Progenesis QI turns food standards detective

Basmati rice being rinsed before boilingAt Nonlinear, we always like to see our software being applied to new scientific areas – it feels very much like we’re on the right track when that happens! With that in mind, I’d like to highlight a new involvement in food forensics: adulteration testing of Basmati rice.

Basmati is an aromatic, high quality rice grown only in certain regions of India and Pakistan but is often mixed with lower quality rice. In the UK, for example, the maximum non-Basmati content before rice must be labelled a mixture is 7%, but 16% of samples labelled as Basmati rice, assessed as recently as 2009/10, were in violation of this standard [1].

Currently, adulteration testing methods focus on DNA microsatellite analysis [2], but our colleagues at Waters have been bringing mass spectrometry and the multivariate statistical visualisations in Progenesis QI to bear on the problem, with a view to developing novel and complementary approaches.

As a proof-of-principle study, Cleland et al. [3] studied off-the-shelf rice samples using Atmospheric Pressure Gas Chromatography (AP-GC) followed by HD-MSE analysis with a SYNAPT G2-Si mass spectrometer. The samples included Basmati rice from four manufacturers, and one long grain and two Jasmine rice samples as comparators. The data generated were analysed in Progenesis QI with supplementary visualisations in EZinfo (Umetrics).

One of the strengths of metabolomics and subsequent multivariate statistical analysis is that a huge range of data points can be simultaneously quantitated, and samples classified sensitively based on correlated differences arising across the data set. Naturally, our no-missing-values approach helps to ensure the conclusions reached are statistically robust! In this study, 3885 compound ions were detected using Progenesis QI; when the data were plotted using PCA, two conclusions were easily reached (Figure 1).

Figure 1 Figure 1. Separation of rice by type and manufacturer. Note that of the four Basmati samples, one clusters with Jasmine rice (behaving similarly to the Jasmine rice from the same manufacturer) and one with Long Grain rice. Reproduced from [3].

Firstly, the method appears reproducible in terms of inter- versus intra-sample variation. Secondly – and very interestingly given the purpose of the study – some of the Basmati rice samples cluster very distinctively with Jasmine and Long Grain samples, potentially with implications about the provenance or purity of those samples. It should be noted that this is a proof-of-principle study only, so no definitive conclusions on causation can be drawn, but it seems that the methodology can distinguish different grains effectively.

Naturally, to function as a test for adulteration on an ongoing basis, it would be important to identify the species driving this separation such that appropriate targeted assays can then be designed. Towards this, Progenesis QI and EZinfo also allowed the application of OPLS-DA (Orthogonal Projection to Latent Structures Discriminant Analysis) to determine the compounds most associated with the sample clustering discrimination, and the visualisation of their abundance profiles across the groups (Figure 2). Potential markers could then be provisionally identified through database searching.

Figure2 Figure 2. A cluster of potential markers elevated in Basmati rice relative to other grains derived from Progenesis QI analysis. (A) and (C) show the cluster in hierarchical dendrogram and abundance profile views. Reproduced from [3].

While it is early days as yet, this proof-of-principle study raises the prospect of novel tests for rice adulteration, which might increase the accuracy and confidence of its detection. Further work would initially focus on validation of the results using a larger, well-characterised sample set.

Want to know more?

If you are interested in learning more about this study, there are two ways you can do this. The application note itself is available for downloading; but there is also an opportunity to hear from the author directly when Gareth Cleland delivers a webinar on the 9th of December on this work.


  1. Food Standards Agency, UK. “UK Local Authorities Imported Food and Feed Sampling Report 2009/10.
  2. Nader W.F., Brendel T., Schubbert R. (2013) “DNA-analysis: enhancing the control of food authenticity through emerging technologies.” Agro FOOD Industry Hi Tech 24(1): 42-46.
  3. Cleland G., Ladak A., Lai S. and Burgess J. (2014) “The Use of HRMS and Statistical Analysis in the Investigation of Basmati Rice Authenticity and Potential Food Fraud.” Waters application note, part number 720005218en.

QC Metrics: Helping you make the most of your time

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.

overview metrics blog

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.

Proteins per condition

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.

