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Systems Biology Guided by XCMS Online Metabolomics
- Source :
- Huan, T, Forsberg, E M, Rinehart, D, Johnson, C H, Ivanisevic, J, Benton, H P, Fang, M, Aisporna, A, Hilmers, B, Poole, F L, Thorgersen, M P, Adams, M W W, Krantz, G, Fields, M W, Robbins, P, Niedernhofer, L J, Ideker, T, Majumder, E L, Wall, J D, Rattray, N, Goodacre, R, Lairson, L & Siuzdak, G 2017, ' Systems Biology Guided by XCMS Online Metabolomics ', Nature Methods, vol. 14, pp. 461-462 . https://doi.org/10.1038/nmeth.4260
- Publication Year :
- 2017
-
Abstract
- Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min.
- Subjects :
- 0301 basic medicine
Extramural
Systems biology
Systems Biology
Cell Biology
Computational biology
Biology
ResearchInstitutes_Networks_Beacons/manchester_institute_of_biotechnology
Biochemistry
Mass Spectrometry
Article
RS
03 medical and health sciences
Multiple data
030104 developmental biology
0302 clinical medicine
Metabolomics
Manchester Institute of Biotechnology
Molecular Biology
030217 neurology & neurosurgery
Software
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 15487105
- Database :
- OpenAIRE
- Journal :
- Huan, T, Forsberg, E M, Rinehart, D, Johnson, C H, Ivanisevic, J, Benton, H P, Fang, M, Aisporna, A, Hilmers, B, Poole, F L, Thorgersen, M P, Adams, M W W, Krantz, G, Fields, M W, Robbins, P, Niedernhofer, L J, Ideker, T, Majumder, E L, Wall, J D, Rattray, N, Goodacre, R, Lairson, L & Siuzdak, G 2017, ' Systems Biology Guided by XCMS Online Metabolomics ', Nature Methods, vol. 14, pp. 461-462 . https://doi.org/10.1038/nmeth.4260
- Accession number :
- edsair.doi.dedup.....08cf361c7b981fe224c7b972c0ea98f0