ASMS 2015: Mass specs, plugs and rock ‘n’ roll

Vicki Elliff and her giant colleague, Brad, at ASMS 2015 “Progenesis QI – for small AND large molecules”

Just over 3 weeks ago now, I set off on the first of 3 flights to travel from Newcastle-upon-Tyne, UK, all the way to St. Louis, MO, for ASMS 2015. This was my first overseas trip for Nonlinear, so I was both nervous and excited for the week ahead. We arrived on the Friday evening (which felt more like Saturday morning due to the time difference!) so it was more or less straight to bed ready for our bright and early start at the Waters Users’ Meeting the following morning.

The users’ meeting kicked off with breakfast and a chance to mingle before an exciting introductory talk about what’s new from Waters – this included the unveiling of their new mass spec, the Vion IMS QTof, a preview of REIMS research system with the iKnife, and also the launch of v2.1 of Progenesis QI (more on this below). This preceded the 2 key note presentations, both of which discussed applications of the iKnife. The first talk was by Professor Zoltan Takats from Imperial College London, the pioneer behind the iKnife – he gave us a very emotive overview of how it could help to prevent surgical removal of healthy tissue, with the example of full mastectomies in the case of breast cancer, as well as reducing time spent on the operating table waiting for histology results. With an estimated 1 in 3 people developing cancer at some point in their lifetime, this was a poignant talk showing a real life benefit of a new technology.

Next up was Chris Elliott from Queen’s University Belfast – his talk was on the very different, but nevertheless important, topic of food fraud. Food fraud is something that has received a lot of media attention over the last few years, from the melamine milk scandal in China, to the horse meat adulteration in Europe so it was great to see that the science industry is coming up with new and innovative ways to tackle these issues, and perhaps more importantly, that the wider world is beginning to see the importance of more stringent testing and regulations. Chris gave a very enlightening talk about how big the problem really is, and how it’s not just limited to these headline grabbing stories; one such example is about how twice as much organic food is sold as is manufactured. Some serious “food for thought” just as we finished for lunch.

After lunch, we split into groups of different areas of interest, although there did seem to be a theme that was consistent across them all: the use of Progenesis QI for the data analysis. There was a certain sense of pride at seeing how well represented we were, and also at how far we’ve come since our acquisition by Waters. This was a good taster of what was to come for the week ahead.

Sunday was a chance for us to explore a bit of what St. Louis had to offer, specifically the zoo which is located inside the enormous Forest Park. Personal highlights included the sea lion exhibit where you walked through a glass  tunnel with them swimming over you, and also the sight of a sleeping chimp right up against the glass.

Sea lion at the St Louis ZooA pensive chimp at the St Louis ZooSeal at the St Louis Zoo

On the Monday morning, the hospitality suites opened, which was where my Nonlinear colleagues and I were based for the duration of the conference, delivering software demos and also answering whatever questions were thrown at us from both new and experienced users. In addition to demos and answering questions, we received loads of great suggestions of what we could do next, and also heard about novel applications for which people are already using Progenesis.

As mentioned earlier, ASMS was our first opportunity to demo the soon to be released v2.1 of Progenesis QI. If you weren’t lucky enough to hear about it then, here’s your chance to see what’s coming:

  • Integration with the NIST MSMS libraries
  • Improvements to ChemSpider functionality with the option to filter your search results based on fragmentation data and elemental composition
  • Integration with the IPA pathways tool from QIAGEN

Dr Ian Morns demonstrating Progenesis QI at ASMS 2015We were more or less fully booked for appointments during the daytime sessions, even having to take an extra PC station to keep up with demand, and with Progenesis featuring on a high number of posters this year, word was getting around about us which meant all hands were on deck for the evenings when the suite was open to all. The evening sessions were slightly more relaxed – even if they were no less busy – with 80’s rock blaring in the background, and flashing neon guitar necklaces hanging off everyone’s necks to fit with Waters’ “Science is my rock ‘n’ roll” theme. The atmosphere was buzzing, drinks were flowing, and we were glowing with pride at how complimentary everyone was about the software – a particular favourite seemed to be the new Progenesis SDF Studio; after all, we all love a freebie!

