Automated visualization of multiomics (metabolomics, proteomics, fuxomics and transcriptomics) data on Garuda, a connectivity platform for biological analytics

Significance of the topic

Integrating multiomics data streams—metabolomics, proteomics, fluxomics and transcriptomics—is critical to decipher complex biological systems and generate new hypotheses at scale. However, the sheer volume and heterogeneity of such data necessitate automated, user‐friendly visualization frameworks to accelerate insight generation and experimental design.

Study objectives and overview

This work describes the development of a pipeline on the Garuda connectivity platform to automate visualization of four omics layers simultaneously. Building on prior Garuda‐based tools for metabolite, protein and flux mapping, the new workflow incorporates transcriptomic data, enabling comprehensive overlay of gene expression, protein abundance, metabolite levels and flux measurements onto metabolic pathway maps.

Methodology and instrumentation

The cyanobacterium Synechocystis sp. PCC 6803 was cultured under three trophic regimes (autotrophic, mixotrophic and photoheterotrophic) to generate matched datasets. Key steps included:

• Conversion of microarray transcriptome outputs into text files compatible with Garuda gadgets.
• Data import via the “Shimadzu MS Data Import” gadget.
• Integration and mapping by the “Multiomics Data Mapper” gadget.
• Downstream network visualization connecting to Cytoscape, VANTED and iPath2 gadgets.

Instrumentation

• Garuda platform (connectivity framework and dashboard)
• Shimadzu MS Data Import gadget
• Multiomics Data Mapper gadget
• Cytoscape, VANTED and iPath2 visualization tools

Key results and discussion

The pipeline successfully rendered four‐layered omics data onto the Calvin–Benson cycle map, focusing on the RuBisCO reaction. Under autotrophic, mixotrophic and photoheterotrophic conditions, metabolic flux through RuBisCO correlated more closely with changes in rbcL/rbcS transcript levels than with substrate (RuBP) or product (3‐PGA) pool sizes, underscoring the benefit of integrating transcriptomic and fluxomic data to interpret regulation of carbon fixation.

Benefits and practical applications

• Rapid, automated multiomics visualization aids hypothesis generation in systems biology and metabolic engineering.
• Modular Garuda gadgets allow flexible workflows and straightforward extension to new data types.
• Researchers can compare conditions, identify regulatory bottlenecks and prioritize targets for metabolic intervention.

Future trends and opportunities

Advancements may include real‐time multiomics streaming, machine‐learning–driven pattern recognition and integration of additional layers such as epigenomics or single‐cell data. Community‐driven expansion of Garuda gadgets will further democratize comprehensive data exploration across laboratories.

Conclusion

This study extends the Garuda platform’s capabilities to fully integrated multiomics visualization by incorporating transcriptomic data alongside proteomic, metabolomic and fluxomic information. The resulting pipeline accelerates interpretation of complex datasets and fosters deeper understanding of biological regulation under diverse conditions.

Reference

• Garuda Alliance. http://www.garuda‐alliance.org
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