Nonlinear scales up support and development teams

We’ve been very busy over here at Nonlinear HQ with the recent release of v2.0 of Progenesis QI for proteomics, a number of conferences to prepare for, and training up our 2 most recent additions to the team: Angus and Janusz.

Angus Black

Angus Black, the latest addition to our customer support team, has joined us from a medical devices company that specialises in diagnostics using electrophoretic techniques – in fact, it’s the same company that Vicki joined us from last year.

Angus is an avid music enthusiast (think heavy metal!) and plays  bass in his spare time as part of a band. When he’s not deafening his neighbours, he can be found either at the gym or enjoying a bit of PC gaming.



Janusz NykielJanusz Nykiel is our newest software engineer and has relocated all the way from Wroclaw (Poland) to join us. Developing scientific software is new for Janusz but is the challenge that attracted him to the job initially, having previously worked on shrink-wrap, line-of-business software.

Janusz is another fan of heavy music and also enjoys console gaming.



I’m pleased to say they’ve both settled in extremely well, with plenty to keep them busy now that development work for v2.0 of Progenesis QI is well underway. You’ll be making use of Janusz’s work in all future releases of Progenesis, and if you’ve got a query on the use of the software, if you email our support team, you may find Angus is the one to help you out.

Nonlinear in the Netherlands

Last Friday, I visited a team of proteomics users who adopted Progenesis as early as 2009.  I feel connected with this group because I made the first phone call to Prof Theo Luider, Head of Laboratory, Neuro-Oncology, Erasmus Medical Centre in the Netherlands back in 2008.  I remember it vividly, as I was calling to see if he’d be interested in trying out the new Progenesis technology, having got his name via a chain of referrals.  Theo managed to reply to my introduction with 2 words (polite ones!) that made me burst out laughing and from there a great collaboration was born :).  Theo’s group evaluated the software and bought a license early in 2009; they have gone on to be our most prolific publishers with over 16 papers citing the use of Progenesis QI for proteomics.

This visit was a good one; I was accompanied by 2 of my Waters colleagues, Dr Perry Derwig (Product Specialist) and Dr Ian Edwards (Omics Business Development Manager), both very knowledgeable about instrumentation.  We spoke with Theo and his colleagues, Christoph Stingl and Dr Lennard Dekker, and over coffee and cakes we learned about the work that the team at Erasmus is doing and where they perceive the current limitations of the technology to be.  As a company, we are keen to form collaborations with our users and, over the years, the team at Erasmus have helped us with their suggestions and feedback.  Our development team here at Nonlinear Dynamics insists upon fully understanding the problem so that they can find the best solution, which is why good collaborations are so important to us; it takes time and effort to fully explain a detailed problem in the research workflow.

During a very nice lunch at The Harbour Club, I explained how Nonlinear Dynamics is run and roughly what our schedule for releases is.  We ended with a tour of the labs on various floors of the building, including a stunning view over Rotterdam from floor 23.  The group has 10 instruments from various vendors and, of course, they can analyse their data from all these different instruments using Progenesis QI for proteomics, as it is multi-vendor.  This is one of the strengths most cited by our users when asked what they like about Progenesis.


I managed to get a quick photo of Theo, Ian and Perry by their SYNAPT with an M-class UHPLC system just before we departed for Schiphol.  Theo said that the life of the group changed dramatically after they started using Progenesis, which is very encouraging to hear!  Here is a new quote Theo kindly gave us after the visit:

“We use Progenesis for label free analysis of nanoLC-high resolution MS data, the software gives good opportunities for alignment, quantitative comparison and assigning identification to quantitative data. We were able to use Progenesis software for relative large numbers of samples (hundreds).”

If you’d like to read more about the work being done by Theo and his team, take a look here.

Progenesis QI draws the crowds at HUPO 2014

This year, the 13th session of HUPO was held in the beautiful city of Madrid. We had such lovely warm weather compared to Paris or Newcastle Upon Tyne, with temperatures 10°C higher in Madrid. Sadly, with all the activity going on at the conference, there wasn’t much time to enjoy the sun. I could however enjoy the long evenings, eating at the very (very!) late Spanish time and having a walk around the old city, visiting the famous Placa de Majorque. I even had the chance to see some students singing serenades as a traditional tuna.


