Transform raw mass spectrometry data into deep biological insights. Our deep learning platform powers peptide identification, protein quantification, and post-translational modification analysis with robust accuracy.
Mass spectrometry generates complex datasets requiring sophisticated computational analysis. Our AI-driven proteomics platform combines deep learning algorithms with proven bioinformatics workflows to deliver accurate, reproducible results from raw MS data.
Whether analyzing DIA-MS, TMT-labeled samples, or performing label-free quantification, our platform adapts to your experimental design and research questions. Every project includes expert consultation to optimize analysis strategy.
Neural network models trained on curated spectral libraries support peptide identification across diverse organisms and experimental conditions.
Support for label-free quantification (LFQ), TMT isobaric labeling, DIA/SWATH-MS, and targeted PRM analysis with robust statistical modeling.
Every project includes dedicated consultation to align analysis strategy with your experimental design and research goals.
Comprehensive mass spectrometry data analysis powered by state-of-the-art AI algorithms and proven bioinformatics workflows.
AI-powered peptide identification using neural network models trained on curated spectral libraries. Supports diverse organisms and experimental conditions with robust statistical validation.
Accurate protein abundance measurement using label-free, TMT, or DIA approaches with rigorous statistical modeling and normalization for biological significance.
End-to-end processing of raw mass spectrometry files including quality control, feature detection, identification, and quantification with comprehensive quality metrics.
Sensitive detection and localization of protein modifications including phosphorylation, acetylation, ubiquitination, and glycosylation with site-specific quantification.
Comprehensive proteome-wide profiling for biomarker discovery, disease mechanisms, and drug target identification. Integration with transcriptomics for multi-omics insights.
Identify protein-protein interactions and complex composition using co-immunoprecipitation, cross-linking MS, and proximity labeling approaches.
Our deep learning models are trained on curated spectral libraries containing millions of peptide-spectrum matches from diverse organisms and instrument platforms, enabling robust performance across a wide range of experimental conditions.
Convolutional neural networks trained on raw spectrum data learn optimal feature representations for peptide identification.
Enginoma baseline models performance-calibrated on project-specific spectral libraries for optimal performance on your experimental system.
Multiple models combined for robust peptide identification and PTM site localization with confidence scores.
We accept all major vendor formats including Thermo RAW, Bruker BAF/D, Sciex WIFF/WIFF2, Agilent MFD, and Waters RAW files. Converted formats (mzML, mzXML) are also accepted.
We use target-decoy searching with concatenated or reversed databases and apply FDR estimation at both peptide-spectrum match and protein levels. Standard threshold is 1% FDR unless otherwise specified.
Yes, we offer cross-linking mass spectrometry analysis including spectrum identification, cross-link site localization, and interaction network visualization using specialized tools.
We apply appropriate statistical tests (t-test, ANOVA) with multiple testing correction (Benjamini-Hochberg) for differential abundance analysis. Biological replication and power analysis are considered in experimental design consultation.
Yes, all processed data, search results, and analysis scripts are provided along with publication-ready figures. Data can be delivered via secure cloud transfer or physical media.
Selected references supporting our analytical approaches and technology platform.
Kong AT, et al. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics through fragment-ion indexing. Nat Methods. 2017. PMID: 28581497
Gessulat S, et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods. 2019. PMID: 30643269
Feng X, et al. TMTpro reagents: a set of isobaric labeling mass tags enables proteome-wide profiling in up to 16 samples per experiment. Nat Methods. 2021. PMID: 32203386
Zhong Y, et al. AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nat Commun. 2022. PMID: 36414611
Cox J, Mann M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics. 2012. PMID: 23176165
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Common questions about our proteomics analysis services and workflows.
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). Other platforms can be accommodated upon consultation.
Label-free quantification (LFQ) analyzes each sample separately without 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.
Every analysis uses target-decoy database searching with concatenated or reversed sequences and applies FDR estimation at both PSM and protein levels. Standard thresholds are 1% FDR. We also perform sample correlation, PCA analysis, and technical replicate reproducibility validation to ensure data quality.
Yes, we offer specialized PTM analysis services including phosphorylation, ubiquitination, acetylation, methylation, and glycosylation. Our workflows include AI-enhanced PTM site localization confidence scoring and can distinguish between isobaric modifications.
Standard turnaround varies by project complexity. We'll provide a detailed timeline estimate during project consultation. Expedited processing is available for time-sensitive deadlines.
