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Real-Time Cloud Processing For High-Throughput Proteomics


Research results in realtime.


State of art published peer reviewed algorithm.


Collaborative, multi-user access

Results quality

Advanced Proteomics, Powered by AI

InfineQ is an easy-to-use, vendor agnostic,AI-based raw data processing tool for LC-MS/MS proteomics, supporting MS-DIA methods. Perfect for high-throughput proteomics, it features the most advanced published algorithm and delivers excellent data even in complex, short DIA runs. Scalable to studies of any size.


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Scalable Performance

InfineQ offers groundbreaking features

  • Automatic Per Run Calibration

    Optimal parameters identified automatically for each run and RT calibrated. No spike in peptides required.

  • Elution profiling

    Perfect peak borders identification via per peptide elution profiling - less noise, more robust signal

  • Advanced PTM Localisation

    Probabilistic PTM localisation analysis using Bayesian graph network.

  • Robust Quantification

    Single best value returned for each peptide / protein. No more MS1, MS2 discussions

Powerful and Efficient

Native Cross-Run Alignment

Cross-run alignment reduces the number of missing values and improves false discovery rates in large cohort studies. However, it is notoriously problematic for large data-sets.

With its native cross-run alignment, InfineQ treats your experiment like a single cohort, not as a collection of files.

Tailored To Your Needs

Both GUI & flexible API Available

InfineQ comes with both graphical and machine interface. GUI enables every proteomics user to generate great results effortlessly. An integrated API support enables fast customization and advanced analysis.

On-Demand Pricing

Only pay for what you use!

Easy & quick single file pricing available for light, sporadic access. Dedicated cloud environment with per processing / hour payment model for heavy users.

If you use InfineQ for your studies please cite the following publication:

„DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.”

Demichev et al., Nature Methods 17, (41-44); 25 Nov 2019

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