Phylogenetic analysis of ITS data from Endophytic fungi using Massive Parallel Bayesian Tree Inference with Exabayes

Abstract

Ecological studies of fungal communities have been favored thanks to the emergence and improvement of independent culture techniques that use the ITS region as a molecular marker. This has allowed a more accurate identification compared to traditional culture-dependent methods.

Next-generation sequencing techniques have increased the amount of data available for the understanding of endophytic fungal communities. An important part of this process is the phylogenetic inference to decipher how the different taxa are related and interact, however, this may become one of the bioinformatic analysis that demands more time.

In response to this, the bioinformatics along with high-performance computing offer solutions to accelerate and make more efficient the tools available for data processing through the implementation of supercomputers and the parallelization of tools

In this study we carried out the processing of ITS sequences to then use the parallelization of Exabayes, software specialized in the analysis and creation of phylogenetic trees.

Thanks to the use of this technique, it was possible to reduce the running time of Exabayes from more than 400 hours to 6 hours, which demonstrates the benefits of the use of high-performance computing platforms.