Our breakfast seminar was on Wednesday morning, with talks on metabolomics and proteomics by Geert Goeminne, Ghent University, VIB Department of Plant Systems Biology and Richard Sprenger, Department of Biochemistry and Molecular Biology, University of Southern Denmark, respectively. I’m pleased to say we had a great turnout, with standing room only. The seminar was recorded, so keep an eye on our Twitter for details.

The Progenesis QI breakfast seminar at ASMS 2015Dr Richard Sprenger presenting at the Progenesis QI breakfast seminar, ASMS 2015

After a hectic, but enjoyable, few days, we started our journey home with freshly inflated egos – it’s a good feeling knowing that we’re on the winning team.

Strong technical support: a founding principle of Progenesis QI

Everyone can tell a personal tale of poor customer service that they’ve experienced, but here at Nonlinear, we’d like to think that none of those tales will involve us. We take support very seriously indeed, so it’s nice to hear that many of you appreciate the efforts we put into making your use of Progenesis as rewarding an experience as it can be.

Just recently, we’ve added a selection of customer quotes to our website that let you see for yourself that when you buy Progenesis QI, you’re not just getting some software to install – you’re also getting the benefit of our support team’s many, many years of data analysis experience. And you can rest assured that you’ll be supported promptly – something that can’t be said for all software.

Here are just a few examples of what people are saying…

“Technical support is fast and has been very helpful”

“We have been using the new version of Progenesis QI for proteomics to analyze a complex set of samples from an ambitious experiment. New users have got familiar with the application very quickly and have had the opportunity to try different protocols and experimental designs fairly easily. It is user friendly and allows you to go back and forth through the workflow and to clearly visualize the results.

“Moreover, the technical support is fast and has been very helpful every time we needed it. The attention provided by the technical staff from Nonlinear was an added value of Progenesis from the beginning.”

Prof. José Antonio Bárcena
University of Córdoba, Spain

“User-friendly and intuitive and the technical support is great”

“In just a short period of time, Progenesis QI for proteomics has become a key software tool in our data analysis platform. We use it for quick quality checks of our data as well as for complete, integrated label-free quantitative analyses. Its strengths include the accuracy of the alignment algorithm, the highly appreciated QC metrics section and the availability of the multivariate statistics tools. Moreover, it is very user-friendly and intuitive and the technical support is great. A must if you go for label-free!”

Elisabeth Govaert, PhD student
Laboratory of Pharmaceutical Biotechnology, Ghent University, Belgium

“As usual, Nonlinear provided wonderful technical support”

“To our surprise, with Progenesis QI for proteomics we practically started getting publishable data within the first few hours. We started writing the paper, while doing more experiments to confirm the quantitation. In 4 weeks, our paper was written! As usual, Nonlinear provided wonderful technical support for us.”

Dr. Lam Yun Wah
Department of Biology and Chemistry, City University of Hong Kong, China

“A prompt response to everything”

“We like Progenesis because it allows us to separate quantification from identification which lets us build complex workflows, it is easy to use which is important in our academic lab, it is very sensitive to finding features of interest, the license system works very well such that we can install Progenesis on many PCs without restriction, and it is easy to use.

“We have worked very well with the Progenesis team for more than 7 years and have great support in terms of prompt response to everything from simple questions to complex features requests.”

Prof. Mark E. McComb
Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA, USA

If your support contract is coming to an end, has already ended, or you simply want to extend your contract early to make sure you have continuous cover, get in touch now. And remember, every annual support contract comes with a further year of free upgrades. What’s not to like?! :)

Come and see us at ASMS 2015!

They say time flies when you’re having fun, so that must explain how it’s already time for ASMS 2015 – the  63rd annual conference for the American Society of Mass Spectrometry. This year it’s being held in St. Louis, MO, in America’s Center Convention Complex, with hospitality suites in the nearby Renaissance Grand Hotel. The conference starts officially on Sunday 31st May and runs until Thursday 4th June, which gives you plenty of time to come and see us for a chat about how Progenesis QI can (or already is) helping you with your ‘omics data analysis. We’ll be attending alongside our colleagues at Waters again this year, so come by to either booth #160 or the Waters hospitality suite, located at the Renaissance Grand Hotel.

