Method for assessing the statistical significance of mass spectral similarities using basic local alignment search tool statistics
Abstract
A novel method for assessing the statistical significance of mass spectral similarities was developed using modified basic local alignment search tool (BLAST; Karlin-Altschul) statistics. In gas chromatography/mass spectrometry-based metabolomics, many signals in raw metabolome data are identified on the basis of unexpected similarities among mass spectra and the spectra of standards. Since there is inevitably noise in the observed spectra, a list of identified metabolites includes some false positives. In the developed method, electron ionization (EI) mass spectrometry-BLAST, a similarity score of two mass spectra is calculated using a general scoring scheme, from which the probability of obtaining the score by chance (P value) is calculated. For this purpose, a simple rule for converting a unit EI mass spectrum to a mass spectral sequence as well as a score matrix for aligned mass spectral sequences was developed. A Monte Carlo simulation using randomly generated mass spectral sequences demonstrated that the null distribution or the expected number of hits (E value) follows modified Karlin-Altschul statistics. A metabolite data set obtained from green tea extract was analyzed using the developed method. Among 171 metabolite signals in the metabolome data, 93 signals were identified on the basis of significant similarities (P < 0.015) with reference data. Since the expected number of false positives is 2.6, the false discovery rate was estimated to be 2.8%, indicating that the search threshold (P < 0.015) is reasonable for metabolite identification.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In untargeted metabolomics and lipidomics, false discovery rates are difficult to compute and largely not accurate.
“In untargeted metabolomics and lipidomics false discovery rates are difficult to compute and largely not accurate (Stein, 1994; Jeong et al., 2011; Matsuda et al., 2013; Kim and Zhang, 2014).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Matsuda et al., 2013
Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669 · see citing works
Amino acid substitution matrices from protein blocks.
Henikoff, S., & Henikoff, J. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.22.10915 · Cited by 6,218 · see citing works
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925 · see citing works
MassBank: a public repository for sharing mass spectral data for life sciences
Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529 · see citing works
Self-citationGas chromatography mass spectrometry–based metabolite profiling in plants
Lisec, J., Schauer, N., Kopka, J., Willmitzer, L., & Fernie, A. R. (2006) · Nature Protocols · doi:10.1038/nprot.2006.59 · Cited by 2,176 · see citing works
Karlin, S., & Altschul, S. F. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.6.2264 · Cited by 1,535 · see citing works
Optimization and testing of mass spectral library search algorithms for compound identification
Stein, S. E., & Scott, D. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)87009-8 · Cited by 882 · see citing works
Lommen, A. (2009) · Analytical Chemistry · doi:10.1021/ac900036d · Cited by 735 · see citing works
Assigning Significance to Peptides Identified by Tandem Mass Spectrometry Using Decoy Databases
Käll, L., Storey, J. D., MacCoss, M. J., & Noble, W. S. (2008) · Journal of Proteome Research · doi:10.1021/pr700600n · Cited by 607 · see citing works
mProphet: automated data processing and statistical validation for large-scale SRM experiments
Reiter, L., Rinner, O., Picotti, P., Hüttenhain, R., Beck, M., Brusniak, M.-Y., Hengartner, M. O., & Aebersold, R. (2011) · Nature Methods · doi:10.1038/nmeth.1584 · Cited by 532 · see citing works
