MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks

Kelley, James J.; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S.

doi:10.1093/bioinformatics/btx240

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…For example, glycolysis, the citric acid cycle and the pentose phosphate pathway are visualized together to visualize data and regulation of central metabolism [42]. Global maps [43,44] aim to provide a zoomable metabolic bird-eye’s view. However, metabolomics data sets usually contain metabolic end-products such as fatty acids or amino acids but not intermediates such as acetyl-CoA or oxaloacetate.…”

Section: Introductionmentioning

confidence: 99%

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Barupal

Fan

Fiehn

2018

Current Opinion in Biotechnology

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Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design. Special considerations need to be taken when adopting approaches from genomics for use in metabolomics. R and Python programming language are enabling an easier exchange of diverse tools to deploy integrated workflows. This review summarizes the key ideas and latest developments in regards to these approaches.

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Section: Introductionmentioning

confidence: 99%

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Barupal

Fan

Fiehn

2018

Current Opinion in Biotechnology

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“…Measurements of culture growth were made using a spectrophotometer PE 5400, using a 1 ml plastic cuvette to measure culture from a shaker and a 10 ml glass cuvette for measuring culture from a bioreactor. To build the genome-scale model and its subsequent modifications, we harnessed a sequenced genome [NZ_JPYA00000000.1] and a set of tools [3][4][5].…”

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confidence: 99%

Genome-scale modeling of carbon assimilation in Geobacillus icigianus

2018

Симпозиум «Математическое Моделирование И Высокопроизводительные Вычисления В Биоинформатике, Биомедицине И Биотехнологии»

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Motivation and Aim: In the last decade, genome-scale modeling became a powerful and useful tool to investigate whole-cell microbial metabolism [1]. For model microbes, such as E. coli, a huge amount of experimental data on different hierarchical levels of cellular organization is a basis for the most detailed and complete metabolic reconstruction of the organism [2], while advances in high-throughput techniques and in theoretical approaches pave the way to explore metabolic potential of the organism for non-model microbial species. Here, we represent recently obtained theoretical and experimental results for Geobacillus icigianus, a thermophilic bacterium isolated from a hot spring three years ago. Methods and Algorithms: G. icigianus was cultivated under the conditions of a bioreactor at pH = 7 with the constant rate of air supply, and in microbiological shakers at pH = 8. Measurements of culture growth were made using a spectrophotometer PE 5400, using a 1 ml plastic cuvette to measure culture from a shaker and a 10 ml glass cuvette for measuring culture from a bioreactor. To build the genome-scale model and its subsequent modifications, we harnessed a sequenced genome [NZ_JPYA00000000.1] and a set of tools [3][4][5]. Results:We conducted experiments on the cultivation of G. icigianus on various substrates and under different conditions. As a result, it was shown that the most effective for cultivation are the substrates: glucose and glycerine, and the optimal conditions: a temperature of 62 °C and pH = 7. To enable the growth of culture we needed to add salts of iron, magnesium, calcium, vitamins, nitrilotriacetic acid and trace elements into the media. The growth rate in experiments with bioreactor was equal to 0.5 h -1 , while the theoretically predicted value of the growth rate was 0.66 h -1 . Conclusions: Initial results allowed us to determine the optimal growth parameters that will facilitate further experiments and also demonstrated that developed genome-scale model qualitatively corresponds to the data observed in the experiments. However, it still requires both refinement of the biomass equation and consideration of transcriptomic data for different growth conditions in order to quantitatively reproduce measured growth rate and make reliable predictions on the essentiality of diverse metabolic pathways. References

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SAMMI: a semi-automated tool for the visualization of metabolic networks

Schultz

Akbani

2019

Bioinformatics

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Summary Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. Availability and implementation SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. Supplementary information Supplementary data are available at Bioinformatics online.

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MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 4 publications

References 13 publications

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Genome-scale modeling of carbon assimilation in Geobacillus icigianus

SAMMI: a semi-automated tool for the visualization of metabolic networks

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