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:Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no comprehensive and direct accuracy evaluations of these tools is published. Here, we evaluate and determine the precision of five commonly used computational NIPT aneuploidy analysis tools, considering diverse sequencing depth (coverage), arbitrary sequencing read placement, and fetal DNA fraction on clinically validated NIPT samples.
Project description:To investigate the potential mechanisms by which m6A contributes to ALS disease degeneration, we utilized a direct RNA sequencing platform, allowing for the identification of the m6A modification sites at single-nucleotide resolution. With the croreference of the ALS risk genes and transcriptomic RNA-seq data in ALS, we could further indntify the m6A-dependent potential genes and pathways in ALS.
Project description:Ambient RNA contamination in single-cell RNA sequencing introduces exogenous transcripts from lysed cells, distorting cell-type annotation and biological interpretation. We performed a systematic benchmark of 7 decontamination tools using datasets with well-defined ground truth. Our evaluation across accuracy, robustness, and subtype sensitivity reveals complementary strengths: CellBender and scCDC excel in contamination estimation accuracy, SoupX is the most robust and sensitive for rare subtypes. For overall decontamination efficacy, scAR best balanced robustness and accuracy at the cell-type level. Our study offers a practical guideline for context-dependent tool selection and highlights key directions for future algorithmic development.