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Related Concept Videos

Genomics02:02

Genomics

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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Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Proteomics01:33

Proteomics

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 proteomics...

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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Omics and literature mining.

Vinod Kumar1

  • 1Computational Biology, Quantitative Sciences, GlaxoSmithKline, King of Prussia, PA, USA. Vinod.D.Kumar@gsk.com

Methods in Molecular Biology (Clifton, N.J.)
|March 4, 2011
PubMed
Summary
This summary is machine-generated.

Interpreting large omics datasets is challenging. This study introduces a method to identify overrepresented biological concepts (MeSH terms) in gene sets, aiding understanding of underlying biological phenomena.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput omics platforms generate vast datasets, making interpretation difficult.
  • Translating gene or protein lists into biological insights remains a significant challenge.

Purpose of the Study:

  • To develop an approach for identifying biological concepts within large gene sets.
  • To facilitate the interpretation of complex omics data by linking gene lists to established biological knowledge.

Main Methods:

  • Utilizing Medical Subject Headings (MeSH terms) extracted from MEDLINE.
  • Identifying MeSH terms significantly overrepresented in a specific gene set compared to a background gene collection.
  • Applying statistical methods to determine overrepresentation significance.

Main Results:

  • The developed method successfully identifies relevant biological concepts from omics data.
  • Demonstrates the ability to pinpoint shared biological themes across different dimensions, including structure, function, and disease.

Conclusions:

  • This approach offers a powerful tool for interpreting large-scale omics data.
  • It enhances biological understanding by connecting gene expression patterns to a broad spectrum of biological and clinical information.