, BioLINCC datasets, and COVID-19 datasets.

View a summary of the data you have access to by viewing the Data Access Table.

This table displays information about the study and associated data, including the full and abbreviated name of the study, study design and focus, the number of clinical variables, participants, and samples sequenced, additional information with helpful links, consent group information, and the dbGaP accession number (or phs number). You are also able to see which studies you are authorized to access in the Access column of the table. For information from dbGaP on submitting a data access request, refer to Tips for Preparing a Successful Data Access Request documentation. Note that studies with a sickle cell disease focus contain links to the Cure SCi Metadata Catalog for additional information.

You can also check the data you have access to by going to the page on the BDC website and clicking Check My Access.

If you are authorized to access any dbGaP dataset(s), the Authorized Access tab at the top will be visible. PIC-SURE Authorized Access provides access to complete, participant-level data, in addition to aggregate counts, and access to the Tool Suite.

A. Select Variables Action: Click the Select Variables icon to include variables when retrieving data. Users can select variables individually or at the dataset level.

• Individually select variables: You can individually select variables from two locations:

  • Variable search results: From the search results you can click the data retrieval icon to include the variable in your data retrieval.

  • Variable modal variable data retrieval: The data retrieval icon next to the variable adds the variable to your data retrieval.

• Select from a dataset or group of variables: In the variable modal the data retrieval icon next to the dataset opens a modal to allow you to select variables from the dataset table or group of variables.

B. Data Summary: In addition to the total number of participants in the filtered cohort, the number of variables the user has selected for data retrieval is also displayed.

There are four concept paths that are automatically included with any data export from PIC-SURE Authorized Access. These fields are listed and described below.

• Patient ID: Internal PIC-SURE participant identifier. Please note that this field is not linking participants between studies and therefore should not be used for data correlation between different data sources or to the original data files.

• Parent Study Accession with Subject ID: PIC-SURE generated identifier for parent studies. These identifiers are a combination of the study accession number and the subject identifier.

C. Tool Suite: The Tool Suite contains tools that can be used to further explore filtered cohorts of interest. Note that at least one filter must be added to the query before using the Tool Suite.

• Select and Package Data: Retrieve participant-level data corresponding to your filters and variable selections. Variables selected for data retrieval can be reviewed and modified. To learn more about the options associated with this tool, please refer to the Select and Package Data section.

• Variable Distributions: View the distributions of query variables based on the filtered cohort. Note that there is a limit to the number of variable distributions that can be viewed at a given time. Additionally, genomic variables nor variables associated with any-record-of filter (e.g. entire datasets) will not be graphed.

Select and Package Data

The Select and Package Data tool is used to select and export participant-level data corresponding to your filters and variable selections. There are several options for selecting and exporting the data, which are shown using this tool.

In the top left corner of the modal, the number of participants and number of variables included in the query is shown. This is used to display the estimated number of data points in the export.

Note: Queries with more than 1,000,000 data points will not be exportable.

The table below displays a summary of the variables included in the export. Using the Selected column, variables that have been added to the export can be selected or deselected for the final dataframe.

Note: Variables with filters are automatically included in the export.

The Package Data button in the top right corner is used to prepare the data for export once the variable filters and selections have been finalized.

Once this button is clicked, there are several options to complete the export.

To export into a BDC analysis workspace, the Export to Seven Bridges or Export to Terra buttons can be used. Once clicking either of these buttons, a new modal will be displayed with all information and instuctions needed to complete the export. This includes your personalized access token, the query ID associated with the dataframe. Additionally, there is the option to Copy Query ID without accessing Seven Bridges or Terra if you wish to use a different analysis platform.

The Export to Seven Bridges option includes a Go to Seven Bridges button, which will open a new tab to the Public PIC-SURE API Project on BDC-Seven Bridges.

The Export to Terra option includes a Go to Terra via R button and a Go to Terra via Python button, which will open the Public PIC-SURE API R Examples workspace and the Public PIC-SURE API Python Examples workspace on BDC-Terra, respectively.

