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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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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.
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MOPED 2.5--an integrated multi-omics resource: multi-omics profiling expression database now includes transcriptomics

Elizabeth Montague1, Larissa Stanberry, Roger Higdon

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Omics : a Journal of Integrative Biology
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Summary
This summary is machine-generated.

The updated Multi-Omics Profiling Expression Database (MOPED) 2.5 integrates gene and protein expression data with extensive metadata. This resource enhances multi-omics data exploration and reproducibility for life sciences research.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Proteomics
  • Data Science

Background:

  • Multi-omics data discovery relies on high-throughput technologies and data sharing.
  • Current omics data are fragmented across repositories with inconsistent formats and limited metadata.
  • An integrated multi-omics data resource is needed to address these challenges.

Purpose of the Study:

  • To introduce MOPED version 2.5, an enhanced, freely accessible multi-omics expression database.
  • To integrate gene and protein expression data with comprehensive metadata for improved data exploration.
  • To facilitate reproducibility and interdisciplinary knowledge sharing in life sciences.

Main Methods:

  • Integrated gene relative expression data with existing protein absolute and relative expression data.
  • Incorporated extensive metadata using the Data-Enabled Life Sciences Alliance (DELSA Global) checklist.
  • Expanded the database with over 500,000 proteomics and 4 million transcriptomics records.

Main Results:

  • MOPED 2.5 now offers a unified platform for gene and protein expression data from over 250 experiments.
  • The database features an intuitive user interface with dedicated tabs for querying omics data types.
  • New data visualization tools and direct protein-gene connections are available for enhanced exploration.

Conclusions:

  • MOPED 2.5 serves as a pivotal public database and interdisciplinary knowledge resource.
  • The integrated data and metadata facilitate multi-omics data exploration and increase reproducibility.
  • Researchers are encouraged to contribute their omics data for consistent processing and integration.