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An efficient tool dedicated to call somatic variants from whole-exome sequencing data using tumor and its matched normal tissue, plus a user-defined control panel of non-cancer samples. The program requires users to provide a control panel consisting of normal samples processed using similar procedures as the test sample (tumor), and uses this control to estimate site-specific error rates. Then, a binominal test is performed to determinate whether the ratio of altered reads of the tumor variant exceeds the background error rate. Compared with other methods, we showed superior performance of LoLoPicker with significantly improved specificity. The algorithm of LoLoPicker is particularly useful for calling variants from low-quality cancer samples such as formalin-fixed and paraffin-embedded samples.

Software type:
Command line interface
Restrictions to use:
Operating system:
Programming languages:
Computer skills:
pysam (>=0.8.4), pysamstats
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Department of Human Genetics, McGill University, Montreal, Quebec, Canada; McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada

  • (Carrot-Zhang and Majewski, 2016) LoLoPicker: Detecting Low Allelic-Fraction Variants in Low-Quality Cancer Samples from Whole-exome Sequencing Data. bioRxiv.
    DOI: 10.1101/043612
  • (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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