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Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification

Overview of attention for article published in BMC Medical Genomics, May 2015
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Title
Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification
Published in
BMC Medical Genomics, May 2015
DOI 10.1186/1755-8794-8-s2-s7
Pubmed ID
Authors

Yul Kim, Chiyong Kang, Bumki Min, Gwan-Su Yi

Abstract

Post-translational modification (PTM) plays a crucial role in biological functions and corresponding disease developments. Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can help to estimate the various PTM candidates involved in diseases, therefore, an integrated analysis between SNPs, PTMs and diseases is necessary. However, only a few types of PTMs affected by nsSNPs have been studied without considering disease-association until now. In this study, we developed a new database called PTM-SNP which contains a comprehensive collection of human nsSNPs that affect PTM sites, together with disease information. Total 179,325 PTM-SNPs were collected by aligning missense SNPs and stop-gain SNPs on PTM sites (position 0) or their flanking region (position -7 to 7). Disease-associated SNPs from GWAS catalogs were also matched with detected PTM-SNP to find disease associated PTM-SNPs. Our result shows PTM-SNPs are highly associated with diseases, compared with other nsSNP sites and functional classes including near gene, intron and so on. PTM-SNP can provide an insight about discovering important PTMs involved in the diseases easily through the web site. PTM-SNP is freely available at http://gcode.kaist.ac.kr/ptmsnp.

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Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sri Lanka 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 27%
Student > Master 8 18%
Student > Ph. D. Student 6 13%
Student > Bachelor 2 4%
Student > Postgraduate 2 4%
Other 6 13%
Unknown 9 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 36%
Agricultural and Biological Sciences 8 18%
Computer Science 2 4%
Medicine and Dentistry 2 4%
Immunology and Microbiology 2 4%
Other 3 7%
Unknown 12 27%