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Related Experiment Video

Updated: Feb 26, 2026

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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Visibiome: an efficient microbiome search engine based on a scalable, distributed architecture.

Syafiq Kamarul Azman1, Muhammad Zohaib Anwar2, Andreas Henschel3

  • 1Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, Masdar City, Abu Dhabi, UAE.

BMC Bioinformatics
|July 26, 2017
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Summary

Visibiome is a new search engine for microbiome data. It allows users to compare 16S rRNA profiles against a large database, aiding in understanding microbial community assembly.

Keywords:
Microbial diversityMicrobiomeSearch engine

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Increasing volume of 16S rRNA sequencing data requires efficient search and comparison tools.
  • Contextualizing novel microbiome samples aids in identifying compositional features and understanding microbial community assembly.

Purpose of the Study:

  • To present Visibiome, a novel microbiome search engine.
  • To enable exhaustive, phylogeny-based similarity searches and contextualization of user-provided samples against a comprehensive 16S rRNA profile dataset.

Main Methods:

  • Developed a distributed system combining web framework, task queueing, cloud computing, and a dedicated database.
  • Implemented Nearest Neighbor search algorithms and an optimized Earth Mover Distance (EMD) based weighted UniFrac implementation.
  • Incorporated pairwise (adaptive) rarefaction and optional 16S rRNA copy number correction.

Main Results:

  • Visibiome performs exhaustive, phylogeny-based similarity searches against a diverse database of environmental microbiome samples.
  • The system is scalable and efficient, utilizing distributed computing and advanced search algorithms.
  • Query results include contextualization, compositional comparisons, and rankings of closest matches, visualized through interactive figures.

Conclusions:

  • Visibiome offers a convenient, scalable, and efficient framework for microbiome data searching.
  • It effectively leverages computationally intensive phylogeny-based distance metrics.
  • Provides significant advantages over existing state-of-the-art microbiome analysis tools.