Release Note

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Release Note

Written by Mithila in

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. 

  1. 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. 

  1. 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. 

  1. MixOmicsPLS and sPLS 

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

  1. MixOmicsPLS-DA and sPLS-DA 

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

  1. MixOmicsrCCA 

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

  1. MixOmicsMINT 

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

  1. 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. 

  1. MixOmicsMINT Public Project 

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

  1. 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. 

  1. 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. 

Mithila