Release notes

August 2026 Public Apps Gallery Updates 

Recently Released Platform Enhancements, Public Projects, and Scientific Workflows 

This month, we expanded the availability of key scientific workflows across Seven Bridges platforms, introduced new resources for single-cell data analysis, released eight nf-core Nextflow workflows on academic platforms (CGC, BDC, and CAVATICA), refreshed public imaging projects with integrated OHIF Viewer and 3D Slicer guides, and upgraded QIIME2 workflows for microbiome research and STAR-based tools for RNA-seq analysis. These updates are designed to improve reproducibility, usability, and accessibility for researchers working across genomics, imaging, and multi-omics workflows. 

Nextflow updates 

1. Added Eightnf-core Nextflow Workflows on CGC, BDC, and CAVATICA Platforms 

To provide a consistent Public Apps Gallery experience across platforms, eight existing and optimized nf-core Nextflow workflows were published across multiple environments, including CAVATICA, CGC, BDC, and EU platforms. The workflows are listed below. 

• chipseq 

• mag 

• nf-rnavar 

• pgsc-calc 

• rnafusion 

• rnaseq 

• Sarek 

• Scrnaseq 

This release makes widely used Nextflow workflows more broadly accessible and simplifies adoption for researchers working across different Velsera ecosystems. 

2. Enhancements to thesbpack_nf CWL app 

Several improvements were delivered to the sbpack_nf CWL App to streamline onboarding and deployment of Nextflow workflows on Seven Bridges platform. Updates include: 

• Support for importing Nextflow workflow packages directly from GitHub repository URLs, reducing the need for manual archive uploads. 

• Validation checks to prevent unsupported string output types during workflow packaging. 

• Improved compatibility with EPI2ME and nf-core workflows 

Together, these updates improve workflow portability, reduce deployment errors, and simplify the packaging process for Nextflow users on the platform. 

Updated Public Imaging Projects 

3. The Cancer Imaging Archive (TCIA) Imaging Project with OHIF Viewer

The TCIA public project was updated and integrated with an OHIF Viewer guide for the pre-segmentation QC validation workflow, supporting DICOM image review, image quality assessment, and basic measurements. It is now published as a public project across multiple environments, including CAVATICA, CGC, and BDC platforms. These updates help users more easily explore and analyze radiology imaging datasets hosted within the project. 

4. COVID-19 Image Segmentation with Deep Learningand 3-D Slicer Visualization 

The COVID-19 Image Segmentation public project was upgraded and integrated with a 3D Slicer guide for the post-segmentation QC workflow. The upgraded project is now published as a public project across multiple environments, including CAVATICA, CGC, and BDC platforms. These changes strengthen the project’s value as a learning and reference resource for medical imaging and AI-assisted segmentation workflows. 

Single-Cell Analysis Resources 

5. New Monocle Workflow for Single-Cell Trajectory Analysis

A standardized Monocle workflow has been developed to support scalable and reproducible single-cell RNA-seq trajectory analysis across platforms. The workflow enables researchers to: 

• Reconstruct developmental and cellular trajectories. 

• Perform pseudotime analyses. 

• Explore branching lineage relationships. 

• Investigate gene expression changes associated with cell state transitions. 

By providing a configurable and reusable workflow, this release simplifies advanced single-cell trajectory analysis for a broad range of biological applications.  

6. Public Project on Monocle 3-Based Single-Cell Trajectory Inference

This public project demonstrates the reconstruction of disease-associated cellular trajectories from raw single-cell count data using the publicly available GSE253587 dataset from Bui et al. 

The resulting trajectory resolves four T-cell populations and recapitulates the major patterns reported in the original study, including a continuum from conventional T-cell states toward cycling regulatory T cells (cycTregs). Together, these results demonstrate Monocle 3’s ability to recover biologically meaningful disease-associated transitions from publicly available single-cell data. 

