Dataset Information

Consensus and conflict cards for metabolic pathway databases.

ABSTRACT:

Background

The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial.

Results

We introduce the concept of Consensus and Conflict Cards (C?Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C?Cards(Human), as a web application http://www.molgenis.org/c2cards, covering five human pathway databases.

Conclusions

C?Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C?Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.

SUBMITTER: Stobbe MD

PROVIDER: S-EPMC3703255 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Publications

Consensus and conflict cards for metabolic pathway databases.

Stobbe Miranda D MD Swertz Morris A MA Thiele Ines I Rengaw Trebor T van Kampen Antoine H C AH Moerland Perry D PD

BMC systems biology 20130626

<h4>Background</h4>The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial.<h4>Results</h4>We introduce the concept of Consensus and Conflict ...[more]

PMID: 23803311

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Project description:The PathoLogic program constructs Pathway/Genome databases by using a genome's annotation to predict the set of metabolic pathways present in an organism. PathoLogic determines the set of reactions composing those pathways from the enzymes annotated in the organism's genome. Most annotation efforts fail to assign function to 40-60% of sequences. In addition, large numbers of sequences may have non-specific annotations (e.g., thiolase family protein). Pathway holes occur when a genome appears to lack the enzymes needed to catalyze reactions in a pathway. If a protein has not been assigned a specific function during the annotation process, any reaction catalyzed by that protein will appear as a missing enzyme or pathway hole in a Pathway/Genome database.We have developed a method that efficiently combines homology and pathway-based evidence to identify candidates for filling pathway holes in Pathway/Genome databases. Our program not only identifies potential candidate sequences for pathway holes, but combines data from multiple, heterogeneous sources to assess the likelihood that a candidate has the required function. Our algorithm emulates the manual sequence annotation process, considering not only evidence from homology searches, but also considering evidence from genomic context (i.e., is the gene part of an operon?) and functional context (e.g., are there functionally-related genes nearby in the genome?) to determine the posterior belief that a candidate has the required function. The method can be applied across an entire metabolic pathway network and is generally applicable to any pathway database. The program uses a set of sequences encoding the required activity in other genomes to identify candidate proteins in the genome of interest, and then evaluates each candidate by using a simple Bayes classifier to determine the probability that the candidate has the desired function. We achieved 71% precision at a probability threshold of 0.9 during cross-validation using known reactions in computationally-predicted pathway databases. After applying our method to 513 pathway holes in 333 pathways from three Pathway/Genome databases, we increased the number of complete pathways by 42%. We made putative assignments to 46% of the holes, including annotation of 17 sequences of previously unknown function.Our pathway hole filler can be used not only to increase the utility of Pathway/Genome databases to both experimental and computational researchers, but also to improve predictions of protein function.

Project description:BackgroundMultiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.ResultsWe compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.ConclusionsOur results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.

Project description:Importance:Armed conflict in the 21st century poses new challenges to a humanitarian surgical response, including changing security requirements, access to patients, and communities in need, limited deployable surgical assets, resource constraints, and the requirement to address both traumatic injuries as well as emergency surgical needs of the population. At the same time, recent improvements in trauma care and systems have reduced injury-related mortality. This combination of new challenges and medical capabilities warrants reconsideration of long-standing humanitarian surgery protocols. Objective:To describe a consensus framework for surgical care designed to respond to this emerging need. Design, Setting, and Participants:An international group of 35 representatives from humanitarian agencies, US military, and academic trauma programs was invited to the Stanford Humanitarian Surgical Response in Conflict Working Group to engage in a structured process to review extant trauma protocols and make recommendations for revision. Main Outcomes and Measures:The working group's method adapted core elements of a modified Delphi process combined with consensus development conference from August 3 to August 5, 2018. Results:Lessons from civilian and military trauma systems as well as recent battlefield experiences in humanitarian settings were integrated into a tiered continuum of response from point of injury through rehabilitation. The framework addresses the security and medical requirements as well as ethical and legal principles that guide humanitarian action. The consensus framework includes trained, lay first responders; far-forward resuscitation/stabilization centers; rapid damage control surgical access; and definitive care facilities. The system also includes nontrauma surgical care, injury prevention, quality improvement, data collection, and predeployment training requirements. Conclusions and Relevance:Evidence suggests that modern trauma systems save lives. However, the requirements of providing this standard of care in insecure conflict settings places new burdens on humanitarian systems that must provide both emergency and trauma surgical care. This consensus framework integrates advances in trauma care and surgical systems in response to a changing security environment. It is possible to reduce disparities and improve the standard of care in these settings.

Dataset Information

Consensus and conflict cards for metabolic pathway databases.

Background

Results

Conclusions

Publications

Consensus and conflict cards for metabolic pathway databases.

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