Transform complex mass spectrometry data into precise, actionable protein quantitation. Our deep learning platform delivers robust identification and quantification across diverse proteomics workflows.
Traditional proteomics analysis faces challenges with data complexity, dynamic range limitations, and the need for increasing sensitivity. Our AI-powered platform addresses these challenges through deep learning algorithms that improve identification accuracy and quantification precision.
By leveraging neural networks trained on millions of annotated spectra, we support confident quantification of proteins across diverse experimental designs, including low-abundance targets, post-translationally modified peptides, and complex sample comparisons.
Comprehensive proteomics services from discovery to targeted quantitation, powered by cutting-edge AI algorithms and rigorous quality control.
Deep learning-enhanced peptide identification using neural networks trained on curated spectral libraries. Supports diverse organisms and complex proteomes with robust statistical validation.
Precise protein absolute quantitation using spike-in standards, PRM/SRM targeted assays, or SureQuant methods. Delivers copy number estimates with defined accuracy based on standard curve analysis.
Comprehensive processing of raw mass spectrometry files from all major instrument vendors. Full workflow from raw data to validated protein groups with comprehensive quality reports.
Unbiased, cost-effective protein quantification without isotopic labeling. Ideal for large sample sets and discovery-phase experiments with appropriate statistical modeling.
Multiplexed quantification enabling analysis of multiple samples simultaneously with tandem mass tag labeling. Supports time-course and comparative studies with high throughput.
Data-independent acquisition analysis for reproducible, comprehensive proteome coverage with library-free or spectral library-based approaches.
From raw mass spectrometry files to validated, publication-ready results
Raw MS file conversion and quality assessment from Thermo, Bruker, Sciex, or Agilent instruments
Deep learning-powered peptide identification with spectrum prediction and hybrid database search
Probabilistic protein grouping and FDR-controlled validation at PSM and protein levels
Label-free, TMT, or DIA quantification with normalization and statistical modeling
Interactive results, protein tables, figures, and raw data exports for downstream analysis
Our services extend beyond simple identification and quantitation to provide deep characterization of your proteomes across a wide range of sample types.
HeLa, HEK293, primary cells, stem cells, and patient-derived cells
Liver, tumor, brain, muscle, plant tissue, and FFPE samples
Serum, plasma, CSF, urine, saliva, and other biofluids
Mitochondria, nucleus, membrane fractions, and organelle isolates
Phospho, ubiquitin, acetyl enrichment samples
Co-IP, IP, and pull-down samples
Selected references supporting our analytical approaches and technology platform.
Gessulat S, et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods. 2019.
PMID: 30643269Kong AT, et al. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics through fragment-ion indexing. Nat Methods. 2017.
PMID: 28581497Gillet LC, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012.
PMID: 22261725Wu G, Wan X, Xu B. A new estimation of protein-level false discovery rate in proteomics research. BMC Genomics. 2018.
PMID: 30367581Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics. 2014.
PMID: 24942700Trusted by Researchers at
We support data from all major mass spectrometry vendors including Thermo Fisher Scientific (Q Exactive, Orbitrap Fusion, Exploris series), Bruker (timsTOF Pro/Ultra), Sciex (TripleTOF, QTRAP), and Agilent (6550 iFunnel Q-TOF). We can also work with data from other platforms upon consultation.
Label-free quantification (LFQ) analyzes each sample separately without any chemical labeling, making it cost-effective for large sample sets. TMT (Tandem Mass Tag) labeling chemically tags peptides from different samples, allowing them to be combined and analyzed in a single MS run — ideal for comparing multiple samples simultaneously with reduced missing data and high reproducibility.
We offer multiple approaches for absolute quantitation: (1) Spike-in synthetic peptides with known concentrations for each target protein, (2) Parallel Reaction Monitoring (PRM) with stable isotope-labeled standard (SIS) peptides, (3) SureQuant targeted acquisition. We deliver copy number per cell estimates with defined accuracy based on standard curve analysis.
Every analysis includes: (1) Raw data quality assessment, (2) Decoy database search for FDR estimation at both PSM and protein levels, (3) Sample correlation and PCA analysis to identify outliers, (4) Technical replicate reproducibility validation. We provide comprehensive QC reports alongside your results.
Yes, we offer specialized PTM analysis services including phosphorylation, ubiquitination, acetylation, methylation, and glycosylation. Our workflows include AI-enhanced PTM site localization confidence scoring. For phosphorylated samples, we recommend enrichment before MS analysis.
Optimal sample preparation depends on your sample type and research goals. General recommendations include: sufficient protein input, clean protein extraction with protease/phosphatase inhibitors, proper storage (snap-frozen pellets or in SDS buffer at -80 degrees C), and minimal contaminants. We also offer sample preparation services if needed.
Whether you need discovery proteomics, targeted quantitation, or absolute protein measurements, our AI-powered platform delivers results. Get a custom project quote today.
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