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The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes

Overview of attention for article published in PLoS Computational Biology, May 2012
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Title
The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes
Published in
PLoS Computational Biology, May 2012
DOI 10.1371/journal.pcbi.1002540
Pubmed ID
Authors

Adam Alexander Thil Smith, Eugeni Belda, Alain Viari, Claudine Medigue, David Vallenet

Abstract

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.

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Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 9%
Brazil 2 3%
France 1 1%
Mexico 1 1%
United Kingdom 1 1%
Unknown 58 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 39%
Student > Ph. D. Student 14 20%
Student > Bachelor 6 9%
Student > Master 6 9%
Student > Postgraduate 3 4%
Other 6 9%
Unknown 7 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 58%
Biochemistry, Genetics and Molecular Biology 11 16%
Computer Science 5 7%
Chemistry 3 4%
Nursing and Health Professions 1 1%
Other 2 3%
Unknown 7 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 June 2012.
All research outputs
#16,737,737
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,220
of 8,964 outputs
Outputs of similar age
#113,927
of 179,143 outputs
Outputs of similar age from PLoS Computational Biology
#87
of 109 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 179,143 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.