1. Processing of MS data using open source software - MZmine
Mark Earll, Mark Seymour, Mark Forster, Dave Portwood, Chris Pudney - Syngenta

2. We bring plant potential to life
Syngenta is one of the world’s leading companies with more than 24,000 employees in over 90 countries dedicated to our purpose: Bringing plant potential to life. Our Crop Protection and Seeds products help growers increase crop yields and productivity. We contribute to meeting the growing global demand for food, feed and fuel and are committed to protecting the environment, promoting health and improving the quality of life. 2

3. Metabolomics Metabolomics applications within Syngenta Plant breeding
Early detection of desirable traits is very valuable
Sensory and nutritional profiling, relating chemical composition to taste and health
Fundamental research
Drought tolerance
Ripening processes
Effect of genetic manipulation
New agrochemical products
Mode of action studies

4. MZmine project history
MZmine project was initiated by Matej Orešic of Quantitative Biology and Bioinformatics group at VTT Technical Research Centre of Finland and Mikko Katajamaa of Computational Systems Biology Research group at Turku Centre for Biotechnology in 2004.
First release in 2005 introduced the data processing workflow and implemented simple methods for each data processing task and data visualization.
The project entered a second phase as Tomáš Pluskal of G0 Cell Unit at Okinawa Institute of Science and Technology joined the project in 2006 and started a process of redesigning the software framework towards modularity. A paper about MZmine 2 was published in BMC Bioinformatics in 2010.
2010 Syngenta sponsors two rounds of development 2011 and 2012 extending the software for GC-MS use and improving peak picking and adding various usability features.
T. Pluskal, S. Castillo, A. Villar-Briones, M. Orešič, MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics 11:395 (2010).

5. Summary of Syngenta sponsored improvements
Added an interface to R to allow incorporation of R based tools
Baseline correction module (ptw)
XCMS Centwave peak picking
XCMS CAMERA deconvolution
Identification
NIST search for GC-MS data
Chemspider
Customisable adduct list
Various usability and plotting improvements
Save batch mode workflows
3D & 2D plot enhancements

6. Extracted single ion chromatograms Chromatographic peak detection
How MZmine works
Mass Detection
MZmine detects mass features
Constructs a series of extracted ion chromatograms
Detects peaks in extracted ion chromatograms
Extracted single ion chromatograms
Chromatographic peak detection

7. Alignment across samples
Multiple runs.
Alignment made at the peak list level
Retention time normalisation by reference to common peaks
Scans are then merged with m/z and RT tolerances and optionally charge, ID and isotope pattern matching.
Retrospective gap filling is used to obtain values where peaks not detected in some runs. This enables more meaningful statistical analysis.

8. Fundamental problems with LC-MS and GC-MS data
3 dimensional data
Mass dimension
Accurate mass enormous range of possible masses
Drift in mass
Mass feature detection
Fragmentation + adducts
Chromatographic dimension
RT drift
Column bleed / artefacts
Peak detection

9. Baseline correction Baseline correction added from R ptw package.
Uses asymmetric least squares (Eilers 2004)
Assigns weights to points above and below an estimated trendline
Smoothing parameter – larger value = more smoothing (103 to 107)
Asymmetry parameter weight for points above/below trend line (0 to 1)
Baseline correction applied either to whole scan or to narrow strips.
1 or 0.5 m/z works well in practice
Eilers, P.H.C. Eilers, P.H.C. (2004) "Parametric Time Warping", Analytical Chemistry, 76 (2), 404– 411.
Boelens, H.F.M., Eilers, P.H.C., Hankemeier, T. (2005) "Sign constraints improve the detection of differences between complex spectral data sets: LC-IR as an example", Analytical Chemistry, 77,7998 – 8007.

10. How does baseline correction affect results?
Ratio
1/2
1/3
2/3
Area Orig
Area
Area
Area
Area
Area
Area
mean
0.49
0.69
1.41 sd
0.06
0.14
0.20 SE
0.12 CV
12.04
19.98
13.94
Preliminary work: Ratios calculated for 3 well characterised peaks
Various settings of smoothing parameter and asymmetry
CV values same order of magnitude as typical metabolomic experimental variation

11. Mass detection
Mass detection stage detects features in the mass domain
Aim is to detect features and avoid noise, preview options assist with choosing parameters
MZmine then extracts single ion chromatograms

12. Peak detection MZmine splits ion chromatograms into individual peaks
Problems: peak recognition is rarely perfect
Split peaks
Noisy features
2 new features:
Maximum peak width (simple but effective)
Implemented CENTWAVE module from XCMS
Ralf Tautenhahn, Christoph Böttcher, and Steffen Neumann "Highly sensitive feature detection for high resolution LC/MS" BMC Bioinformatics 2008, 9:504

13. UPLC-MS Peak Identification
In-house library generated by UPLC
Positive / negative ion
Polar / Apolar methods
Retention time and m/z values stored as .CSV file
Common adducts added to database ( i.e. [M+Na] )
Custom database search in MZmine with RT and m/z tolerances
Also experimented with RT modelling to predict samples not yet run in combination with accurate mass
Highly approximate used for scouting for suspects to run next in UPLC-RT library

14. Extending MZmine for GC data
GC-MS data similar to UPLC from a chromatographic viewpoint
Sharpness of peaks similar
MZmine handles data well
Rather longer runs
need more RAM
And/or collect in centroid mode
GC-MS data is fragmented
Searchable
NIST MS Library search

15. GC Data I – NIST Search First implementation:
Send simple time slice to NIST MS search (RT Tolerance)
Limit numbers of peaks sent
Forward and reverse match factor
All matching peaks get labelled with ID

16. GC Data II - Deconvolution
Experimenting with CAMERA* algorithm from XCMS
CAMERA algorithm correlates peak shapes and clusters features to form “pseudo spectra”
Enables deconvolution of closely overlapping peaks with differing peak shapes
Pseudo spectra are generated which are sent to NIST
* Carsten Kuhl, Ralf Tautenhahn, Steffen Numann IPB Halle

17. NIST search of Pseudo spectra
“Must have same identities” enables the selection of pseudo spectra
NIST identities are then overwritten onto the peak list identities
Currently under development!

18. 2D and 3D plotting enhancements
3D plot: An intensity slider has been added and the ability to change font and font colour
2D plot: In cases of many overlaid chromatograms the legend would obscure the plots so a new icon now toggles the legend
Peak annotations from a peaklist may now be overlaid on TIC plots

19. Identification Improvements
Adduct list is now customisable
Import/export as CSV file
Online Chemspider search
Requires chemspider security token

20. Batch mode analysis
Batch mode allows you to concatenate analysis steps into a batch
Allows unattended operation (i.e. overnight)
Can now load and save batch scripts as XML files

21. Design of Experiments (DoE) approach to testing
How to optimise the many parameters in MZmine?
Design of Experiments is useful for testing software and sensitivity of parameters
Requires some sensible measure of peak picking quality
Trygg, Johansson et al. have used dilution series of a pooled sample. The linear regression r2 vs concentration may be used to distinguish ‘good’ from ‘bad’ peaks
Some DoE already used in MZmine, more planned. Here the time taken to do baseline correction is studied using a CCC design
A Strategy for Optimizing UPLC-MS Data-Processing Parameters – a DoE approach Mattias Eliasson, Stefan Rännar, Rasmus Madsen, Erik Johansson Emma Marsden-Edwards, John P. Shockcor and Johan Trygg Submitted to Analytical Chemistry

22. Summary of Our Identification Process
UPLC-MS
Search against custom accurate mass/ retention time library
Search against accurate mass library and predicted RT from QSRM model
Search individual peaks against Online databases using Isotope pattern matching
A great deal of hand curation!
GC-MS
NIST MS-search
Hand curation (still painful)
“Gotchas”
Full search of public databases brings up many unsuitable hits
Gap filling averages accurate mass across samples which can prevent peak recognition. Search custom database before gap filling.

23. The Future
Tomáš Pluskal has recently been discussing version 3 of MZmine with the community.
MZmine GUI seen as attractive feature to be retained
Data model may be extended to allow GCxGC-MS and MSe data
Possible incorporation of guineu software (Sandra Castillo)
Stephan Beisken (EBI) has adapted some MZmine code into KNIME nodes
For us this is a very attractive combination
Workflow tools are ideal for multistep data processing
Gives transparency and reproducibility advantages
Audit trail capability
Integrate with downstream processing already done in KNIME
T. Pluskal, T. Uehara, M. Yanagida, Highly accurate chemical formula prediction tool utilizing high-resolution mass spectra, MS/MS fragmentation, heuristic rules, and isotope pattern matching , Anal Chem (2012).

24. Advantages of open source model
We have found the open source model to be very useful
Rapid development
Ability to tailor software to our needs
Direct communication with developers
Incorporate cutting edge tools and algorithms
Cost effective
Substantial initial investment but no licence restriction on deployment across many users
Finding new MS applications outside metabolomics
Technical support
Active MZmine forum
Community
We hope by donating code we can encourage further development by the community

25. Thanks and Acknowledgements
MZmine team
Tomas Pluskal ,
Matej Orešič
Mikko Katajamaa
Chris Pudney
Syngenta
Mark Seymour
Mark Forster
Martin Cip
Dave Portwood
Aniko Kende
IPB-Halle
Carsten Kuhl,
Ralf Tautenhahn
Steffen Neumann
EBI
Christoph Steinbeck
Stephan Beisken
Dominic Clark
Thermo
Madalina Oppermann
Umetrics / Umeå University
Erik Johansson
Johan Trygg