Release notes

Smarter Session Management for Data Studio Analysis

General

This release delivers improved session management for Data Studio, ensuring that interactive user activity (typing, running cells, UI interactions, mouse movement) is correctly recognized as active platform use. This prevents unintended logouts during active work while maintaining full FedRAMP compliance for truly inactive sessions. Together with redesigned session notifications and long-running session support, these changes provide a predictable, uninterrupted analysis experience.

New Features

• Interactive Data Studio activity- including typing, running cells, executing commands, UI clicks, navigation, and mouse movement – now counts as active platform use and resets the inactivity timer, preventing unintended session timeouts while users are actively working.

• A new “Session Expiring Warning” pop-up now appears before logout (at 10 minutes of inactivity on academic platforms; at 55 minutes on commercial platforms), giving users a 5-minute countdown warning with an “OK” button to dismiss and remain on the same page.

• A new “Session Expired” pop-up now appears at logout (at 15 minutes on academic; 60 minutes on commercial) with a “Log In Again” button that redirects to the login page.

• A new informational banner – “You are no longer inactive – your session is extended by [15/60] minutes” – now appears when a user returns during the session-expiring warning window. The banner fades after 10 seconds.

Improvements

• Session notification experience has been consolidated into a single pop-up system, replacing the previous banner + pop-up combination for a cleaner, less disruptive experience during active use.

• The countdown timer in the session-expiring warning pop-up now dynamically decrements to zero before transitioning to the session-expired pop-up.

• Long-running sessions now ensure that background work (computations, file writes, running jobs) continues independently even after the user is logged out due to inactivity. Background activity keeps the Data Studio instance alive but does not keep the user logged in – login sessions are governed exclusively by user-presence activity.

• Session management behavior is now consistent across all supported platforms.

Resources

If you have questions or experience issues with Data Studio session management, please contact your Velsera Seven Bridges representative or email [support@velsera.com].

Learn more about [Data Studio].

IAM Role Authentication for AWS S3 Volume Connections

General 

This release introduces IAM role authentication as a supported method for connecting AWS S3 buckets as volumes, alongside the existing IAM user method. 

 New Features 

A new feature that enables: 

• Authentication of AWS S3 volume connections using an IAM role in place of an IAM user access key 

• Temporary, automatically-renewed session credentials with no long-term keys to manage 

• Custom IAM policy support scoped to a bucket, or a sub-path within it, for read-only or read-write access 

• Optional External ID configuration for additional trust-policy protection against unauthorized role assumption 

• Volume creation and management via both the visual interface and dedicated API endpoints (create, get details, update) 

 Resources 

For questions or support related to IAM role volume connections, contact your Velsera Seven Bridges representative or email support@velsera.com. 

Learn more about IAM Roles. 

Dynamic File Addition and Enhanced Input File Visibility in Data Studio

General

This release introduces the ability to add files dynamically to a running Data Studio analysis and provides enhanced visibility into input files associated with each session, improving workflow efficiency and file traceability.

Improvements 

• Users can now add single or multiple files from the Current Project to a running Data Studio analysis via a new “+Add files” button on the Data studio View Details page, without needing to stop or restart the session.
• A new “Input Files” section is now displayed on the Data Studio View Details page, listing all files selected at launch as well as any files added dynamically during the session. This section remains visible in read-only mode for completed or stopped analyses.
• “Create New Analysis” popup now includes additional guidance:

1. New informative message: “Add files dynamically to your running Data studio analysis without interrupting. Learn more”.

Resources

If you have questions or experience issues related to Data Studio analyses, please contact your Velsera Seven Bridges representative or email [support@velsera.com].

Nextflow Head Instance Customization and Enhanced Execution Controls

General 

This release introduces customizable compute resources for Nextflow head (executor) instances at both the app and task level. You can now right-size the head job separately from worker instances, improving reliability and cost efficiency for complex Nextflow workflows while preserving backward compatibility for existing apps. 

New Features 

• Customizable Head Job Instance for Nextflow Tasks : Nextflow tasks now support a dedicated “Nextflow Head Instance type” configuration under Task → Execution settings. When “Nextflow Multi-Instance Execution” is enabled, you can select a custom head instance type and adjust attached storage. App-level defaults can be defined in the app YAML configuration or managed via the public API, with changes reflected bidirectionally between YAML and the UI. 

