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dbCNS: A New Database for Conserved Noncoding Sequences.

Jun Inoue1,2, Naruya Saitou1,3

  • 1Population Genetics Laboratory, Department of Genomics and Evolutionary Biology, National Institute of Genetics, Mishima, Japan.

Molecular Biology and Evolution
|November 16, 2020
PubMed
Summary
This summary is machine-generated.

We created dbCNS, a new database for conserved noncoding sequences (CNSs) across vertebrate genomes. This resource aids in understanding gene regulation, morphological changes, and genetic diseases linked to CNSs.

Keywords:
cis-regulatory elementsconserved noncoding sequencesdbCNSsingle-nucleotide polymorphismsvertebrates

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Conserved noncoding sequences (CNSs) are crucial for gene expression regulation in eukaryotes.
  • Mutations within CNSs are implicated in morphological alterations and genetic disorders.
  • Previous efforts identified numerous CNSs, particularly in primate genomes.

Purpose of the Study:

  • To introduce dbCNS, a novel database for conserved noncoding sequences.
  • To provide a powerful pipeline for identifying CNSs in vertebrate genomes.
  • To link CNSs to potential roles in morphological changes and genetic diseases.

Main Methods:

  • Development of a precise CNS identification pipeline for multiple vertebrate genomes.
  • Integration of approximately 6.9 million CNSs from diverse vertebrate species.
  • Incorporation of genome sequences from 161 species and links to dbSNP for human CNSs.

Main Results:

  • dbCNS enables efficient extraction of CNSs near specific genes via keyword searches.
  • The database supports phylogenetic analysis and evaluation of CNS evolution (modifications, acquisitions, losses).
  • Fast analysis (30s for 38 gnathostome genomes) and comparison with other CNS identification tools are supported.

Conclusions:

  • dbCNS serves as a valuable resource for studying conserved noncoding sequences and their functions.
  • The database facilitates research connecting genetic variations in CNSs to phenotypic outcomes and diseases.
  • dbCNS enhances the analysis of genome-scale data for conserved noncoding elements.