Final build for bioRxiv submission

README.md

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 ## To do:
-- Wrap up test cases by this weekend
-- Packaging and beta release
-- Submit to bioRxiv on Monday
-
-- beta testing feedback
-
 - Continue to clean up some visualization aspects (e.g., options for display in perturbation networks)
-  - Option to hide NA value/stat labels
 - Flux integration
 - Interactive motif searcher
-- Submission to Cell Systems

content/01.abstract.md

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 ## Abstract {.page_break_before}
 
-Metabolism and its component reactions are complex, each with variable inputs, outputs, and modifiers. The harmony between these factors consequently determines the health and stability of a cell or organism. Perturbations to any component of a reaction can have rippling downstream effects, which can be difficult to trace across the global reaction network, particularly when the effects occur between canonical representations of pathways. Researchers have primarily utilized reductionist approaches to understand metabolic reaction systems; however, customary methods often limit the scope of the analysis. Even the power of systems-centric omics approaches can be limited when only a handful of high magnitude signals in the data are prioritized. To address these challenges, we developed Metaboverse, an interactive tool for the exploration and automated extraction of potential regulatory events, patterns, and trends from multi-omic data within the context of the metabolic network and other global reaction networks. This framework will be foundational in increasing our ability to holistically understand static and temporal metabolic events and perturbations as well as gene-metabolite intra-cooperativity. Metaboverse is freely available under a GPL-3.0 license at [https://github.com/Metaboverse/](https://github.com/Metaboverse/).
+Metabolism and its component reactions are complex, each with variable inputs, outputs, and modifiers. The harmony between these factors consequently determines the health and stability of a cell or an organism. Perturbations to any reaction component can have rippling downstream effects, which can be challenging to trace across the global reaction network, particularly when the effects occur between canonical representations of pathways. Researchers have primarily utilized reductionist approaches to understand metabolic reaction systems; however, customary methods often limit the analysis scope. Even the power of systems-centric omics approaches can be limited when only a handful of high magnitude signals in the data are prioritized. To address these challenges, we developed Metaboverse, an interactive tool for the exploration and automated extraction of potential regulatory events, patterns, and trends from multi-omic data within the context of the metabolic network and other global reaction networks. This framework will be foundational in increasing our ability to holistically understand static and temporal metabolic events and perturbations as well as gene-metabolite intra-cooperativity. Metaboverse is freely available under a GPL-3.0 license at [https://github.com/Metaboverse/](https://github.com/Metaboverse/).
 
 Graphical Abstract (displayed as Figure @fig:overview)
 
 ![
   **Metaboverse conceptual overview.**
-  Illustration summarizing the usage of Metaboverse to model biological data on the global reaction network to rapidly identify regulatory hotspots. Traditionally, when scientists perform an omics experiment, they tend to focus on a few features that are differentially regulated. Metaboverse allows a user to input multiple omics data types, which it layers across the metabolic network. Metaboverse then uses this integrated model to identify patterns of putative regulatory potential in the data. The user can also dynamically explore metabolic pathways and other network representations.
+  Illustration summarizing the usage of Metaboverse to model biological data on the global reaction network to rapidly identify regulatory hotspots. Traditionally, when scientists perform an omics experiment, they tend to focus on a few features that are differentially regulated. Metaboverse allows a user to input multiple omics data types, which it layers upon the metabolic network. Metaboverse then uses this integrated model to identify patterns of putative regulatory potential in the data. The user can also dynamically explore metabolic pathways and other network representations.
 ](./content/figures/graphical_abstract.png "Square image"){#fig:overview}

content/04.discussion.md

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 ## Discussion
 
-In this manuscript, we introduce a new software tool for the analysis and exploration of user data layered on the metabolic reaction network. To improve on tools with similar capabilities, we introduced several new analytical tools and methods to aid the user in the automated identification and discovery of regulatory patterns within their data in a reaction network context. These tools and methods include the automated ability to identify reaction regulatory events across the reaction network, such as a reaction where an input has a high measured abundance and an output has a low measured abundance. Metaboverse also provides dynamic and interactive visualization capabilities to search for patterns and features within the user data manually within classical pathway representations. If a user is interested in how a reaction motif is propagating across the reaction network and not just a single pathway, they can explore a reaction component's nearest reaction neighborhood. The user can also explore the connectedness of perturbations across the network and begin to explore hypotheses around the role of connected or disconnected reactions within a particular biological model.
+In this manuscript, we introduce a new software tool for the analysis and exploration of user data layered on the metabolic reaction network. To improve on tools with similar capabilities, we introduced several new analytical tools and methods to aid the user in the automated identification and discovery of regulatory patterns within their data. These tools and methods include the automated ability to identify regulatory events across reactions, such as a reaction where an input has a high measured abundance and an output has a low measured abundance. Metaboverse also provides dynamic and interactive visualization capabilities to search for patterns and features within the user data manually within classical pathway representations. If a user is interested in how a reaction motif is propagating across the reaction network and not just a single pathway, they can explore an entity's nearest reaction neighborhood. The user can also explore the connectedness of perturbations across the network and begin to explore hypotheses around the role of connected or disconnected reactions within a particular biological model.
 
