Background With the rise of single-cell RNA sequencing new bioinformatic tools have been developed to handle specific demands, such as quantifying unique molecular identifiers and correcting cell barcodes. Here, we benchmarked several datasets with the most common alignment tools for single-cell RNA sequencing data. We evaluated differences in the whitelisting, gene quantification, overall performance, and potential variations in clustering or detection of differentially expressed genes. We compared the tools Cell Ranger version 6, STARsolo, Kallisto, Alevin, and Alevin-fry on 3 published datasets for human and mouse, sequenced with different versions of the 10X sequencing protocol. Results Striking differences were observed in the overall runtime of the mappers. Besides that, Kallisto and Alevin showed variances in the number of valid cells and detected genes per cell. Kallisto reported the highest number of cells; however, we observed an overrepresentation of cells with low gene content and unknown cell type. Conversely, Alevin rarely reported such low-content cells. Further variations were detected in the set of expressed genes. While STARsolo, Cell Ranger 6, Alevin-fry, and Alevin produced similar gene sets, Kallisto detected additional genes from the Vmn and Olfr gene family, which are likely mapping artefacts. We also observed differences in the mitochondrial content of the resulting cells when comparing a prefiltered annotation set to the full annotation set that includes pseudogenes and other biotypes. Conclusion Overall, this study provides a detailed comparison of common single-cell RNA sequencing mappers and shows their specific properties on 10X Genomics data.

Epigenetic mechanisms represent a possible mechanism for achieving a rapid response of long‐lived trees to changing environmental conditions. However, our knowledge on plant epigenetics is largely limited to a few model species. With increasing availability of genomic resources for many tree species, it is now possible to adopt approaches from model species that permit to obtain single‐base pair resolution data on methylation at a reasonable cost. Here, we used targeted bisulfite sequencing (TBS) to study methylation patterns in the conifer species Norway spruce (Picea abies). To circumvent the challenge of disentangling epigenetic and genetic differences, we focused on four clone pairs, where clone members were growing in different climatic conditions for 24 years. We targeted >26.000 genes using TBS and determined the performance and reproducibility of this approach. We characterized gene body methylation and compared methylation patterns between environments. We found highly comparable capture efficiency and coverage across libraries. Methylation levels were relatively constant across gene bodies, with 21.3 ± 0.3%, 11.0 ± 0.4% and 1.3 ± 0.2% in the CG, CHG, and CHH context, respectively. The variance in methylation profiles did not reveal consistent changes between environments, yet we could identify 334 differentially methylated positions (DMPs) between environments. This supports that changes in methylation patterns are a possible pathway for a plant to respond to environmental change. After this successful application of TBS in Norway spruce, we are confident that this approach can contribute to broaden our knowledge of methylation patterns in natural tree populations.