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Human Metabolome Project

From EverybodyWiki Bios & Wiki


  • Comment: Neutrality issues are still there. Further, I'm not sure this has standalone notability from Human Metabolome Database and likely any useful info from this should just be incorporated there. Sulfurboy (talk) 03:16, 26 July 2026 (UTC)
  • Comment: Needs to be rewritten from scratch without the use of LLMs. Helpful Raccoon (talk) 00:06, 9 June 2026 (UTC)

Human Metabolome Project

The Human Metabolome Project (HMP) is a Canadian initiative which aims to identify and quantify all known and unknown metabolites in human tissues and biofluids. Metabolites are small molecules, having molecular weights < 1500 Daltons, that participate in metabolism or other cellular functions. The collection of all metabolites in a given cell, tissue or organism is known as the metabolome. The HMP was established at the University of Alberta under the direction of Dr. David Wishart. The HMP team consists of spectroscopists, organic chemists, analytical chemists, physicians and bioinformaticians.[1] Since its inception in January 2005, the HMP has built and continues to develop and maintain databases such as the Human Metabolome Database[2] and webservers such as MetaboAnaylst.[3] These resources were established for use in metabolomics research and applications in human health.

Background and History

Metabolomics, as a field of omics science, emerged after genomics (in 1986) and proteomics (in 1994). While the term “metabolomics” was first used in 1998, few papers were published in the first years of the field’s existence.[4] This was due to the labor-intensiveness of the technique and the fact that metabolomics did not have centralized DNA sequence and protein sequence databases such as GenBank[5] and UniProt.[6]

In 1999, Dr. Wishart published a paper that showed how machine learning could enable automated metabolite identification and quantification from nuclear magnetic resonance (NMR) spectra of a human biofluid.[7] Based on this discovery, Wishart applied for funding from Genome Canada in 2004 to support “The Human Metabolome Project” in an effort to catalog the "chemical equivalent of the human genome".[1] According to an interview with Wishart in 2007,[8] the official HMP aims were 1) to identify, quantify, and catalogue all detectable human metabolites at concentrations greater than 1 micromolar (цM), 2) to create an electronic database and 3) to generate a physical library of human metabolites that would be freely available.

The HMP aims to provide free, open-access tools and infrastructure, similar to those established for genomics and proteomics, to enable high throughput metabolomics studies.[8] Initial efforts (2005-2009) of the HMP were focused on enumerating and quantifying all known human metabolites through large-scale literature surveys and targeted, quantitative experimental studies of human biofluids. Later efforts (2010 to present) have moved towards elucidating unknown metabolites via spectral and biotransformation predictions using machine learning and artificial intelligence (AI).[9]

Human Metabolome Database

The HMP developed the HMDB as a central, online, web-based database to serve metabolomics researchers, in the same way that GenBank serves genomic researchers and UniProt serves proteomics researchers.  

The first release of the HMDB occurred on Jan. 1, 2007. It was assembled by a team of 53 scientists[1] and trainees over a period of two years. Approximately two-thirds of the team combed scientific literature and performed metabolomics assays to identify, quantify and manually annotate 2180 metabolites in various biofluids and tissues. The other one-third of the team built the HMDB compute infrastructure to house and display the information. Prior to the first release of the HMDB, only 690 human metabolites had been catalogued in other databases.[10]

As metabolomics technologies improved over the years, the HMP was able to expand its annotation efforts. Subsequent versions increased the size and scope of the database. HMDB 2.0[11] (released Jan.1, 2009), 3.0[12] (released on Sept. 2013), 4.0[13] (released on Jan. 2018) and 5.0[14] (released on Jan. 2022) contained 6408,[11] 40,153,[12] 114,100[13] and 220,945[14] metabolites, respectively. As of August 31, 2026, HMDB houses 253,245 metabolites.[15]

Human Biofluid and Excreta Metabolomes

The HMP team also spent time characterizing the metabolomes of human biofluids (serum, urine, saliva, and cerebrospinal fluid or CSF) and excreta to increase the number of detectable human metabolites and report reference ranges. Using extensive literature surveys and/or gas chromatography–mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), NMR spectroscopy or inductively coupled plasma mass spectrometry (ICP-MS), 4,200 compounds were identified and quantified in the serum metabolome (in 2011),[16] 2,651 metabolites in the urine metabolome (in 2013),[17] 476 metabolites in the CSF metabolome (in 2012),[18] 1008 compounds in the saliva metabolome (in 2015)[19] and 6700 compounds in the fecal metabolome (in 2018).[20]

