Coffee leaf rust caused by the fungus Hemileia vastatrix is one of the most important leaf diseases of coffee plantations worldwide. Current knowledge of the H . vastatrix genome is limited and only a small fraction of the total fungal secretome has been identified. In order to obtain a more comprehensive understanding of its secretome, we aimed to sequence and assemble the entire H . vastatrix genome using two next-generation sequencing platforms and a hybrid assembly strategy. This resulted in a 547 Mb genome of H . vastatrix race XXXIII (Hv33), with 13,364 predicted genes that encode 13,034 putative proteins with transcriptomic support. Based on this proteome, 615 proteins contain putative secretion peptides, and lack transmembrane domains. From this putative secretome, 111 proteins were identified as candidate effectors (EHv33) unique to H . vastatrix , and a subset consisting of 17 EHv33 genes was selected for a temporal gene expression analysis during infection. Five genes were significantly induced early during an incompatible interaction, indicating their potential role as pre-haustorial effectors possibly recognized by the resistant coffee genotype. Another nine genes were significantly induced after haustorium formation in the compatible interaction. Overall, we suggest that this fungus is able to selectively mount its survival strategy with effectors that depend on the host genotype involved in the infection process.

The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post transcriptional modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. The source code and user manuals are available at https://modupeore.github.io/VAP/.

This work was supported by: R01HD060769 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD), 2P20GM103446 and P20GM103464 from the National Institute of General Medical Sciences (NIGMS), and Nemours Biomedical Research. The authors have no competing interests to declare.