Improvements 

• Right-sized head job resources: Workflows with heavy orchestration logic can now run on more capable head instances, reducing failures caused by out-of-memory conditions or controller overload. Lighter workloads can use smaller instances to reduce cost. 
• Predictable default behavior: If only an app-level head instance is configured, it serves as the default. If both app-level and task-level are configured, the task-level takes precedence. Existing apps without head instance configuration continue using platform defaults. 
• Clearer UI terminology: The existing “Instance type” for worker tasks is now labeled “Nextflow Worker Instance type” to clearly distinguish worker resources from head job configuration.
• Network restriction compatibility: For projects with network restrictions enabled, the Nextflow head instance now also honours the project’s network access policy, consistent with worker instance behavior.
• Division name in task notifications: Addition of Division name in mail notifications for tasks making it easier for users in multi-division organizations to quickly identify and prioritize relevant task notifications 

Resources 

If you have questions or experience issues, please contact your Velsera Seven Bridges representative or email support@velsera.com. 

Recently published apps

MONAI is an open-source framework for deep learning in healthcare. In this release, following 2 applications wrapped with Monai wrapper are added in Public Apps Gallery.

1. MONAI Auto3DSeg is a wrapper for the AutoRunner utility of Auto3DSeg. It is used to automatically train, tune hyperparameters, and evaluate multiple 3D segmentation models with minimal user intervention.
2. MONAI nnUNetV2 is a wrapper for the nnUNetV2Runner utility of nnUNetV2. It is a fully automated, self‑configuring deep learning framework for biomedical image segmentation which can automatically train, tune hyper parameters and evaluate multiple 3D segmentation models end-to-end without human tuning by automatically adapting itself to any new dataset.

MOFA2 is an unsupervised multi-omics integration framework that learns latent factors capturing shared and view-specific sources of variation across multiple omics datasets. In this release, following 3 tools are added in Public Apps Gallery

1. Data Harmonizer reads omics data files (tabular, .h5ad, or .h5mu) and prepares matrices for MOFA2 input structure. It supports matrix and long-format tabular data, along with AnnData and MuData objects, and performs format harmonization, basic data-type-aware transformation, and sample alignment when needed. The app is intended for MOFA2 input preparation and does not substitute for full assay-specific preprocessing or quality control.
2. MOFA2 takes a set of multi-omics data files and performs Multi-Omics Factor Analysis (MOFA2) to infer latent factors that represent the underlying biological signals across diverse modalities. This process allows for the integration of data types such as transcriptomics, methylation, and proteomics into a unified latent variable model.
3. MOFAx-0-3-7 generates visualization plots from trained MOFA2 multi-omics models. It helps interpret latent factors and explore relationships among samples, features, views, and covariates derived from multi-omics datasets.

Resources

If you need help with accessing controlled study details, please contact your Velsera Seven Bridges representative or email [support@velsera.com].

DRS Bulk Import and File Upload Improvements

General

This release includes a usability improvement to the DRS bulk import interface and updates to file upload and import reliability. 

Bug Fixes

• Drag-and-drop file uploads are now available on the file upload interface, for both direct file uploads and DRS manifest uploads. Confirmation text styling has been updated to clearly indicate upload state and next steps. 
• Long-running directory and volume import jobs now recover automatically from session expiry during the import. The system requests a new session and retries the failed operation once, allowing imports to complete successfully. 

Resources

If you have questions or experience issues with DRS imports or file uploads, please contact your Velsera/Seven Bridges representative or email support@velsera.com.

Learn more about importing data from a DRS server.

Introducing GUI‑Based Imaging Workflows in Data Studio

General

This release adds Graphical User Interface (GUI) based visualization tools to Data Studio, enabling researchers to work with complex imaging data directly on the platform. OHIF Viewer, 3D Slicer, and ImageJ can now be launched as cloud-hosted, desktop-like applications from Data Studio or from the Files tab, without local installation or configuration. Annotated outputs and result files from 3D Slicer and ImageJ can be exported back to the project workspace for downstream analysis.

New features

GUI-based imaging tools in Data Studio (OHIF Viewer, 3D Slicer, ImageJ)

Data Studio now supports Graphical User Interface (GUI) GUI-based imaging applications, enabling advanced visualization and annotation workflows alongside existing code-based environments such as JupyterLab and RStudio. OHIF Viewer, 3D Slicer, and ImageJ are available directly from the Create new analysis dialog in Data Studio, and from the Files tab via Open in Data Studio → Create new analysis, providing a consistent launch experience across both entry points. As part of this update, the existing SlicerJupyter (Beta) a remains available, with the new GUI 3D Slicer added alongside it.

The following capabilities are available when working with these tools:

• Custom extensions and plugins can be installed for OHIF Viewer, 3D Slicer, and ImageJ, with changes persisted and available on session restart.
• Annotated outputs and result files from 3D Slicer and ImageJ can be exported back to the project workspace for downstream analysis.
• Access to external data sources, including external repositories and data endpoints, is available subject to the project’s network access settings.
• GPU-enabled instance types are available for compute-intensive imaging workloads.
• Multi-cloud provider selection is supported to help minimize data egress costs.

