NDLI: Identifying novel protein phenotype annotations by hybridizing protein–protein interactions and protein sequence similarities

Content Provider	Springer Nature Link
Author	Chen, Lei Zhang, Yu Hang Huang, Tao Cai, Yu Dong
Copyright Year	2016
Abstract	Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein–protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.
Starting Page	913
Ending Page	934
Page Count	22
File Format	PDF
ISSN	16174615
Journal	Molecular and General Genetics MGG
Volume Number	291
Issue Number	2
e-ISSN	16174623
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-01-04
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Phenotype Protein–protein interaction Protein sequence similarity Shortest path algorithm Cell Biology Biochemistry Microbial Genetics and Genomics Plant Genetics & Genomics Animal Genetics and Genomics
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine Molecular Biology

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