Bioinformatics approaches in molecular plant pathology: from genomic data to disease diagnosis

Abstract

Recent advancements in molecular plant pathology have generated a huge volume of biological data, necessitating the integration of bioinformatics for effective data management and analysis. Bioinformatics, at the intersection of biology and computer science, is essential for deciphering genomic data and understanding plant molecular processes. It facilitates identifying and isolating pathogenicity factors, 
virulence-related genes, and disease resistance genes (R genes) in crop plants. The specialized tools and genomic databases are instrumental in locating the nucleotide leucine-rich (NLR) domains which are pivotal for plant pathogen resistance. It predicts viral suppressors of RNA silencing, often utilizing machine-learning approaches, there by shedding light on viral pathogenesis. Bioinformatic tools used in molecular docking and dynamics simulation techniques help deepen our insights into plant-pathogen interactions at the molecular level. Furthermore, the burgeoning field of microbiome profiling, focusing on bacterial-fungal interactions in plant ecosystems, is gaining momentum by using bioinformatics tools. It enables metabarcoding, metagenomic, metatranscriptomic, and metaproteomic studies, unveiling intricate interactions with implications for plant health. Beyond these, bioinformatics supports genome editing for enhanced plant resistance and predictive modeling of agronomic traits. These multifaceted applications underscore bioinformatics’ pivotal role in understanding plant-pathogen dynamics. This review summarizes different bioinformatic strategies, tools used, and their applications, emphasizing molecular mechanisms in plant pathology and also applications in microbiome analysis for sustainable agriculture.