Dataset Information

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

ABSTRACT: As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-compiled VCF-file of targeted clinically relevant variants it associates this dataset with a single arbiter parameter. Intrinsically, EphaGen estimates the probability to miss any variant from the defined spectrum within a particular NGS dataset. Such performance measure virtually resembles the diagnostic sensitivity of given NGS dataset. Here we present case studies of the use of EphaGen in context of BRCA1/2 and CFTR sequencing in a series of 14 runs across 43 blood samples and 504 publically available NGS datasets. EphaGen is superior to conventional bioinformatics metrics such as coverage depth and coverage uniformity. We recommend using this software as a QC step in NGS studies in the clinical context. Availability: https://github.com/m4merg/EphaGen or https://hub.docker.com/r/m4merg/ephagen.

SUBMITTER: Ivanov M

PROVIDER: S-EPMC6868350 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

Ivanov Maxim M Ivanov Mikhail M Kasianov Artem A Rozhavskaya Ekaterina E Musienko Sergey S Baranova Ancha A Mileyko Vladislav V

Nucleic acids research 20191201 21

As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-com ...[more]

PMID: 31511888

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Project description:Molecular technologies have allowed laboratories to detect and establish the profiles of human cancers by identifying a variety of somatic variants. In order to improve personalized patient care, we have established a next-generation sequencing (NGS) test to screen for somatic variants in primary or advanced cancers. In this study, we describe the laboratory quality management program for NGS testing, and also provide an overview of the somatic variants identified in over 1000 patient samples as well as their implications in clinical practice.Over the past one-and-a-half years, our laboratory received a total of 1028 formalin-fixed, paraffin-embedded (FFPE) tumor tissues, which consisted of non-small-cell lung carcinomas (NSCLCs), colon adenocarcinomas, glioma/glioblastomas, melanomas, breast carcinomas, and other tumor types. During this time period, we implemented a series of quality control (QC) checks that included (1) pre-DNA extraction, (2) DNA quantification, (3) DNA quality, (4) library quantification, (5) post-emulsification PCR, and (6) post-sequencing metrics. At least 10 ng of genomic DNA (gDNA) were used to prepare barcoded libraries using the AmpliSeq CHPv2. Samples were multiplexed and sequenced on Ion Torrent 318 chips using the Ion PGM System. Variants were identified using the Variant Caller Plugin, and annotation and functional predictions were performed using the Golden Helix SVS.A total of 1005 samples passed QC1-3, and following additional library preparation QC checkpoints, 877 samples were sequenced. Samples were classified into two categories: wild-type (127) and positive for somatic variants (750). Somatic variants were classified into clinically actionable (60%) and non-actionable (40%).The use of NGS in routine clinical laboratory practice allowed for the detection of tumor profiles that are essential for the selection of targeted therapies and identification of applicable clinical trials, contributing to the improvement of personalized patient care in oncology.

Dataset Information

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

Publications

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

Similar Datasets

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