The week kicked off for Nonlinear with a pre-conference course on Progenesis, organised by the congress. It was a great success, with a lot of interest and, since it was fully booked, we had to run an extra session!

This year, we had a joint booth for Waters and Nonlinear which was perfect to show off the latest MS instruments from Waters with ion mobility, and the newest version 2.0 of Progenesis QI for proteomics which is fully compatible with this additional dimension in resolution.


Waters gave a lunch seminar on Monday where we showed the latest developments in Progenesis QI for proteomics in more detail; the QC Metrics along with the automation of the workflow were highly appreciated.

There are now many QC metrics tools in Progenesis, aimed at both process optimisation and validation of the input data. If you wish to know more, just contact us and we will arrange a demonstration for you! Or you can simply download it and have a try at your convenience!

Next year, the 14th HUPO congress will be held in Vancouver – we hope to see you there to show off even more new features.

Out now – Progenesis QI for proteomics v2.0

We’re delighted to announce that Progenesis QI for proteomics v2.0 has been released and is now available to download. We’ve enhanced the software in a number of important directions for this release, from increasing your productivity to helping put your observations in a wider biological context – and all of the improvements are a direct response to your feedback.

What’s New?

Highlights of this release include:

  • Pathway analysis: export your identified proteins to external pathway tools.
  • QC metrics: diagnostic charts to help you identify any problems with your analysis.
  • Automated data processing: run from Import Data to a completed peptide search without intervention.
  • “Hi-N” quantification: for both relative and absolute protein quantification.

You can read about these new features in more detail here.

Where can I download it?

If you’re an existing customer with an up to date coverwise plan, this upgrade is totally free of charge and very simple – you will receive an email with a direct download link as well as specific instructions on how to upgrade your dongle. In addition, if your Progenesis PC is connected to the internet, there should be a message in the Experiments list sidebar notifying you of this new version – if you click this, and your dongle is plugged in, you’ll be sent to the download page.


If you’re thinking of trying Progenesis QI for proteomics for the first time, you can download the software from here.

How will I know how to get the most out of the new features?

We’ve expanded our FAQs to cover the new features, as well as updating any previously available FAQs to correctly reflect new behaviour.

Alternatively, if you’d like a private demonstration, we’ll be at HUPO from 5th-8th October at Waters booth numbers 20 and 22, so get in touch to book an appointment.

Special thanks

One final thing: we’d like to say a massive THANK YOU to the participants of our Early Access Program for their feedback in evaluating beta versions of this release; the comments received are vital in helping us to build a high quality product.

How to save your samples before it’s too late!

Picture this: You’ve planned your experiment, collected all of your samples and you’re ready to run them on your LC-MS. Time is tight, so you get your samples through as quickly as possible, making the best use of the time available to you. Finally, it’s complete and your data is ready for analysis – great, so far so good. You start analysing your data in your preferred software — or simply what’s available to you — but after several painstaking hours of frustrating analysis, you’ve realised that something’s not quite right. After a bit of investigation, you find that your data just isn’t usable, due to sample running problems at the time of acquisition. Time to beg for some more time on that LC-MS… assuming you have some sample left, of course!

Sound familiar? Wish there was a quick and easy way of checking your data for problems while you’re still running samples, so that any issues could be identified and rectified quickly to prevent wasting time collecting “garbage” data? Here’s how Progenesis can help:

Quality control comes first in Progenesis

The first stage of the workflow in Progenesis is Import Data. Here, the raw data is imported and immediately visualised in the form of a 2D ion intensity map.

The ion map for a single LC-MS run viewed at the Import Data screen

It’s this ion map that can be used to quickly spot a multitude of common sample running problems. We recommend installing Progenesis on the acquisition PC and checking the quality of your data every set number of runs using the ion intensity map to prevent any nasty surprises during the analysis.

If you’re worried about needing an additional Progenesis licence for this QC, have no fear: importing data into Progenesis doesn’t require a licence, so you can–

Wait a second! That’s a really important point that’s worth repeating:

Importing data into Progenesis doesn’t require a licence.

