GENPPI: standalone software for creating protein interaction networks from genomes

Abstract

BackGround Bacterial genomes are being deposited into online databases at an increasing rate. Genome annotation represents one of the first efforts to understand organisms and their diseases. Some evolutionary relationships capable of being annotated only from genomes are conserved gene neighbourhoods (CNs), phylogenetic profiles (PPs), and gene fusions. At present, there is no standalone software that enables networks of interactions among proteins to be created using these three evolutionary characteristics with efficient and effective results. Results We developed GENPPI software for the ab initio prediction of interaction networks using predicted proteins from a genome. In our case study, we employed 50 genomes of the genus Corynebacterium. Based on the PP relationship, GENPPI differentiated genomes between the ovis and equi biovars of the species Corynebacterium pseudotuberculosis and created groups among the other species analysed. If we inspected only the CN relationship, we could not entirely separate biovars, only species. Our software GENPPI was determined to be efficient because, for example, it creates interaction networks from the central genomes of 50 species/lineages with an average size of 2200 genes in less than 40 min on a conventional computer. Moreover, the interaction networks that our software creates reflect correct evolutionary relationships between species, which we confirmed with average nucleotide identity analyses. Additionally, this software enables the user to define how he or she intends to explore the PP and CN characteristics through various parameters, enabling the creation of customized interaction networks. For instance, users can set parameters regarding the genus, metagenome, or pangenome. In addition to the parameterization of GENPPI, it is also the user’s choice regarding which set of genomes they are going to study. Conclusions GENPPI can help fill the gap concerning the considerable number of novel genomes assembled monthly and our ability to process interaction networks considering the noncore genes for all completed genome versions. With GENPPI, a user dictates how many and how evolutionarily correlated the genomes answer a scientific query.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…As they mention, this type of method is not intended for the discovery of direct interactions. Recently, a new tool: GENPPI ( Anjos et al, 2021 ), allowing the generation of PPI networks by taking into account evolutionary relationships that can only be annotated from genomes, namely, conserved gene neighborhoods (CN), phylogenetic profiles (PPs), and gene fusions, has been introduced, showing that these three methods mainly allow the annotation of missing data and thus the understanding of a limited number of interactions. At present, the tool is being tested in their laboratory.…”

“…As they mention, this type of method is not intended for the discovery of direct interactions. Recently, a new tool: GENPPI (Anjos et al, 2021), allowing the generation of PPI networks by String (Szklarczyk et al, 2019), BioGRID (Oughtred et al, 2021), Hippie (Alanis-Lobato, Andrade-Navarro and Schaefer, 2017), IntAct (Hermjakob et al, 2004a), HPRD (Keshava Keshava Prasad et al, 2009) Machine learning algorithm Supervised learning: support vector machine, artificial neural networks, naïve Bayes learning, decision trees (Sarkar and Saha, 2019;Chakraborty et al, 2021) Handling multi-dimensional and multi-variety data, high efficiency Data acquisition (massive datasets), High error susceptibility, requires significant IT resources…”

Unveiling the Brazilian Kefir Microbiome: Discovery of a novel Lactobacillus kefiranofaciens (LkefirU) genome and in silico prospection of bioactive peptides with potential anti-Alzheimer properties

“…PANNOTATOR assigns color tags based on three similarity levels of alignment size and protein identity: green for certain annotations (≥95%), yellow for high similarity (<95% and ≥70%), and red for annotations with lower con dence (<70%). To assess pangenome conservation involving over two hundred genomes simultaneously, we used the GENPPI software [13]. Although initially developed for predicting protein interactions, GENPPI also generates core and accessory pangenomes for subsequent interaction analyses.…”

“…Additionally, we investigated annotated proteins for conserved interactions within the genus. The GENPPI parameters were set based on examples from the software tutorial, employing dynamic neighborhood expansion and phylogenetic pro le conservations [13].…”