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.

Recently Published Apps

1. CellTypist: Automated cell type annotation in single-cell RNA-seq data. It uses pre-trained machine learning models on curated datasets to deliver fast, standardized, and reproducible cell labeling. This is especially valuable for large-scale immunology and tissue profiling studies, reducing manual effort and subjectivity. 
2. GATK VariantToTable: Transform raw VCF data into clean, structured tables. Select the INFO, FORMAT, or genotype fields you need, and the tool generates a ready-to-use CSV/TSV file for effortless filtering, visualization, and downstream analysis in Excel, R, and Python. 
3. GATK GenomicsDBImport: Efficiently combine multiple gVCF files into a GenomicsDB workspace, enabling scalable joint genotyping. It organizes variant data in a tiled database format, which allows faster queries and reduces storage requirements compared to raw gVCFs. This step is typically performed before running GenotypeGVCFs in large cohort analyses. The updated version 4.6.2.0, now featuring a DB update option.
4. The MCmicro workflow: A scalable and reproducible pipeline for processing multiplexed tissue imaging data. It integrates tools for image registration, illumination correction, segmentation, and feature extraction. These tools produce high-quality single-cell spatial datasets and are designed for large-scale studies, MCmicro standardizes analysis across experiments, enabling downstream biological insights from multiplex imaging platforms. This updated workflow version, with existing models and support for custom models.

Enable Project Download Restrictions Retroactively

This release introduces the ability to apply download restrictions to a project retroactively. Previously, the download restriction could only be applied while creating a project. With this release, the admins and project creator can modify the project settings to enable download restrictions at anytime.

The Download Restriction feature has been upgraded to also be applicable retroactively to existing projects, without recreating projects or migrating files.  This enables enhanced restriction of data access on the platform, aligning with security policies.  

• The restriction is irreversible, ensuring consistent security policies.  
• Internal teams can support rollback based on requests, ensuring flexibility without compromising control and reducing time   

Enabling new AWS families with GPU-enabled Instances on Data Studio

This release introduces adding new AWS families with GPU enabled Instances within Seven Bridges Data Studio. It enables users to perform advanced medical image visualization, segmentation, GPU acceleration and secure data integration.

Enabled AWS Instances on Data Studio

We are thrilled to announce that we have enabled new AWS families with GPU enabled Instances on Data Studio to provide even more options for optimizing your computational workloads.

New AWS Instance Families Include:

• Compute-Optimized: C7i, C6i
• Memory-Optimized: R7i, R7iz, R6i, R6in, X2idn, X2iedn, X2iezn
• General Purpose: M7i, M6i
• GPU-Enabled: G6, G5

This update empowers researchers and analysts to execute high-performance computational tasks directly from Data Studio without external infrastructure setup.

It simplifies workflow execution, reducing processing time and improving efficiency for GPU-dependent applications.

3D Slicer now available in Data Studio

General

This release introduces 3D Slicer as a fully integrated analysis environment within Data Studio, enabling advanced medical image visualization and segmentation.

New feature

Changes in the main navigation

The main navigation now features a Home button which returns you to the dashboard at any given point. In addition, the icon for accessing the account settings now shows your initials instead of the username.

Improved UX for Enterprise users

We have introduced a small change in order to improve and optimize the UX for the Enterprise users. The menu for accessing Divisions is now in the upper right hand side, right next to the Account Settings.

Somatic small variant callers for long read data, ClairS (0.2.0) and ClairS-TO (0.1.0) (for matched tumor-normal pairs and tumor-only data, respectively) have been published to the Seven Bridges Platform.

We have published the GCTA 1.94.1 tool on the Seven Bridges Platform. GCTA is a suite of tools for various genetic analyses using genome-wide data. GCTA (Genome-wide Complex Trait Analysis) was initially developed to estimate the proportion of phenotypic variance explained by all genome-wide SNPs for a complex trait but has been greatly extended for many other analyses of data from genome-wide association studies (GWASs)