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A computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ~150X coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. SomVarIUS can be useful in a variety of clinical or research settings, where matched normal samples are not yet routinely collected (e.g. precision medicine initiatives) or for archival samples (e.g. FFPE and fresh frozen tumor samples stored in the tissue banks).

Software type:
Command line interface
Restrictions to use:
Input data:
SomVarIUS accepts sorted alignment files (.bam) as input.
Output data:
SomVarIUS outputs predicted somatic mutations in the variant call format (.vcf), thereby enabling its easy integration into any standard genome analysis pipeline. It also generates an optional output providing information about the status of known cancer/disease-associated mutations in the sample.
Operating system:
Programming languages:
MIT License
Computer skills:
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  • Subhajyoti De <subhajyoti.de at ucdenver.edu>


Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA; Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA; Computational Biosciences Training Program, University of Colorado School of Medicine, Aurora, CO, USA; University of Colorado Cancer Center, Aurora, CO, USA; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA

Funding source(s)

Boettcher Foundation Webb-Waring grant

  • (Smith et al., 2015) SomVarIUS: Somatic variant identification from unpaired tissue samples. Bioinformatics.
    PMID: 26589277
  • (Spencer et al., 2014) Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data. The Journal of molecular diagnostics.
    PMID: 24211364
  • (Stead et al., 2013) Accurately identifying low-allelic fraction variants in single samples with next-generation sequencing: applications in tumor subclone resolution. Human mutation.
    PMID: 23766071
  • (Roberts et al., 2013) A comparative analysis of algorithms for somatic SNV detection in cancer. Bioinformatics.
    PMID: 23842810
  • (Wang et al., 2013) Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome medicine.
    PMID: 24112718

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