QC metrics into Progenesis QI for proteomics

Originally posted NonLinear

Proteomics as a field is rapidly maturing; there is a real sense that established techniques are being improved, and excitement at emerging techniques for quantitation. Central to this revolution is the application of effective quality control (QC) – understanding what adversely affects proteomics data, monitoring for problems, and being able to pin down and address them when they arise.

We’ve been at the forefront of QC implementation over the years, from our early involvement in the Fixing Proteomics campaign to our staff (in a previous guise!) publishing on proteomics QC^[1], and it’s an area that’s very important to us – we want you to have confidence in your data and your results, as well as our software.

For that reason we’re making the application of QC easier, by introducing automatic QC metrics into Progenesis QI for proteomics. These constitute a range of charts that present your LC-MS data in an easy to visualise way, summarising key experimental statistics and allowing you to check for any batch-to-batch and run-to-run variation in your processing. We’re really digging in to the data; you can examine overview charts for the whole experiment and also detailed ones focussing in at the run level.

If you’d like to get a flavour of the comprehensive range of metrics we’re offering, have a look at the FAQ page. They range from overview metrics including abundance dynamic range, summaries of identifications obtained and precursor mass errors, and missed cleavages, to detailed run monitoring including MS1 scan rates. We’ve also included a chart detailing the level of overlap between your conditions in Venn diagram format.

You’ll see these charts at the QC Metrics page after you initially run an analysis using the auto-processing wizard  so that you’ll get an immediate flavour of your data, and you can also visit the QC Metrics page in the workflow at any time – the charts update with changes you make, so that you can both evaluate your data quality, and also the choices you’ve made as you have processed it. They’ll also re-plot the data as you change your experimental design so you can investigate any variable you like, and you can flag up particular charts with comments or export them as a QC report. You can also add new runs to an existing experiment to measure the metrics over an extended period.

The intention is that QC Metrics will provide you with a versatile and simple aid for process optimisation, troubleshooting and quality assurance. We think this should be a very useful addition to the software, and we’re always interested in feedback on developing this further – please have a look and let us know what you think!

[1] Jackson, D. and Bramwell, D., 2013: “Application of clinical assay quality control (QC) to multivariate proteomics data: a workflow exemplified by 2-DE QC.” J Proteomics 95: 22-37.