This means you can install it on as many PCs as you like without fear of delaying anyone else’s analysis (or even having to make an initial purchase of the software!). You’ll also be pleased to hear that as you’re not using any of the more powerful analytical processes and algorithms involved later in the Progenesis workflow, you can carry out this QC step on a reasonably basic PC.

Don’t just take our word for it

If all of this sounds too good to be true, here’s what Dr Paul Langlais from the Research Division at the Mayo Clinic (Arizona, US) had to say about the early QC in Progenesis:

“Progenesis is label-free quantitative proteomics at its easiest. What surprised me is how much I was able to improve the quality of my mass spec runs, using the 2D map feature combined with the charge state distribution analysis…

What nobody realizes is how powerful Progenesis is in analysing LC and instrument method design. I’ve completely redesigned everything. I look at other people’s .RAW files and I see how much they could use the 2D feature alone.”

And when you consider that Progenesis supports data from a wide range of instrument vendors as well as several generic file formats, this makes it a suitable and important resource for pretty much any LC-MS lab.

Spread the word

Did you find this article helpful enough to share with other researchers like yourself? If so, click here to share the tip on Twitter – if we can save even one experiment together, it’ll be well worth it!

Back to basics – No missing values

Missing values

When writing the blog, it’s sometimes easy to get distracted by what’s new and exciting with a product rather than to focus on its core features and functionality. With this in mind, I thought I’d take us back to basics and talk about the “no missing values” approach taken by Progenesis – one of its core, and arguably most important, features.

Missing values can occur for a number of reasons:

  • Not present (the analyte truly isn’t in the sample)
  • Present but below threshold detection limits on the instrument
  • Present but missing due to instrument error
  • Analysis error: present but misaligned, misdetected or misidentified

Missing values in your data can cause a number of problems:

  • Reduced effectiveness of statistical analysis through lost data points
  • Misleading statistics – missing values can result in the data being misleadingly reported as statistically insignificant.
  • Problems with useful multivariate visualisations caused by the missing data

The chance of missing values increases with the number of replicates you run and since replicates are important in increasing the statistical power of your experiment, this poses a significant problem. There are a few approaches used to attempt to address this:

  • Imputation (assigning values for missing data) which must be done with care and carries a risk of biasing the data depending on why the values are missing
  • Removal of observations with missing values (increasing fidelity for the remaining measurements but potentially sacrificing useful intact data or introducing bias)
  • Interpolation (e.g. modelling missing parts of a peak from the rest)

Or the Progenesis approach: co-detection, using accurate alignment and combined aggregate detection to eliminate missing values

How does Progenesis do this?

While no software can perfectly restore raw data that are absent due to instrument error / inaccuracy, Progenesis does remove the possibility of missing values caused by misalignment, misdetection and misidentification, giving you a true reading for everything reaching the detector. It does this by accurately aligning all the runs in a dataset to a reference run on the retention time axis, with the reference run being the run with the greatest similarity to all other runs in the experiment.

Once the runs are aligned, a single aggregate run is created containing all analytes from all the runs in the experiment, with the retention time alignment correcting any drifts due to inconsistencies in the chromatography. Co-detection is then performed on this aggregate run with the detected isotope profiles being applied to all the individual aligned runs:


This ensures that like-for-like measurements are made for the same point of the intensity profile across all the runs, with the same boundary. Abundance is consistently and accurately measured for every analyte in every run, with a value of 0 only being reported where there is truly no signal present above background. This approach also allows identifications to be confidently aggregated and passed across all runs for the same analytes. This aggregation of MS2 data allows more confident results and also eliminates missing identifications as information in one run can be applied to all runs.

If you’d like to try the Progenesis approach to no missing values with your own proteomics or small molecules LC-MS data, get in touch and we’ll arrange a demo.

“Making decisions is easy. Getting them right is the hard part.”

A phrase I’m sure we can all relate to, and one that’s got me thinking about the way we make decisions here at Nonlinear – decisions that affect our users and, ultimately, the way they analyse their data. So, how do we ensure we make the right decisions?

