Published May 20, 2026 | Version v10.0.6

MagmaFlow_v10.0.6: A desktop platform for artificial intelligence-driven expression analysis

Authors/Creators

Contributors

Researcher:

  • 1. Universite Libre de Bruxelles Faculté de Pharmacie

Description

MagmaFlow v10.0.6


MagmaFlow: A desktop platform for artificial intelligence-driven expression analysis

MagmaFlow is a cross-platform desktop application combining interactive volcano plot visualization with automated annotation, integrated literature mining, and pathway-level contextual analysis. The platform retrieves relevant PubMed references, pathway memberships, and disease associations directly within an interactive visualization environment, enabling efficient, reproducible, and publication-ready analysis of transcriptomic datasets.

MagmaFlow transforms volcano plot analysis from static display into dynamic biological interpretation, representing the first tool integrating AI-powered literature contextualization with enrichment analysis to convert differential expression data into actionable insights.

What's New in v10.0.6

Enrichr pathway enrichment databases updated to the latest available versions:

  • Gene Ontology updated to 2026 (GO_Biological_Process_2026, GO_Molecular_Function_2026, GO_Cellular_Component_2026)
  • KEGG confirmed at 2026, WikiPathways at 2024, Reactome at 2024, MSigDB Hallmark at 2020
  • All database versions aligned with the latest Enrichr library releases as of May 2026

Files in This Release

File Platform Architecture Compatibility Signing Status
MagmaFlow-MacOsSilicon-arm64-10.0.6.dmg macOS Apple Silicon (arm64) macOS 11 Big Sur or later — M1, M2, M3, M4 chips (2020 and later) Signed and notarized
MagmaFlow-MacOs-MacOsIntel-x86_64_10.0.6.dmg macOS Intel (x86_64) All Intel Macs — macOS 10.12 Sierra or later Unsigned (see note below)
MagmaFlow-10.0.6_Windows.exe Windows x86_64 Windows 10 (64-bit) or later Unsigned

How to Identify Your Mac

Go to Apple menu → About This Mac:

  • If Chip shows Apple M1/M2/M3/M4 → your Mac is Apple Silicon (2020 or later) → download the arm64 DMG
  • If Processor shows Intel Core → your Mac is Intel → download the Intel DMG

Important Note on the Intel Build

The MagmaFlow-MacOs_x86_64_10.0.6.dmg is intentionally distributed without Apple notarization. This is due to a fundamental and unresolvable incompatibility between Apple's notarization requirements and the JVM memory model on Intel Macs running older macOS versions.

Apple's notarization process requires the hardened runtime flag, which restricts executable memory allocation. The Java Virtual Machine requires dynamic executable memory mapping for its code cache, a requirement that cannot be disabled without breaking the application. This conflict affects Intel Macs on macOS 10.14 Mojave and earlier, where Apple had not yet updated the memory management model. To maximize compatibility across all Intel Macs regardless of macOS version, we distribute a single unified Intel build without notarization.

To open the Intel build when macOS displays a security warning:

  1. Right-click the DMG file and select Open
  2. Click Open in the security dialog that appears
  3. Drag MagmaFlow to your Applications folder and launch normally

This is a standard one-time override for trusted third-party software and is fully documented in Apple's official Gatekeeper guidance.

Key Features

Interactive Volcano Plot Visualization

Feature Description
Double-Click Gene Selection Instantly add/remove target genes by double-clicking data points
Drag and Drop Labels Reposition gene annotations with collision avoidance for publication-ready figures
Smart Edge Connections Five layout modes (Auto, Left, Right, Center, Smart) to reduce overlap
Advanced Zoom and Pan SHIFT+drag panning, mouse wheel zooming, one-click fit-to-viewport
Real-time Hover Tooltips Live gene details displaying name, log₂FC, p-value, and adjusted p-value

Target Gene Management

Feature Description
Checkbox Control Synchronized checkboxes for real-time activation/deactivation of annotations
File Import Load target gene lists from text files
Manual Entry Type or paste gene names with smart parsing
Smart Positioning Auto-prevent label overlaps with intelligent spacing

Color Systems

Style Description
Gurzov Classic Traditional blue/red coloring for down/up-regulated genes
MagmaFlow Classic 1 P-value based gradients
MagmaFlow Classic 2 Log₂ fold change based gradients
MagmaFlow Classic 3 Combined p-value and fold change gradients
Viridis and Magma Perceptually uniform, color-blind-friendly scientific palettes
Custom Color Pickers Direct RGB/Hex color refinement with adjustable outlines and transparency

Precision Customization

Feature Description
Independent Font Controls Separate sizes for title, axes, ticks, and annotations
Dynamic Thresholds Real-time p-value and log₂FC cutoff adjustment
Smart Tick Spacing Auto or manual axis intervals for perfect scaling
Advanced Dot Styling Opacity, outlines, sizes with live preview
Publication Export High-resolution PNG (72–1200 DPI) with scalable off-screen rendering

Project Workflow

Feature Description
Smart CSV Import Automatic detection of standard columns via regular expressions; manual mapping dialog for non-standard headers
Complete Project Files JSON format preserving gene data, thresholds, annotations, label positions, and display preferences
Auto-save Tracking Visual indicators for unsaved changes to prevent data loss
Session Persistence Remember your work across application restarts

R Integration

Feature Description
MagmaFlowR Package Companion R package for integration with DESeq2, edgeR, limma, and Seurat pipelines
mag_landragem() Launch MagmaFlow GUI and preload expression data directly from R environment
Repository https://github.com/carlosbuss1/magmaflowR

