What is MuTect?

MuTect is a method developed at the Broad Institute for the reliable and accurate identification of somatic point mutations in next generation sequencing data of cancer genomes.

For complete details, please see our publication in Nature Biotechnology:

Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnology (2013).doi:10.1038/nbt.2514

How does it work?

In brief, muTect consists of three steps.

  1. Preprocessing the aligned reads in the tumor and normal sequencing data. In this step we ignore reads with too many mismatches or very low quality scores since these represent noisy reads that introduce more noise than signal.
  2. A statistical analysis that identifies sites that are likely to carry somatic mutations with high confidence. The statistical analysis predicts a somatic mutation by using two Bayesian classifiers – the first aims to detect whether the tumor is non-reference at a given site and, for those sites that are found as non-reference, the second classifier makes sure the normal does not carry the variant allele. In practice the classification is performed by calculating a LOD score (log odds) and comparing it to a cutoff determined by the log ratio of prior probabilities of the considered events. For the tumors we calculate
    , and for the normal
    Since we expect somatic mutations to occur at a rate of ~1 in a Mb, we require  which guarantees that our false positive rate, due to noise in the tumor, is less than half of the somatic mutation rate. In the normal, not in dbSNP sites, we require  since non-dbSNP germline variants occur roughly at a rate of 100 in a Mb. This cutoff guarantees that the false positive somatic call rate, due to missing the variant in the normal, is also less than half the somatic mutation rate.
  3. Post-processing of candidate somatic mutations to eliminate artifacts of next-generation sequencing, short read alignment and hybrid capture. For example, sequence context can cause hallucinated alternate alleles but often only in a single direction. Therefore, we test that the alternate alleles supporting the mutations are observed in both directions.

As muTect attempts to call mutations it also generates a coverage file (in a wiggle file format, which indicates for every base whether it is sufficiently covered in the tumor and normal to be sensitive enough to call mutations). We currently use cutoffs of at least 14 reads in the tumor and at least 8 in the normal (these cutoffs are applied after removing noisy reads in the preprocessing step).  In addition, wiggle files can also be generated of the observed depth in the tumor and in the normal.

Most cancer genome studies at the Broad Institute have made use of MuTect and have validated the mutation calls as a part of their cancer biology papers, showing that MuTect has a very low false positive rate.  A summary of validation rates from these papers are show below: