Progenesis QI for Proteomics

Discover the significantly changing proteins in your samples

Progenesis QI for proteomics software enables you to quantify and identify proteins in your complex samples using the advantages of label-free analysis. With support for all common vendor data formats and a highly visual guided workflow, Progenesis QI for proteomics enables you to rapidly, objectively and reliably discover proteins of interest from single or fractionated samples and using multi-group experimental designs. As well as conventional data-dependent analysis (DDA), Progenesis QI for proteomics supports the analysis of Waters MS^E and HDMS^E data-independent (DIA) data. Uniquely, the software also takes advantage of the additional dimension of resolution offered by Ion Mobility separations to give improvements in accuracy and precision of identification and quantification.

What's new in Progenesis QI for proteomics?

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Overview

Literature

Application Notes

Support

Advantages of label-free analysis

Label-free LC‑MS provides a wide range of benefits when compared to labeled techniques, including:

Reduced protein loading
No labeling reagent costs
Reduced fractionation and sample handling
Increased sequence coverage per protein
Increased overall proteome coverage
Ability to compare more conditions within one experiment

Consistently measure the peptides in all samples

The quantify-the-identify approach taken by Progenesis QI for proteomics allows you to automatically align the features in each sample and create an in-silico aggregate map containing every peptide in the complete sample set. This aggregate map is used to consistently detect and quantify features across all samples and create a data matrix with no missing values, irrespective of the number of samples or replicates. This means maintenance of statistical power in the analysis with no sacrifice of potentially important data, without the need for imputation.

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QC metrics are available for a range of processes from sample preparation to instrument operation

QC Metrics to evaluate the quality of LC-MS input data (v2.0 feature)

QC metric tools aim to prevent you wasting valuable time performing analysis on sub-optimal LC-MS data, and include readouts such as LC peak width, feature dynamic range, precursor mass error, missed cleavage count and peptide per protein count. In addition, these QC metric tools can be used to guide process optimization and troubleshooting.

Reliable quantification based on ion abundance

Progenesis QI for proteomics quantifies peptides based on ion abundance. Version 2.0 offers the facility to utilize a spiked internal standard and user selectable "HiN" metrics to estimate absolute abundance.

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Protein characterization based on unique peptides

zoom	Protein quantification based on unique peptides Progenesis QI for proteomics automatically combines peptide ion quantification and identification from search results and if desired, allows the quantification of proteins based only on unique peptides.

Protein identification using multiple database search engines

Progenesis QI for proteomics is highly flexible in that it can be used to search both DIA and DDA data using a variety of user-selectable search engines. Data from multiple searches can be combined in a single experiment. If required, the software can be supplied with ProteinLynx Global Server (PLGS) facilitating the analysis of Waters MS^E, HDMS^E, DDA and HD-DDA data.

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Guided data-processing workflow

The menu-guided workflow in Progenesis QI for proteomics helps to guide you through the experimental steps in the software. If required, automation routines allow you to seamlessly move through multiple stages to maximize opportunities for unsupervised overnight and weekend data processing (v2.0 feature).

Data import
Automatic reference run selection and alignment
Automatic peak picking and normalization
Automatic protein quantification
Protein database search

Pathways Analysis (v2.0 feature)

How do we understand the protein differences in our experiment? One option is to use Pathway Analysis, which determines which biological pathways are implicated in the data and thus provides the next level of information for biological contextualisation. In Progenesis QI for proteomics, we have provided export tools to easily and quickly interface with third-party Pathway Analysis programs.

Support for all major instrument vendors

Progenesis QI for proteomics is instrument platform independent supporting LC-MS data from Waters, Thermo, ABSciex, Agilent, and Bruker. Also supported are universal formats such as .mzML, .mzXML, and NetCDF.

For more information about Progenesis QI for proteomics, please see www.nonlinear.com.