International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 90-99 http://medscipublisher.com/index.php/ijmms 91 1 Sources and Types of Multi-omics Data Multi-omics data encompass biological data from various levels, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics. These data are typically obtained through high-throughput sequencing technologies and other high-throughput experimental methods. 1.1 Acquisition and application of genomics data Genomics data are acquired through high-throughput sequencing technologies (Manni et al., 2021). After obtaining DNA samples, sequencing instruments are used to sequence them. Genome sequencing can produce a complete genome sequence of an individual, helping researchers understand the structure and function of the genome. Genomics data have extensive applications in personalized therapy. Genomic data can help determine whether an individual carries genes associated with certain genetic disease risks, enabling early prevention and intervention. Genomic data can also be used for drug efficacy prediction. By analyzing an individual's genome sequence, it is possible to determine the efficacy and side effect risks of specific drugs for that individual, providing a basis for personalized drug therapy. Additionally, in cancer treatment, genomic data help identify driver gene mutations in tumors, providing guidance for selecting appropriate targeted therapy methods. 1.2 Acquisition and application of transcriptomics data Transcriptomics data are a type of high-throughput experimental data that study gene expression levels within the genome. The acquisition and application of transcriptomics data provide researchers with important tools for understanding gene expression regulation and biological processes, and they are significant for biomedical research, disease diagnosis, and personalized therapy. The acquisition and application of transcriptomics data generally involve the following steps. First, RNA is extracted from the tissues or cells to be studied (Liu et al., 2022). The extracted RNA can be total RNA, or specific types of RNA can be selected as needed, such as mRNA or non-coding RNA. Next, high-throughput sequencing technology (e.g., RNA-seq) is used to sequence the extracted RNA samples. RNA-seq can generate a large number of short-read sequences, providing information on gene expression levels and alternative splicing. For the sequenced data, data processing and analysis are required. This includes quality control, removing low-quality sequences, adapter trimming, and bioinformatics analyses such as aligning to the reference genome or transcriptome, quantifying gene expression, and differential expression analysis. Transcriptomics data have a wide range of applications. By analyzing gene expression profiles, it is possible to understand the patterns and changes in gene expression in different tissues, developmental stages, and conditions, thereby revealing gene regulatory networks and potential biological functions. By comparing transcriptomics data from different samples, genes with significant expression differences in various biological processes, disease progression, and drug responses can be identified, leading to the discovery of potential biomarkers and drug targets. Furthermore, transcriptomics data can reveal alternative splicing events, and by integrating multiple transcriptome datasets, reconstruct the patterns of gene expression profiles and model gene regulatory networks. These analyses help in understanding gene regulatory mechanisms and identifying disease-associated splicing variants. 1.3 Acquisition and application of proteomics data Proteomics is a discipline that studies the composition, structure, and function of proteins. The acquisition and application of proteomics data provide researchers with important tools to understand protein composition, structure, and function, as well as to reveal biological processes and disease mechanisms within organisms. When conducting proteomics research, proteins must first be extracted from the biological samples under study. This typically involves steps such as cell lysis, protein extraction, purification, and concentration. Next, proteins can be separated using techniques such as two-dimensional gel electrophoresis or liquid chromatography. Two-dimensional gel electrophoresis separates proteins based on their molecular weight and isoelectric point,
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==