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  • #1

    What's the best tools to detect somatic mutations?

    Hi guys, I am quite new in cancer genome. Can anyone give me some suggestions on tools that can detect somatic mutations from a patient's tissue sequencing data? Thanks!
    Tags: None
  • #2
    I'm partial to bambino:

    https://cgwb.nci.nih.gov/goldenPath/bamview/documentation/index.html


    hooking up annovar on the back end provides some good annotation.

    Comment

    • #3
      The Genome Institute at Washington University in St. Louis makes software available at
      Not Found
      http://tvap.genome.wustl.edu/tools/


      You can obtain support through biostar using the "music" tag.

      Detection Tools

      Bassovac
      Improved Bayesian inversion somatic caller

      BreakDancer
      SV caller using paired-end sequence data

      CMDS
      Recurrent CNV caller using population data

      CNVHMM
      An HMM-based copy number caller

      CopyCat
      A read depth based copy number caller

      CREST
      SV caller using soft-clipped reads

      PASSion
      Paired-end RNA-Seq splice site detection

      Pindel
      Indel caller using pattern growth

      SomaticSniper
      Bayesian somatic SNV caller
      (Useful for situations with normal contamination (ie adjacent normal or tissue infiltrating leukemias)

      SquareDancer
      Split reads for SV discovery

      TIGRA_SV
      De novo assembly of SV breakpoints

      VarScan
      Germline and somatic SNV and CNV caller
      Last edited by m_two; 07-26-2013, 09:44 AM.

      Comment

      • #4
        Comparing somatic mutation-callers: beyond Venn diagrams
        Su Yeon Kim and Terence P Speed
        Comparing somatic mutation-callers: beyond Venn diagrams - BMC Bioinformatics
        http://www.biomedcentral.com/1471-2105/14/189
        Background Somatic mutation-calling based on DNA from matched tumor-normal patient samples is one of the key tasks carried by many cancer genome projects. One such large-scale project is The Cancer Genome Atlas (TCGA), which is now routinely compiling catalogs of somatic mutations from hundreds of paired tumor-normal DNA exome-sequence data. Nonetheless, mutation calling is still very challenging. TCGA benchmark studies revealed that even relatively recent mutation callers from major centers showed substantial discrepancies. Evaluation of the mutation callers or understanding the sources of discrepancies is not straightforward, since for most tumor studies, validation data based on independent whole-exome DNA sequencing is not available, only partial validation data for a selected (ascertained) subset of sites. Results To provide guidelines to comparing outputs from multiple callers, we have analyzed two sets of mutation-calling data from the TCGA benchmark studies and their partial validation data. Various aspects of the mutation-calling outputs were explored to characterize the discrepancies in detail. To assess the performances of multiple callers, we introduce four approaches utilizing the external sequence data to varying degrees, ranging from having independent DNA-seq pairs, RNA-seq for tumor samples only, the original exome-seq pairs only, or none of those. Conclusions Our analyses provide guidelines to visualizing and understanding the discrepancies among the outputs from multiple callers. Furthermore, applying the four evaluation approaches to the whole exome data, we illustrate the challenges and highlight the various circumstances that require extra caution in assessing the performances of multiple callers.

        Comment

        • #5
          Related:

          Global optimization of somatic variant identification in cancer genomes with a global community challenge
          http://www.nature.com/ng/journal/v46/n4/full/ng.2932.html

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