Literature Mining Module

Feature Description
PubTator3 Integration AI-powered named entity recognition across 36 million PubMed abstracts and 6 million PMC full-text articles
Dynamic Context Definition Autocomplete for Disease/Condition (required) and Treatment/Chemical (optional) with validated MeSH identifiers
Relation Types Configuration Positive/negative correlation, stimulation, inhibition, or general association to match expression patterns
Dual-API Strategy PubTator3 Relations API for Total and Context papers; NCBI E-utilities for Recent papers (default: 2020 onwards, customizable) and PMIDs
Disease Synonym Expansion Automatic inclusion of abbreviations and related terms for disease-specific searches
Log-Scaled Scoring Composite score (2×log(1+Total) + 8×log(1+Context) + 5×log(1+Recent) + StatsBonus) preventing highly-studied genes from dominating
Clickable PMID Links Direct access to publications sorted by date (newest first)

Pathway Enrichment and Circle Plot Visualization

Feature Description
Enrichr API Integration Over-Representation Analysis across seven pathway databases
Supported Databases Gene Ontology 2026 (BP, MF, CC), KEGG 2026, Reactome 2024, WikiPathways 2024, MSigDB Hallmark 2020. Database release versions are explicitly specified in each API call to ensure reproducibility of enrichment results across MagmaFlow versions
Species Support Human, Mouse, Zebra fish and Cow (Bos taurus, GO only)
Analysis Modes All significant genes combined, or separate up/down-regulated analyses
Circle Plot Visualization Multi-layer circular diagrams with pathway enrichment, gene expression, and database annotations
Cross-Pathway Detection Curved lines connecting genes appearing in multiple pathways reveal functional relationships
Bidirectional Workflow Pathway discovery informs gene prioritization; volcano visualization contextualizes shared pathway genes

System Requirements

macOS — Apple Silicon (arm64)

  • Chip: Apple M1, M2, M3, or M4 (Mac purchased 2020 or later)
  • macOS 11 Big Sur or later (macOS 13 Ventura or later recommended)
  • 4 GB RAM minimum — 8 GB recommended for datasets >30,000 genes
  • 1280 × 720 display minimum — Full HD recommended

macOS — Intel (x86_64)

  • Chip: Intel Core i5, i7, or i9 (64-bit) — any Intel Mac
  • macOS 10.12 Sierra or later
  • 4 GB RAM minimum — 8 GB recommended for datasets >30,000 genes
  • 1280 × 720 display minimum — Full HD recommended

Windows

  • Windows 10 (64-bit) or later — Windows 11 recommended
  • 4 GB RAM minimum — 8 GB recommended for datasets >30,000 genes
  • 1280 × 720 display minimum — Full HD recommended

Installation

macOS — Apple Silicon

  1. Download MagmaFlow-MacOs_arm64-10.0.6.dmg
  2. Open the DMG and drag MagmaFlow to your Applications folder
  3. Launch from Applications — no security prompts expected

macOS — Intel

  1. Download MagmaFlow-MacOs_x86_64_10.0.6.dmg
  2. Right-click the DMG and select Open
  3. Click Open in the security dialog
  4. Drag MagmaFlow to your Applications folder
  5. Launch from Applications

Windows

  1. Download MagmaFlow-10.0.6_Windows.exe
  2. Run the installer and follow the setup wizard
  3. Launch from Start Menu or desktop shortcut

Technical Implementation

Component Technology
Framework JavaFX 17.0.2
Compiler JDK 17 (LTS)
Architecture Model-View-Controller (MVC)
Rendering JavaFX Canvas API with GPU-accelerated GraphicsContext
Precision Double-precision floating-point arithmetic

External API Integration

API Purpose Endpoint
PubTator3 AI-powered named entity recognition https://www.ncbi.nlm.nih.gov/research/pubtator3-api
NCBI E-utilities Date-filtered publication queries (default: 2020 onwards, customizable) https://eutils.ncbi.nlm.nih.gov/entrez/eutils/
Enrichr Over-Representation Analysis https://maayanlab.cloud/Enrichr/

License

MagmaFlow is distributed under the MagmaFlow Academic Binary End-User License Agreement (EULA v1, October 2025). The binary is freely available for academic and non-commercial use without registration or fee. The source code is maintained as closed-source under the stewardship of the Knowledge and Technology Transfer Office (KTO) of the Université libre de Bruxelles (ULB). Commercial use requires prior written consent from ULB.

Commercial licensing inquiries: carlos.eduardo.buss@ulb.be

Related Resources

  • GitHub Repository: https://github.com/carlosbuss1/MagmaFlow
  • MagmaFlowR Package: https://github.com/carlosbuss1/magmaflowR

Main Publication:

Buss CE, Li A, Gilglioni EH, Bansal M, Singh SP, Bakiri L, Cardozo AK, Gurzov EN. MagmaFlow: A desktop platform for artificial intelligence-driven expression analysis. FEBS Open Bio. 2026 Jun 3. doi: 10.1002/2211-5463.70288.

Files

Files (309.0 MB)

Name Size
md5:e12325a632e9690ade06e6384646a3bb
134.7 MB Download
md5:290353dc006789fe26dfdfa9126037b0
131.9 MB Download
md5:349368adcda203452acac130984abdbb
42.3 MB Download

Additional details

Dates

Updated
2026-05-20

Software

Repository URL
https://github.com/carlosbuss1/MagmaFlow
Programming language
Java