Project description:To reannotate the genome of Zymoseptoria tritici IPO323, RNA-Seq and Iso-Seq runs were performed on different growth media to provide new source of evidence for gene model predictors. New gene models were predicted and combined with existing annotation releases. Finally, selection of best gene models was done by congruence with evidence data like transcript assembled from RNA-Seq, Iso-Seq cDNA and fungal proteins from databases.
Project description:The skin commensal yeast Malassezia is associated with several skin disorders. To establish a reference resource, we sought to determine the complete genome sequence of Malassezia sympodialis and identify its protein-coding genes. A novel genome annotation workflow combining RNA sequencing, proteomics, and manual curation was developed to determine gene structures with high accuracy.
Project description:Gene expression estimates detected by RNA-sequencing technology vary with the updates of reference genome and gene annotation, which might confound existing expression-based prognostic signatures, making them inapplicable to clinical practice. In this study, we proposed a method to decrease these effects and developed a qualitative signature for stage I lung adenocarcinoma, whose classification was based on within-sample relative expression orderings (REOs) of gene pairs. The signature was validated in 471 stage I samples derived from public RNA-sequencing and microarray data (both log-rank p < 0.001). Notably, our signature could effectively predict prognosis for 30 stage I patients with severely degraded FFPE tissues (log-rank p = 0.0177). More important, the risk classification was stable in the latest annotation. In summary, our signature would be a promising signature for clinical individualized application because of its excellent prognostic performance and classification robustness.