HMDB Reception

Upon the initial release of HMDB, some researcher immediately started using the database for their studies. Dr. Adrian Arakaki et al. of Georgia Institute of Technology stated that HMDB was a "crucial" resource and searched HMDB to find normal concentrations of metabolites to compare to predicted levels in leukemia cells.[4][21] Dr. Ivano Bertini et al. from the University of Italy used HMDB to study celiac disease.[4][22] Dr. Hongyu Zhang et al. from the University of Michigan searched for elevated metabolites found in the urine of mice treated with a drug and found most of them in HMDB by their molecular weight.[4][23]

Related Resources

The HMP was followed by large scale metabolomics research initiatives in the USA,[24] the Netherlands[4] and Australia.[25] The HMP also led to increasing awareness that metabolites can originate from both exogenous (originating within the organism) and endogenous (from food, drugs, microbiota, cosmetics, and environment) sources. HMP team members developed additional resources to catalogue these compounds and facilitate their identification in various biological samples. DrugBank, which compiles information about drugs, drug targets and drug actions, includes details about pharmacokinetics, drug mechanism of action and absorption.[26] Likewise, the Toxic Exposure Database (T3DB), which captures information about common toxic chemicals found in human,[27] also includes toxic threshold data (lethal dose 50 or LD50) and treatment options. Another exposome database, FooDB, contains details about thousands of food chemicals found in humans, with information about flavor and aroma contributing metabolites.[28] Similarly, the Microbial Metabolome Database (MiMeDB) has compiled data about microbial metabolites found in or on the human body along with information about thousands of human microbes, their genomes and metabolites and their relation to human health and disease.[29]

In addition to characterizing known human metabolites, the HMP has worked to identify unknown metabolites using machine learning and AI. In many metabolomic studies, only 5-10% of MS-detectable features can be identified, meaning 95% are unknown.[9] To address the identification of unknown metabolites, the HMP team has developed and continues to develop freely available software to help identify these unknown metabolites. Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) was created to predict tandem MS spectra and identify compounds by comparing their predicted tandem MS data with measured tandem MS data.[30] Other tools such as RTpred[31] and RIpred[32] were developed to help identify metabolites from liquid chromatography retention times (RT) and gas chromatography retention indices (RIs). Similarly, PROSPRE was created to predict the proton (1H) NMR spectra of metabolites, with the aim of expediting their identification via NMR spectroscopy.[33]

While most metabolites are generated from catabolism (the breakdown of molecules) or anabolism (synthesis of compounds), many can also be created via biotransformation (chemically converted for use or excretion from the body) by numerous enzymes, particularly those found in the liver.[34] Many of the products of biotransformations of drugs, toxins and microbial products are still unknown or poorly characterized. To facilitate the identification of biotransformed chemicals, the HMP team has created BioTransformer,[35] an AI-based software tool that can predicts dozens of plausible metabolite biotransformations from a single starting compound. These predicted compounds can be used to help identify previously unknown or uncharacterized metabolites in human biofluids or excreta.

To help with the interpretation of metabolite data and understand how certain metabolites impact human health, the HMP developed the Small Molecule Pathway Database (SMPDB).[36] The SMPDB contains "more than 450 highly detailed, hand-drawn pathways describing small molecule metabolism or small molecule processes that are specific to humans".[37] Subsequently the SMPDB has been transitioned to PathBank,[38] which now has 194,705 human pathways covering metabolic, signaling, disease, drug action, drug metabolism and physiological pathways.

The Metabolomics Innovation Centre (TMIC) was established using technologies, equipment and software acquired or developed for the HMP. As of August 31, 2026, TMIC continues to host and support many HMP databases and HMP research activities, but it also provides low-cost access to extensive metabolomics equipment and services, allowing academic and industry researchers to conduct advanced metabolomics research.

