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CoagVDb: a comprehensive database for coagulation factors and their associated SAPs

Overview of attention for article published in Biological Research, July 2015
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Title
CoagVDb: a comprehensive database for coagulation factors and their associated SAPs
Published in
Biological Research, July 2015
DOI 10.1186/s40659-015-0028-5
Pubmed ID
Authors

Shabana Kouser Ali, C George Priya Doss, D Thirumal Kumar, Hailong Zhu

Abstract

The current state of the art in medical genetics is to identify and classify the functional (deleterious) or non-functional (neutral) single amino acid substitutions (SAPs), also known as non-synonymous SNPs (nsSNPs). The primary goal is to elucidate the mechanisms through which functional SAPs exert their effects, and ultimately interrogating this information for association with complex phenotypes. This work focuses on coagulation factors involved in the coagulation cascade pathway which plays a vital role in the maintenance of homeostasis in the human system. We developed an integrated coagulation variation database, CoagVDb, which makes use of the biological information from various public databases such as NCBI, OMIM, UniProt, PDB and SAPs (rsIDs/variant). CoagVDb enriched with computational prediction scores classify SAPs as either deleterious or tolerated. Also, various other properties are incorporated such as amino acid composition, secondary structure elements, solvent accessibility, ordered/disordered regions, conservation, and the presence of disulfide bonds. This specialized database provides integration of various prediction scores from different computational methods along with gene, protein, and disease information. We hope our database will act as a useful reference resource for hematologists to reveal protein structure-function relationship and disease genotype-phenotype correlation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 27%
Student > Postgraduate 2 18%
Student > Ph. D. Student 1 9%
Student > Bachelor 1 9%
Student > Master 1 9%
Other 1 9%
Unknown 2 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 45%
Biochemistry, Genetics and Molecular Biology 2 18%
Computer Science 1 9%
Unknown 3 27%
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 20 July 2015.
All research outputs
#20,657,128
of 25,374,917 outputs
Outputs from Biological Research
#527
of 642 outputs
Outputs of similar age
#201,210
of 275,154 outputs
Outputs of similar age from Biological Research
#10
of 21 outputs
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So far Altmetric has tracked 642 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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