PT  - JOURNAL ARTICLE
AU  - Yi-Heng Zhu
AU  - Chengxin Zhang
AU  - Yan Liu
AU  - Gilbert S. Omenn
AU  - Peter L. Freddolino
AU  - Dong-Jun Yu
AU  - Yang Zhang
TI  - TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations
AID  - 10.1101/2021.11.25.470058
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.11.25.470058
4099  - http://biorxiv.org/content/early/2021/11/27/2021.11.25.470058.short
4100  - http://biorxiv.org/content/early/2021/11/27/2021.11.25.470058.full
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.