Hiller, K., Hangebrauk, J., Jäger, C., Spura, J., Schreiber, K., & Schomburg, D. (2009) · Analytical Chemistry · doi:10.1021/ac802689c · Cited by 440 · see citing works
Sadygov, R. G., Cociorva, D., & Yates, J. R. (2004) · Nature Methods · doi:10.1038/nmeth725 · Cited by 409 · see citing works
False Discovery Rates and Related Statistical Concepts in Mass Spectrometry-Based Proteomics
Choi, H., & Nesvizhskii, A. I. (2007) · Journal of Proteome Research · doi:10.1021/pr700747q · Cited by 217 · see citing works
Tsugawa, H., Tsujimoto, Y., Arita, M., Bamba, T., & Fukusaki, E. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-131 · Cited by 217 · see citing works
Self-citationMetAlign 3.0: performance enhancement by efficient use of advances in computer hardware
Lommen, A., & Kools, H. J. (2011) · Metabolomics · doi:10.1007/s11306-011-0369-1 · Cited by 211 · see citing works
Pongsuwan, W., Fukusaki, E., Bamba, T., Yonetani, T., Yamahara, T., & Kobayashi, A. (2006) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf062330u · Cited by 181 · see citing works
Self-citationProbability based matching of mass spectra. Rapid identification of specific compounds in mixtures
McLafferty, F. W., Hertel, R. H., & Villwock, R. D. (1974) · Organic Mass Spectrometry · doi:10.1002/oms.1210090710 · Cited by 177 · see citing works
Altschul, S. F., & ERICKSON, B. (1985) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040370 · Cited by 165 · see citing works
Tsugawa, H., Bamba, T., Shinohara, M., Nishiumi, S., Yoshida, M., & Fukusaki, E. (2011) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2011.05.001 · Cited by 122 · see citing works
Self-citationX-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectrometry
Mylonas, R., Mauron, Y., Masselot, A., Binz, P., Budin, N., Fathi, M., Viette, V., Hochstrasser, D. F., & Lisacek, F. (2009) · Analytical Chemistry · doi:10.1021/ac900954d · Cited by 79 · see citing works
Matsuda, F., Shinbo, Y., Oikawa, A., Hirai, M. Y., Fiehn, O., Kanaya, S., & Saito, K. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007490 · Cited by 69 · see citing works
Self-citationKim, S., Koo, I., Jeong, J., Wu, S., Shi, X., & Zhang, X. (2012) · Analytical Chemistry · doi:10.1021/ac301350n · Cited by 67 · see citing works
Yoshida, R., Tamura, T., Takaoka, C., Harada, K., Kobayashi, A., Mukai, Y., & Fukusaki, E. (2010) · Aging Cell · doi:10.1111/j.1474-9726.2010.00590.x · Cited by 64 · see citing works
Self-citationWhat’s Driving False Discovery Rates?
Tabb, D. L. (2007) · Journal of Proteome Research · doi:10.1021/pr700728t · Cited by 53 · see citing works
Herod, A. A., Islas, C. A., Lázaro, M. J., Dubau, C., Carter, J. F., & Kandiyoti, R. (1999) · Rapid Communications in Mass Spectrometry · doi:10.1002/(sici)1097-0231(19990228)13:4 · Cited by 31 · see citing works
Lommen, A., van der Kamp, H., Kools, H. J., van der Lee, M. K., van der Weg, G., & Mol, H. G. J. (2012) · Journal of Chromatography A · doi:10.1016/j.chroma.2012.09.056 · Cited by 24 · see citing works
Database searching with DNA and protein sequences: An introduction
Sansom, C. (2000) · Briefings in Bioinformatics · doi:10.1093/bib/1.1.22 · Cited by 16 · see citing works
Wegner, A., Sapcariu, S. C., Weindl, D., & Hiller, K. (2013) · Analytical Chemistry · doi:10.1021/ac303774z · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of This Field
Dupree, E. J., Jayathirtha, M., Yorkey, H., Mihăşan, M., Petre, B. A., & Darie, C. C. (2020) · Proteomes · doi:10.3390/proteomes8030014 · Cited by 357 · see citing works
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
“In untargeted metabolomics and lipidomics false discovery rates are difficult to compute and largely not accurate (Stein, 1994; Jeong et al., 2011; Matsuda et al., 2013; Kim and Zhang, 2014).”
