Ultrafast prediction of somatic structural variations by filtering out reads matched to pan-genome k-mer sets.

Variant callers typically produce massive numbers of false positives for structural variations, such as cancer-relevant copy-number alterations and fusion genes resulting from genome rearrangements. Here we describe an ultrafast and accurate detector of somatic structural variations that reduces read-mapping costs by filtering out reads matched to pan-genome k-mer sets. The detector, which we named ETCHING (for efficient detection of chromosomal rearrangements and fusion genes), reduces the number of false positives by leveraging machine-learning classifiers trained with six breakend-related features (clipped-read count, split-reads count, supporting paired-end read count, average mapping quality, depth difference and total length of clipped bases). When benchmarked against six callers on reference cell-free DNA, validated biomarkers of structural variants, matched tumour and normal whole genomes, and tumour-only targeted sequencing datasets, ETCHING was 11-fold faster than the second-fastest structural-variant caller at comparable performance and memory use. The speed and accuracy of ETCHING may aid large-scale genome projects and facilitate practical implementations in precision medicine.

Junction Location Identifier (JuLI): Accurate Detection of DNA Fusions in Clinical Sequencing for Precision Oncology.

Accurate detection of genomic fusions by high-throughput sequencing in clinical samples with inadequate tumor purity and formalin-fixed, paraffin-embedded tissue is an essential task in precise oncology. We developed the fusion detection algorithm Junction Location Identifier (JuLI) for optimization of high-depth clinical sequencing. Novel filtering steps were implemented to minimize false positives in the clinical setting. The algorithm was comprehensively validated using high-depth sequencing data from cancer cell lines and clinical samples and genome sequencing data from NA12878. JuLI showed improved performance mainly in positive predictive value over state-of-the-art fusion callers in cases with high-depth clinical sequencing and rescued a driver fusion from false negative in plasma cell-free DNA using joint calling.

Performance evaluation of commercial library construction kits for PCR-based targeted sequencing using a unique molecular identifier.

BACKGROUND: Target enrichment is a critical component of targeted deep next-generation sequencing for the cost-effective and sensitive detection of mutations, which is predominantly performed by either hybrid selection or PCR. Despite the advantages of efficient enrichment, PCR-based methods preclude the identification of PCR duplicates and their subsequent removal. Recently, this limitation was overcome by assigning a unique molecular identifier(UMI) to each template molecule. Currently, several commercial library construction kits based on PCR enrichment are available for UMIs, but there have been no systematic studies to compare their performances. In this study, we evaluated and compared the performances of five commercial library kits from four vendors: the Archer® Reveal ctDNA™ 28 Kit, NEBNext Direct® Cancer HotSpot Panel, Nugen Ovation® Custom Target Enrichment System, Qiagen Human Comprehensive Cancer Panel(HCCP), and Qiagen Human Actionable Solid Tumor Panel(HASTP). RESULTS: We evaluated and compared the performances of the five kits using 50 ng of genomic DNA for the library construction in terms of the library complexity, coverage uniformity, and errors in the UMIs. While the duplicate rates for all kits were dramatically decreased by identifying unique molecules with UMIs, the Qiagen HASTP achieved the highest library complexity based on the depth of unique coverage indicating superb library construction efficiency. Regarding the coverage uniformity, the kits from Nugen and NEB performed the best followed by the kits from Qiagen. We also analyzed the UMIs, including errors, which allowed us to adjust the depth of unique coverage and the length required for sufficient complexity. Based on these comparisons, we selected the Qiagen HASTP for further performance evaluations. The targeted deep sequencing method based on PCR target enrichment combined with UMI tagging sensitively detected mutations present at a frequency as low as 1% using 6.25 ng of human genomic DNA as the starting material. CONCLUSION: This study is the first systematic evaluation of commercial library construction kits for PCR-based targeted deep sequencing utilizing UMIs. Because the kits displayed significant variability in different quality metrics, our study offers a practical guideline for researchers to choose appropriate options for PCR-based targeted sequencing and useful benchmark data for evaluating new kits.

A Method to Evaluate the Quality of Clinical Gene-Panel Sequencing Data for Single-Nucleotide Variant Detection.

Customized gene-panel tests, based on next-generation sequencing, have demonstrated their usefulness in a plethora of clinical settings. As with other clinical diagnostic techniques, gene-panel sequencing for clinical purposes requires precise quality control (QC) measures to ensure its reliability. Only detected variants are currently recorded in clinical reports; however, identifying whether a nondetected variant is a true or false negative is regarded essential in a clinical setting and, thus, a comprehensive QC measure is in demand. Conventional QC metrics, such as mean coverage and uniformity, are considered inadequate for such an evaluation. As such, a more specific measure focused on clinically important variants is herein proposed. In this study, we suggest a new scoring method for assessing the quality of clinical gene-panel sequencing data, specifically for the detection of a set of single-nucleotide variants. The performance of the method was analyzed using 2295 clinical samples (1012 formalin-fixed, paraffin-embedded and 1283 fresh-frozen tissues), and was shown to provide additional information that conventional methods do not show, such as mean depth and uniformity. Customized sequencing protocols, which include QC criteria, have been optimized by each genomic laboratory. The pass rate scoring method proposed in this study provides an appropriate QC response variable for the customized panel, which strengthens the reliability of calls on clinically relevant variants implicated in clinical reports.

ARID1B alterations identify aggressive tumors in neuroblastoma.

Targeted panel sequencing was performed to determine molecular targets and biomarkers in 72 children with neuroblastoma. Frequent genetic alterations were detected in ALK (16.7%), BRCA1 (13.9%), ATM (12.5%), and PTCH1 (11.1%) in an 83-gene panel. Molecular targets for targeted therapy were identified in 16 of 72 patients (22.2%). Two-thirds of ALK mutations were known to increase sensitivity to ALK inhibitors. Sequence alterations in ARID1B were identified in 5 of 72 patients (6.9%). Four of five ARID1B alterations were detected in tumors of high-risk patients. Two of five patients with ARID1B alterations died of disease progression. Relapse-free survival was lower in patients with ARID1B alterations than in those without (p = 0.01). In analysis confined to high-risk patients, 3-year overall survival was lower in patients with an ARID1B alteration (33.3 ± 27.2%) or MYCN amplification (30.0 ± 23.9%) than in those with neither ARID1B alteration nor MYCN amplification (90.5 ± 6.4%, p = 0.05). These results provide possibilities for targeted therapy and a new biomarker identifying a subgroup of neuroblastoma patients with poor prognosis.

Investigation of Splicing Quantitative Trait Loci in Arabidopsis thaliana.

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.