Publications describing HMDB, DrugBank and MetaboAnalyst have received substantial numbers of citations. According to Google Scholar, the full suite of HMDB, DrugBank and MetaboAnalyst publications has been cited >63,000 times. These resources have been used by the research community with HMDB[28] and Drugbank[26] combined reporting over 40 million page views per year and MetaboAnalyst processing over 2.2 million jobs annually.[39]

See also

References

  1. ↑ 1.0 1.1 1.2 "Alberta scientists map human metabolome chemicals". CBC/Radio-Canada. January 24, 2007. Retrieved July 7, 2026.
  2. ↑ Wishart, D. S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A. C.; Young, N.; Cheng, D.; Jewell, K.; Arndt, D.; Sawhney, S.; Fung, C.; Nikolai, L.; Lewis, M.; Coutouly, M.-A.; Forsythe, I. (2007-01-03). "HMDB: the Human Metabolome Database". Nucleic Acids Research. 35 (Database): D521–D526. doi:10.1093/nar/gkl923. ISSN 0305-1048. PMC 1899095. PMID 17202168.
  3. ↑ Xia, Jianguo; Psychogios, Nick; Young, Nelson; Wishart, David S. (2009). "MetaboAnalyst: a web server for metabolomic data analysis and interpretation". Nucleic Acids Research. 37 (Web Server issue): W652–660. doi:10.1093/nar/gkp356. ISSN 1362-4962. PMC 2703878. PMID 19429898.
  4. ↑ 4.0 4.1 4.2 4.3 4.4 Scudellari, Megan (May 1, 2005). "Charting the human metabolome". Scientist. 23 (5).
  5. ↑ Sayers, Eric W.; Cavanaugh, Mark; Frisse, Linda; Pruitt, Kim D.; Schneider, Valerie A.; Underwood, Beverly A.; Yankie, Linda; Karsch-Mizrachi, Ilene (2025-01-06). "GenBank 2025 update". Nucleic Acids Research. 53 (D1): D56–D61. doi:10.1093/nar/gkae1114. ISSN 1362-4962. PMC 11701615 Check |pmc= value (help). PMID 39558184 Check |pmid= value (help).
  6. ↑ UniProt Consortium (2015). "UniProt: a hub for protein information". Nucleic Acids Research. 43 (Database issue): D204–212. doi:10.1093/nar/gku989. ISSN 1362-4962. PMC 4384041. PMID 25348405.
  7. ↑ Bamforth, F. J.; Dorian, V.; Vallance, H.; Wishart, D. S. (1999). "Diagnosis of inborn errors of metabolism using 1H NMR spectroscopic analysis of urine". Journal of Inherited Metabolic Disease. 22 (3): 297–301. doi:10.1023/A:1005531432766. ISSN 0141-8955. PMID 10384391.
  8. ↑ 8.0 8.1 Ph.D, Dr Sanchari Sinha Dutta (2020-02-14). "The Human Metabolome Project". AZoLifeSciences. Retrieved 2026-07-07.
  9. ↑ 9.0 9.1 Mitchell Crow, James (2021-11-18). "Canada's scientists are elucidating the dark metabolome". Nature. 599 (7885): S14–S15. Bibcode:2021Natur.599S..14M. doi:10.1038/d41586-021-03062-9. ISSN 0028-0836.
  10. ↑ Kanehisa, Minoru (2002). "The KEGG database". Novartis Foundation Symposium. 247: 91–101, discussion 101–103, 119–128, 244–252. ISSN 1528-2511. PMID 12539951.
  11. ↑ 11.0 11.1 Wishart, David S.; Knox, Craig; Guo, An Chi; Eisner, Roman; Young, Nelson; Gautam, Bijaya; Hau, David D.; Psychogios, Nick; Dong, Edison; Bouatra, Souhaila; Mandal, Rupasri; Sinelnikov, Igor; Xia, Jianguo; Jia, Leslie; Cruz, Joseph A. (2009). "HMDB: a knowledgebase for the human metabolome". Nucleic Acids Research. 37 (Database issue): D603–610. doi:10.1093/nar/gkn810. ISSN 1362-4962. PMC 2686599. PMID 18953024.
