Variant allele detection in sequencing data containing unique molecular identifiers

Targeted sequencing using Unique Molecular Identifiers (UMIs) enables detection and quantification of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs and are inflexible, require multi-step workflows or dedicated computing resources. Here, we describe UMIErrorCorrect, a bioinformatics pipeline for analyzing targeted sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. UMIErrorCorrect is a comprehensive and customizable bioinformatics tool that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. We also provide UMIAnalyzer, an R-package, including a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from four different targeted sequencing protocols and accurately quantified rare variants in standardized cell-free DNA reference material. Access to simple, generic, and open-source bioinformatics tools will facilitate the use and implementation of UMI-based sequencing approaches in basic research and clinical applications.