7. Single-Cell RNA-seq End-to-End Analysis Public Project

A new public project on the BDC platform brings together existing single-cell analysis and visualization resources into a single end-to-end learning experience. The project is designed to: 

• Introduce users to single-cell RNA-seq analysis workflows. 

• Demonstrate interoperability between Seurat-based analyses and Vitessce visualizations. 

• Showcase Human Cell Atlas datasets and related resources. 

• Serve as a reference and onboarding environment for platform users and collaborators. 

This consolidated resource makes it easier for researchers to learn and adopt single-cell analysis workflows on the BDC platform. 

Microbiome Analysis Improvements 

8.Updated QIIME2 16S rRNA Public Workflows 

The publicly available QIIME2 workflows for microbiome analysis have been reviewed, updated, and validated to align with current platform standards. Updated workflows include: 

• 16S rRNA Feature Classifier Training 

• 16S rRNA Metagenomic Profiling 

• QIIME2 Core Diversity Reporting 

Improvements include workflow validation, refreshed configurations, updated parameter guidance, enhanced documentation, and verification of referenced assets and metadata. These updates help ensure reliable execution and a more consistent user experience for microbiome researchers. 

RNA-seq Workflow Updates 

9. Updated STAR Toolkit and RNA-seq Alignment Workflows

The STAR toolkit and associated RNA-seq alignment workflows available through the Public Apps Gallery have been updated to the latest supported version, improving consistency and maintaining alignment with current platform standards. The update includes the following components: STAR, STARsolo, STARsoloCellFiltering, STAR Genome Generate, and the RNA-seq Alignment STAR workflow. This release ensures that users performing bulk and single-cell RNA-seq analyses have access to the most up-to-date STAR-based tools while maintaining compatibility with existing analysis workflows. The refreshed toolkit supports reliable sequence alignment, genome indexing, and single-cell transcriptomic preprocessing across a variety of RNA-seq applications. 

 

Resources 

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

 

June–July 2026 Public Apps Gallery Updates 

We are pleased to announce a new set of applications, projects, and tool upgrades released to the Public Apps Gallery during June and July 2026. These additions expand support for clinical machine learning, multi-omics integration, genomic data analysis, and biomedical imaging workflows, providing researchers with a broader portfolio of production-ready CWL applications for translational and bioinformatics research. 

 

Clinical AI and COMET Framework Applications 

Three new applications have been added to support machine learning workflows built around electronic health record (EHR) data and the COMET framework. Together, these tools provide an end-to-end workflow for representation learning, model training, evaluation, and clinical prediction. 

1. COMET Train Word2Vec

Generates patient embeddings and recurrent neural network (RNN)-ready datasets from preprocessed EHR data. This application helps researchers transform longitudinal clinical records into structured representations suitable for downstream machine learning analyses. 

2. COMET Run Experiments

Supports training and evaluation of GRU-based EHR models and enables zero-shot testing, fine-tuning, and integrated COMET experiments. The application simplifies model development and benchmarking across clinical machine learning studies. 

3. COMET Prediction

Performs inference using trained COMET models on new patient datasets. Researchers can apply previously trained models to generate predictions and support downstream clinical and translational research applications. 

 

Expanded mixOmics Toolkit Portfolio 

June introduces a comprehensive set of mixOmics 6.32.0 applications for multivariate and integrative omics analyses. These tools strengthen support for biomarker discovery, dimensionality reduction, data integration, and predictive modeling across multiple omics datasets. 

1. MixOmicsPCA 

Supports exploratory data analysis and dimensionality reduction, helping users identify major sources of variation within complex biological datasets. 

2. MixOmicsPLS and sPLS 

Enables modeling relationships between datasets while incorporating variable selection capabilities, making it easier to identify the most informative molecular features. 

3. MixOmicsPLS-DA and sPLS-DA 

Provides supervised classification workflows for biomarker discovery and sample discrimination across biological conditions. 

