RT Journal
A1 Dondi, Arthur
A1 Borgsmüller, Nico
A1 Ferreira, Pedro F.
A1 Haas, Brian J.
A1 Jacob, Francis
A1 Heinzelmann-Schwarz, Viola
A1 Tumor Profiler Consortium
A1 Beerenwinkel, Niko
T1 De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data
JF Genome Research 
JO Genome Research 
YR 2025 
FD April 01 
VO 35 
IS 4 
SP 900 
OP 913 
DO 10.1101/gr.279281.124 
UL http://genome.cshlp.org/content/35/4/900.abstract 
AB In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variant calling, LongSom reannotates marker gene-based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.