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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Synthetic Biology02:55

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Updated: Oct 9, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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GraphOmics: an interactive platform to explore and integrate multi-omics data.

Joe Wandy1, Rónán Daly2

  • 1Glasgow Polyomics, University of Glasgow, Glasgow, G61 1BD, UK.

BMC Bioinformatics
|December 19, 2021
PubMed
Summary
This summary is machine-generated.

GraphOmics integrates multiple omics data, like transcriptomics and proteomics, for deeper biological insights. This platform aids hypothesis generation by mapping biological entities to pathways, simplifying complex data analysis.

Keywords:
Data explorationOmics integrationPathway analysisReactomeVisualisation

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Multi-omics studies generate complex datasets requiring advanced computational analysis.
  • Integrating multiple omics data (e.g., transcriptomics, proteomics, metabolomics) provides deeper biological understanding than analyzing them separately.
  • Horizontal data integration maps biological entities across omics datasets to common reactions and pathways, but faces challenges in complexity and interpretation.

Purpose of the Study:

  • To present GraphOmics, a user-friendly platform for exploring and integrating multiple omics datasets.
  • To support hypothesis generation by facilitating the analysis of integrated omics data.
  • To overcome the challenges of multi-omics data integration and interpretation.

Main Methods:

  • GraphOmics allows users to upload transcriptomics, proteomics, and metabolomics data.
  • It connects relevant biological entities based on biochemical relationships and maps them to Reactome pathways.
  • Interactive features include data browsing, ranking/filtering by statistical significance and fold change, context-sensitive information panels, heatmaps, and clustering.

Main Results:

  • GraphOmics provides a user-friendly interface for exploring and integrating multi-omics data.
  • The platform successfully mapped entities and pathways, enabling interactive exploration and hypothesis generation.
  • Case studies using Zebrafish regeneration and COVID-19 datasets demonstrated the platform's utility in revealing biological insights.

Conclusions:

  • GraphOmics is an open-source, freely accessible platform for horizontal multi-omics data integration.
  • It facilitates the mapping of entities across different omics datasets to common reactions and pathways.
  • Interactive exploration with GraphOmics enables rapid discovery of biological insights and hypothesis generation.