BACKGROUND: The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns". RESULT: We present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing. CONCLUSIONS: Our data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction.
BACKGROUND: The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns". RESULT: We present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing. CONCLUSIONS: Our data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction.
Authors: Arun Sreekumar; Laila M Poisson; Thekkelnaycke M Rajendiran; Amjad P Khan; Qi Cao; Jindan Yu; Bharathi Laxman; Rohit Mehra; Robert J Lonigro; Yong Li; Mukesh K Nyati; Aarif Ahsan; Shanker Kalyana-Sundaram; Bo Han; Xuhong Cao; Jaeman Byun; Gilbert S Omenn; Debashis Ghosh; Subramaniam Pennathur; Danny C Alexander; Alvin Berger; Jeffrey R Shuster; John T Wei; Sooryanarayana Varambally; Christopher Beecher; Arul M Chinnaiyan Journal: Nature Date: 2009-02-12 Impact factor: 49.962
Authors: Antonio Noto; Giulia Pomero; Michele Mussap; Luigi Barberini; Claudia Fattuoni; Francesco Palmas; Cristina Dalmazzo; Antonio Delogu; Angelica Dessì; Vassilios Fanos; Paolo Gancia Journal: Ann Transl Med Date: 2016-11
Authors: David J Beale; Farhana R Pinu; Konstantinos A Kouremenos; Mahesha M Poojary; Vinod K Narayana; Berin A Boughton; Komal Kanojia; Saravanan Dayalan; Oliver A H Jones; Daniel A Dias Journal: Metabolomics Date: 2018-11-17 Impact factor: 4.290
Authors: Anjaritha A R Parijadi; Sobir Ridwani; Fenny M Dwivany; Sastia P Putri; Eiichiro Fukusaki Journal: Metabolomics Date: 2019-05-03 Impact factor: 4.290
Authors: Pablo R Cortina; Ana N Santiago; María M Sance; Iris E Peralta; Fernando Carrari; Ramón Asis Journal: Metabolomics Date: 2018-03-31 Impact factor: 4.290