Use Case: Investigating Comorbidities of Breast Cancer in Authorized Access

In this section, the functionalities of PIC-SURE Authorized Access will be described in the context of a scientific use case. Specifically, let’s say I am interested in investigating some comorbidities of breast cancer in women with BRCA1 and BRCA2 gene variants, such as hypertension and COPD.

I already have been authorized to access the Women’s Health Initiative (WHI) study and am interested in a single cohort: women with breast cancer and variants of the BRCA1 and BRCA2 genes. I want to select hypertension-related variables of interest, check the distributions of some variables, and export all the data to an analysis workspace.

First, let’s apply our variable filters for the WHI study.

1. Search “breast cancer” in Authorized Access.

2. Add the WHI study tag to filter search results to only age variables found within the WHI study.

3. Filter to participants with breast cancer by clicking the filter icon next to the variable of interest. Select values to filter your variable on and click “Add Filter to Query”.

The BDC ecosystem hosts several datasets from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. The PIC-SURE platform has integrated the clinical and genomic data from all studies listed in the Data Access Dashboard. Through the ingestion process, occasionally PIC-SURE will ingest phenotypic data for the TOPMed studies prior to the genomic data.

Harmonized Data (TOPMed Harmonized Clinical Variables)

There are limited amounts of harmonized data available at this time. The TOPMed Data Coordinating Center (DCC) curation team has identified 44 variables that are shared across 17 NHLBI studies and normalized the participant values for these variables.

The 44 harmonized variables available are listed in the table in Appendix 2. For more information on this initiative, you can view the or on the .

Table of Studies Included in the TOPMed Harmonized Dataset Available in PIC-SURE

Login to the BDC-Gen3 Platform

In order to navigate and access data available on the Gen3 platform, start by visiting the login page. You will need an eRA Commons account as well as access permissions through the Database of Genotypes and Phenotypes (dbGaP). If you are a researcher, login by selecting NIH Login and using your eRA Commons account. BDC consortia developers can login using their Google accounts. Make sure to use the correct login method that contains access to your available projects.

Once logged in, your username will appear in the upper right-hand corner of the page. You will also see a display with aggregate statistics for the total number of subjects, studies, aliquots and files available within the BDC platform.

NOTE: These numbers may differ from those displayed in the dbGaP records as they include TOPMed studies as well as the associated parent studies.

Types of Hosted Data

Phenotypic

DCC Harmonized clinical data:

A number of clinical variables have been harmonized by the in order to facilitate cross-study analysis. Faceted search over the DCC Harmonized Variables is available via the page, under the "Data" tab.

Unharmonized clinical data:

Unharmonized clinical files are also available on the Gen3 platform and contain all of the raw phenotypic information for the hosted studies. Unlike the DCC Harmonized Variables, these files are located and searchable under the "" tab in the page.

Genomic

The Gen3 platform hosts genomic data provided by the (TOPMed) program and the plus synthetic tutorial data from Terra. At present, these projects include CRAM and VCF files together with their respective index files. Specifically for TOPMed projects, each project will contain at least one multi-sample VCF that comprises all subjects within the consent group. CRAM and VCF are based on an individual level, whereas multi-sample VCFs are based on the study consent level.

All files are available under the "Files" tab in the page. More detailed information on currently hosted data on the Gen3 platform can be found .

Gen3 Pages

The BDC-Gen3 platform contains five pages described below:

• : An interactive data dictionary display that details the contents and relationships between clinical and biospecimen data

• : The facet filter custom cohort creation tool

• : The GraphQL query tool to retrieve specific data within the graph model

Table of BDC dbGAP/TOPMed Identifiers

Current Project IDs

A list of current project IDs can be found in the Data tab, under Filters>Project>Project Id. The current project IDs are:

Overview

The Query page can search and return metadata from either the Flat Model or the Graph Model of a commons. Using GraphQL, these searches can be tailored to filter and return fields of interest for the data sets being queried. These queries can be made immediately after data submission as this queries the model directly.

For more information about how to use the Query page, refer to the Gen3 documentation.

The PIC-SURE v2 API is a meta-API used to host any number of resources exposed through a unified set of generalized operations.