Resources

To learn more about enabling GUI-based imaging tools for your organization or to get help planning a migration of existing imaging workflows, please contact your Velsera Seven Bridges representative or reach out to the Seven Bridges support team at [support@velsera.com].

Recently published apps

General

DeepVariant workflow  is an analysis pipeline that uses a deep neural network to call genetic variants from NGS DNA sequencing data. It is a highly accurate variant caller for ONT, PacBio, and multi-technology categories. It was updated from version 1.5.0 to 1.9.0.

Giraffe-DeepVariant workflow is a pipeline for calling small variants using the pangenome reference. Giraffe-DeepVariant workflow starts with sequenced reads (FASTQs, CRAM) and does analysis to detect small variants (VCF). Reads are mapped to a pangenome with vg giraffe and pre-processed (e.g. indel realignment) before the variant calling step. DeepVariant is used for calling small variants. It was updated from version 1.0 to 1.1.

Genotype GVCFs & Filter Variants  workflow performs the main part of the Joint Discovery analysis of GATK. It starts with GVCF files, performs joint genotyping and gives back filtered vcf files. All independent tools of the workflow were upgraded to GATK 4.6.2.0 version. 

nnUNet (nonewUNet) toolkit  is a fully automated, self-configuring deep learning framework for biomedical image segmentation. It eliminates manual architecture engineering by automatically adapting itself to any new dataset. It analyzes dataset properties, configures all training components, and delivers strong baseline performance without human tuning.

Below is the list of tools included in this toolkit, arranged in an nnUNetstyle workflow.

1 .nnUNetv2 convert_MSD_dataset  tool converts a Medical Segmentation Decathlon (MSD) dataset into the standardized nnUNet folder structure. It generates a valid dataset.json and restructures images/labels according to nnUNet conventions.

2 . nnUNetv2 plan_and_preprocess tool analyzes dataset properties (voxel spacing, intensity statistics, modalities) and generates the experiment plan that determines patch size, architecture, and configuration. It performs preprocessing steps like:

• Resampling
• Normalization
• Cropping
• Dataset caching

3 .nnUNetv2 train tool initiates model training for a given configuration (e.g. 2d, 3d_fullres) and fold (0–4).
The training includes data augmentation, optimization, checkpointing, and validation to produce trained model weights.

4 .nnUNetv2 find_best_config tool aggregates cross validation results and identifies the best performing configuration and checkpoint. It is used to decide which model or ensemble should be deployed for test time inference.

5 .nnUNetv2 predict tool runs inference on unseen images using the selected trained model which gives following outputs:

• Segmentation masks
• Optional probability maps

6 .nnUNetv2 determine_postprocessing tool evaluates several postprocessing heuristics (e.g. removing small, connected components) on validation outputs.It automatically selects the rule set that improves performance metrics such as Dice and IoU.

7 .nnUNetv2 apply_postprocessing tool applies the chosen postprocessing operations to raw prediction outputs to refine segmentation masks and enhance overall accuracy.

8 .nnUNetv2 ensemble  tool combines predictions from multiple folds or configurations (e.g. 2D + 3D) by averaging their probability maps. Itensembles which improves robustness and generally increases accuracy.

9 .nnUNetv2 evaluate_folder tool computes metrics by comparing a folder of predicted labels with groundtruth labels (e.g., Dice, IoU, depending on configuration). It summarizes percase and aggregate scores, enabling quick validation of model performance or comparison across checkpoints, folds, or ensembles.

Resources

If you need help with accessing controlled study details, please contact your Velsera Seven Bridges representative or email [support@velsera.com].

Enhanced Data Studio Session Backup and Shutdown

General

This release includes stability enhancements to Data Studio session shutdown and backup, reducing the occurrence of stuck sessions and improving the reliability of analysis recovery.

Improvements 

The reliability of the process that runs when you stop a Data Studio session has been improved. The updated logic better handles large and complex analysis environments, ensuring that backup and cleanup steps complete more consistently before a session is terminated.

Fixed Bugs

Several issues that could cause Data Studio sessions to remain in a saving or stopping state for extended periods and result in incomplete backups have been resolved. The following underlying problems were fixed:

• Locked files during backup: Improved handling of files that remain in use while the backup is running, so they no longer prevent proper cleanup and completion of the backup process.
• File upload reliability: Strengthened error handling for timeouts when uploading large files, reducing incomplete transfers that could interrupt session backup.
• Complex environment handling:  Fixed failures when saving sessions with many installed packages and dependencies, improving stability for complex analysis environments.
• System metrics handling: Fixed an issue where unexpected internal data could interrupt the backup workflow.
• Bulk file operations: Optimized how large batches of files are processed during backup, preventing platform slowdowns during session shutdown.

Resources

If you experience issues with stopping or recovering a Data Studio session, please contact your Velsera Seven Bridges representative or email support@velsera.com.

Learn more about Data Studio.