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A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens

Overview of attention for article published in The HUGO Journal, April 2011
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Title
A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens
Published in
The HUGO Journal, April 2011
DOI 10.1007/s11568-011-9152-7
Pubmed ID
Authors

Umashankar Vetrivel, Gurunathan Subramanian, Sudarsanam Dorairaj

Abstract

In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae-2, H. influenzae-6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Tunisia 1 2%
Brazil 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 22%
Student > Ph. D. Student 9 17%
Student > Master 5 9%
Researcher 4 7%
Professor > Associate Professor 3 6%
Other 4 7%
Unknown 17 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 15%
Agricultural and Biological Sciences 8 15%
Immunology and Microbiology 4 7%
Engineering 4 7%
Medicine and Dentistry 3 6%
Other 8 15%
Unknown 19 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 December 2012.
All research outputs
#18,321,703
of 22,687,320 outputs
Outputs from The HUGO Journal
#14
of 19 outputs
Outputs of similar age
#95,489
of 109,045 outputs
Outputs of similar age from The HUGO Journal
#1
of 2 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 19 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one scored the same or higher as 5 of them.
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 109,045 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them