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In Silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B

Overview of attention for article published in Journal of Biomedical Science, January 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

patent
1 patent

Citations

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12 Dimensions

Readers on

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30 Mendeley
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Title
In Silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B
Published in
Journal of Biomedical Science, January 2012
DOI 10.1186/1423-0127-19-30
Pubmed ID
Authors

George Priya Doss C

Abstract

In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in F8 and F9 gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of F8 and F9 are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of F8 and F9 from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge. We performed an in silico analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability. Out of 510 nsSNPs in F8, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In F9, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both F8 and F9. The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These in silico tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 3%
India 1 3%
Italy 1 3%
Unknown 27 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 27%
Student > Ph. D. Student 5 17%
Professor > Associate Professor 4 13%
Professor 2 7%
Student > Postgraduate 2 7%
Other 8 27%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 33%
Biochemistry, Genetics and Molecular Biology 8 27%
Medicine and Dentistry 4 13%
Computer Science 1 3%
Nursing and Health Professions 1 3%
Other 4 13%
Unknown 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 01 August 2013.
All research outputs
#2,583,051
of 9,623,359 outputs
Outputs from Journal of Biomedical Science
#91
of 440 outputs
Outputs of similar age
#34,018
of 100,382 outputs
Outputs of similar age from Journal of Biomedical Science
#1
of 4 outputs
Altmetric has tracked 9,623,359 research outputs across all sources so far. This one has received more attention than most of these and is in the 57th percentile.
So far Altmetric has tracked 440 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 54% 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 100,382 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 65% of its contemporaries.
We're also able to compare this research output to 4 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