Prediction of Novel Domains, Motifs, Genes, and Proteins through Integrative Omics Approaches

The rapid advancement of high-throughput omics technologies has created unprecedented opportunities to elucidate the functional roles of novel domains, motifs, genes, and proteins across diverse biological systems. While the identification of coding and non-coding sequences has been accelerated by next-generation sequencing, a critical gap remains in accurately predicting and validating their biological functions. This challenge is particularly acute for uncharacterized proteins, lineage-specific domains, and newly discovered motifs, whose potential regulatory or structural roles often overlooked by conventional annotation pipelines.Integrative multi-omics approaches offer a comprehensive framework to infer functionality by linking sequence information with expression profiles, interaction networks, and phenotypic outcomes. By incorporating systems biology, machine learning, and structural bioinformatics, omics-driven predictions can reveal conserved or context-specific functions of newly identified molecular features. This Research Topic aims to highlight innovative methodologies and applications that advance functional prediction of novel biomolecules using omics data.The goal of this Research Topic is to advance predictive frameworks for uncovering the functions of novel domains, motifs, genes, and proteins using integrative omics data. By leveraging plant omics technologies alongside computational and structural biology, this collection seeks to highlight innovative strategies that transcend traditional annotation pipelines. Special emphasis will be given to studies applying machine learning, network biology, and comparative omics to reveal hidden functional modules. Ultimately, this Research Topic aims to accelerate the transition from discovery to function and deepen insights into the complexity of biological systems.This Research Topic invites original research, reviews, perspectives, and methods papers that focus on the prediction and functional characterization of novel domains, motifs, genes, and proteins using omics data. We encourage studies that integrate multiple omics layers to generate systems-level predictions, particularly those that focus on:• Computational prediction of novel domains and motifs using omics-driven datasets• Multi-omics integration for functional annotation of uncharacterized genes and proteins• Machine learning and deep learning approaches in protein/domain function prediction• Comparative genomics and evolutionary insights into newly discovered motifs or genes• Structural bioinformatics coupled with omics for function inference• Case studies linking predicted functions to stress response, development or disease• Experimental validation of predicted domain or motif functions, including biochemical assays, mutagenesis studies, protein interaction analyses, or overexpression phenotypes Topic editors: Debajit Das, Weimin Li, Zhen Yang Source: https://www.frontiersin.org/research-topics/73895/prediction-of-novel-domains-motifs-genes-and-proteins-through-integrative-omics-approaches