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MoMI-G: modular multi-scale integrated genome graph browser.

Toshiyuki T Yokoyama1, Yoshitaka Sakamoto1, Masahide Seki1

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.

BMC Bioinformatics
|November 7, 2019
PubMed
Summary
This summary is machine-generated.

We developed MoMI-G, a genome graph browser, to visualize complex structural variations in large genomes. This tool aids in the intuitive recognition and rapid inspection of genomic data, improving cancer genome analysis.

Keywords:
Genome browserGenome graphsLong-read sequencingStructural variantVariation graphsVisualization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome graphs offer a more natural representation for structural variants in genomes, especially for complex cancer genomes.
  • Existing visualization tools struggle to display the increasing number of structural variants identified by long-read sequencing.
  • There is a demand for effective visualization methods for large genome graphs, such as those from human cancer studies.

Purpose of the Study:

  • To develop a novel web-based genome graph browser for visualizing large and complex structural variations.
  • To enable intuitive recognition and analysis of structural variations with supporting evidence like read alignments.
  • To provide a tool for efficient manual inspection and filtering of numerous structural variants.

Main Methods:

  • Developed MoMI-G, a modular, multi-scale, integrated genome graph browser.
  • Implemented view modules for multi-scale visualization, from whole genomes to nucleotide-level alignments.
  • Integrated features for displaying read alignments, read depth, annotations, and an Interval Card Deck for rapid inspection.

Main Results:

  • MoMI-G effectively visualizes genome graphs with structural variants and supporting evidence.
  • The browser facilitates intuitive recognition of large, nested, and complex structural variations.
  • Demonstrated utility with examples of structural variations in LC-2/ad and CHM1 cell lines.

Conclusions:

  • MoMI-G enables smoother and more intuitive inspection of complex structural variations in large genomes from long-read data.
  • The tool allows users to efficiently filter false positives by manually reviewing variants with supporting data.
  • Facilitates rapid analysis of hundreds of structural variants, improving the accuracy of genomic studies.