Multiomic Approach
Multiomic Approach - Related Videos
07:47
Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
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2023
Here, a protocol is presented in which multiple bioinformatic tools are combined to study the biological functions of TMEM200A in cancer. In addition, we also experimentally validate the bioinformatics predictions.
01:20
Frustration and Conflict: Approach-Approach, Approach-Avoidance
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2025
Frustration occurs when people are obstructed or prevented from achieving a desired goal or fulfilling a perceived need. For example, when someone's input is ignored in a discussion, it can lead to feelings of frustration. Conflict, however, arises from opposing interests, goals, or actions. Conflicts can take various forms based on the nature of these opposing desires or goals. One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable options,...
09:09
Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
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Cited by 6 •
2022
Here, we present a protocol for isolating nuclei from flash-frozen, archived liver tissues for single-nucleus RNA-seq, ATAC-seq, and joint multiomics (RNA-seq and ATAC-seq).
05:21
General Approach to the Physical Exam
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2023
Source: Jaideep S. Talwalkar, MD, Internal Medicine and Pediatrics, Yale School of Medicine, New Haven, CT The examination of the body is fundamental to the practice of medicine. Since the Roman Empire, physicians have described the connection between alterations in function of specific parts of the body and specific disease states and have sought to further scientific understanding to improve bedside diagnosis. However, in this modern age of increasing technology within medical diagnostics, it...
01:10
The Three Approaches
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2025
The Three Approaches to Calculating GDPGross Domestic Product (GDP) represents the total market value of all final goods and services produced within a country during a given period. Economists use three main approaches to calculate GDP: the expenditure approach, the income approach, and the production approach.The expenditure approach is the most widely used. The expenditure approach focuses on the spending on final goods and services. These expenditures come from four sectors: households,...
FAQs
The value of a multiomic approach depends on relating measurements from the same or matched biological samples. Genomic data can be considered alongside gene expression, epigenomic regulation, protein activity, and metabolite patterns. Computational analysis then searches for relationships across these layers, helping distinguish coordinated molecular changes from observations that appear isolated in a single dataset.
Computational integration provides the framework for connecting molecular measurements that describe different stages of cellular function. It can relate genetic variation to gene regulation, protein activity, and metabolic state rather than treating each dataset independently. This combined analysis supports interpretations of how molecular changes work together within pathways and contribute to cellular phenotypes.
A single assay can highlight one molecular layer without showing how it relates to other cellular processes. Integrating several layers may reveal coordinated pathway activity, links between regulation and expression, or connections between protein behavior and metabolic state. These relationships can expose mechanisms that remain hidden when genomic, transcriptomic, epigenomic, proteomic, or metabolomic results are examined separately.
Each layer contributes a different perspective on cellular systems. Genomics describes genetic variation, transcriptomics measures gene expression, and epigenomics addresses regulatory state. Proteomics adds information about proteins, while metabolomics reflects metabolic state. Considering these perspectives together helps researchers connect molecular regulation with functional activity instead of interpreting any layer in isolation.
A study first selects the biological question and determines which molecular layers can address it. Researchers then obtain measurements from the same or matched samples and combine the resulting datasets through computational analysis. The integrated results are examined for relationships among pathways, cell types, cellular states, or molecular mechanisms relevant to the original question.
Researchers can use this strategy when a biological question spans several molecular processes and cannot be adequately addressed by one assay. The approach is relevant to studies of development, disease, and treatment response, where genetic, regulatory, protein, and metabolic changes may interact. It is especially useful for building a more precise systems-level view of cellular behavior.
Integrated analysis can help identify coordinated pathways, distinguish cell types and cellular states, and clarify mechanisms underlying biological changes. It may also support more precise biological models by showing how molecular layers relate to one another. These outcomes move interpretation beyond lists of individual measurements toward explanations of system-level organization and function.