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A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins

Overview of attention for article published in BMC Bioinformatics, November 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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140 Mendeley
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Title
A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins
Published in
BMC Bioinformatics, November 2015
DOI 10.1186/s12859-015-0792-9
Pubmed ID
Authors

James T. Morton, Stefan D. Freed, Shaun W. Lee, Iddo Friedberg

Abstract

Bacteriocins are peptide-derived molecules produced by bacteria, whose recently-discovered functions include virulence factors and signaling molecules as well as their better known roles as antibiotics. To date, close to five hundred bacteriocins have been identified and classified. Recent discoveries have shown that bacteriocins are highly diverse and widely distributed among bacterial species. Given the heterogeneity of bacteriocin compounds, many tools struggle with identifying novel bacteriocins due to their vast sequence and structural diversity. Many bacteriocins undergo post-translational processing or modifications necessary for the biosynthesis of the final mature form. Enzymatic modification of bacteriocins as well as their export is achieved by proteins whose genes are often located in a discrete gene cluster proximal to the bacteriocin precursor gene, referred to as context genes in this study. Although bacteriocins themselves are structurally diverse, context genes have been shown to be largely conserved across unrelated species. Using this knowledge, we set out to identify new candidates for context genes which may clarify how bacteriocins are synthesized, and identify new candidates for bacteriocins that bear no sequence similarity to known toxins. To achieve these goals, we have developed a software tool, Bacteriocin Operon and gene block Associator (BOA) that can identify homologous bacteriocin associated gene blocks and predict novel ones. BOA generates profile Hidden Markov Models from the clusters of bacteriocin context genes, and uses them to identify novel bacteriocin gene blocks and operons. We provide a novel dataset of predicted bacteriocins and context genes. We also discover that several phyla have a strong preference for bacteriocin genes, suggesting distinct functions for this group of molecules. https://github.com/idoerg/BOA.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 139 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 24%
Student > Master 25 18%
Researcher 21 15%
Student > Bachelor 17 12%
Student > Doctoral Student 7 5%
Other 13 9%
Unknown 23 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 27%
Agricultural and Biological Sciences 38 27%
Immunology and Microbiology 8 6%
Computer Science 7 5%
Engineering 4 3%
Other 14 10%
Unknown 31 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 17 November 2015.
All research outputs
#7,174,980
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,657
of 7,454 outputs
Outputs of similar age
#86,563
of 285,343 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 145 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 63% of its peers.
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 285,343 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.