This work is supported by National Key Research and Development Program (2018YFC0910200/2017YFA0505001) and the Guangdong Key R&#38;D Program (2019B020226001).

1. Prepare metabolomics datasets for analysis
NOTE: In this demonstration, we use metabolomics datasets without QC sample. Data for case and control groups are needed. For demonstration, we use a public dataset in GNPS database27.
<ol>
	<li>Go to the webpage&#160;https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp. Click&#160;Browse Datasets.</li>
	<li>Search the keyword &#34;msv000084112&#34; in the Title column....

<ol>
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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Challenges,+progress+and+promises+of+metabolite+annotation+for+LC-MS-based+metabolomics.">Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics.</a> Current Opinion in Biotechnology. 55, 44-50 (2019).</li><li>Palmer, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FDR-controlled+metabolite+annotation+for+high-resolution+imaging+mass+spectrometry.">FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.</a> Nature Methods. 14 (1), 57-60 (2017).</li><li>Scheubert, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Significance+estimation+for+large+scale+metabolomics+annotations+by+spectral+matching.">Significance estimation for large scale metabolomics annotations by spectral matching.</a> Nature Communications. 8 (1), 1494 (2017).</li><li>Schrimpe-Rutledge, A. C., Codreanu, S. G., Sherrod, S. D., McLean, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Untargeted+metabolomics+strategies-challenges+and+emerging+directions.">Untargeted metabolomics strategies-challenges and emerging directions.</a> Journal of the American Society for Mass Spectrometry. 27 (12), 1897-1905 (2016).</li><li>Bla&#382;enovi&#263;, I., Kind, T., Ji, J., Fiehn, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Software+tools+and+approaches+for+compound+identification+of+LC-MS/MS+data+in+metabolomics.">Software tools and approaches for compound identification of LC-MS/MS data in metabolomics.</a> Metabolites. 8 (2), (2018).</li><li>Katajamaa, M., Miettinen, J., Oresic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MZmine:+toolbox+for+processing+and+visualization+of+mass+spectrometry+based+molecular+profile+data.">MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.</a> Bioinformatics. 22 (5), 634-636 (2006).</li><li>Tsugawa, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hydrogen+rearrangement+rules:+computational+MS/MS+fragmentation+and+structure+elucidation+using+MS-FINDER+software.">Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software.</a> Analytical chemistry. 88 (16), 7946-7958 (2016).</li><li>Wang, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CFM-ID+4.0:+More+accurate+ESI-MS/MS+spectral+prediction+and+compound+identification.">CFM-ID 4.0: More accurate ESI-MS/MS spectral prediction and compound identification.</a> Analytical Chemistry. 93 (34), 11692-11700 (2021).</li><li>Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J., Neumann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetFrag+relaunched:+incorporating+strategies+beyond+in+silico+fragmentation.">MetFrag relaunched: incorporating strategies beyond in silico fragmentation.</a> Journal of Cheminformatics. 8, 3 (2016).</li><li>Delabriere, A., Warmer, P., Brennsteiner, V., Zamboni, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SLAW:+A+scalable+and+self-optimizing+processing+workflow+for+untargeted+LC-MS.">SLAW: A scalable and self-optimizing processing workflow for untargeted LC-MS.</a> Analytical chemistry. 93 (45), 15024-15032 (2021).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Target-decoy-based+false+discovery+rate+estimation+for+large-scale+metabolite+identification.">Target-decoy-based false discovery rate estimation for large-scale metabolite identification.</a> Journal of Proteome Research. 17 (7), 2328-2334 (2018).</li><li>Li, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=XY-Meta:+a+high-efficiency+search+engine+for+large-scale+metabolome+annotation+with+accurate+FDR+estimation.">XY-Meta: a high-efficiency search engine for large-scale metabolome annotation with accurate FDR estimation.</a> Analytical Chemistry. 92 (8), 5701-5707 (2020).</li><li>Wen, B., Mei, Z., Zeng, C., Liu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=metaX:+a+flexible+and+comprehensive+software+for+processing+metabolomics+data.">metaX: a flexible and comprehensive software for processing metabolomics data.</a> BMC Bioinformatics. 18 (1), 183 (2017).</li><li>Aberg, K. 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No conflicts of interest.

The FDR control of untargeted metabolites has been a great challenge. Here, we demonstrated a complete pipeline of large-scale untargeted metabolomics analysis (qualitative and quantitative) with FDR control. This effectively reduces the false positives, which are very common in MS analysis.
Preparing an appropriate reference spectral library for your study is a key point. A successful and sensitive MS/MS identification requires not only proper matching algorithms, but also proper reference sp...

