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Mapping Mammalian 3D Genome Interactions with Micro-C-XL
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Published on: November 3, 2023

MGcV: the microbial genomic context viewer for comparative genome analysis.

Lex Overmars1, Robert Kerkhoven, Roland J Siezen

  • 1Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, Geert Grooteplein Zuid 26-28, Nijmegen, 6525GA, The Netherlands. L.Overmars@cmbi.ru.nl

BMC Genomics
|April 4, 2013
PubMed
Summary

The Microbial Genomic context Viewer (MGcV) is a new web tool that helps researchers analyze bacterial gene function and regulation. It integrates various data types for easier comparative genomics and discovery of regulatory elements.

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Published on: December 22, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Microbiology

Background:

  • Conserved gene context is crucial for comparative genome analysis, aiding in gene function prediction, regulatory sequence discovery, and metabolic network reconstruction.
  • Manual comparative genome context analysis is a laborious but important practice in microbiology.

Purpose of the Study:

  • To introduce the Microbial Genomic context Viewer (MGcV), an interactive web-based application designed to enhance manual comparative genome context analysis for bacteria.
  • To provide a versatile and user-friendly tool for visualizing and analyzing genomic contexts.

Main Methods:

  • MGcV renders visualizations of genomic contexts for selected genes, phylogenetic trees, genomic segments, or regulatory elements.
  • The tool integrates diverse data, including NCBI annotations, Pfam domains, sub-cellular location predictions, and gene sequence characteristics (e.g., GC content).
  • Interactive features enable graphical selection of genes for data retrieval and facilitate the analysis of transcription regulation, including the visualization of experimental data (RNA-seq, microarray).

Main Results:

  • MGcV facilitates gene function annotation by integrating multiple data sources.
  • The application supports the discovery of regulatory elements and aids in reconstructing gene regulatory networks.
  • Ranked comparative context maps allow for the integrated interpretation of predicted regulatory elements and experimental data.

Conclusions:

  • MGcV streamlines manual comparative analysis of genes and regulatory elements through efficient data integration and retrieval.
  • The tool advances the practice of comparative genomics by offering a fast and flexible approach to data analysis.
  • MGcV is accessible online at http://mgcv.cmbi.ru.nl.