| Title: |
Single nucleotide variations: Biological impact and theoretical interpretation |
| Authors: |
Katsonis, Panagiotis; Koire, Amanda; Wilson, Stephen Joseph; Hsu, Teng‐Kuei; Lua, Rhonald C.; Wilkins, Angela Dawn; Lichtarge, Olivier |
| Contributors: |
National Institute of Health; National Science Foundation |
| Source: |
Protein Science ; volume 23, issue 12, page 1650-1666 ; ISSN 0961-8368 1469-896X |
| Publisher Information: |
Wiley |
| Publication Year: |
2014 |
| Collection: |
Wiley Online Library (Open Access Articles via Crossref) |
| Description: |
Genome‐wide association studies (GWAS) and whole‐exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease‐causing mutations are exonic non‐synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1002/pro.2552 |
| Availability: |
https://doi.org/10.1002/pro.2552; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fpro.2552; https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.2552 |
| Rights: |
http://creativecommons.org/licenses/by-nc/3.0/ |
| Accession Number: |
edsbas.C3BD424 |
| Database: |
BASE |