Advances in next-generation sequencing methodologies have facilitated the assembly of an ever-increasing number of genomes. Gene annotations are typically conducted via specialized software, but the most accurate results require additional manual curation that incorporates insights derived from functional and bioinformatic analyses (e.g., transcriptomics, proteomics, and phylogenetics). In this study, we improved the annotation of the Leishmania donovani (strain HU3) genome using publicly available data from the deep sequencing of ribosome-protected mRNA fragments (Ribo-Seq). As a result of this analysis, we uncovered 70 previously non-annotated protein-coding genes and improved the annotation of around 600 genes. Additionally, we present evidence for small upstream open reading frames (uORFs) in a significant number of transcripts, indicating their potential role in the translational regulation of gene expression. The bioinformatics pipelines developed for these analyses can be used to improve the genome annotations of other organisms for which Ribo-Seq data are available. The improvements provided by these studies will bring us closer to the ultimate goal of a complete and accurately annotated L. donovani genome and will enhance future transcriptomics, proteomics, and genetics studies.
Keywords: Leishmania; Ribo-seq; genome; ribosome profiling; transcriptome; uORFs.