PIC-SURE Repositories:

• PIC-SURE API: This is the repository for version 2+ of the PIC-SURE API.

• : This is the wiki page for version 2+ of the PIC-SURE API.

• : This is the repository for the BDC environment of PIC-SURE.

• : This is the repository for PIC-SURE-ALL-IN-ONE.

Additional PIC-SURE Links:

• : A link to the Avillach Lab Jenkins repository.

• : A repository for Avillach Lab Jenkins development release control.

Client Libraries

The following are the collected client libraries for the entire PIC-SURE project.

PIC-SURE User Interface

The PIC-SURE User Interface acts as a visual aid for running normal queries of resources through PIC-SURE.

PIC-SURE User Interface Repositories:

• : The main High Performance Data Store (HPDS) UI repository.

Additional PIC-SURE User Interface Links:

• : Links to a google drawing of the PIC-SURE UI flow.

PIC-SURE Auth Micro-App (PSAMA)

The PSAMA component of the PIC-SURE ecosystem authorizes and authenticates all actions taken within PIC-SURE.

PSAMA Repos:

Additional PSAMA Links:

• : This is where the core of the PSAMA application is stored in GitHub

High Performance Data Store (HPDS)

HPDS is a datastore designed to work with the PIC-SURE meta-API. It grants researchers fast, dependable access to static datasets and the ability to produce statistics-ready dataframes filtered on any variable they choose at any time.

HPDS Repositories:

• : The main HPDS repository.

• : Python client library to run queries against a PIC-SURE HPDS resource.

• : R client library to run queries against a PIC-SURE HPDS resource.

Overview

The Dictionary page contains an interactive visual representation of the Gen3 data model. The default graph model view, as pictured below, displays all of the nodes and relationships between nodes in a hierarchical structure. The model further specifies the node types and links between nodes, as highlighted in the legend located at the top right side of the page.

Graph View

Users can click on any of the graph nodes in order to learn more about their respective properties. By clicking on a node, the graph will highlight that specific node and all associated links that connect it to the Program node. A "Data Model Structure" list will also appear on the left side toolbar. This will display the node path required to reach the selected node from the Program node.

When a second node in the path is selected, it will then gray out the other possible paths and only highlight the selected path. It will also change the "Data Model Structure" list on the left side toolbar.

The left side toolbar has two options available:

• Open properties: Will download the submission files for all the nodes in the "Data Model Structure" list. This option can also be found on the node that was first selected.

• Download templates: Will open the node properties in a new pop-up window; an example is displayed in the following screenshot.

This property view will display all properties in the node and information about each property:

• Property: Name of the property.

• Type: The type of input for the node. Examples of this are string, integer, Boolean and enumerated values (enum), which are displayed as preset strings.

Table View

The Table view is similar to the Properties view, and nodes are displayed as a list of entries grouped by their node category.

Clicking on one of the nodes will open the Properties view of the node.

Dictionary Search

The Dictionary contains a text-based search function that will search through the names of the properties and the descriptions. While typing, a list of suggestions appears below the search bar. Click on a suggestion to search for it.

When the search function is used, it will default to the graph model and highlight nodes that contain the search term. Frames around the node boxes indicate whether the searched word was identified in the name of the node (full line) or in the node's description and properties' names/descriptions (dashed line).

Clicking on one of these nodes, it will only display the properties that have this keyword present in either the property name or the description.

Click Clear Search Result to clear the free text search if needed.

The search history is saved below the search bar in the "Last Search" list. Click on an item here to display the results again.

Profile Page

The Profile page contains two sections: API keys and Project access.

API key(s)

To download large amounts of data, an API key will be required as a part of the . To create a key on your local machine, click Create API key, which will activate the following pop-up window:

Click Download json to save the credential file to your local machine. After completion, a new entry will appear in the API key(s) section of the Profile page. It will display the API key key_id and the expiration date (one month after the key creation). The user should delete the key after it has expired. If for any reason a user feels that their API key has been compromised, the key should be deleted before subsequently creating a new one.