So, what have we got planned for this year?

Announcement of v2.1 of Progenesis QI

We’ll be confirming what’s coming next for Progenesis QI with the opportunity to book a demo to see these new features for yourself. If you’re not able to attend ASMS this year, don’t worry – we’ll be posting full details of this release after the announcement and you can still send us an email to arrange your own demo.

Breakfast Seminar

We’re hosting a breakfast seminar on the Wednesday morning from 7-8am with talks from Geert Goeminne, Ghent University, VIB Department of Plant Systems Biology and Richard Remko Sprenger, Department of Biochemistry and Molecular Biology, University of Southern Denmark. Please note that pre-registration is required to attend this event, so please register now to avoid disappointment.

Software demos

We’ll be taking bookings for software demos in the suite, so just pop along to the suite (or the booth) to reserve your slot. In addition to demonstrating the software, you’ll have the chance to meet with some of our development team to get your questions answered. Please note bookings are required during the day, but the suite is open to all from 8-11pm Monday – Wednesday which is when we’ll also be giving out some exciting freebies.

In addition to the above, I’m also very excited to confirm Progenesis QI features in a number of posters that will be presented this year so keep an eye out for those.

Hopefully we will see you very soon. Smile

Spectral counting: why not?

One of the key considerations in bottom-up label-free proteomics analysis is the means of feature quantitation. Being peptide ions, measurements of these features are ‘rolled up’ into inferred proteins, but two main approaches can be taken to generating the data for this purpose.

The first, and most commonly used, approach is MS1 (precursor-based) measurement such as calculating the area under the MS peak for the feature, or the height (maximum intensity) of the peak. The former is the method used by Progenesis QI for Proteomics. These readings can then be summed for all the features comprising inferred proteins.

The second approach is MS2 (product/fragment-based) measurement. Prominent among this type of method, in Data-Dependent-Analysis (DDA) experiments, is quantitating a protein by summing the number of identified MS2 spectra derived from and matched against its peptides. This approach is known as spectral counting. The value obtained will depend on the intensity of the protein’s precursor peptide ions, as in DDA analyses more abundant features will be sampled more often than lower abundance ones.

Good reviews of these approaches in the wider context of MS-based quantitation as a whole are available (for example, [1-3]).

Why don’t we use spectral counting?

We are often asked about spectral counting by customers. It is an easy-to-apply and convenient method for relative quantitation purposes, for which the same process required to identify the proteins present in the sample also provides the quantitative data. It also allows a comparison to be made between very different samples, by reducing the comparison to the identification level. However, it is not an approach we employ within our software workflow, because of i) deficiencies in the method for quantitation, and ii) the assumptions upon which it is based running contrary to our approach.

i) Quantitative performance

Fundamentally, MS1-based measurements are more accurate and precise than spectral counting with a better linear dynamic range. This arises due to a number of weaknesses of spectral counting:

  • There is no direct measurement of peptide ion properties inherent to the approach, discarding potentially important characteristics of a peak.
  • The response in terms of spectra per peptide ion is not constant across different features.
  • Measurements can also be affected by the level of competition with other features for DDA selection, which may vary within and across samples.
  • The linear dynamic range of the method can be limited by saturation effects.
  • There is a stochastic aspect to DDA sampling, hampering reproducibility; DDA sampling is also biased towards more abundant species, for this reason.
  • Dynamic exclusion methods, designed to improve DDA coverage, can also affect the response.
  • Any changes to the base MS2 sampling conditions between runs will prevent inter-run comparisons.
  • It is problematic to deal with the complication of peptide ions being shared between proteins, and assigning counts appropriately.

