SUMMER, a shiny utility for metabolomics and multiomics exploratory research

Metabolomics - Tập 16 Số 12 - 2020
Ling Huang1, António Currais2, Maxim N. Shokhirev1
1Razavi Newman Integrative Genomics and Bioinformatics Core, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
2Cellular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA

Tóm tắt

Abstract Introduction Cellular metabolites are generated by a complex network of biochemical reactions. This makes interpreting changes in metabolites exceptionally challenging. Objectives To develop a computational tool that integrates multiomics data at the level of reactions. Methods Changes in metabolic reactions are modeled with input from transcriptomics/proteomics measurements of enzymes and metabolomic measurements of metabolites. Results We developed SUMMER, which identified more relevant signals, key metabolic reactions, and relevant underlying biological pathways in a real-world case study. Conclusion SUMMER performs integrative analysis for data interpretation and exploration. SUMMER is freely accessible at http://summer.salk.edu and the code is available at https://bitbucket.org/salkigc/summer.

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Tài liệu tham khảo

Cambiaghi, A., Ferrario, M., & Masseroli, M. (2017). Analysis of metabolomic data: Tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics, 18, 498–510.

Cavill, R., Jennen, D., Kleinjans, J., & Briede, J. J. (2016). Transcriptomic and metabolomic data integration. Briefings in Bioinformatics, 17, 891–901.

Chang, W., Cheng, J., Allaire, J.J., Xie, Y., & McPherson, J. (2018). Shiny: Web Application Framework for R. R package version 1.1.0. Retrieved from https://CRAN.R-project.org/package=shiny.

Currais, A., Huang, L., Goldberg, J., Petrascheck, M., Ates, G., Pinto-Duarte, A., et al. (2019). Elevating acetyl-CoA levels reduces aspects of brain aging. Elife, 8, e47866.

Forsberg, E. M., Huan, T., Rinehart, D., Benton, H. P., Warth, B., Hilmers, B., & Siuzdak, G. (2018). Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online. Nature Protocols, 13, 633–651.

Kanehisa, M., Sato, Y., Furumichi, M., Morishima, K., & Tanabe, M. (2019). New approach for understanding genome variations in KEGG. Nucleic Acids Research, 47, D590–D595.

Khatri, P., Sirota, M., & Butte, A. J. (2012). Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Computational Biology, 8, e1002375.

Machado, D., & Herrgard, M. (2014). Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Computational Biology, 10, e1003580.

Marco-Ramell, A., Palau-Rodriguez, M., Alay, A., Tulipani, S., Urpi-Sarda, M., Sanchez-Pla, A., & Andres-Lacueva, C. (2018). Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinformatics, 19, 1.

Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 13, 263–269.

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43, e47.

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Muller, M. (2011). prOC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77.

Shannon, P., Markiel, A., Ozier, O. B., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498–2504.

Spicer, R., Salek, R. M., Moreno, P., Canueto, D., & Steinbeck, C. (2017). Navigating freely-available software tools for metabolomics analysis. Metabolomics, 13, 106.

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS, 102, 15545–15550.

Vital-Lopez, F. G., Wallqvist, A., & Reifman, J. (2013). Bridging the gap between gene expression and metabolic phenotype via kinetic models. BMC Systems Biology, 7, 63.

Wang, J., Vasaikar, S., Shi, Z., Greer, M., & Zhang, B. (2017). WebGestalt 2017: A more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Research, 45, W130–W137.

Yu, H., Xing, S., Nierves, L., Lange, P. F., & Huan, T. (2020). Fold-Change compression: An unexplored but correctable quantitative bias caused by nonlinear electrospray ionization responses in untargeted metabolomics. Analytical Chemistry, 92, 7011–7019.