BackgroundAdvances in computational metabolomics and databases deepen the understanding of metabolisms
Tsugawa, H. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.01.008 · Cited by 97 · see citing works
Self-citationType V Secretion Systems in Bacteria
Fan, E., Chauhan, N., Udatha, D. B. R. K. G., Leo, J. C., & Linke, D. (2016) · Microbiology Spectrum · doi:10.1128/microbiolspec.vmbf-0009-2015 · Cited by 92 · see citing works
The musk chemical composition and microbiota of Chinese forest musk deer males
Li, D., Chen, B., Zhang, L., Gaur, U., Ma, T., Jie, H., Zhao, G., Wu, N., Xu, Z., Xu, H., et al. (2016) · Scientific Reports · doi:10.1038/srep18975 · Cited by 83 · see citing works
Sputum Bacterial and Fungal Dynamics during Exacerbations of Severe COPD
Su, J., Liu, H., Tan, X.-L., Ji, Y. I., Jiang, Y., Prabhakar, M., Rong, Z., Zhou, H., & Zhang, G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130736 · Cited by 59 · see citing works
Zhang, J., Wang, E. T., Singh, R. P., Guo, C., Shang, Y., Chen, J., & Liu, C.-Y. (2018) · Journal of Applied Microbiology · doi:10.1111/jam.14124 · Cited by 47 · see citing works
Tsugawa, H., Satoh, A., Uchino, H., Čajka, T., Arita, M., & Arita, M. (2019) · Metabolites · doi:10.3390/metabo9060119 · Cited by 46 · see citing works
Self-citationRehman, A., Wang, N., Peng, Z., He, S., Zhao, Z., Gao, Q., Wang, Z., Li, H., & Du, X. (2021) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2021.06.166 · Cited by 33 · see citing works
Application of Metabolomics for High Resolution Phenotype Analysis
Fukusaki, E. (2014) · Mass Spectrometry · doi:10.5702/massspectrometry.s0045 · Cited by 32 · see citing works
Self-citationRehman, A., Atif, R. M., Azhar, M. T., Peng, Z., Li, H., Qin, G., Jia, Y., Pan, Z., He, S., Qayyum, A., et al. (2021) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2021.05.016 · Cited by 27 · see citing works
Chen, L., Zhang, Y., Huang, T., & Cai, Y. (2016) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1157-9 · Cited by 27 · see citing works
Toward the Heterologous Biosynthesis of Plant Natural Products: Gene Discovery and Characterization
Wang, H., Guo, H., Wang, N., & Huo, Y. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00315 · Cited by 25 · see citing works
Li, Z., Liu, Z., Wei, Y., Liu, Y., Xing, L., Liu, M., Li, P., Lú, Q., & Peng, R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254111 · Cited by 25 · see citing works
Genome wide analysis of IQD gene family in diploid and tetraploid species of cotton (Gossypium spp.)
Rehman, A., Peng, Z., Li, H., Qin, G., Jia, Y., Pan, Z., He, S., Qayyum, A., & Du, X. (2021) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2021.06.115 · Cited by 21 · see citing works
Jing, X., Wang, X., Zhang, W., An, J., Luo, P., Nie, Y., & Xu, Y. (2019) · ACS Omega · doi:10.1021/acsomega.9b00983 · Cited by 19 · see citing works
Effects of α-pinene on the pinewood nematode (Bursaphelenchus xylophilus) and its symbiotic bacteria
Wang, X., Yu, Y., Ge, J., Xie, B., Zhu, S., & Cheng, X. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221099 · Cited by 19 · see citing works
Rethinking Mass Spectrometry-Based Small Molecule Identification Strategies in Metabolomics
Matsuda, F. (2014) · Mass Spectrometry · doi:10.5702/massspectrometry.s0038 · Cited by 18 · see citing works
Self-citationRehman, A., Tian, C., Li, X., Wang, X., Li, H., He, S., Jiao, Z., Qayyum, A., Du, X., & Peng, Z. (2024) · Plant Cell Reports · doi:10.1007/s00299-024-03178-y · Cited by 16 · see citing works
Discovery of false identification using similarity difference in GC–MS‐based metabolomics
Kim, S., & Zhang, X. (2014) · Journal of Chemometrics · doi:10.1002/cem.2665 · Cited by 15 · see citing works
Peng, Z., Jiang, X., Wang, Z., Wang, X., Li, H., He, S., Pan, Z., Qayyum, A., Rehman, A., & Du, X. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222312649 · Cited by 12 · see citing works
Type V Secretion Systems in Bacteria
Fan, E., Chauhan, N., Udatha, D. B. R. K. G., Leo, J. C., & Linke, D. (2016) · ASM Press eBooks · doi:10.1128/9781555819286.ch11 · Cited by 12 · see citing works
Khan, U. M., Rana, I. A., Shaheen, N., Raza, Q. S., Rehman, H. M., Maqbool, R., Khan, I. A., & Atif, R. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-28665-2 · Cited by 11 · see citing works
