Dilution series of two cancer cell lines analyzed using ultra deep duplex sequencing.
Detection of low-frequency mutations constitutes a critical challenge in oncology and precision medicine. As next-generation sequencing and liquid biopsy become more prevalent in the clinic and research, there is an increasing need to improve the sensitivity of mutation detection as well as other sources of cancer genome variation. Technical artefacts are introduced by sequencing technologies and a common solution utilizes unique molecular identifiers (UMIs) to distinguish errors from true variants found across multiple reads of the same molecule. While these methods are effective, they often rely on over-amplification and redundant sequencing resulting in inefficient error correction at lower sequencing depths. Here, we describe ConsensusCruncher, an open-source python software program for generalized processing of UMI-tagged, paired-end sequencing reads that enables double-strand error suppression for both redundant (consensus sequences) and non-redundant (singleton) reads. Traditional UMI-based error suppression methods only correct redundant reads and have strict UMI specifications. ConsensusCruncher supports UMIs of any length and produces versatile consensus sequences in standardized binary alignment map (BAM) files that are primed for downstream calling of mutations, copy number alterations, and structural rearrangements. We provide best practices for variant calling and quantification, using consensus sequences for highly accurate detection and all unique reads for quantification.