SCSilicon: a tool for synthetic single-cell DNA sequencing data generation

SCSilicon: a tool for synthetic single-cell DNA sequencing data generation

Blog Article

Abstract Background Single-cell DNA sequencing is getting indispensable in the study of cell-specific cancer genomics.The performance of computational tools that tackle single-cell Action Figure genome aberrations may be nevertheless undervalued or overvalued, owing to the insufficient size of benchmarking data.In silicon simulation is a cost-effective approach to generate as many single-cell genomes as possible in a controlled manner to make reliable and valid benchmarking.Results This study proposes a new tool, SCSilicon, which efficiently generates single-cell in silicon DNA reads with minimum manual intervention.SCSilicon automatically creates a set of genomic aberrations, including SNP, SNV, Indel, and CNV.

Besides, SCSilicon yields the ground truth of CNV segmentation breakpoints and subclone cell labels.We have manually inspected a series of synthetic variations.We conducted a sanity check of the start-of-the-art single-cell CNV callers and found SCYN was the most robust one.Conclusions SCSilicon is a user-friendly software package for users to develop and benchmark single-cell Wooden Car CNV callers.Source code of SCSilicon is available at https://github.

com/xikanfeng2/SCSilicon.