TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction
Year: 2022
Type: article
Abstract: Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. Here, 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 profile, genetic sequence alignment, protein sequence alignment, and naïve probability. TripletGO was tested on a large set of 5754 genes from 8 species (human, mouse, Arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2433 proteins with available expression data from the third Critical Assessment of Protein Function Annotation challenge (CAFA3). Experimental results show that TripletGO achieves 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 expression profiles. 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://zhanggroup.org/TripletGO/.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,500
Funders: National Science Foundation, National Natural Science Foundation of China, China Scholarship Council, Natural Science Foundation of Jiangsu Province, National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Environmental Health Sciences
Awards: G1035356945, IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus, G1491571464, III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction, G1492819423, G1692205820, G179862484, University of Michigan Proteogenomics Data Analysis Center, G2117560495, G2232767831, G2464480468, G2990727520, G3226045097, G3260064559, Advanced approaches to protein structure prediction, XSEDE 2.0: Integrating, Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement, G4021502096, MTM 2: Combining structural informatics and crosslinking mass spectrometry to predict the key protein-protein interactions shaping symbiotic microbial communities, G4584606584, G4630214672, G4913448354, G5877044679, G5964975137, G6532479989, G6975977380, G7051324606, G7063014021, G758395984, G7632130495, G7637742026, 高精度多尺度蛋白质残基接触图及距离图预测研究, G7897283973, High-Performance Computing Cluster for Biomedical Research, G8702286652, 大规模多源异质蛋白质数据挖掘中的若干关键问题研究, Structure-based functional annotation of microbial genomes, Michigan Center on Lifestage Environmental Exposures and Disease (M-LEEaD)
OpenAlex ID: W4280609620