<p>Microbial co-occurrence network inference is often hindered by low accuracy and tool dependency. We introduce <i>microbetag</i>, a comprehensive software ecosystem designed to annotate microbial networks. Nodes, representing taxa, are enriched with phenotypic traits, while edges are enhanced with metabolic complementarities, highlighting potential cross-feeding relationships. <i>microbetag</i>’s online version relies on <i>microbetagDB</i>, a database of 34,608 annotated representative genomes. <i>microbetag</i> can be applied to custom (metagenome-assembled) genomes via its stand-alone version. <i>MGG</i>, a Cytoscape app designed to support <i>microbetag</i>, offers a streamlined, user-friendly interface for network retrieval and visualization. <i>microbetag</i> effectively identified known metabolic interactions and serves as a robust hypothesis-generating tool.</p>

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microbetag: simplifying microbial network interpretation through annotation, enrichment tests, and metabolic complementarity analysis

  • Haris Zafeiropoulos,
  • Ermis Ioannis Michail Delopoulos,
  • Andi Erega,
  • Aline Schneider,
  • Annelies Geirnaert,
  • John Morris,
  • Karoline Faust

摘要

Microbial co-occurrence network inference is often hindered by low accuracy and tool dependency. We introduce microbetag, a comprehensive software ecosystem designed to annotate microbial networks. Nodes, representing taxa, are enriched with phenotypic traits, while edges are enhanced with metabolic complementarities, highlighting potential cross-feeding relationships. microbetag’s online version relies on microbetagDB, a database of 34,608 annotated representative genomes. microbetag can be applied to custom (metagenome-assembled) genomes via its stand-alone version. MGG, a Cytoscape app designed to support microbetag, offers a streamlined, user-friendly interface for network retrieval and visualization. microbetag effectively identified known metabolic interactions and serves as a robust hypothesis-generating tool.