For these (and yet more!) reasons, spectral counting is particularly weak at robustly estimating low fold changes in peptides between samples, and requires a large number of spectra per feature to be reasonably accurate; it could be considered a semi-quantitative technique, and with our focus on robust accuracy, we did not feel that it was suitable for inclusion as a quantitation method in our software.

There have been a number of efforts to improve the effectiveness of spectral counting for quantitation, and variations on the approach. These include normalisation of the counts to various parameters, and the development of more complex indices such as emPAI [4] and APEX [5]. An element of direct quantitation can also be introduced by measuring the intensities of the fragment ions themselves for spectra assigned to a given feature [6]. It is fair to say that MS2-based methods can perform reasonably well for relative quantitation, albeit not as well as MS1-based methods (e.g. [7,8]) and we certainly don’t dismiss them out of hand. However, there are crucial and fundamental limitations to spectral counting analysis, which discards a great deal of quantitative information from the run.

ii) The involvement of identification in quantitation

Spectral counting uses identification and assignment of spectra as its basic measurement. This also carries several weaknesses. For one, the measurements are not only affected by ‘experimental’ factors such as instrumentation settings, but also subject to variation in the identification process. Results are contingent upon external identification databases, their curation, and the search settings, introducing extra dependencies into the quantitative side of the analysis. This would affect the benefits of our quantify-then-identify approach, in which we identify only after extracting maximum information from the raw data for optimal normalisation and multivariate visualisations.

More drastically, unidentified features simply cannot be quantified. This would prevent any identification-free classification, normalisation, or QC approaches – three areas where this really does matter.

Quantifying first is much more future-proof. Identifications may always be added to unknown, but fully quantified features of interest in an MS1 map via later targeted runs; you can’t add quantitative results to unidentified features in spectral counting.

Finally, one of the challenges commonly ascribed to MS1-based approaches is that valid MS1 quantitation requires accurate alignment of precursor features between complex runs, given that the process is not ID-driven. However, this is achievable, and we provide means by which you can overcome this challenge; there is no restriction to driving cross-run comparisons via identification-level matching. Instead, we can truly compare each precursor feature directly using like-for-like measurements.

Given all this, can I still get spectral counts from Progenesis QI for Proteomics?

Of course! We do understand that some users may wish to obtain spectral counts from their data, and it’s never been our policy to deny you data that may be of use to you. Because of this, we do allow the export of spectral counts for your own ends. If you wish, you can then perform your own analyses using MS2-based approaches.

To obtain these data, follow the instructions in our FAQ on the topic of data export. You can obtain the spectral counts at the protein level using the instructions under “Protein Data”.

References

[1] Bantscheff M. et al. (2007). “Quantitative mass spectrometry in proteomics: a critical review”. Anal Bioanal Chem 389(4):1017–1031 (Open access).

[2] Bantscheff M. et al. (2012). “Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present”. Anal Bioanal Chem 404(4):939-65.

[3] Soderblom E.J., Thompson J.W. and Moseley M.A. (2014). “Overview and Implementation of Mass Spectrometry-Based Label-Free Quantitative Proteomics”. Chapter 6, pages 131-53 in: Quantitative Proteomics, Issue 1 of “New Developments in Mass Spectrometry Series”. Editors: Eyers C.E and Gaskell S.J., Publisher: Royal Society of Chemistry, ISSN: 2044-253X, ISBN: 9781849738088.

[4] Ishihama Y. et al. (2005). “Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein”. Mol Cell Proteomics 4(9):1265-72 (Open access).

[5] Braisted J.C. et al. (2008). “The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results”. BMC Bioinformatics 9:529 (Open access).

[6] Griffin N.M. et al. (2010). “Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis”. Nat Biotechnol 28(1):83-9 (Open access for linked PMC version).

[7] Grossman J. et al. (2010). “Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods”. J Proteomics 73(9):1740-6.

[8] Krey J.F. et al. (2014). “Accurate label-free protein quantitation with high- and low-resolution mass spectrometers”. J Proteome Res 13(2):1034-44 (Open access for linked PMC version).

