Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling

Pham, Nhung; Heck, Ruben G. A. van; Dam, Jesse C. J. van; Schaap, Peter J.; Saccenti, Edoardo; Suárez-Diez, María

doi:10.3390/metabo9020028

Cited by 38 publications

(33 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In spite of our efforts, some relationships were still missing which evidence the complexity of the problem. Since recent efforts have made clearer the type of issues arising in different databases [69], we emphasize the importance of standards, which could make easier the identification of synonyms because of the presence of high-quality information, and the need of an outstanding mapping system.…”

Section: Discussionmentioning

confidence: 99%

A systematic assessment of current genome-scale metabolic reconstruction tools

et al. 2019

View full text Add to dashboard Cite

Background Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. Results In this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool’s output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis , representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features. Conclusions Model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software. Electronic supplementary material The online version of this article (10.1186/s13059-019-1769-1) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 99%

A systematic assessment of current genome-scale metabolic reconstruction tools

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For instance, the genes modality comprises gene, protein, and RNA BEL nodes. While this modality is clearly defined, there is a higher degree of variability in the entity types that can be that can be classified with the metabolites modality since the latter comprises a broad range of abundance BEL nodes (i.e., small molecules, cellular components, clinical measurements, or categories that do not fit in other BEL node classes; [34]). Without the use of standard nomenclatures by the source databases, an extensive manual effort would be required to partition these modalities into more granular classifications.…”

Section: Resultsmentioning

confidence: 99%

PathMe: merging and exploring mechanistic pathway knowledge

et al. 2019

View full text Add to dashboard Cite

Background The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions. Results Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/ ) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies. Conclusions This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level. Electronic supplementary material The online version of this article (10.1186/s12859-019-2863-9) contains supplementary material, which is available to authorized users.

show abstract

“…In spite of our efforts, still some relationships were missing which evidence the complexity of the problem. Since recent efforts have made clearer the type of issues arising in different databases [62], we emphasize the importance of standards, which could make easier the identification of synonyms because of the presence of high-quality information, and the need of an outstanding mapping system.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

Mendoza

Olivier

Molenaar

et al. 2019

Preprint

View full text Add to dashboard Cite

Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. In this work, we performed a systematic assessment of the current genome-scale reconstruction software platforms. To meet our goal, we first defined a list of features for assessing software quality related to genome-scale reconstruction, which we expect to be useful for the potential users of these tools. Subsequently, we used the feature list to evaluate the performance of each tool. In order to assess the similarity of the draft reconstructions to high-quality models, we compared each tool's output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We showed that none of the tools outperforms the others in all the defined features and that model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. Author SummaryMetabolic networks that comprise biochemical reactions at genome-scale have become very useful to study and predict the phenotype of important microorganisms. Several software platforms exist to build these metabolic networks. Based on different approaches and utilizing a variety of databases it is, unfortunately, unclear what are the best scenarios to use each of these tools. Hence, to understand the potential uses of these tools, we created a list of relevant features for metabolic reconstruction and we evaluated the tools in all these categories. Here, we show that none of the tools is better than the other in all the evaluated categories; instead, each tool is more suitable for particular purposes. Therefore, users should carefully select the tool(s) that best fit the purpose of their research. This is the first time these tools are systematically evaluated and this overview can be used as a guide for selecting the correct tool(s) for each case. Current state of genome-scale reconstruction toolsHere we provide a brief description of the current reconstruction tools (see also S1 Table).AutoKEGGRec (2018). AutoKEEGRec [13] is an easy-to-use automated tool that uses the KEGG databases to create draft genome-scale models for any microorganism in that database. It runs in MATLAB and is compatible with COBRA Toolbox v3 [14]. One of the advantages of this tool is that multiple queries (microorganisms) can be processed in one run making it appropriate for cases where several microorganisms need to be reconstructe...

show abstract

Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling

Cited by 38 publications

References 48 publications

A systematic assessment of current genome-scale metabolic reconstruction tools

A systematic assessment of current genome-scale metabolic reconstruction tools

PathMe: merging and exploring mechanistic pathway knowledge

A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

Contact Info

Product

Resources

About