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R-loopDB: a database for R-loop forming sequences (RLFS) and R-loops.

Piroon Jenjaroenpun1, Thidathip Wongsurawat1, Sawannee Sutheeworapong2

  • 1Department of Genome and Gene Expression Data Analysis, Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, 138671, Singapore.

Nucleic Acids Research
|December 1, 2016
PubMed
Summary
This summary is machine-generated.

R-loopDB now includes experimental data and genome-wide predictions of R-loop forming sequences (RLFSs) in humans, animals, and yeast. This enhanced database facilitates comparative genomics and R-loop biology research.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • R-loop forming sequences (RLFSs) are implicated in various biological processes and diseases.
  • Previous R-loopDB versions focused on computationally predicted RLFSs in human genic regions.
  • There was a need for a more comprehensive and updated resource incorporating experimental data and broader genomic coverage.

Purpose of the Study:

  • To update and enhance R-loopDB with new features, including experimental data integration.
  • To expand genome-scale prediction of RLFSs to multiple species beyond humans.
  • To provide advanced tools for searching, analyzing, and comparing RLFSs across genomes.

Main Methods:

  • Utilized an extended quantitative model of RLFSs (QmRLFS) for improved prediction accuracy.
  • Performed genome-scale predictions of RLFSs in human, six animal, and yeast genomes.
  • Integrated Ensembl gene annotation system for comprehensive data referencing and analysis.

Main Results:

  • Significantly increased the number of predicted RLFSs in human genes using the QmRLFS model.
  • Identified RLFSs across multiple eukaryotic genomes, including non-human species.
  • R-loopDB now contains 1,565,795 RLFSs across 121,056 genic/proximal regions in covered organisms.
  • Comprehensive annotation of RLFS-positive genes, including protein-coding, ncRNA, and pseudogenes.

Conclusions:

  • The updated R-loopDB is a valuable, expanded resource for R-loop biology research.
  • It enables genome-scale comparative evolutionary analyses of RLFSs.
  • The new interface and tools facilitate efficient querying and analysis across multiple species.