Project Access

This section of the Profile page lists the projects and the methods of access for the data within in the BDC-Gen3 system. If you do not see access to a specific study, check that you have been granted access within . If access has been granted for over a week, contact the BDC Help Desk: bdcat-support@datacommons.io

Using Exploration

The Exploration page located in the upper right-hand section of the toolbar allows users to search through data and create cohorts. The Exploration portal contains a dynamic summary statistics display, as well as search facets leveraging the DCC Harmonized Variables.

Data Accessibility

Users can navigate through data on the Exploration page by selecting any of the three Data Access categories.

• Data with Access: A user can view all of the summary data and associated study information for studies the user has access to, including but not limited to Project ID, file types, and clinical variables.

• Data without Access:

  • Locks next to the project ID signify to users that they do not have subject-level access but they can still search through the available studies but only view summary statistics. Users can request access to data by visiting the

• All Data: Users can view all of the data available in the BDC-Gen3 platform, including studies with and without access. As a result, studies not available to a user will be locked as demonstrated below.

By default, all users visiting the Exploration page will be assigned to Data with Access.

The Data Tab

Under the "Data" tab, users can leverage the to create custom cohorts. When facets are selected and/or updated to cover a desired range of values, the display will reflect the information relevant to the new applied filter. If no facets have been selected, all of the data accessible to the user will be displayed. At this time, a user can filter based on three categories of clinical information:

• Project: Any specifically defined piece of work that is undertaken or attempted to meet a single investigative question or requirement.

• Subject: The collection of all data related to a specific subject in the context of a specific experiment.

• Harmonized Variables: A selection of different clinical properties from multiple nodes, defined by the Consortium.

NOTE: The facet filters are based on the DCC Harmonized Variables, which are a selected subset of clinical data that have been transformed for compatibility across the dbGaP studies. TOPMed studies that do not contain harmonized clinical data at this time will be filtered out when a facet is chosen, unless the no data option is also selected for certain facets.

Exporting Data from the Data Tab

After a cohort has been selected, the user has four different options for exporting the data.

Export

The options for export are as follows:

• Export All to Terra : Initiate a export of all clinical data and file GUIDs for the selected cohort to . At this time the max number of subjects that can be exported to Terra is 120,000.

• Export All to Seven Bridges: Initiate a export of all clinical data and file GUIDs for the selected cohort to

NOTE: PFB export times can take up to 60 minutes, but often will complete in less than 10 minutes.

The Files Tab

The Files tab displays study files from the facets chosen on the left-side panel (Project ID, Data Type, Data Format, Callset, and Bucket Path). Each time a facet selection is made, the data summary and displays will update to reflect the applied filters.

Locating Unharmonized Clinical Data

The Files tab also contains files that are either case-independent or project-level. This is important for files that are part of the Unharmonized Clinical Data category under the Data Type field. Unharmonized clinical files are made available in two distinct data formats:

• TAR : Contain a complete directory of phenotypic datasets as XML and TXT files that are direct downloads of unharmonized clinical data from dbGaP on a study consent level project.

• AVRO: These files are the same as the unharmonized clinical data from dbGaP as the TAR files, but in form of a file.

NOTE: The unharmonized clinical data sets contains all data from the dbGaP study, but it is not cross-compatible across all studies within BDC.

Exporting/Downloading Data from the Files Tab

Once the user has selected a cohort, there are five options for accessing the files:

• Download Manifest: Download the file manifest and use this manifest to download the enlisted data files using the .

• Export to Workspace: The files can be exported to a Gen3 workspace.

• Export All PFB

File Information Page

A user can visit the File Information Page after clicking on any of the available GUID link(s) in the Files tab page. The page will display details such as data format, size, object_id, the last time it was updated and the md5sum. The page also contains a button to download the file via the browser (see below). For files that are 5GB or more, we suggest using the .

Free text search for Submitter IDs and File Names

Both the Data and File tabs contain a text-based search function that will initiate a list of suggestions below the search bar while typing.

In the Data tab, Submitter IDs can be searched under the Subject tab.

In the File tab, File Names can be searched under the File tab.

Click either on a single or on multiple suggestions in the list appearing underneath the search bar to create a cohort and export/download the data. The selections can be again clicked to be removed from the created cohort.