Dataset Information

MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.

ABSTRACT: BACKGROUND:Rapid progress in high-throughput sequencing (HTS) and the development of novel library preparation methods have improved the sensitivity of detecting mutations in heterogeneous samples, specifically in high-depth (>?500×) clinical applications. However, HTS methods are bounded by their technical and theoretical limitations and sequencing errors cannot be completely eliminated. Comprehensive quantification of the background noise can highlight both the efficiency and the limitations of any HTS methodology, and help differentiate true mutations at low abundance from artifacts. RESULTS:We introduce MERIT (Mutation Error Rate Inference Toolkit), designed for in-depth quantification of erroneous substitutions and small insertions and deletions. MERIT incorporates an all-inclusive 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 single-base and 16 double-base indels. We applied MERIT to ultra-deep sequencing data (1,300,000 ×) obtained from the amplification of multiple clinically relevant loci, and showed a significant relationship between error rates and genomic contexts. In addition to observing significant difference between transversion and transition rates, we identified variations of more than 100-fold within each error type at high sequencing depths. For instance, T >G transversions in trinucleotide GTCs occurred 133.5 ±?65.9 more often than those in ATAs. Similarly, C >T transitions in GCGs were observed at 73.8 ±?10.5 higher rate than those in TCTs. We also devised an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of expected true mutations. Our analyses showed that increasing sequencing depth might improve sensitivity for detecting some mutations based on their genomic context. For example, T >G rate of error in GTCs did not change when sequenced beyond 10,000 ×; in contrast, T >G rate in TTAs consistently improved even at above 500,000 ×. CONCLUSIONS:Our results demonstrate significant variation in nucleotide misincorporation rates, and suggest that genomic context should be considered for comprehensive profiling of specimen-specific and sequencing artifacts in high-depth assays. This data provide strong evidence against assigning a single allele frequency threshold to call mutations, for it can result in substantial false positive as well as false negative variants, with important clinical consequences.

SUBMITTER: Hadigol M

PROVIDER: S-EPMC5994075 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.

Hadigol Mohammad M Khiabanian Hossein H

BMC bioinformatics 20180608 1

<h4>Background</h4>Rapid progress in high-throughput sequencing (HTS) and the development of novel library preparation methods have improved the sensitivity of detecting mutations in heterogeneous samples, specifically in high-depth (> 500×) clinical applications. However, HTS methods are bounded by their technical and theoretical limitations and sequencing errors cannot be completely eliminated. Comprehensive quantification of the background noise can highlight both the efficiency and the limit ...[more]

PMID: 29884116

Similar Datasets

Project description:Intratumor heterogeneity (ITH) contributes to cancer progression and chemoresistance. We sought to comprehensively describe ITH of somatic mutations, copy number, and transcriptomic alterations involving clinically and biologically relevant gene pathways in colorectal cancer (CRC). We performed multiregion, high-depth (384× on average) sequencing of 799 cancer-associated genes in 24 spatially separated primary tumor and nonmalignant tissues from four treatment-naïve CRC patients. We then used ultra-deep sequencing (17 075× on average) to accurately verify the presence or absence of identified somatic mutations in each sector. We also digitally measured gene expression and copy number alterations using NanoString assays. We identified the subclonal point mutations and determined the mutational timing and phylogenetic relationships among spatially separated sectors of each tumor. Truncal mutations, those shared by all sectors in the tumor, affected the well-described driver genes such as APC, TP53, and KRAS. With sequencing at 17 075×, we found that mutations first detected at a sequencing depth of 384× were in fact more widely shared among sectors than originally assessed. Interestingly, ultra-deep sequencing also revealed some mutations that were present in all spatially dispersed sectors, but at subclonal levels. Ultra-high-depth validation sequencing, copy number analysis, and gene expression profiling provided a comprehensive and accurate genomic landscape of spatial heterogeneity in CRC. Ultra-deep sequencing allowed more sensitive detection of somatic mutations and a more accurate assessment of ITH. By detecting the subclonal mutations with ultra-deep sequencing, we traced the genomic histories of each tumor and the relative timing of mutational events. We found evidence of early mixing, in which the subclonal ancestral mutations intermixed across the sectors before the acquisition of subsequent nontruncal mutations. Our findings also indicate that different CRC patients display markedly variable ITH, suggesting that each patient's tumor possesses a unique genomic history and spatial organization.

Dataset Information

MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.

Publications

MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets