Chapter 10 Software/Web Application/Workflow

10.1 Platform for metabolomics

Here is a list for related open source projects

10.1.1 XCMS online

XCMS online is hosted by Scripps Institute. If your datasets are not large, XCMS online would be the best option for you. Recently they updated the online version to support more functions for systems biology. They use metlin and iso metlin to annotate the MS/MS data. Pathway analysis is also supported. Besides, to accelerate the process, xcms online employed stream (windows only). You could use stream to connect your instrument workstation to their server and process the data along with the data acquisition automate. They also developed apps for xcms online, but I think apps for slack would be even cooler to control the data processing.

10.1.2 PRIMe

PRIMe is from RIKEN and UC Davis. They update their database frequently(Tsugawa et al. 2016). It supports mzML and major MS vendor formats. They defined own file format ABF and eco-system for omics studies. The software are updated almost everyday. You could use MS-DIAL for untargeted analysis and MRMOROBS for targeted analysis. For annotation, they developed MS-FINDER and statistic tools with excel. This platform could replaced the dear software from company and well prepared for MS/MS data analysis and lipidomics. They are open source, work on Windows and also could run within mathmamtics. However, they don’t cover pathway analysis. Another feature is they always show the most recently spectral records from public repositories. You could always get the updated MSP spectra files for your own data analysis.

If you make GC-MS based metabolomics, this paper(Matsuo et al. 2017) could be nice start.

10.1.3 OpenMS

OpenMS is another good platform for mass spectrum data analysis developed with C++. You could use them as plugin of KNIME. I suggest anyone who want to be a data scientist to get familiar with platform like KNIME because they supplied various API for different programme language, which is easy to use and show every steps for others. Also TOPPView in OpenMS could be the best software to visualize the MS data. You could always use the metabolomics workflow to train starter about details in data processing. pyOpenMS and OpenSWATH are also used in this platform. If you want to turn into industry, this platform fit you best because you might get a clear idea about solution and workflow.

10.1.4 MZmine 2

MZmine 2 has three version developed on Java platform and the lastest version is included into MSDK. Similar function could be found from MZmine 2 as shown in XCMS online. However, MZmine 2 do not have pathway analysis. You could use metaboanalyst for that purpose. Actually, you could go into MSDK to find similar function supplied by ProteoSuite and Openchrom. If you are a experienced coder for Java, you should start here.

10.1.5 XCMS

xcms is different from xcms online while they might share the same code. I used it almost every data to run local metabolomics data analysis. Recently, they will change their version to xcms 3 with major update for object class. Their data format would integrate into the MSnbase package and the parameters would be easy to set up for each step. Normally, I will use msconvert-IPO-xcms-xMSannotator-metaboanalyst as workflow to process the offline data. It could accelerate the process by parallel processing. However, if you are not familiar with R, you would better to choose some software above.

10.1.6 Emory MaHPIC

This platform is composed by several R packages from Emory University including apLCMS to collect the data, xMSanalyzer to handle automated pipeline for large-scale, non-targeted metabolomics data, xMSannotator for annotation of LC-MS data and Mummichog for pathway and network analysis for high-throughput metabolomics. This platform would be preferred by someone from environmental science to study exposome. I always use xMSannotator to annotate the LC-MS data.

10.1.7 DIA data analysis

Skyline is a freely-available and open source Windows client application for building Selected Reaction Monitoring (SRM) / Multiple Reaction Monitoring (MRM), Parallel Reaction Monitoring (PRM - Targeted MS/MS), Data Independent Acquisition (DIA/SWATH) and targeted DDA with MS1 quantitative methods and analyzing the resulting mass spectrometer data.

MSstats is an R-based/Bioconductor package for statistical relative quantification of peptides and proteins in mass spectrometry-based proteomic experiments. It is applicable to multiple types of sample preparation, including label-free workflows, workflows that use stable isotope labeled reference proteins and peptides, and work-flows that use fractionation. It is applicable to targeted Selected Reactin Monitoring(SRM), Data-Dependent Acquisiton(DDA or shotgun), and Data-Independent Acquisition(DIA or SWATH-MS). This github page is for sharing source and testing.

10.1.8 Others

  • MAVEN from Princeton University

  • MAIT based on xcms and you could find source code here(Fernández-Albert et al. 2014).

  • metabolomics is a CRAN package for analysis of metabolomics data.

  • LipidFinder A computational workflow for discovery of new lipid molecular species

  • enviGCMS from environmental non-targeted analysis.

  • pySM provides a reference implementation of our pipeline for False Discovery Rate-controlled metabolite annotation of high-resolution imaging mass spectrometry data.

  • MetabolomeExpress a public place to process, interpret and share GC/MS metabolomics datasets.

  • PhenoMeNal is an easy-to-use, cloud-based metabolomic research environment.

  • MetAlign&MSClust

  • MetaboliteDetector is a QT4 based software package for the analysis of GC/MS based metabolomics data.

10.2 Data sharing

See this paper(Haug, Salek, and Steinbeck 2017):

10.3 Contest

  • CASMI predict smail molecular contest


Tsugawa, Hiroshi, Tobias Kind, Ryo Nakabayashi, Daichi Yukihira, Wataru Tanaka, Tomas Cajka, Kazuki Saito, Oliver Fiehn, and Masanori Arita. 2016. “Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software.” Anal. Chem. 88 (16): 7946–58. doi:10.1021/acs.analchem.6b00770.

Matsuo, Teruko, Hiroshi Tsugawa, Hiromi Miyagawa, and Eiichiro Fukusaki. 2017. “Integrated Strategy for Unknown EIMS Identification Using Quality Control Calibration Curve, Multivariate Analysis, EIMS Spectral Database, and Retention Index Prediction.” Anal. Chem. 89 (12): 6766–73. doi:10.1021/acs.analchem.7b01010.

Fernández-Albert, Francesc, Rafael Llorach, Cristina Andrés-Lacueva, and Alexandre Perera. 2014. “An R Package to Analyse LC/MS Metabolomic Data: MAIT (Metabolite Automatic Identification Toolkit).” Bioinformatics 30 (13): 1937–9. doi:10.1093/bioinformatics/btu136.

Haug, Kenneth, Reza M Salek, and Christoph Steinbeck. 2017. “Global Open Data Management in Metabolomics.” Current Opinion in Chemical Biology, Omics, 36 (February): 58–63. doi:10.1016/j.cbpa.2016.12.024.

Guitton, Yann, Marie Tremblay-Franco, Gildas Le Corguillé, Jean-François Martin, Mélanie Pétéra, Pierrick Roger-Mele, Alexis Delabrière, et al. 2017. “Create, Run, Share, Publish, and Reference Your LCMS, FIAMS, GCMS, and NMR Data Analysis Workflows with the Workflow4Metabolomics 3.0 Galaxy Online Infrastructure for Metabolomics.” The International Journal of Biochemistry & Cell Biology 93 (Supplement C): 89–101. doi:10.1016/j.biocel.2017.07.002.