MERIT: a Mutation Error Rate Identification Toolkit for Ultra-deep Sequencing Applications

Rapid progress in high-throughput sequencing (HTS) has enabled the molecular characterization of mutational landscapes in heterogeneous populations and has improved our understanding of clonal evolution processes. Analyzing the sensitivity of detecting genomic mutations in HTS requires comprehensive profiling of sequencing artifacts. To this end, we introduce MERIT, designed for in-depth quantification of erroneous substitutions and small insertions and deletions, specifically for ultra-deep applications. MERIT incorporates an allinclusive variant caller and considers genomic context, including the nucleotides immediately at 5′ and 3′, thereby establishing error rates for 96 possible substitutions as well as four singlebase and 16 double-base indels. We apply MERIT to ultra-deep sequencing data (1,300,000×) and show a significant relationship between error rates and genomic contexts. We devise an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of true mutations. Finally, we assess nucleotide-incorporation fidelity of four high-fidelity DNA polymerases in clinically relevant loci, and demonstrate how fixed detection thresholds may result in substantial false positive as well as false negative calls. Introduction Advances in high-throughput sequencing (HTS) technologies have revolutionized the genomic, transcriptomic, and epigenomic characterization of biological states. HTS platforms produce large Correspondance: [email protected] 1 peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/184291 doi: bioRxiv preprint first posted online Sep. 4, 2017;