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Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets

Overview of attention for article published in BMC Genomics, November 2011
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Title
Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets
Published in
BMC Genomics, November 2011
DOI 10.1186/1471-2164-12-s3-s20
Pubmed ID
Authors

Hufeng Zhou, Limsoon Wong

Abstract

M. tuberculosis is a formidable bacterial pathogen. There is thus an increasing demand on understanding the function and relationship of proteins in various strains of M. tuberculosis. Protein-protein interactions (PPIs) data are crucial for this kind of knowledge. However, the quality of the main available M. tuberculosis PPI datasets is unclear. This hampers the effectiveness of research works that rely on these PPI datasets. Here, we analyze the two main available M. tuberculosis H37Rv PPI datasets. The first dataset is the high-throughput B2H PPI dataset from Wang et al's recent paper in Journal of Proteome Research. The second dataset is from STRING database, version 8.3, comprising entirely of H37Rv PPIs predicted using various methods. We find that these two datasets have a surprisingly low level of agreement. We postulate the following causes for this low level of agreement: (i) the H37Rv B2H PPI dataset is of low quality; (ii) the H37Rv STRING PPI dataset is of low quality; and/or (iii) the H37Rv STRING PPIs are predictions of other forms of functional associations rather than direct physical interactions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Singapore 2 5%
Unknown 36 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 25%
Student > Ph. D. Student 8 20%
Student > Master 4 10%
Lecturer 3 8%
Student > Bachelor 3 8%
Other 9 23%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 38%
Computer Science 8 20%
Medicine and Dentistry 3 8%
Immunology and Microbiology 2 5%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 2 5%
Unknown 9 23%
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 24 April 2014.
All research outputs
#15,168,964
of 25,373,627 outputs
Outputs from BMC Genomics
#5,391
of 11,244 outputs
Outputs of similar age
#156,165
of 246,003 outputs
Outputs of similar age from BMC Genomics
#65
of 134 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 49th percentile – i.e., 49% 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 246,003 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.