A computational protocol to analyze metatranscriptomic data capturing fungal–host interactions

Abstract

Plant diseases cause significant losses to agricultural production and pose serious threats to food security worldwide. Understanding the mechanism of host–pathogen interaction is essential for the development of novel diagnostic methods and disease management strategies. RNA sequencing (or RNA-Seq) technology enables a global characterization and quantification of all transcripts of organisms from which RNA can be obtained, and it is particularly useful in identifying pathogen virulence factors involved in disease development and host immunity involved in the development of resistance. This chapter describes a computational protocol to manage, analyze and interpret RNA-Seq data. We have included two transcriptome analysis approaches, one reference-guided and the other de novo assembly-based, and discuss pros and cons for each method. We have also presented visualization methods to generate high quality figures as well as data mining strategies for identifying candidate genes/pathways involved in host immunity and pathogen virulence. In summary, this protocol captures the fungal-plant interactions at the transcriptional level and facilitates rapid gene discovery and expression analysis using next-generation sequencing data of mixed host and pathogen transcripts (i.e., metatranscriptomics). All bioinformatic tools used to build this protocol are publically available, and we strove to make them accessible to researchers with limited computational skills and applicable to metatranscriptomic data analysis in a wide range of plant–fungal interactions.