Listening to our customers

We regularly conduct surveys for both Progenesis QI and Progenesis QI for proteomics, asking about the top 3 challenges people face while using the software. So far, we’ve had great responses to these and they’ve given us valuable insight into anything we’re missing or just not doing quite right. While we can’t guarantee to implement all the suggestions we receive, every single one of them is reviewed by the development team, and we do our best to ensure we use these suggestions to improve the “Progenesis experience” for all our users. For example, lots of our users asked for more automation which has led us to develop the automation wizard for Progenesis QI for proteomics v2.0.

As well as specifically targeted questionnaires, we’ll happily receive comments and suggestions via emails to our support team. It might be that a request comes in for something Progenesis can already do, in which case we’ll guide you through the process; it might even prompt the writing of a new FAQ or a redesign of the functionality in the software.

The Progenesis Improvement Program

When you opt-in to the Progenesis Improvement Program, the software will collect information about how it’s being used and about any problems you encounter. Back at Nonlinear HQ, we can then use this information to identify which features are used most heavily, which are rarely used, and which parts of the analysis are fast or slow. This helps us to see where we need to improve performance, where we could add a small but helpful feature, where we need better instructions, and much more. If you haven’t opted in already, give it a go – you can always opt out again at any time.

Our Scientific Advisory Board

Our long term decision making is helped by our Scientific Advisory Board.  This is a group of prominent scientists who meet once a year and discuss with us what we should be considering long term.  These are lively and interesting sessions;  the board is candid and tells us if it thinks we are going in the wrong direction or wasting time and resource.  An example of this is that we were considering developing a pathway analysis tool but the board said, “Don’t.  Better to integrate with what’s already available”. Hence Progenesis QI for proteomics v2.0 now includes direct support for export to third party pathway analysis programs such as IMPaLA and PANTHER.

So if you’ve got an idea on how to make Progenesis even better, or simply want to try the software for yourself, get in touch.

Three years and a million thanks!

andyHi, I’m Andy, a software engineering intern at Nonlinear Dynamics. I recently graduated from Newcastle University with a degree in Computing Science, having also spent the past three summers at Nonlinear as a way of gaining hands-on experience to complement my academic studies.

What I’ve worked on

In my time here, I’ve worked on tasks of increasing depth and difficulty:

  • My first internship saw me working with another student to develop an in-house tool to analyse and react to trends in the issues being reported from our software.
  • During my second internship, I was responsible for my own project, indexing all of our test datasets, allowing the development team to efficiently find the data they need to test new features. Managing the project myself meant that I was in charge of organising and prioritising tasks, and responsible for the project in each stage of the software development life cycle.
  • This year, I’ve been working on something slightly different: an SDF Viewer. The tool will be made available later this year and will make processing SDF files much easier by helping to combat the issue of badly formatted compound databases for use with Progenesis QI.

All of my internships have been great experiences, giving me first-hand insight into working as a professional software engineer in a world-leading company. This year’s project, however, has been especially rewarding; knowing that I’ve contributed to software that will make it into the hands of research scientists all over the world is a real buzz. :) I’m delighted to have been able to develop it for you and, while it’s not quite complete yet, I’m really looking forward to its launch!

The Nonlinear Dynamics Experience

One of the best things about Nonlinear Dynamics is the relaxed, flexible working environment. I can explore new ideas, develop innovative solutions to problems, and learn freely. Here, I’m able to plug in my headphones and work independently, or, when necessary, collaborate with my more experienced colleagues. They’ve always been available, and keen to discuss the progression of my project whenever I need reassurance or guidance.

Each summer working here has pushed my abilities. I’ve gained new skills and had the opportunity to improve upon things I had been introduced to at university. The enthusiasm of my colleagues has been contagious and inspiring, and I now feel confident in my ability to develop software as part of a team, to meet deadlines, and to create useful software.

I can’t thank Nonlinear Dynamics enough for this brilliant leg-up in my academic progression and future career as a software engineer. It’s an exciting, cutting-edge company and the learning and experiences I have gained here have been invaluable. If you’re a software developer looking for a new challenge, I can heartily recommend keeping an eye out for job vacancies with Nonlinear. Hint: at the time of writing, they’re currently recruiting — tell them I sent you! ;)