Proteomics experiments generate highly complex data matrices and must be planned, executed and analyzed with extreme care to ensure the most accurate and relevant knowledge can be obtained. We take a modular approach allowing clients to enter and exit the pipeline at any stage, whilst ensuring seamless integration of each module. Our team of highly qualified and experienced scientists, bioinformaticians and biostatisticians will work with you throughout to provide a comprehensive service – from initial careful study design and planning through to detailed interpretation of your results.
|Computational MS||Proteome Discoverer||Peptide sequence and TMT® quantitation|
|Data Assembly and Pre-processing||SQuaT,CalDIT,DIANA||Normalized quantitative values and functional annotation at peptide and protein level|
|Statistical and Exploratory Analysis of Expression||FeaST||Visualization of data quality, class identifier model, biomarker candidate lists|
|Functional Analysis||FAT||Identification of biological processes and cellular components showing variance|
Computational MS, QC and data integration are standard components. Feature selection and functional analysis are optional components and strongly recommended for clients with limited experience of processing proteomics data
We have developed separate modules to integrate and process Proteome Discoverer output data for each of our core workflows. SQuaT (SysQuant®), CalDIT (TMTcalibrator™) and DIANA (TMT®MS3) perform similar functions including isotopic correction, removal of peptides lacking TMT® quantitative values, data normalization within each TMT®10plex, calculation of expression ratio and functional annotation. The output is used for feature selection (FeaST) and is included in the QuantSheet™, an Excel file that is provided to our clients.
The Feature selection module FeaST takes the output from SQuaT, CalDIT or DIANA and applies data normalization between TMT®10plexes to remove batch effects before calculating relative fold-change and significance of differential expression between groups (p-value, adjusted p-value). FeaST also performs quality assessment to remove any outlier samples and exploratory analysis before applying multivariate statistical models (LIMMA) to the processed data matrix to identify the main peptide and protein features that drive separation between experimental groups. Each iteration of the model removes features exhibiting variance due to technical or confounding clinical features (age, gender etc.) unrelated to the key biological question.
Box and Whisker Plots - Before normalization (left image) and after batch effect removal (right image).
The Functional Analysis Tool is an optional, bespoke bioinformatics package that provides biological context around regulated proteins and peptides within each experiment. Analysis is performed following data processing by FeaST to reveal detailed information on regulatory and signaling pathways affected by disease or treatment aiding compound prioritization. In the case of fluid biomarkers, the tool can identify which aspects of disease biology are represented in the proteomics data, providing detailed knowledge of disease and drug mechanisms and supporting selection of pharmacodynamics markers of drug mechanisms. Outputs can include biological pathway and Gene Ontology enrichment and protein interaction network maps. More specialist analyses include kinase substrate and functional domain enrichments. As each experiment is different, the functional analysis package is tailored to individual requirements in consultation with the client.
Enrichment Analysis Volcano Plots - Enrichment of kinase substrates based on phosphopeptide expression (left figure). Enrichment of microRNA substrates based on protein expression (right figure).
Learn more about how our bioinformatics services provide an optimized solution to your discovery projects.