Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays.

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

An Updated Taxonomy and a Graphical Summary Tool for Optimal Classification and Comprehension of Omics Research.

The volume of publications and the type of research approaches used in omics system sciences are vast and continue to expand rapidly. This increased complexity and heterogeneity of omics data are challenging data extraction, sensemaking, analyses, knowledge translation, and interpretation. An extended and dynamic taxonomy for the classification and summary of omics studies are essential. We present an updated taxonomy for classification of omics research studies based on four criteria: (1) type and number of genomic loci in a research study, (2) number of species and biological samples, (3) the type of omics technology (e.g., genomics, transcriptomics, and proteomics) and omics technology application type (e.g., pharmacogenomics and nutrigenomics), and (4) phenotypes. In addition, we present a graphical summary approach that enables the researchers to define the main characteristics of their study in a single figure, and offers the readers to rapidly grasp the published study and omics data. We searched the PubMed and the Web of Science from 09/2002 to 02/2018, including research and review articles, and identified 90 scientific publications. We propose a call toward omics studies' standardization for reporting in scientific literature. We anticipate the proposed classification scheme will usefully contribute to improved classification of published reports in genomics and other omics fields, and help data extraction from publications for future multiomics data integration.

Toward a Taxonomy for Multi-Omics Science? Terminology Development for Whole Genome Study Approaches by Omics Technology and Hierarchy.

Omics is a form of high-throughput systems science. However, taxonomies for omics studies are limited, inviting us to rethink new ways in which we classify, prioritize, and rank various omics systems science studies. In this overarching context, the genome-wide study approaches have proliferated in number and popularity over the past decade. However, their hierarchy is not well organized and the development of attendant terminology is not controlled. In the present study, we searched the literature in PubMed and the Web of Science databases published from March 1999 to September 2016 using the keywords, including genome-wide, association, whole genome, transcriptome-wide, metabolome, epigenome, and phenome. We identified the whole genome study approaches and sorted them according to the omics technology types (genomics, proteomics, and so on) and hierarchy. Thirty-four studies from over 90 publications were sorted into 10 omics groups: DNA level, transcriptomics, proteomics, interactomics, metabolomics, epigenomics, miRNomics/ncRNomics, phenomics, environmental omics, and pharmacogenomics. We suggest here modifications of terminology for study approaches, which share the same acronyms such as EWAS for epigenome-wide association and environment-wide association studies, and MWAS for methylome-wide association and metabolome-wide association studies. Taken together, our study presented here provides the first systematic review and analyses of whole genome approaches and presents a baseline for further controlled terminology development, with a view to a new taxonomy for omics and multi-omics studies in the future. Finally, we call for greater dialogue and collaboration across diverse omics knowledge domains and applications, for example, across plants, animals, clinical medicine, and ecology.