MSAID provides an AI-powered cloud platform for proteomics data analysis, utilizing the CHIMERYS™ algorithm to enhance the deconvolution of chimeric MS2 spectra in tandem mass spectrometry. The platform enables researchers to achieve up to three times more peptide-spectrum matches than traditional methods, improving the accuracy and reliability of proteomic data interpretation.
Funding
Funding not disclosed
Founders
Product
Problem
Proteomics researchers face challenges in accurately interpreting tandem mass spectrometry data due to the complexity of chimeric MS2 spectra. Traditional methods often struggle to deconvolute these spectra, leading to incomplete or inaccurate peptide-spectrum matches (PSMs). This limitation hinders the comprehensive analysis of proteomic samples and the discovery of novel biological insights.
Solution
MSAID offers an AI-powered cloud platform designed to streamline and enhance proteomics data analysis. The platform leverages the CHIMERYS algorithm to effectively deconvolute chimeric MS2 spectra, enabling researchers to achieve significantly more PSMs compared to conventional search engines. By integrating data storage, processing, and browser-based analysis into a scalable cloud environment, MSAID simplifies proteomics workflows and accelerates the generation of actionable insights. The platform supports data-dependent acquisition (DDA), data-independent acquisition (DIA), and parallel reaction monitoring (PRM) data, providing a versatile solution for various proteomics experiments.
Target Audience
The primary target audience includes proteomics researchers, mass spectrometry facilities, and biotechnology companies seeking advanced tools for comprehensive and accurate proteomic data analysis.
Features
- AI-driven CHIMERYS algorithm for enhanced deconvolution of chimeric MS2 spectra in tandem mass spectrometry
- Cloud-based platform for scalable proteomics data storage, processing, and analysis
- Support for DDA, DIA, and PRM data analysis
- INFERYS Rescoring for Sequest™ HT to improve the accuracy of spectral matches
- API integration for seamless data transfer and integration with existing workflows
- Browser-based interface for intuitive result visualization and interpretation
- Customizable deep learning models and in-silico spectral libraries tailored to specific research needs