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Progenesis QI for proteomics

The next generation in LC-MS proteomics data analysis software.
Discover the significantly changing proteins in your samples.


What is Progenesis QI for proteomics?

Progenesis QI for proteomics enables you to quantify and identify proteins in your complex samples using the advantages of label-free analysis. It has a simple, guided workflow for differential protein expression and protein characterisation of single or fractionated samples.

Key benefits

Using Progenesis QI for proteomics, you'll benefit from:

Progenesis LC-MS helps minimise data loss

See how data loss occurs in a typical experiment.

Minimise data loss in your experiments

When performing LC‑MS/MS in data-dependent acquisition mode, it's not possible to probe every ion. As a result, information is lost. Typically, this lost information is concentrated in the low-abundance peptides.

But what if you could preserve and build upon this information?

What if you could confidently quantify all detected peaks, safe in the knowledge that their identifications can be retrieved later, once you've found that they're exhibiting interesting behaviour? It could revolutionise your analysis!

The quantify-then-identify approach taken by Progenesis QI for proteomics, coupled with the use of inclusion lists or gas-phase fractionation, helps you to keep all of your peptide information until you decide it doesn't warrant further investigation.

Comparison of peptide sequence coverage by technique

Comparison of peptide sequence coverage by technique [1]

What are the advantages of label-free analysis?

Label-free LC‑MS provides a wide range of benefits when compared to other techniques, including those that use labelling:

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

Read more on the benefits of label-free analysis.

Ion abundance as seen in the peaks of the 3D view

Progenesis QI for proteomics quantifies peptides based on ion abundance

Reliable quantification based on ion abundance

Progenesis QI for proteomics uses the ion intensities recorded in your MS data to provide reliable measurements for your peptides. While we recognize that spectral counting has, in the past, seen wide use, it's a technique we've avoided. At best, it can only ever provide an approximate assessment of protein abundance.

This approach was vindicated by a poster [2] presented at ASMS 2010. Here's a quote from the abstract:

Quantification by MS intensity clearly outperformed spectrum counting. Only 0.5% of all intensity values (661 out of 133042) were missed in the Progenesis data, while 52.2% (26751 out of 51272) of all spectrum counting values were zero.

For further technical details on how Progenesis QI for proteomics analyses your data, see the page on how it works.

Automated data processing saves operator time and increases objectivity

Data import, reference run selection and alignment are performed automatically by the software. This provides more objective, reproducible analysis and reduces time spent at your computer. After performing these critical steps, alignment quality scores and visual displays are reported. These give you confidence that automatic peak picking and normalisation will achieve the best results possible or, quickly guide you to problems within your data. Find out more...

.d and .raw folders, plus mzXML, .raw and .cdf files

Some of the data formats supported by Progenesis QI for Proteomics

Support for all major machine vendors and peptide databases

Thermo, Bruker, Waters, Agilent, AB Sciex; machines from all of these vendors are supported by Progenesis LC‑MS. Both vendor-specific and generic, cross-vendor file formats (e.g. mzXML) are supported for your run data, and many inclusion list formats are also supported, helping you to better target interesting proteins.

Progenesis QI for proteomics offers a specific advantage for Waters customers by fully integrating the workflow to identify peptides quantified by MSᴱ and HDMSᴱ data-independent analysis.

Full details of the support for different machines and data formats, inclusion list formats, and peptide databases are available in the FAQ section. And remember, if your machine or file format isn't supported yet, contact us and we'll see what we can do to help.

Correlation Analysis. Select a dendrogram node and report proteins with common expression

Correlation Analysis. Select a dendrogram node and report proteins with common expression patterns.

Protein quantification based on unique peptides

Peptide ion quantification and identification from search results are automatically combined. The result is a protein view of your experiment, based on unique peptides, to help answer biological questions and highlight post-translational modifications. You can apply easy-to-use multivariate statistics to protein measurements.

Enhanced data analysis features with your Waters research solution

  • Fully compatible with ion mobility to achieve three dimensions of resolution and increase peak capacity
  • Powerful data visualization and guided-workflow for data-independent analysis using MSE as well as data-dependant analysis

Want to learn more?

For more information on Progenesis QI for proteomics and its approach to data analysis, please see the following:

If you already own Progenesis QI for proteomics, but want to know about the features in the latest release, please see the following:

What's new in the latest version?

Finally, if you have any questions that are not covered by these pages, please ask us a question. We aim to respond to all enquiries within 1 working day.


  1. A Comparison of Labelling and Label-Free Mass Spectrometry-Based Proteomics Approaches. Vibhuti J. Patel, Konstantinos Thalassinos, Susan E. Slade, Joanne B. Connolly, Andrew Crombie, J. Colin Murrell and James H. Scrivens. J. Proteome Res., 2009, 8 (7), pp 3752–3759. May 12, 2009
  2. Comparison of label-free protein quantification approaches for chemical proteomics. Zhixiang Wu, Kurt Fellenberg, Simone Lemeer and Bernhard Kuster. 58th ASMS Conference on Mass Spectrometry and Allied Topics. Utah, USA. May 2010.