  12. ↑ 12.0 12.1 Wishart, David S.; Jewison, Timothy; Guo, An Chi; Wilson, Michael; Knox, Craig; Liu, Yifeng; Djoumbou, Yannick; Mandal, Rupasri; Aziat, Farid; Dong, Edison; Bouatra, Souhaila; Sinelnikov, Igor; Arndt, David; Xia, Jianguo; Liu, Philip (2013). "HMDB 3.0--The Human Metabolome Database in 2013". Nucleic Acids Research. 41 (Database issue): D801–807. doi:10.1093/nar/gks1065. ISSN 1362-4962. PMC 3531200. PMID 23161693.
  13. ↑ 13.0 13.1 Wishart, David S.; Feunang, Yannick Djoumbou; Marcu, Ana; Guo, An Chi; Liang, Kevin; Vázquez-Fresno, Rosa; Sajed, Tanvir; Johnson, Daniel; Li, Carin; Karu, Naama; Sayeeda, Zinat; Lo, Elvis; Assempour, Nazanin; Berjanskii, Mark; Singhal, Sandeep (2018-01-04). "HMDB 4.0: the human metabolome database for 2018". Nucleic Acids Research. 46 (D1): D608–D617. doi:10.1093/nar/gkx1089. ISSN 1362-4962. PMC 5753273. PMID 29140435.
  14. ↑ 14.0 14.1 Wishart, David S.; Guo, AnChi; Oler, Eponine; Wang, Fei; Anjum, Afia; Peters, Harrison; Dizon, Raynard; Sayeeda, Zinat; Tian, Siyang; Lee, Brian L.; Berjanskii, Mark; Mah, Robert; Yamamoto, Mai; Jovel, Juan; Torres-Calzada, Claudia (2022-01-07). "HMDB 5.0: the Human Metabolome Database for 2022". Nucleic Acids Research. 50 (D1): D622–D631. doi:10.1093/nar/gkab1062. ISSN 1362-4962. PMC 8728138 Check |pmc= value (help). PMID 34986597 Check |pmid= value (help).
  15. ↑ "Human Metabolome Database". hmdb.ca. Archived from the original on 2026-01-21. Retrieved 2026-07-08.
  16. ↑ Psychogios, Nikolaos; Hau, David D.; Peng, Jun; Guo, An Chi; Mandal, Rupasri; Bouatra, Souhaila; Sinelnikov, Igor; Krishnamurthy, Ramanarayan; Eisner, Roman; Gautam, Bijaya; Young, Nelson; Xia, Jianguo; Knox, Craig; Dong, Edison; Huang, Paul (2011-02-16). "The human serum metabolome". PLOS ONE. 6 (2). Bibcode:2011PLoSO...616957P. doi:10.1371/journal.pone.0016957. ISSN 1932-6203. PMC 3040193. PMID 21359215. Unknown parameter |article-number= ignored (help)
  17. ↑ Bouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick (2013). "The human urine metabolome". PLOS ONE. 8 (9). Bibcode:2013PLoSO...873076B. doi:10.1371/journal.pone.0073076. ISSN 1932-6203. PMC 3762851. PMID 24023812. Unknown parameter |article-number= ignored (help)
  18. ↑ Mandal, Rupasri; Guo, An Chi; Chaudhary, Kruti K.; Liu, Philip; Yallou, Faizath S.; Dong, Edison; Aziat, Farid; Wishart, David S. (2012-04-30). "Multi-platform characterization of the human cerebrospinal fluid metabolome: a comprehensive and quantitative update". Genome Medicine. 4 (4): 38. doi:10.1186/gm337. ISSN 1756-994X. PMC 3446266. PMID 22546835.
  19. ↑ Dame, Zerihun T.; Aziat, Farid; Mandal, Rupasri; Krishnamurthy, Ram; Bouatra, Souhaila; Borzouie, Shima; Guo, An Chi; Sajed, Tanvir; Deng, Lu; Lin, Hong; Liu, Philip; Dong, Edison; Wishart, David S. (2015). "The human saliva metabolome". Metabolomics. 11 (6): 1864–1883. doi:10.1007/s11306-015-0840-5. ISSN 1573-3882.
  20. ↑ Karu, Naama; Deng, Lu; Slae, Mordechai; Guo, An Chi; Sajed, Tanvir; Huynh, Hien; Wine, Eytan; Wishart, David S. (2018-11-07). "A review on human fecal metabolomics: Methods, applications and the human fecal metabolome database". Analytica Chimica Acta. 1030: 1–24. Bibcode:2018AcAC.1030....1K. doi:10.1016/j.aca.2018.05.031. ISSN 1873-4324. PMID 30032758.
  21. ↑ Arakaki, Adrian K.; Mezencev, Roman; Bowen, Nathan J.; Huang, Ying; McDonald, John F.; Skolnick, Jeffrey (2008-06-17). "Identification of metabolites with anticancer properties by computational metabolomics". Molecular Cancer. 7. doi:10.1186/1476-4598-7-57. ISSN 1476-4598. PMC 2453147. PMID 18559081. Unknown parameter |article-number= ignored (help)
