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

Schultz, André; Akbani, Rehan

doi:10.1093/bioinformatics/btz927

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Escher has been integrated into other Python COBRA packages such as cameo to visualize flux analysis results. Additional interactive visualization packages include SAMMI for semi-automated visualization and d3flux for d3.js based plots ( 85 , 86 ). Due to open-source nature of Python packages, future COBRA web applications can be deployed for public use without licensing limitations.…”

Section: Cobra Methods In Pythonmentioning

confidence: 99%

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

Lee

Baloni

et al. 2022

Front. Oncol.

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The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

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Section: Cobra Methods In Pythonmentioning

confidence: 99%

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

Lee

Baloni

et al. 2022

Front. Oncol.

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“…Placing those targets into context and visualizing the measurements are keys to obtain ideas about the effects of a given treatment. There are many tools dedicated to visualization of omics data in a network context (see, e.g., the review by Gehlenborg et al [ 1 ]), and tools like Paintomics [ 2 , 3 ], BiNA [ 4 ] or MONGKIE [ 5 ] have started to address the issue of simultaneous multi-omics visualisation with recent tools like MetaboMAPS [ 6 ] or SAMMI [ 7 ], giving increasingly better and more satisfying results. Escher [ 8 ] and Pathway Tools [ 9 ] are popular applications tailored for building and sharing biological pathway and metabolic visualizations across multiple model organisms.…”

Section: Introductionmentioning

confidence: 99%

IDARE2—Simultaneous Visualisation of Multiomics Data in Cytoscape

et al. 2021

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Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.

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