Abstract
The accurate annotation and functional prediction of proteins are critical for understanding biological systems, especially in non-model organisms where genomic and proteomic resources are scarce. Traditional annotation methods often struggle with the complexity and incompleteness of data from these organisms. This paper proposes a novel framework that integrates diverse multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) to enhance de novo protein annotation and functional prediction. By leveraging advanced computational approaches, including machine learning and network analysis, our method aims to overcome the limitations of single-omic data and improve the accuracy and comprehensiveness of protein function assignment. We discuss the challenges associated with de novo assembly and annotation in non-model organisms [1, 4, 5], the importance of integrating multiple data sources for robust predictions [11, 12, 14, 17], and the potential of multi-omics integration for uncovering novel biological insights. This approach is expected to significantly advance our understanding of the molecular mechanisms underlying biological processes in understudied species, paving the way for new discoveries in fields ranging from evolutionary biology to applied biotechnology. The integration strategy will focus on identifying conserved functional modules and pathways across different omics layers, thereby providing a more holistic view of cellular functions.
Keywords
multi-omics integration, de novo annotation, protein function prediction, non-model organisms, transcriptomics, proteomics, metabolomics, computational biology