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).

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.

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.

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.

Investigation of Splicing Quantitative Trait Loci in .

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 with publicly available next generation sequencing datasets.