Q&A: Elemental Composition in Progenesis QI, with Dr Jayne Kirk

Photo of Dr Jayne KirkLast month, we released version 2.0 of Progenesis QI, with a number of improvements in its compound identification workflow. One of these new features was the ability to calculate a compound’s elemental composition.

Here, we’ll interview Dr Jayne Kirk, a Senior Applications Chemist at Waters, to learn a little more about the feature and how it can help your small molecule analysis.

Mal Ross: Hi Jayne. Thanks for talking to us. Can you start by telling us a little about your job and the type of analyses that you perform, please?

Jayne Kirk: Hi Mal, I work in the Applications Laboratory in Wilmslow, UK, and have been working on metabolomic and lipidomic applications for 8 years now. Before joining Waters, I completed my PhD at York University, UK, in Chemistry.

My role in the laboratory is to perform small molecule demonstrations for clients from all over Europe, provide training and also to offer support to our MS specialists. Last year plant metabolomics (my personal favourite) was a hot topic, whereas this year lipidomics requests are flooding in!

Mal: So, we’re here to talk about how calculating elemental composition can help your compound identification. That ability is new in Progenesis QI v2.0, but how does it work? How much control do you have over the composition?

Jayne: A QToF Mass Spectrometer provides accurate mass information. Data processing within Progenesis QI generates a list of markers with m/z, retention time (and collisional cross section) information. After acquisition and processing, identification of markers is the next step. The elemental composition calculator allows assignment of a molecular formula to those markers.

The tool within Progenesis QI gives full control over the elements and the number of elements included in the search. It’s also possible to set and save several ‘typical’ search parameters for different classes of compounds, making the process very efficient.

The Elemental Composition parameters dialog in Progenesis QI v2.0

Mal: OK, so when should I use this feature? Don’t I already get this information in the IDs returned by my compound database searches?

Jayne: There can be times when your markers may not match anything in your compound database; in these cases, it’s necessary to go to the elemental composition calculator.

Mal: So, how do you typically use the calculation of elemental composition in your own small molecule analysis?

Jayne: Elemental composition is a building block or another piece of the puzzle and without that piece of information, it’s impossible to complete the jigsaw. Getting the elemental composition, whether it’s from the calculator or database searching, is essential.

It might not be necessary (depending on the experiment) to assign an elemental composition to all of the markers, however; instead, you can use the statistical tools to determine the important markers in the metabolomic or lipidomic study. Identification of these key markers is really the critical part of the process and if no database hit is returned, a molecular formula can still be obtained, giving you a starting point for further investigation.

Integration with ChemSpider, for instance, is another new feature within Progenesis QI v2.0 and another great building block. Here, the workflow would be to perform a ChemSpider search on the markers by mass and then filter that list of hits based on the elemental composition. Depending on the application area, certain elements are going to be of more interest in the search than others, so this is a way of filtering that information appropriately.

It’s great that you’re incorporating so many tools like this, helping scientists like myself to investigate, characterise and identify the markers in what are increasingly complex experiments.

Mal: We try our best!

So, rounding off, is this something you’d recommend to most people using Progenesis QI?

Jayne: Most definitely, yes. It’s another tool in the box which can be used in combination with the isotopic match, databases and pathway options within Progenesis QI.

Mal: Thanks, Jayne. It was great talking to you.

If you want to take advantage of the support for calculating elemental composition, as well as ChemSpider, LipidBlast and pathways integration, why not download Progenesis QI today and try it out?

Wall to wall proteomics in Berlin

A few weeks ago, I attended the Proteomic Forum in Berlin, which was held in The Technical University, from 22nd to 25th March. It was my first time in Berlin, a cosmopolitan city with a fascinating mix of people where, just as in London, anything can happen. :) Since it was my first visit to Berlin, it was also the first time I attended this event, and a great opportunity for me to meet with German scientists and my Waters colleagues based in Germany.

As usual, the interesting content of the program kept me busy all day long, but I had the opportunity to do some sightseeing during the evenings: from the Brandenburg Gate to Potsdamer Platz, an entire quarter built from scratch since 1995, after the Wall came down.

