Evidence map›Paper›PMID 22233419›Full record

ArticleBMC bioinformatics2012

MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases.

Akhil Kumar, Patrick F Suthers, Costas D Maranas

Abstract read
In one paragraph

Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

55 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Akhil KumarDepartment of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA. costas@psu.edu
Patrick F Suthers
Costas D Maranas

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity). DESCRIPTION: MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.

conclusionsThe standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).

Indexed as

Knowledge BasesMetabolomicsSoftwareAlgorithmsDatabases, FactualHumansInternetMetabolic Networks and PathwaysModels, Biological

Identifiers

PMID22233419
PMCPMC3277463

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.