  22. ↑ Bertini, Ivano; Calabrò, Antonio; De Carli, Valeria; Luchinat, Claudio; Nepi, Stefano; Porfirio, Berardino; Renzi, Daniela; Saccenti, Edoardo; Tenori, Leonardo (2009-01-02). "The Metabonomic Signature of Celiac Disease". Journal of Proteome Research. 8 (1): 170–177. doi:10.1021/pr800548z. ISSN 1535-3893. PMID 19072164.
  23. ↑ Zhang, Hongyu; Saha, Jharna; Byun, Jaeman; Schin, MaryLee; Lorenz, Matthew; Kennedy, Robert T.; Kretzler, Matthias; Feldman, Eva L.; Pennathur, Subramaniam; Brosius, Frank C. (2008). "Rosiglitazone reduces renal and plasma markers of oxidative injury and reverses urinary metabolite abnormalities in the amelioration of diabetic nephropathy". American Journal of Physiology. Renal Physiology. 295 (4): F1071–1081. doi:10.1152/ajprenal.90208.2008. ISSN 1931-857X. PMC 2576144. PMID 18667486.
  24. ↑ Kaddurah-Daouk, Rima (August 31, 2026). "Metabolomics Network for Drug Response Phenotype". Grantome. Retrieved August 31, 2026. Unknown parameter |url-status= ignored (help)
  25. ↑ "Metabolomics Australia: A Commonwealth Government Initiative Available to All Life Science Researchers". Metabolomics Australia. August 31, 2026. Retrieved August 31, 2026. Unknown parameter |url-status= ignored (help)
  26. ↑ 26.0 26.1 Knox, Craig; Wilson, Mike; Klinger, Christen M.; Franklin, Mark; Oler, Eponine; Wilson, Alex; Pon, Allison; Cox, Jordan; Chin, Na Eun Lucy; Strawbridge, Seth A.; Garcia-Patino, Marysol; Kruger, Ray; Sivakumaran, Aadhavya; Sanford, Selena; Doshi, Rahil (2024-01-05). "DrugBank 6.0: the DrugBank Knowledgebase for 2024". Nucleic Acids Research. 52 (D1): D1265–D1275. doi:10.1093/nar/gkad976. ISSN 1362-4962. PMC 10767804 Check |pmc= value (help). PMID 37953279 Check |pmid= value (help).
  27. ↑ Wishart, David; Arndt, David; Pon, Allison; Sajed, Tanvir; Guo, An Chi; Djoumbou, Yannick; Knox, Craig; Wilson, Michael; Liang, Yongjie; Grant, Jason; Liu, Yifeng; Goldansaz, Seyed Ali; Rappaport, Stephen M. (2015). "T3DB: the toxic exposome database". Nucleic Acids Research. 43 (Database issue): D928–934. doi:10.1093/nar/gku1004. ISSN 1362-4962. PMC 4383875. PMID 25378312.
  28. ↑ 28.0 28.1 Wishart, David S. (2024-09-01). "Knowledge translation and knowledge mobilization from the FoodBAll project". Applied Physiology, Nutrition, and Metabolism = Physiologie Appliquee, Nutrition et Metabolisme. 49 (9): 1279–1285. doi:10.1139/apnm-2023-0573. ISSN 1715-5320. PMID 39011902 Check |pmid= value (help).
  29. ↑ Wishart, David S.; Oler, Eponine; Peters, Harrison; Guo, AnChi; Girod, Sagan; Han, Scott; Saha, Sukanta; Lui, Vicki W.; LeVatte, Marcia; Gautam, Vasuk; Kaddurah-Daouk, Rima; Karu, Naama (2023-01-06). "MiMeDB: the Human Microbial Metabolome Database". Nucleic Acids Research. 51 (D1): D611–D620. doi:10.1093/nar/gkac868. ISSN 1362-4962. PMC 9825614 Check |pmc= value (help). PMID 36215042 Check |pmid= value (help).
  30. ↑ Wang, Fei; Allen, Dana; Tian, Siyang; Oler, Eponine; Gautam, Vasuk; Greiner, Russell; Metz, Thomas O.; Wishart, David S. (2022-07-05). "CFM-ID 4.0 - a web server for accurate MS-based metabolite identification". Nucleic Acids Research. 50 (W1): W165–W174. doi:10.1093/nar/gkac383. ISSN 1362-4962. PMC 9252813 Check |pmc= value (help). PMID 35610037 Check |pmid= value (help).