4. MixOmicsrCCA 

Facilitates the exploration of correlations between datasets, supporting the identification of biologically relevant relationships across data modalities. 

5. MixOmicsMINT 

Enables integration of multiple independent studies measuring common variables, helping researchers identify robust and reproducible molecular signatures. 

6. DIABLO

Supports outcome-driven multi-omics integration and biomarker discovery by identifying correlated features across multiple data types associated with a phenotype or clinical outcome. 

These additions significantly enhance the platform’s capabilities for integrative omics analysis, biomarker identification, and advanced statistical learning workflows. 

 

New Public Projects 

Several new public projects have been published to provide users with reference implementations and reproducible examples across multi-omics and imaging domains. 

1. MixOmicsDIABLO Public Project 

Demonstrates outcome-driven multi-omics integration using DIABLO to identify correlated transcriptomic and proteomic signatures associated with COVID-19 severity, based on the Harriott et al. 2025 study. 

2. MixOmicsMINT Public Project 

Showcases multivariate integrative analysis using MAQC datasets to identify reproducible molecular signatures across independent microarray studies and platforms. 

3. nnU-Net Public Project

Provides an accessible implementation of the self-configuring nnU-Net v2 framework for biomedical image segmentation. The project supports automated preprocessing, model training, and inference for both 2D and 3D imaging datasets. 

4. MOFA2 Public Project

Brings together MOFA2, MEFISTO, and MOFACell workflows for unsupervised multi-omics factor analysis, temporal and spatial omics integration, and single-cell or multicellular program discovery. 

 

Tool Upgrades 

VCFtools Suite for Variant Processing and Comparison 

A comprehensive collection of VCFtools-based applications has been upgraded to support routine variant-processing and quality-control workflows. These tools improve interoperability across genomic analysis pipelines while streamlining common data manipulation tasks. 

The upgraded applications enable users to: 

• Filter variants using quality, genotype, region, and variant-level criteria with VCFtools Filter. 

• Separate SNPs and indels using VCFtools Keep SNPs and VCFtools Keep Indels. 

• Merge, concatenate, sort, and subset variant datasets using VCFtools Merge, Concat, Sort, and Subset. 

• Perform variant intersection analyses with VCFtools Isec, Venn2, and Venn3. 

• Assess Hardy-Weinberg Equilibrium using VCFtools Hardy and VCFtools Hwe. 

• Convert between VCF versions using VCFtools Convert to support downstream tool compatibility. 

These enhancements simplify routine variant curation and analysis workflows for genomic researchers. 

GATK Best-Practice Components 

Several key Genome Analysis Toolkit (GATK) applications have been upgraded, enabling users to build variant-calling workflows aligned with widely adopted best-practice methodologies. 

The upgraded applications include: 

• BaseRecalibrator 

• ApplyBQSR 

• GatherBQSRReports 

• HaplotypeCaller 

• VariantFiltration 

• GenomicsDBImport 

Together, these tools support base-quality score recalibration, germline variant discovery, joint genotyping workflows, and downstream variant filtering to improve sequencing analysis quality and consistency. 

Regenie 

The Public Apps Gallery has also been upgraded with Regenie, a widely used tool for whole-genome regression and large-scale genetic association studies. 

Regenie is designed for efficient analysis of large cohorts and supports genome-wide association testing across quantitative and binary traits, making it a valuable addition for population-scale genomics and genetic epidemiology research. 

Resources 

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

This release includes performance and reliability improvements across billing, Docker registry compatibility, and project administration, along with an updated CLI version

• Billing Reports: Billing Reports are exported and are shared over mail for easier access and download.

• Docker Registry: Support extended for Docker version 25 and above, including full visibility of the images in the Repository UI

• Task Logs (Nextflow): Actionable messages on Nextflow task execution failures for easier troubleshooting.

• Project Membership: Enabled deletion of disabled members from automation projects via the CLI, using the updated CLI version (0.25.3) included in this release.

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

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.