Metabolites play important roles in biological processes. Metabolites are often regulators of various processes like energy transfer, hormone regulations, regulation of neurotransmitters, cellular communications, and protein post-translational modifications, etc1,2,3,4. Untargeted metabolomics provides a global view of numerous metabolites5,6. With advances in mass spectrometry and chromatography technologies, the throughput of metabolome MS/MS spectra is rapidly increasing in recent years7,8,9,10,11. To identify metabolites from these huge datasets, various annotation software were developed11, such as MZmine12, MS-FINDER13, CFM-ID14, MetFrag15, and SLAW16. However, these identifications often contain many false positives. The reasons include: (1) The MS/MS spectra contain random noise, which may mislead the peak matching. (2) Isomers and differences in fragmentation energies cause multiple spectra fingerprints and thus increase the volume of the reference library. (3) The quality of reference libraries varies. A proper standard to build a good reference spectral library is needed. Therefore, a systematic false discovery rate (FDR) control for untargeted metabolomics is essential for functional metabolome research7,8,9,17.
Both the Empirical Bayes approach and Target-Decoy strategy tackled the FDR control problem generally. Kerstin Scheubert et al. showed that the Target-Decoy strategy on decoy database generated from fragmentation tree-based method is the best method for FDR control9. Xusheng Wang et al. designed a method for decoy generation based on the octet rule in chemistry and improved the precision of FDR estimation17. The spectral library for generating decoy database was demonstrated for better performance18. Here, we improved the spectral library-based method and developed a software called XY-Meta19 that can further improve FDR estimation&#39;s precision. It uses the existing reference spectral library to generate a decoy library for the FDR control under the Target-Decoy scheme. XY-Meta supports its own spectra matching and cosine similarity algorithms. It allows conventional search and iterative search modes. In the step of FDR assessment, it supports Target-Decoy concatenated mode and separated mode. For better flexibility, XY-Meta accepts external decoy libraries.
Peak detection and quantification of metabolites is also an important step of untargeted metabolome analysis. Peak detection is the main method for metabolome identification. In general, the accuracy of peak detection of metabolites was affected by multiple factors, such as noise signals of mass spectrometry, low abundance of metabolites, contaminants, and degradation products of metabolites20. When the number of samples of is too large or the liquid chromatography column was replaced in experiments of untargeted metabolome, remarkable batch effects may appear, which is a major challenge for metabolome quantitation21,22,23. Currently, software like XCMS24, Workflow4Metabolomic25, iMet-Q26, and metaX19 can perform peak detection and quantitation of untargeted metabolome, but we suggest that the pipeline of metaX is more complete and easier to use. Here, we demonstrate the process of identification and FDR control for a publicly available dataset msv000084112 using XY-Meta, and the peak detection and quantification of metabolites using metaX. This workflow only requires two groups, and each group needs at least two samples. MS/MS spectra data is needed, regardless of the mass spectrometer platform, ionization mode, charge mode, and sample type, and can support sample-based normalization and peak-based normalization. Following this example, researchers can perform metabolomics identification and quantification in an easy-to-handle way. Using this pipeline requires R programming capability. To help the researcher without any programming knowledge, we also developed a cloud analysis platform for metabolomics analysis. We demonstrated this cloud analysis platform in Supplementary Material 5.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>GNPS</td>
			<td>open source</td>
			<td>n/a</td>
			<td>https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp</td>
		</tr>
		<tr>
			<td>XY-Meta</td>
			<td>open source</td>
			<td>n/a</td>
			<td>https://github.com/DLI-ShenZhen/XY-Meta</td>
		</tr>
		<tr>
			<td>metaX</td>
			<td>open source</td>
			<td>n/a</td>
			<td>https://github.com/wenbostar/metaX</td>
		</tr>
		<tr>
			<td>ProteoWizard</td>
			<td>Free Download</td>
			<td>3.0.22116.18c918b-x86_64</td>
			<td>https://proteowizard.sourceforge.io/download.html</td>
		</tr>
		<tr>
			<td>CHI.Client</td>
			<td>Free Download</td>
			<td>ndp48-x86-x64-allos-enu</td>
			<td>http://www.chi-biotech.com/technology.html?ty=ypt</td>
		</tr>
	</tbody>
</table>

an integrated workflow of identification and quantification on fdr control-based untargeted metabolome

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an enormous amount of spectra, setting challenges for quality control of the mass spectrometry spectra. To reduce the false positives, false discovery rate (FDR) quality control is necessary. Recently, we developed a software for FDR control of untargeted metabolome identification that is based on a Target-Decoy strategy named XY-Meta. Here, we demonstrated a complete analysis pipeline that integrates XY-Meta and metaX together. This protocol shows how to use XY-meta to generate a decoy database from an existing reference database and perform FDR control using the Target-Decoy strategy for large-scale metabolome identification on an open-access dataset. The differential analysis and metabolites annotation were performed after running metaX for metabolites peaks detection and quantitation. In order to help more researchers, we also developed a user-friendly cloud-based analysis platform for these analyses, without the need for bioinformatics skills or any computer languages.

