Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller

Abstract

BackgroundDetection of DNA mutations at very low allele fractions with high accuracy will significantly improve the effectiveness of precision medicine for cancer patients. To achieve this goal through next generation sequencing, researchers need a detection method that 1) captures rare mutation-containing DNA fragments efficiently in the mix of abundant wild-type DNA; 2) sequences the DNA library extensively to deep coverage; and 3) distinguishes low level true variants from amplification and sequencing errors with high accuracy. Targeted enrichment using PCR primers provides researchers with a convenient way to achieve deep sequencing for a small, yet most relevant region using benchtop sequencers. Molecular barcoding (or indexing) provides a unique solution for reducing sequencing artifacts analytically. Although different molecular barcoding schemes have been reported in recent literature, most variant calling has been done on limited targets, using simple custom scripts. The analytical performance of barcode-aware variant calling can be significantly improved by incorporating advanced statistical models.ResultsWe present here a highly efficient, simple and scalable enrichment protocol that integrates molecular barcodes in multiplex PCR amplification. In addition, we developed smCounter, an open source, generic, barcode-aware variant caller based on a Bayesian probabilistic model. smCounter was optimized and benchmarked on two independent read sets with SNVs and indels at 5 and 1% allele fractions. Variants were called with very good sensitivity and specificity within coding regions.ConclusionsWe demonstrated that we can accurately detect somatic mutations with allele fractions as low as 1% in coding regions using our enrichment protocol and variant caller.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3425-4) contains supplementary material, which is available to authorized users.

Human Cytomegalovirus Genomes Sequenced Directly from Clinical Material: Variation, Multiple-Strain Infection, Recombination and Mutation

“…The factors listed above may have been responsible for the outliers observed in single-strain infections (Figure 2). In our view, accurate estimates of the levels of intrahost variation in single-strain infections are not available from the present and previous studies, and will require sequencing and bioinformatic approaches that are demonstrably reliable, robust and reproducible [46, 47].…”

smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers

“…N0015 was based on 10% mixture of NA12878 on a custom panel focusing on non-coding regions (catalog number CDHS-13244Z-3587). Both datasets were also used for the development of smCounter and described in [15]. N11582 was generated with pure NA24385 using QIAseq Human Inherited Disease Panel (catalog number CDHS-14433Z-11582).…”

“…A two-step UMI-based variant calling approach that first constructs a consensus read with tools like fgbio [11] and then applies one of the conventional low-frequency variant callers [12] to the consensus reads has been implemented in [3, 13]. In addition to the two-stage method, three UMI-based variant callers, DeepSNVMiner [14], smCounter [15], and MAGERI [16], are publicly available. DeepSNVMiner relies on heuristic thresholds to draw consensus and call variants.…”

RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins

“…the 1,000 Genomes Project) [25,26]. GC percentage may confound such inference by introducing read coverage variations, which is a sensitive parameter in the downstream variant calling process [27,28]. Therefore, we calculated the fraction of rare variants, defined as those with derived allele frequencies (DAFs) less than 0.5%, within the binding sites of each RBP.…”