Project description:Bulk and single-cell RNA-seq are powerful tools for transcriptomic analysis, providing insights into many aspects of molecular and cellular phenotypes. Costs constrain the amount of biological insight obtainable within a given budget, and as sequencing prices decline, efficient library protocols have become a decisive factor. In this study, we introduce an approach to systematically optimize the number of usable reads that RNA-seq protocols generate. We applied this ”funnel strategy” to prime-seq, an early-barcoding bulk RNA-seq protocol, by systematically testing critical protocol steps totaling 1080 samples in 49 libraries. This resulted in the optimized prime-seq2 protocol that increases the number of usable reads by 60 % and improves one of the most cost-efficient bulk RNA-seq protocols available. Our study also suggests that monitoring the filtering of usable reads can serve as a valuable quality control for many RNA-seq protocols and sheds light on the complexity of the conditions and interactions that shape RNA-seq library composition and their interpretation.
Project description:Systematic analysis of the cellular and transcriptional landscape of brain organoids grown from multiple cell lines using four different protocols recapitulating dorsal and ventral forebrain, midbrain, and striatum via time-course bulk-RNA sequencing.
Project description:In this study single cell RNA-Seq data was used to train a deconvolution algorithm. The algorithm was validated on paired bulk RNA-Seq profiles.