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The program was rich and diverse, with discussions on a range of approaches from top-down proteomics to imaging techniques, but common themes across the varying approaches were the importance of PTMs and pathway analysis. Pathway analysis is actually one of the areas we focused on with the release of the latest version of Progenesis QI for Proteomics, helping scientists to understand the biological context of their results. For instance, with the pathway tool IMPaLA, which is directly supported by Progenesis, you can go further and get a biological interpretation of your quantitative results.

20150323_16104520150323_161818

IMPaLA can also easily merge results from a proteomics experiment and a metabolomics experiment, so this functionality is also available in our software for small molecules, Progenesis QI. I had the opportunity to demonstrate this at the Gen2Bio conference, a regional metabolomics meeting held in La Baule, Western France, just after the proteomic forum.

If you want to know more about the latest releases of Progenesis QI or Progenesis QI for proteomics, or want to try it with your own data, please get in touch. If you’d like the opportunity to catch up with us in person at a future event, keep an eye on our events page to see where we’re headed over the next few months.

Announcing Progenesis SDF Studio

We’re excited to announce the release of the Progenesis SDF Studio – a free tool for the viewing and editing of SDF and MOL files.

Pasted image at 2015_03_31 15_29(1)

Why was it developed?

One of the major bottlenecks in LC-MS metabolomics data analysis is identification – something that our latest release of Progenesis QI has targeted by adding more search methods – and one of the biggest issues is sourcing a suitable database for your study. The number of publicly available databases is increasing, but unfortunately not all of these files are correctly formatted. Without some intervention, they can’t be used for identification.

Of course, the MetaScope search engine in Progenesis also allows the searching of Excel databases, which are much easier to fix. However, these are of no use if you want to perform theoretical fragmentation (which requires structure information), or if you want to use an alternative piece of software. After supporting a number of our users by fixing errors in their SDFs, we realised that there’s a distinct lack of a free and easy-to-use SDF editor. While we were happy to carry on fixing these files on behalf of our users, we realised the benefits that such an editor could bring to the wider community. So, here it is: v0.9 of Progenesis SDF Studio.

Why version 0.9?

This first release of Progenesis SDF Studio is an early access edition – we want your feedback on it so we can make tailored improvements before we release v1.0. That’s not to say we’ll stop there; we’ll keep on making changes and issuing new releases based on your feedback, but we wanted to make it clear that we’d value your input on how we should develop the tool in the future. So tell us:

  • Is it easy to use? Which bits could be made easier?
  • Is it missing some functionality you’d like?
  • Does it have functionality that’s not required?
  • Anything else!

What does it do?

The Progenesis SDF Studio allows you to:

  • View your SDFs and MOL files – search to find out whether your database contains the compounds you’re interested in
  • Delete entries – allows you to reduce a database down to only the compounds in which you’re interested
  • Combine SDFs and MOL files – in case you can’t find a single database containing all of your compounds of interest
  • Correct any formatting errors (including automatic highlighting of entries with errors) – fix any formatting problems that are causing your compound ID search to fail

How can I get it?

You can download Progenesis SDF Studio here. And did I mention it’s free? Yes, FREE.

How will I know how to use it?

We’ve written a small number of anticipated FAQ articles which outline some basic concepts – we’ll add to these as your questions come in. Our support team will also be on hand to help with any queries about this early access edition.

Out now – Progenesis QI v2.0

We’re pleased to announce that Progenesis QI v2.0 has been released and is now available to download. This release is focussed on improving the identification process for your compounds – something we know from your feedback is one of the biggest challenges of analysing metabolomics data – but that’s not all that’s new:

What’s New?

Highlights of this release include:

  • Improved access to compound databases: integrated searching of LipidBlast and ChemSpider libraries.
  • Elemental composition elucidation: determine the elemental content of your compound for when you can’t source a suitable database.
  • Pathway analysis: export your identified compounds to IMPaLA.
  • Automated data processing: run from Import Data to Identify Compounds without intervention.
  • Seamless integration with EZinfo 3.0.3: export data from Progenesis for further statistical testing via a single menu-driven command.