Xing, L., Peng, K., Xue, S., Yuan, W., Zhu, B., Zhao, P., Wu, H., Cheng, Y., Fang, M., & Liu, Z. (2022) · Functional & Integrative Genomics · doi:10.1007/s10142-022-00913-0 · Cited by 11 · see citing works
Li, Y., YongQiu, C., Liu, H., Wang, J., Li, Y., & Wang, Y. (2021) · Avian Diseases · doi:10.1637/aviandiseases-d-20-00115 · Cited by 8 · see citing works
Conservation and Divergence of Phosphoenolpyruvate Carboxylase Gene Family in Cotton
Wei, Y., Li, Z., Wedegaertner, T. C., Jaconis, S. Y., Wan, S., Zhao, Z., Liu, Z., Liu, Y., Zheng, J., Hake, K. D., et al. (2022) · Plants · doi:10.3390/plants11111482 · Cited by 8 · see citing works
Pu, L., Jian, Z., Pan, F., Yang, G., He, M., & Liao, P. (2019) · Infection and Drug Resistance · doi:10.2147/idr.s216745 · Cited by 7 · see citing works
Application of metabolomics for high resolution phenotype analysis
Fukusaki, E. (2017) · Japanese Journal of Lactic Acid Bacteria · doi:10.4109/jslab.28.66 · Cited by 6 · see citing works
Self-citationFlores, J. E., Bramer, L., Degnan, D. J., Paurus, V., Corilo, Y., & Clendinen, C. S. (2023) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.3c00039 · Cited by 6 · see citing works
Li, Y., Liu, Z., Zhang, K.-Y., Chen, S., Liu, M., & Zhang, Q. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235317 · Cited by 6 · see citing works
Shahzaib, M., Khan, U. M., Azhar, M. T., Atif, R. M., Khan, S. H., Zaman, Q. U., & Rana, I. A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297473 · Cited by 5 · see citing works
Genome-wide evolution and function analysis of ALOG gene family in cotton
Liu, Z., Shen, S., Cui, Z., Wang, T., Li, P., Wei, Y., & Peng, R. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1625634 · Cited by 3 · see citing works
Qin, N., Tian, Y., Rehman, A., Ma, X., Wang, X., Wang, X., Peng, Z., Pan, Z., He, S., Du, X., et al. (2025) · Industrial Crops and Products · doi:10.1016/j.indcrop.2025.120681 · Cited by 3 · see citing works
Molecular Evolution of clock Genes in Vertebrates
Mao, Y., Shao, F., Zhao, Q., & Peng, Z. (2021) · Journal of Molecular Evolution · doi:10.1007/s00239-021-10020-6 · Cited by 2 · see citing works
Silaiyiman, S., Zheng, Q., Wang, Y., Ouyang, L., Guo, Z., Yu, J., Chen, R., Peng, R., & Shen, C. (2024) · Agronomy · doi:10.3390/agronomy14061214 · Cited by 1 · see citing works
Serinophospholipids: A Third Type of Natural Phospholipid Discovered in a Thermophilic Bacterium
Vyssotski, M. V., Lagutin, K., MacKenzie, A., Mitchell, K., Stewart, A. W., Scott, D., Stott, M. B., & Compton, B. J. (2025) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c01141 · Cited by 1 · see citing works
Ismail, M. T., & Farhana, I. (2014)
Gas Chromatography/Mass Spectrometry Analysis: Nontargeted Metabolomics Based on Scan Mode Analysis
Tsugawa, H., & Lommen, A. (2014) · CRC Press eBooks · doi:10.1201/b17793-5
Self-citationRehman, A., Peng, Z., Li, H., Qin, G., Jia, Y., Pan, Z., He, S., Qayyum, A., & Du, X. (2021) · Research Square · doi:10.21203/rs.3.rs-166037/v1
Mao, Y., Li, D., Chen, R., Ma, C., Xiong, J., & Zhang, K. (2023) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2023.108642
Spectral Library Searching Algorithms in Metabolomics
Tsugawa, H. (2023) · Journal of the Mass Spectrometry Society of Japan · doi:10.5702/massspec.s23-26
Self-citation
- Authors
- Matsuda, F., Tsugawa, H., & Fukusaki, E.
- Affiliations
- RIKEN Center for Sustainable Resource Science (JP)
- The University of Osaka (JP)
- Corresponding author
- Matsuda, Fumio
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-08-14
- Volume
- 85
- Issue
- 17
- Pages
- 8291-8297
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W1971434441
- Cited by
- 41
- References
- 28
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 9 · 2020: 2 · 2019: 4 · 2018: 2 · 2017: 1 · 2016: 4 · 2015: 1 · 2014: 5
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Analytical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (25871136, 25820400)
- Strategic International Collaborative Research Program
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- statistical significance, false discovery rate, metabolite identification, gas chromatography-mass spectrometry, metabolomics, electron ionization mass spectra, Monte Carlo simulation, null distribution, green tea extract
- MeSH terms
- Animals, Gas Chromatography-Mass Spectrometry, Monte Carlo Method, Spectrometry, Mass, Electrospray Ionization, Mice, Metabolome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
