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Identification of genomic features in the classification of loss- and gain-of-function mutation

Authors
 Seunghwan Jung  ;  Sejoon Lee  ;  Sangwoo Kim  ;  Hojung Nam 
Citation
 BMC MEDICAL INFORMATICS AND DECISION MAKING, Vol.15(Suppl1) : 6, 2015 
Journal Title
BMC MEDICAL INFORMATICS AND DECISION MAKING
Issue Date
2015
MeSH
Data Mining/methods* ; Genomics/methods* ; Humans ; Machine Learning* ; Mutation/genetics*
Keywords
Support Vector Machine ; Subcellular Location ; Random Forest ; Hypergeometric Test ; Fumarate Hydratase
Abstract
BACKGROUND: Alterations of a genome can lead to changes in protein functions. Through these genetic mutations, a protein can lose its native function (loss-of-function, LoF), or it can confer a new function (gain-of-function, GoF). However, when a mutation occurs, it is difficult to determine whether it will result in a LoF or a GoF. Therefore, in this paper, we propose a study that analyzes the genomic features of LoF and GoF instances to find features that can be used to classify LoF and GoF mutations.

METHODS: In order to collect experimentally verified LoF and GoF mutational information, we obtained 816 LoF mutations and 474 GoF mutations from a literature text-mining process. Next, with data-preprocessing steps, 258 LoF and 129 GoF mutations remained for a further analysis. We analyzed the properties of these LoF and GoF mutations. Among the properties, we selected features which show different tendencies between the two groups and implemented classifications using support vector machine, random forest, and linear logistic regression methods to confirm whether or not these features can identify LoF and GoF mutations.

RESULTS: We analyzed the properties of the LoF and GoF mutations and identified six features which have discriminative power between LoF and GoF conditions: the reference allele, the substituted allele, mutation type, mutation impact, subcellular location, and protein domain. When using the six selected features with the random forest, support vector machine, and linear logistic regression classifiers, the result showed accuracy levels of 72.23%, 71.28%, and 70.19%, respectively.

CONCLUSIONS: We analyzed LoF and GoF mutations and selected several properties which were different between the two classes. By implementing classifications with the selected features, it is demonstrated that the selected features have good discriminative power.
Files in This Item:
T201501681.pdf Download
DOI
10.1186/1472-6947-15-S1-S6
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
Yonsei Authors
Kim, Sangwoo(김상우) ORCID logo https://orcid.org/0000-0001-5356-0827
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/140248
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