Applications of multi‐omics analysis in human diseases

Abstract

Multi‐omics usually refers to the crossover application of multiple high‐throughput screening technologies represented by genomics, transcriptomics, single‐cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi‐omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi‐omics. This review outlines the existing technical categories of multi‐omics, cautions for experimental design, focuses on the integrated analysis methods of multi‐omics, especially the approach of machine learning and deep learning in multi‐omics data integration and the corresponding tools, and the application of multi‐omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open‐source analysis tools and databases, and finally, discusses the challenges and future directions of multi‐omics integration and application in precision medicine. With the development of high‐throughput technologies and data integration algorithms, as important directions of multi‐omics for future disease research, single‐cell multi‐omics and spatial multi‐omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi‐omics medical research.

MOCCal: A Multiomic CCS Calibrator for Traveling Wave Ion Mobility Mass Spectrometry

“…The use of multiple omics analysis, such as proteomics, metabolomics, and lipidomics, in combination has grown in popularity given the wealth of information that can be obtained about an organism, disease state, or condition when a systems-level view is taken. − However, with this added sample complexity, challenges with data collection, data analysis, and feature identification also increase. The rapid gas-phase and structural separation of ion mobility mass spectrometry (IM-MS) is a promising approach for complex multiomic sample analysis because each class of biomolecule separates based on mass, charge state, and three-dimensional shape, yielding unique trends in IM-MS space. − The addition of IM measurements provides supplementary information to the standard mass-to-charge ( m / z ) and retention time data that allows for an additional level of feature identification validation.…”

A review of Ribosome profiling and tools used in Ribo-seq data analysis

“…For example, in cancer research, multi-omics analysis gave rise to the pan-cancer molecular classification that sets subtypes based on frequently mutated genes, regardless of their tissue of origin [58] , [59] . Using this approach, we may witness the value of multi-omics analysis into medical research; ranging from cancer to aging, to neurodegenerative diseases, and more [60] .…”

Extracellular Vesicles as Delivery Vehicles for Non-Coding RNAs: Potential Biomarkers for Chronic Liver Diseases

“…In recent years, however, clinical and research attention has growingly focused on molecular biomarkers. Molecular biomarkers can be identified in biological samples such as serum, plasma, and tissues and comprise a wide range of molecules, different in size and origin, which can be classified based on their chemical nature or their omics profile, including genomics, transcriptomics, proteomics, and metabolomics, and which can be extracted with big data analysis using machine learning and deep learning technologies [ 16 , 20 , 21 ].…”