  31. ↑ Zakir, Mahi; LeVatte, Marcia A.; Wishart, David S. (2025-04-26). "RT-Pred: A web server for accurate, customized liquid chromatography retention time prediction of chemicals". Journal of Chromatography. A. 1747. doi:10.1016/j.chroma.2025.465816. ISSN 1873-3778. PMID 40023050 Check |pmid= value (help). Unknown parameter |article-number= ignored (help)
  32. ↑ Anjum, Afia; Liigand, Jaanus; Milford, Ralph; Gautam, Vasuk; Wishart, David S. (2023-08-30). "Accurate prediction of isothermal gas chromatographic Kováts retention indices". Journal of Chromatography. A. 1705. doi:10.1016/j.chroma.2023.464176. ISSN 1873-3778. PMID 37413909 Check |pmid= value (help). Unknown parameter |article-number= ignored (help)
  33. ↑ Sajed, Tanvir; Sayeeda, Zinat; Lee, Brian L.; Berjanskii, Mark; Wang, Fei; Gautam, Vasuk; Wishart, David S. (2024-05-19). "Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning". Metabolites. 14 (5): 290. doi:10.3390/metabo14050290. ISSN 2218-1989. PMC 11123270 Check |pmc= value (help). PMID 38786767 Check |pmid= value (help).
  34. ↑ "Using metabolomics and artificial intelligence to explore the universe of the dark metabolome". Research Outreach (109): 118–121. 2019-10-01. doi:10.32907/RO-109-118121.
  35. ↑ Wishart, David S; Tian, Siyang; Allen, Dana; Oler, Eponine; Peters, Harrison; Lui, Vicki W; Gautam, Vasuk; Djoumbou-Feunang, Yannick; Greiner, Russell; Metz, Thomas O (2022-07-05). "BioTransformer 3.0—a web server for accurately predicting metabolic transformation products". Nucleic Acids Research. 50 (W1): W115–W123. doi:10.1093/nar/gkac313. ISSN 0305-1048. PMC 9252798 Check |pmc= value (help). PMID 35536252 Check |pmid= value (help).
  36. ↑ Frolkis, Alex; Knox, Craig; Lim, Emilia; Jewison, Timothy; Law, Vivian; Hau, David D.; Liu, Phillip; Gautam, Bijaya; Ly, Son; Guo, An Chi; Xia, Jianguo; Liang, Yongjie; Shrivastava, Savita; Wishart, David S. (2010). "SMPDB: The Small Molecule Pathway Database". Nucleic Acids Research. 38 (Database issue): D480–487. doi:10.1093/nar/gkp1002. ISSN 1362-4962. PMC 2808928. PMID 19948758.
  37. ↑ Wishart, David (2012), "Systems Biology Resources Arising from the Human Metabolome Project", in Suhre, Karsten, Genetics Meets Metabolomics, New York, NY: Springer New York, pp. 157–175, doi:10.1007/978-1-4614-1689-0_11, ISBN 978-1-4614-1688-3, retrieved 2026-07-08
  38. ↑ Wishart, David S.; Kruger, Ray; Sivakumaran, Aadhavya; Harford, Karxena; Sanford, Selena; Doshi, Rahil; Kehrtarpal, Nitya; Fatokun, Omolola; Doucet, Daphnee; Zubkowski, Ashley; Jackson, Hayley; Sykes, Gina; Ramirez-Gaona, Miguel; Marcu, Ana; Li, Carin (2024-01-05). "PathBank 2.0-the pathway database for model organism metabolomics". Nucleic Acids Research. 52 (D1): D654–D662. doi:10.1093/nar/gkad1041. ISSN 1362-4962. PMC 10767802 Check |pmc= value (help). PMID 37962386 Check |pmid= value (help).
  39. ↑ Pang, Zhiqiang; Lu, Yao; Zhou, Guangyan; Hui, Fiona; Xu, Lei; Viau, Charles; Spigelman, Aliya F.; MacDonald, Patrick E.; Wishart, David S.; Li, Shuzhao; Xia, Jianguo (2024-07-05). "MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation". Nucleic Acids Research. 52 (W1): W398–W406. doi:10.1093/nar/gkae253. ISSN 1362-4962. PMC 11223798 Check |pmc= value (help). PMID 38587201 Check |pmid= value (help).


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