Project description:The goal of this study is to identify the effect of inhibition of Aurora-A kinase activity on gene expression and RNA splicing. The perturbation of Aurora-A is well known to affect cell cycle distribution. Therefore, we coupled the inhibition of Aurora-A with cell synchronization procedure in order to avoid the indirect effect of cell cycle perturbation on splicing changes. The mRNA -seq libraries were prepared and subjected to paired-end sequencing on Illumina HiSeq 2500 lanes. Differential gene expression and splicing analysis were carried using the edgeR tool and VAST-tools respectively. The RNA seq analysis identified that pharmacological inhibition of Aurora-A affects alternative splicing of 505 genes while having a marginal effect on gene expression. Overall our work identified Aurora-A as a novel splicing kinase and for the first time, describes a broad role of Aurora-A in regulating alternative splicing.
Project description:Based on our previous O-Search strategy, we have developed a new search method, O-Search-Pattern, to process searching for O-glycopeptide. In comparison of analyzing our human serum dataset generated from optimized energy, our new method can generate more GPSMs glycopeptide sequences than currently state-of-the-art search tools.
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
Project description:Single-cell transcriptomics allows the identification of cellular types, subtypes and states through cell clustering. In this process, similar cells are grouped before determining co-expressed marker genes for phenotype inference. The performance of computational tools is directly associated to their marker identification accuracy, but the lack of an optimal solution challenges a systematic method comparison. Moreover, phenotypes from different studies are challenging to integrate, due to varying resolution, methodology and experimental design. In this work we introduce matchSCore (https://github.com/elimereu/matchSCore), a measure to fastly match cell populations across tools, experiments and technologies. We compared 14 computational methods and evaluated their accuracy in clustering and gene marker identification in simulated data sets. Further, we used matchSCore to project cell type identities across mouse or human cell atlas projects. Despite originated from different technologies, cell populations could be matched across datasets, allowing the assignment of clusters to reference maps and their annotation.