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.

QI v1.0 with upgrade notice highlighted 1200x800

If you’re thinking of trying Progenesis QI 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.

We’ve also updated our user guide if you’re looking for a step-by-step guide from start to finish.

Big cities and big science in Asia

Hi, my name’s Paul Goulding and I’m Nonlinear’s Business Development Manager for Asia, Africa and Australasia. I’ve been involved with sales to Asia and the Asia-Pacific region for many years now and have travelled to countries such as Japan, China, India, South Korea and Australia many times. This has given me the privilege of visiting (and photographing) some of the most iconic sites in the world whilst introducing the ‘omics researchers of the region to our Progenesis data analysis solutions.

Through repeated visits to the same cities over more than a decade, I’ve been able to see some pretty incredible changes and architectural developments. I have to confess here to a fascination with modern cityscapes which perhaps comes from having grown up in a typical medium-sized English town where the most impressive buildings tend to be medieval or Victorian. I therefore find the ultra-modern skylines of Hong Kong, Shanghai and Sydney to be just as fascinating as the Victorian, medieval and ancient wonders of London, Florence and Rome.

greatwall smallSydney small

HKskyline small

In this post, I’d just like to share some observations of ‘omics research in three of the most exciting countries I’ve visited recently and invite you to tell me about what excites you most. I’ve also shared a few of my photographs of the iconic sights I’ve been lucky enough to visit.

Japan

First on this brief tour of Asia is Japan, a country that’s relentlessly modern, but at the same time, not necessarily new – even the bullet trains on Japan’s amazing high-speed railway are more than 40 years old now.

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While Japan has many highly-regarded research groups studying proteomics, metabolomics or both – and using Progenesis software to do it – I’ve noticed a trend towards focussed metabolomics analysis. Its prevalence in Japan is somewhat in contrast to Europe and North America and it’s great to be able to demonstrate Progenesis QI to research groups moving in this direction; maybe yours is one of those groups that could benefit from it?

China

Epitomised by the incredible skylines of Shanghai which have sprouted from the old city almost entirely within the past 20 years, China is a country I have probably seen change the most as I have visited over the years. Here, building projects which in any European city (including London) would be landmark, once in a decade projects, are implemented routinely, often several at a time. To illustrate this scale of development, in the picture below the 2nd tallest building in the world (towards the right) is nearing completion and at just over 2,000ft, will literally tower over the two adjacent super-tall sky-scrapers, the third tallest of which is more than 200ft taller than the Shard, the tallest building in the European Union.

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For some years, there has been a particular focus in China on developing the country’s proteomics capability with government-led, multi-institute projects. More recently, however, there’s been rapid growth in metabolomics/lipidomics research, including food and traditional Chinese medicine research.

The vast investment in scientific research happening in China, coupled with its enormous talent pool, makes it a truly exciting country in which to demonstrate the advantages of Progenesis and one to watch for major scientific developments in the years and decades to come.

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India

Tajmahal smallAgrah Taj small

While India is, of course, famous for its many beautiful and historic sites, it’s also another country with huge investments changing both the physical and scientific landscapes. ‘Omics research in India is currently dominated by proteomics, with an established Proteomics Society hosting annual conferences with increasingly eminent international attendance.

In terms of techniques, the research in India is refreshingly open-minded, applying suitable tools for the job, meaning that everything from 2D gels to MALDI and, of course, mass spectrometry is used. It’s not all proteomics, however, and the thriving pharmaceutical industry in India is driving the growth of multi-omics research, expanding from production of generics into more of a focus on the development of biosimilars and novel pharmaceuticals.

What excites you?

So, I’ve told you some of the things that make my job so interesting, but I’d love to hear about the global trends and research that excite you. Maybe you’re involved in a project that you think is worthy of a mention here? Share it with us in this post’s comments. :)

Have you read these 21 must-read proteomics articles?

