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STGBench: sequencing-level spatial DNA–RNA simulation for multimodal and virtual cell-oriented benchmarking of genomic alterations

  • The Second Affiliated Hospital of Xi'an Jiaotong University
  • Xi'an Jiaotong University
  • Xi'an Jiaotong University
  • Nanjing Geneseeq Technology Inc.

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Spatially resolved genomics and transcriptomics are reshaping our understanding of tumor evolution and therapeutic resistance, yet benchmarking spatial copy number variation (CNV), single-nucleotide variant (SNV), and spatial mutation-burden proxy analyses is constrained by the scarcity of datasets with known ground truth. Existing simulators often produce only count matrices, lack matched DNA–RNA outputs, or do not propagate genomic variation to sequencing-level signals, limiting end-to-end benchmarking of multi-omics pipelines, including virtual cell-oriented multimodal benchmarks. Here, we present STGBench, a sequencing-level spatial DNA–RNA simulator that generates paired DNA-seq alignments (BAM files) and matched gene expression matrices on a user-defined 2D tissue grid. STGBench builds tissue masks from geometric templates or image-derived masks, overlays spatial CNV landscapes and SNV/VAF fields in boundary, gradient, and nested modes, and synthesizes DNA and RNA readouts by coupling copy number states to expression under a negative binomial model with spatially correlated technical effects; outputs are directly consumable by downstream tools. Using AneuFinder on simulated DNA data, spatial CNV profiles are recovered with Pearson r up to 0.855. Applying InferCNV to simulated transcriptomes, CNV-driven expression signatures are reproduced (r up to 0.996) and support unsupervised structure consistent with clonal organization; DNA-derived and RNA-inferred CNVs show concordance (r ≈ 0.77). CellSNP recovers diverse spatial SNV patterns from simulated reads, and IGV inspection confirms realistic allelic balance and CNV-associated coverage shifts at nucleotide resolution. Collectively, STGBench provides a controllable benchmark generator for spatial CNV/SNV and mutation-burden analyses with explicit ground truth across paired DNA–RNA modalities. STGBench is open source at https://github.com/Icarus200110/STGBench.

Original languageEnglish
Article numberbbag354
JournalBriefings in Bioinformatics
Volume27
Issue number4
DOIs
StatePublished - Jul 2026

Keywords

  • multimodal benchmarking
  • probabilistic generative model
  • sequencing-level simulation
  • spatial clonal heterogeneity
  • tumor mutation burden

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