RT Journal Article
SR Electronic
T1 TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.25.470058
DO 10.1101/2021.11.25.470058
A1 Yi-Heng Zhu
A1 Chengxin Zhang
A1 Yan Liu
A1 Gilbert S. Omenn
A1 Peter L. Freddolino
A1 Dong-Jun Yu
A1 Yang Zhang
YR 2021
UL http://biorxiv.org/content/early/2021/11/27/2021.11.25.470058.abstract
AB Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naïve probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanglab.ccmb.med.umich.edu/TripletGO/.Competing Interest StatementThe authors have declared no competing interest.