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Related Concept Videos

Proteomics01:33

Proteomics

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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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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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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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Using clusterProfiler to characterize multiomics data.

Shuangbin Xu1,2, Erqiang Hu1, Yantong Cai1,3

  • 1Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.

Nature Protocols
|July 17, 2024
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Summary
This summary is machine-generated.

clusterProfiler software enables multidimensional enrichment analysis for multiomics data, aiding in disease mechanism discovery. It integrates various biological databases and offers advanced visualization for gene set variations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiomics data analysis requires sophisticated tools for gene set enrichment.
  • Understanding biological processes and disease pathways is crucial for identifying therapeutic targets.

Purpose of the Study:

  • To present clusterProfiler as a versatile software for multidimensional enrichment analysis.
  • To showcase innovative applications of clusterProfiler in integrating diverse omics data and biological contexts.

Main Methods:

  • Utilizing clusterProfiler for over-representation and gene set enrichment analyses.
  • Integrating Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases.
  • Applying clusterProfiler to metabolomics, metagenomics, transcription factor identification, and single-cell RNA sequencing data.

Main Results:

  • clusterProfiler efficiently performs enrichment analyses using multiple biological databases.
  • The software supports various visualization options for enhanced interpretability of results.
  • Computational steps are completed rapidly, typically within ~2 minutes.

Conclusions:

  • clusterProfiler is a powerful and efficient tool for multiomics data analysis and biological interpretation.
  • Its flexibility in handling diverse datasets and biological knowledge makes it invaluable for research.
  • The software facilitates the elucidation of complex biological mechanisms and potential therapeutic strategies.