The raw data of msv000084112 was converted by msconvert.exe and generated mgf files (Supplementary Material S6).
XY-Meta generated GNPS-NIST14-MATCHES_Decoy.mgf file under /database folder. This is the decoy library generated from the original reference spectral library GNPS-NIST14-MATCHES.mgf. This decoy library can be reused. When reusing this decoy library, the user should set the decoy_pattern as 1 in parameter.default file, and set the decoyinput as the absolute path of t...

Watch this Scientific Journal Video about An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome at JoVE.com

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

We constructed an untargeted metabolomic workflow that integrated XY-Meta and metaX together. In this protocol, we displayed how to use XY-Meta to generate a decoy spectral library from open access spectra reference, and then performed FDR control and used the metaX to quantitate the metabolites after identifying the metabolomics spectra.

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an ...

This protocol enables qualitative and quantitative analysis of untargeted metabolome based on FDR quality control which effectively reduces the false positives of metabolite identification. This workflow integrates XY-Meta that uses the target-decoy strategy to evaluate FDR more accurately for metabolome identification in the qualitative analysis module. This measure can effectively filter false positive results of untargeted metabolome identification, which improves the robustness of discovering biomarkers or key molecules.We expect that researchers can understand and master the target-decoy strategy for FDR control and should try to strictly run this pipeline several times with the default parameters of the protocol. Begin by going to the GNPS database webpage and click browse datasets. Search the key word in the title column, then click the dataset ID number.Download the dataset using FTP and save the raw data in the respective folder. To convert the format of raw data, first install the ProteoWizard software. Under the ProteoWizard installation path, type msconvert.exe, followed by the specific parameters to convert the raw data format to mzXML format. Again, using msconvert. exe convert this data to MGF format and store them in the MGF folder.To prepare the reference spectral library for the metabolites, go to the GNPS webpage. Search the keyword NIST, click view for the details and download the library. Save the library in the database folder.Download the XY-Meta program. Find the parameter configuration file under the config folder and change its contents as described in the text protocol. Set the type of adducts as a list in the adduct folder.Perform the metabolite identification and false discovery rate control using the command XY-Meta.exe. Download and install the metaX software package. Then edit the samplelist.txt file to specify the sample and its corresponding mass spectrometry data as described in the text protocol. Use the R file provided with the text protocol to run the script for quantification of Mock and wild-type groups using the metaX software. Check the output folder in which the results of the quantitative analysis are stored such as the PCA plot.Next, modify the parameters in the R script to annotate the peaks in qualitative and quantitative analysis using metabolite identifications in order to integrate both the results and run the R script. Box plots of quantified metabolites showed that the general distribution of healthy and disease samples was similar with low fluctuation of the mean values. Only 3.39%of the metabolites had more than 30%of the missing values.Principle component analysis plot of the samples from both the groups showed that metaX remarkably increased the proportion of the metabolites with CV less than 0.3. A Venn diagram of differentially detected metabolites from three statistical test methods revealed 119 common metabolites. Retention time and mass by charge distribution of all annotated metabolites were plotted with a false discovery rate of less than 0.01, showing six significant and differentially detected metabolites.It is important to limit the time for workflow testing. Remember, not to select too many samples for analysis and keep at least two samples in each group. This technique enables quality control of metabolized identification from data independent acquisition data constructing a more robust reference spectrum spectral library using the spectrum matching results based on FDR control.

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Research

JoVE Journal

Biochemistry

3.5K 조회수.  Jinan University. 이 프로토콜은 FDR 품질 관리를 기반으로 한 비표적 대사체의 질적 및 정량적 분석을 가능하게하여 대사 산물 식별의 오탐을 효과적으로 줄입니다. 이 워크플로우는 표적 데코이 전략을 사용하여 정성적 분석 모듈에서 대사체 식별을 위해 FDR을 보다 정확하게 평가하는 XY-Meta를 통합합니다. 이 측정은 표적화되지 않은 대사체 식별의 위양성 결과를 효과적으로 필터링 할 수있?...