AUTHOR=Guo Li , Allen Kelly S. , Deiulio Greg , Zhang Yong , Madeiras Angela M. , Wick Robert L. , Ma Li-Jun 

TITLE=A De Novo-Assembly Based Data Analysis Pipeline for Plant Obligate Parasite Metatranscriptomic Studies

JOURNAL=Frontiers in Plant Science

VOLUME=7

YEAR=2016

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2016.00925

DOI=10.3389/fpls.2016.00925

ISSN=1664-462X

ABSTRACT=<p>Current and emerging plant diseases caused by obligate parasitic microbes such as rusts, downy mildews, and powdery mildews threaten worldwide crop production and food safety. These obligate parasites are typically unculturable in the laboratory, posing technical challenges to characterize them at the genetic and genomic level. Here we have developed a data analysis pipeline integrating several bioinformatic software programs. This pipeline facilitates rapid gene discovery and expression analysis of a plant host and its obligate parasite simultaneously by next generation sequencing of mixed host and pathogen RNA (i.e., metatranscriptomics). We applied this pipeline to metatranscriptomic sequencing data of sweet basil (<italic>Ocimum basilicum</italic>) and its obligate downy mildew parasite <italic>Peronospora belbahrii</italic>, both lacking a sequenced genome. Even with a single data point, we were able to identify both candidate host defense genes and pathogen virulence genes that are highly expressed during infection. This demonstrates the power of this pipeline for identifying genes important in host–pathogen interactions without prior genomic information for either the plant host or the obligate biotrophic pathogen. The simplicity of this pipeline makes it accessible to researchers with limited computational skills and applicable to metatranscriptomic data analysis in a wide range of plant-obligate-parasite systems.</p>