At Nonlinear we get a lot of questions on the whole analysis process for proteomics data, from experimental design through to statistical analysis, QC, and database searching for protein and compound identities. For our own software and approaches, you may well find the answers to questions you have in our FAQs, and we’re always happy to help. However, we often get questions that go beyond the ‘number crunching’ into the details of some of these wider concepts. With that in mind, I thought I’d collect together a mini reading list with some starting points for learning more on concepts surrounding the analytical workflow, for anyone new to the field. Of course these are just one selection of topics, but they may be worth a look.

QC approaches

This whole blog entry was prompted first and foremost by an excellent recent review on proteomics LC-MS/MS QC, itself the topic of a recent post in the form of our own QC metrics. Since that post was written, Bereman [1] published a review on the topic that, while requiring a subscription to Proteomics, I would really recommend a look at. It provides a good grounding in the approaches one can take and various software tools available including SimpatiQCo and QuaMeter. An interesting application of QuaMeter itself was also recently provided by Wang et al. [2]. In this work, the authors developed multivariate QC metrics (independent of MS/MS identifications) to identify outlier data by dissimilarity analysis, investigating the effects of different runs, mass spectrometers, laboratories and the application of SOPs. Amidan et al. [3] is another good example, which used classification models to develop ongoing composite control metrics. Both papers either use freely available data or have made their data available, and are well worth a read.

Data sharing

On the topic of quality, there is also a need to share, and standardise the sharing of, proteomics data. Ternent et al. [4] produced a very useful overview of the process for uploading to a key repository, ProteomeXchange, via PRIDE; further recent overviews of ProteomeXchange have been provided by Vizcaíno et al. [5] and Römpp et al. [6]; and a wide-ranging overview of the range of current databases available has been provided by Perez-Riverol et al. [7].

File formats and interconversions

As you’ll know, there is a huge array of file formats in mass spectrometry; Deutsch [8] summarised these very well, discussing both the formats themselves and issues raised by their diversity. Tools for interconverting data between different formats such as ProteoWizard are also discussed in that review.

This also links in to data sharing, as commonality of formats can aid this process. The development of standardised open exchange file formats by the HUPO-PSI group is described in a series of freely available papers [9, 10, 11]. This also points back to QC: Walzer et al. [12] recently provided a good overview of the qcML format, which will provide an expandable but standardised means of reporting quality metrics.

Experimental design and statistics

Karp and Lilley [13] published a review, “Design and Analysis Issues in Quantitative Proteomics Studies”, on this topic a while back – it’s a great starting point and looks at a number of the issues we’re commonly asked about. The consequences of improper experimental design can be critical – Ioannidis [14] published a strikingly titled paper in 2005 discussing aspects of this problem, and the 2012 Institute of Medicine report on the evolution of translational ‘omics has some food for thought in the form of several very interesting case studies [15].

Missing values

We’ve blogged on the issue of missing values, which our software helps to avoid. If you’re interested in learning a bit more about them and how they may be handled when present, then I recommend a look at Karpievitch et al. [16].

Protein & peptide identification

Nesvizhskii published a very in-depth review of computational approaches to MS/MS-based identification in 2010 [17].

Law and Lim [18] have also published a very good summary of recent technical approaches to improving peptide and protein identification coverage, such as DIA (Data Independent Analysis). This covers developments such as MSE, SWATH and AIF. Sajic et al. also produced a general overview of DIA methods, which then goes on to focus on SWATH in particular [19]. Of course these methods have relevance for quantitation as well, and create challenges for software used to analyse their output data, which are also described in those two reviews.

Protein inference

Given peptide identities in bottom-up proteomics, it is then not trivial to assemble these correctly into protein identifications. Two papers that summarise the issues encountered, and look at a range of approaches, are Nesvizhskii & Aebersold [20] and Li & Radivojac [21]. Our own approaches / options are described in an FAQ.

If you’d prefer to view a full list of the articles mentioned in this post, please see our references page.

I hope some of these pointers might be of some use and/or interest to you! As I was saying, we’re always happy to help with any questions you have on our approach, so do get in touch on that, but these recommendations are designed to range more widely than our own software.

Happy reading! :)