Contact us to discuss your project requirements and receive a custom analysis proposal tailored to your experimental design.
Mass spectrometry generates complex datasets requiring sophisticated computational analysis. Our AI-driven proteomics platform combines deep learning algorithms with proven bioinformatics workflows to deliver accurate, reproducible results from raw MS data.
Whether analyzing DIA-MS, TMT-labeled samples, or performing label-free quantification, our platform adapts to your experimental design and research questions. Every project includes expert consultation to optimize analysis strategy.
Neural network models trained on curated spectral libraries support peptide identification across diverse organisms and experimental conditions.
Support for label-free quantification (LFQ), TMT isobaric labeling, DIA/SWATH-MS, and targeted PRM analysis with robust statistical modeling.
Every project includes dedicated consultation to align analysis strategy with your experimental design and research goals.
Comprehensive mass spectrometry data analysis powered by state-of-the-art AI algorithms and proven bioinformatics workflows.
AI-powered peptide identification using neural network models trained on curated spectral libraries. Supports diverse organisms and experimental conditions with robust statistical validation.
Accurate protein abundance measurement using label-free, TMT, or DIA approaches with rigorous statistical modeling and normalization for biological significance.
End-to-end processing of raw mass spectrometry files including quality control, feature detection, identification, and quantification with comprehensive quality metrics.
Sensitive detection and localization of protein modifications including phosphorylation, acetylation, ubiquitination, and glycosylation with site-specific quantification.
Comprehensive proteome-wide profiling for biomarker discovery, disease mechanisms, and drug target identification. Integration with transcriptomics for multi-omics insights.
Identify protein-protein interactions and complex composition using co-immunoprecipitation, cross-linking MS, and proximity labeling approaches.
Our deep learning models are trained on curated spectral libraries containing millions of peptide-spectrum matches from diverse organisms and instrument platforms, enabling robust performance across a wide range of experimental conditions.
Convolutional neural networks trained on raw spectrum data learn optimal feature representations for peptide identification.
Enginoma baseline models performance-calibrated on project-specific spectral libraries for optimal performance on your experimental system.
Multiple models combined for robust peptide identification and PTM site localization with confidence scores.
We accept all major vendor formats including Thermo RAW, Bruker BAF/D, Sciex WIFF/WIFF2, Agilent MFD, and Waters RAW files. Converted formats (mzML, mzXML) are also accepted.
We use target-decoy searching with concatenated or reversed databases and apply FDR estimation at both peptide-spectrum match and protein levels. Standard threshold is 1% FDR unless otherwise specified.
Yes, we offer cross-linking mass spectrometry analysis including spectrum identification, cross-link site localization, and interaction network visualization using specialized tools.
We apply appropriate statistical tests (t-test, ANOVA) with multiple testing correction (Benjamini-Hochberg) for differential abundance analysis. Biological replication and power analysis are considered in experimental design consultation.
Yes, all processed data, search results, and analysis scripts are provided along with publication-ready figures. Data can be delivered via secure cloud transfer or physical media.
Selected references supporting our analytical approaches and technology platform.
Kong AT, et al. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics through fragment-ion indexing. Nat Methods. 2017. PMID: 28581497
Gessulat S, et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods. 2019. PMID: 30643269
Feng X, et al. TMTpro reagents: a set of isobaric labeling mass tags enables proteome-wide profiling in up to 16 samples per experiment. Nat Methods. 2021. PMID: 32203386
Zhong Y, et al. AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nat Commun. 2022. PMID: 36414611
Cox J, Mann M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics. 2012. PMID: 23176165
Trusted by Researchers at
Common questions about our proteomics analysis services and workflows.
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). Other platforms can be accommodated upon consultation.
Label-free quantification (LFQ) analyzes each sample separately without 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.
Every analysis uses target-decoy database searching with concatenated or reversed sequences and applies FDR estimation at both PSM and protein levels. Standard thresholds are 1% FDR. We also perform sample correlation, PCA analysis, and technical replicate reproducibility validation to ensure data quality.
Yes, we offer specialized PTM analysis services including phosphorylation, ubiquitination, acetylation, methylation, and glycosylation. Our workflows include AI-enhanced PTM site localization confidence scoring and can distinguish between isobaric modifications.
Standard turnaround varies by project complexity. We'll provide a detailed timeline estimate during project consultation. Expedited processing is available for time-sensitive deadlines.
Contact us to discuss your project requirements and receive a custom analysis proposal tailored to your experimental design.
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