-In order to address the challenge of data sparsity, particularly regarding metabolomics data and the metabolic reaction network, we introduce a reaction collapsing feature that summarizes a series of connected reactions in which values may be missing between the reactions, but where the terminal ends of the reaction path have measured values. Importantly, this ability to collapse reactions augments the capabilities available within Metaboverse, especially in identifying disease-relevant reaction motifs that may be of interest to the user but would otherwise be hidden.  
+To address the challenge of data sparsity, particularly regarding metabolomics data and the metabolic reaction network, we introduce a reaction collapsing feature. This feature summarizes a series of connected reactions in which values may be missing between the reactions, but where the terminal ends of the reaction path have measured values. Importantly, this ability to collapse reactions augments the capabilities available within Metaboverse, especially in identifying disease-relevant reaction motifs that may be of interest to the user but are otherwise hidden.  
 
-We demonstrated the utility of Metaboverse in exploring single- and multi-omic datasets. We analyzed previously published studies and generated novel analyses that highlight the timecourse and multi-omic capabilities of this framework. We demonstrated that Metaboverse was able to identify regulatory motifs that were expected in the models based on the current literature, as well as identify intriguing patterns that led us to form new hypotheses. We expect these features to be a powerful tool researchers' toolkits as they analyze their data and plan their next steps. In the near future, we will implement several additional features that will expand the applicability of Metaboverse. For example, we will integrate additional common data quality control features for each data type, the ability to visualize and analyze flux metabolomics data, additional data pre-processing modules, other more targeted analysis approaches to aid the user in following up on motif reaction search results, and an interactive motif builder.
+We demonstrated the utility of Metaboverse in exploring single- and multi-omic datasets. We analyzed previously published studies and generated novel analyses highlighting the timecourse and multi-omic capabilities of this tool. We demonstrated that Metaboverse was able to identify regulatory motifs that were expected in the models based on the current literature and identify intriguing patterns that led us to form new hypotheses. We expect these features to be a powerful tool in researchers' toolkits as they analyze their data and plan their next experimental steps. We plan to continue to implement additional features that will expand the applicability of Metaboverse. For example, we will integrate additional standard data quality control features for each data type and add additional data pre-processing modules. We will include the ability to visualize and analyze flux metabolomics data and add other more targeted analysis approaches to aid the user in following up on motif reaction search results. We will also design an interactive tool to allow users to design a motif.
 
-Metaboverse aims to enhance the computational toolkit for data analysis and hypothesis generation in metabolic and other experiments, but a number of challenges remain, which we intend to address in subsequent versions of this software. For example, the reaction collapsing features of Metaboverse aid in identifying patterns across several reactions where data may be missing, but various biological and technical edge cases need to be considered in future implementations of this feature. As technical limitations in metabolomics data analysis are overcome, we hope more complete snapshots of metabolism will be visible. Additionally, we take a more straightforward and somewhat rudimentary approach to statistical significance integration in the reaction motif searches, but more holistic platforms for cross-omics integration are needed and remain a significant challenge across multi-omics research.
+Metaboverse aims to enhance the computational toolkit for data analysis and hypothesis generation in metabolic and other experiments. However, numerous challenges remain, which we intend to address in future versions of this software. For example, the reaction collapsing features of Metaboverse aid in identifying patterns across several reactions where data may be missing, but various biological and technical edge cases will need to be accounted for in future implementations of this feature. As more technical limitations in metabolomics data generation and analysis are overcome, we hope that more complete snapshots of metabolism will be visible. Additionally, we currently take a more straightforward and somewhat rudimentary approach to statistical significance integration in the reaction motif searches. However, more holistic platforms for cross-omics integration are needed and remain a significant challenge across multi-omics research.
 
-In summary, we hope that Metaboverse will bring the user a new perspective on their data. We envision Metaboverse as a staple in the metabolic research toolkit that will help researchers critically and holistically consider their data in the context of biological network interactions and help draw the connections needed to aid them in extracting new and exciting hypotheses that might be challenging to do without this tool.
+In summary, we hope that Metaboverse will bring a new perspective to users' data. We envision Metaboverse will become a staple in the metabolic research toolkit that will help researchers critically and holistically consider their data in the context of biological network interactions and draw the connections needed to extract new and exciting hypotheses that might be challenging without these features.