AlphaDIA enables DIA transfer learning for feature-free proteomics
Revolutionizing Proteomics with AI-Powered Data Analysis
AlphaDIA introduces a novel, open-source framework for data-independent acquisition (DIA) proteomics. By directly applying machine learning to raw mass spectrometry signals, AlphaDIA achieves superior identification and quantification, particularly with high-dimensional time-of-flight (TOF) data. Its innovative DIA transfer learning strategy leverages fully predicted spectral libraries, enabling the generic analysis of any post-translational modification.
Unlocking Deeper Insights in Proteomics
AlphaDIA represents a significant leap forward in proteomics data analysis, offering unparalleled depth, speed, and versatility. By overcoming traditional data processing limitations and embracing AI-driven methods, it empowers researchers to explore complex proteomes with unprecedented accuracy and confidence, accelerating scientific discovery.
0
Precursors Identified (Library-based)
0
Median Protein Group CV
0
Increase in PTM Identifications (Transfer Learning)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Feature-Free Processing for High-Dimensional TOF Data
AlphaDIA’s innovative feature-free processing method directly analyzes raw mass spectrometry signals, circumventing the limitations of traditional centroiding or feature boundary definitions. This approach is particularly effective for high-dimensional time-of-flight (TOF) data, which often presents noisy and discontinuous signals. By aggregating evidence across retention time, ion mobility, and fragments using learned convolution kernels, AlphaDIA can confidently identify peptides even when individual fragment signals are indistinguishable from background noise, leading to more robust and comprehensive proteome coverage.
This method significantly enhances the ability to extract meaningful information from complex DIA data, providing a more complete picture of protein expression and modifications. It's especially valuable for next-generation TOF instruments that generate billions of detector events and require advanced algorithms to utilize the rich, multidimensional data effectively.
Benchmarking Against Established Software
AlphaDIA demonstrates competitive to superior performance compared to established DIA search engines like DIA-NN, Spectronaut, and MaxDIA in library-based searches. Benchmarking studies using complex mouse brain membrane isolates spiked into a yeast background, analyzed on both quadrupole Orbitrap and timsTOF platforms, show that AlphaDIA identifies a comparable or higher number of proteins and peptides. For example, it identified up to 50,600 mouse peptides on QE-HF data and 81,500 on timsTOF, exceeding other algorithms.
Crucially, AlphaDIA maintains a reliable and conservative False Discovery Rate (FDR) control, as validated by entrapment searches with Arabidopsis libraries. This rigorous validation ensures that the increased identification depth does not come at the expense of accuracy, making AlphaDIA a trustworthy tool for quantitative proteomics.
DIA Transfer Learning for Unseen PTMs
One of AlphaDIA’s most innovative aspects is its DIA transfer learning capability, which integrates deep learning-based prediction with the search engine. This allows the system to continuously adapt fully predicted spectral libraries to specific instrument and sample workflows, including those involving post-translational modifications (PTMs) that typically alter peptide retention and fragmentation behavior. By fine-tuning pretrained models on experiment-specific training datasets, AlphaDIA accurately models these changes.
This approach was demonstrated with dimethylated HeLa peptides, showing a substantial improvement in retention time prediction (R² from 0.69 to 0.99) and spectral correlation. This led to a 48% increase in unique precursor identifications and a 25% increase in protein groups compared to using pretrained models alone. This breakthrough closes the gap between DDA's versatility and DIA's performance for PTM analysis.
AlphaDIA Workflow for DIA Transfer Learning
Raw Data Access (alphaRaw/alphaTims)
→
Spectral Library Prediction (alphaPeptDeep)
→
Initial DIA Search (alphaDIA)
→
High-Confidence Precursor Collection
→
Deep Neural Network Fine-tuning (Transfer Learning)
→
Refined Library Generation
→
Second DIA Search (alphaDIA)
→
Label-Free Quantification (directLFQ)
→
Proteomics Report (.tsv)
9,800
Protein groups identified in 21-min, 60-SPD method using fully predicted libraries, demonstrating high depth.
| Feature | AlphaDIA | DIA-NN | Spectronaut | Chimerys |
|---|---|---|---|---|
| Processing Method | Feature-free, raw signal ML | Spectrum-centric | Library-based | Spectrum-centric |
| PTM Support | Generic (Transfer Learning) | Limited | Limited | Limited |
| TOF Data Handling | Optimized | Good | Good | Good |
| Open-Source | ✓ | ✓ | ||
| Protein Groups (HeLa 60-SPD) | 9,883 | 8,849 | 8,556 | 8,629 |
Case Study: Dimethylation Analysis with AlphaDIA
In a study of dimethylated HeLa peptides, AlphaDIA's transfer learning significantly improved identification. The framework precisely modeled the effects of lysine and N-terminal dimethylation on retention time, achieving an R² of 0.99, up from 0.69 without transfer learning. This led to a 48% increase in unique precursor identifications and a 25% increase in protein groups. This demonstrates AlphaDIA's ability to handle complex post-translational modifications without specific pretraining, generalizing to peptide behavior in the actual experiment and improving overall identifications while controlling false discoveries.
Calculate Your Proteomics ROI with AlphaDIA
Estimate the potential time and cost savings for your organization by integrating AlphaDIA's advanced proteomics analysis. Optimize your research, accelerate drug discovery, and gain deeper biological insights faster than ever before.
Estimated Annual Savings
Annual Hours Reclaimed
Your AlphaDIA Implementation Roadmap
Our structured approach ensures a smooth integration and rapid value realization for your proteomics research.
Discovery & Customization
We analyze your current proteomics workflow, instrument setup, and data challenges to tailor AlphaDIA for optimal performance. This includes configuring predicted libraries and transfer learning parameters.
Pilot Integration & Training
AlphaDIA is integrated into your existing computational infrastructure. Your team receives hands-on training for data processing, analysis, and interpretation, focusing on feature-free identification and transfer learning.
Performance Optimization & Scale-up
We fine-tune AlphaDIA for your specific research goals, ensuring maximum peptide identification depth, quantitative accuracy, and efficient processing across large cohorts. We assist with cloud deployment if needed.
Continuous Support & Innovation
Benefit from ongoing support, updates, and access to new algorithmic advancements as AlphaDIA evolves. Join a community that fosters open science and collaborative development in proteomics.
Ready to Transform Your Proteomics Research?
Connect with our experts to explore how AlphaDIA's feature-free, AI-driven approach and transfer learning can revolutionize your data analysis, accelerate discoveries, and streamline your PTM investigations. Book a personalized consultation today.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat