Vande Velde, J. | bioRxiv Science

bioRxiv2026

LRSomatic: a highly scalable and robust pipeline for somatic variant calling in long-read sequencing data

MotivationLong-read sequencing is increasingly used in cancer research and clinical genomics due to its ability to resolve complex genomic variation and previously inaccessible regions of the genome. However, dedidated workflows for comprehensive somatic variant analysis from long-read whole-genome data remain scarce, limiting uptake in cancer genomics. ResultsWe present LRSomatic, a Nextflow-based, nf-core-compliant pipeline supporting somatic SNV, indel, structural variant, and copy number calling from PacBio HiFi and ONT data. LRSomatic supports paired tumor-normal and tumor-only designs, as well as integration of epigenetic integration via Fiber-seq. Benchmarked on COLO829 and HG008 reference cell lines, LRSomatic achieves state-of-the-art performance across both platforms and variant types. Applied to a case of clear cell sarcoma, it recovers all identified driver alterations, including the pathognomonic EWSR1::ATF1 fusion, and resolves haplotype-specific chromatin accessibility via Fiber-seq. Availability and ImplementationFreely available at https://github.com/intgenomicslab/lrsomatic, implemented in Nextflow DSL2, supported via Docker and Singularity.

bioinformatics↗

bioRxiv2025

Long-read single-cell genome, transcriptome and open chromatin profiling links genotype to phenotypes.

Current single-cell multiomics methods typically provide limited genomic information, constraining genotype-phenotype studies. To address this gap, we developed SPLONGGET (Single-cell Profiling of LONG-read Genome, Epigenome, and Transcriptome), which integrates 10X Genomics barcoding with Oxford Nanopore sequencing to simultaneously profile genome, chromatin accessibility, and full-length transcriptomes in thousands of single cells. By retaining all tagmentation fragments during library preparation, SPLONGGET delivers whole-genome coverage, supports target enrichment for effective single-cell genotyping, and remains backwards compatible with existing short-read workflows. SPLONGGET enables comprehensive calling of small variants, structural variants, and copy number alterations. Applying SPLONGGET to paediatric B-cell acute lymphoblastic leukaemia revealed clonal dynamics and the phenotypic effects of somatic variants. Notably, we evidence parallel evolution of immune escape variants with four distinct splice site mutations and loss of heterozygosity in the CAR T-cell therapy CD19 target. In conclusion, SPLONGGET enables integrated high-throughput analysis of genetic variation and molecular phenotypes using off-the-shelf kits, offering a timely and powerful tool to study genetically heterogeneous samples, such as tumours but also ageing normal tissues.

genomics↗