Authors: J. Méndez, A. Monleon-Getino, A. Paytuví i Gallart, W. Sanseverino

Institutions:

  • .Section of Statistics, Departament of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain
  • Sequentia Biotech SL, Carrer Comte d’Urgell 240, Barcelona, Spain
  • Group of Research in Bioestatistics and Bioinformatics (GRBIO), Barcelona, Spain
  • BIOST Group of Research in Clinical Bioestatistics, Bioinformatics and Data Science, Barcelona, Spain

Publication: bioRxiv

Date: June, 2020

Link: https://www.biorxiv.org/content/10.1101/2020.06.10.140103v1.full

Abstract:

High-throughput experimental techniques, such as metagenomics or metatranscriptomics, produce large amounts of data, which interpretation and conversion into understandable knowledge can be challenging and out of reach. We present GANGO, a new algorithm based on the ecological concept of consortium (groups biologically connected) and by using clustering network analysis, gene ontologies and powerful hypothesis test allows the identification and interpretation of complex ecological networks, allowing the identification of the relationship between taxa/genes, the number of groups, their relations and their functionalities using the annotated genes of an organism in a database (e.g. UniProt or Ensembl). Three examples of the use of GANGO are shown: a simulated mixture of fungi and bacteria, alterations in soil fungi communities after a diesel-oil spill and genomic changes in Saccharomyces cerevisae due to abiotic stress.