The ICR142 NGS validation series: a resource for orthogonal assessment of NGS analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Ruark E
PROVIDER: S-EPMC4857748 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Detection of deletions and duplications of whole exons (exon CNVs) is a key requirement of genetic testing. Accurate detection of this variant type has proved very challenging in targeted next-generation sequencing (NGS) data, particularly if only a single exon is involved. Many different NGS exon CNV calling methods have been developed over the last five years. Such methods are usually evaluated using simulated and/or in-house data due to a lack of publicly-available datasets with orthogonally generated results. This hinders tool comparisons, transparency and reproducibility. To provide a community resource for assessment of exon CNV calling methods in targeted NGS data, we here present the ICR96 exon CNV validation series. The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM or PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs, the most difficult type of exon CNV to detect. The FASTQ files for the ICR96 exon CNV validation series can be accessed through the European-Genome phenome Archive (EGA) under the accession number EGAS00001002428.
| S-EPMC5473400 | biostudies-literature
Project description:The analytical sensitivity of a next generation sequencing (NGS) test reflects the ability of the test to detect real sequence variation. The evaluation of analytical sensitivity relies on the availability of gold-standard, validated, benchmarking datasets. For NGS analysis the availability of suitable datasets has been limited. Most laboratories undertake small scale evaluations using in-house data, and/or rely on in silico generated datasets to evaluate the performance of NGS variant detection pipelines. Cancer predisposition genes (CPGs), such as BRCA1 and BRCA2, are amongst the most widely tested genes in clinical practice today. Hundreds of providers across the world are now offering CPG testing using NGS methods. Validating and comparing the analytical sensitivity of CPG tests has proved difficult, due to the absence of comprehensive, orthogonally validated, benchmarking datasets of CPG pathogenic variants. To address this we present the ICR639 CPG NGS validation series. This dataset comprises data from 639 individuals. Each individual has sequencing data generated using the TruSight Cancer Panel (TSCP), a targeted NGS assay for the analysis of CPGs, together with orthogonally generated data showing the presence of at least one CPG pathogenic variant per individual. The set consists of 645 pathogenic variants in total. There is strong representation of the most challenging types of variants to detect, with 339 indels, including 16 complex indels and 24 with length greater than five base pairs and 74 exon copy number variations (CNVs) including 23 single exon CNVs. The series includes pathogenic variants in 31 CPGs, including 502 pathogenic variants in BRCA1 or BRCA2, making this an important comprehensive validation dataset for providers of BRCA1 and BRCA2 NGS testing. We have deposited the TSCP FASTQ files of the ICR639 series in the European Genome-phenome Archive (EGA) under accession number EGAD00001004134.
| S-EPMC6081973 | biostudies-literature
Project description:Next generation sequencing is a transformative technology for discovering and diagnosing genetic disorders. However, high-throughput sequencing remains error-prone, necessitating variant confirmation in order to meet the exacting demands of clinical diagnostic sequencing. To address this, we devised an orthogonal, dual platform approach employing complementary target capture and sequencing chemistries to improve speed and accuracy of variant calls at a genomic scale. We combined DNA selection by bait-based hybridization followed by Illumina NextSeq reversible terminator sequencing with DNA selection by amplification followed by Ion Proton semiconductor sequencing. This approach yields genomic scale orthogonal confirmation of ~95% of exome variants. Overall variant sensitivity improves as each method covers thousands of coding exons missed by the other. We conclude that orthogonal NGS offers improvements in variant calling sensitivity when two platforms are used, better specificity for variants identified on both platforms, and greatly reduces the time and expense of Sanger follow-up, thus enabling physicians to act on genomic results more quickly.
| S-EPMC4836299 | biostudies-literature
Project description:Evaluating, optimising and benchmarking of next generation sequencing (NGS) variant calling performance are essential requirements for clinical, commercial and academic NGS pipelines. Such assessments should be performed in a consistent, transparent and reproducible fashion, using independently, orthogonally generated data. Here we present ICR142 Benchmarker, a tool to generate outputs for assessing germline base substitution and indel calling performance using the ICR142 NGS validation series, a dataset of Illumina platform-based exome sequence data from 142 samples together with Sanger sequence data at 704 sites. ICR142 Benchmarker provides summary and detailed information on the sensitivity, specificity and false detection rates of variant callers. ICR142 Benchmarker also automatically generates a single page report highlighting key performance metrics and how performance compares to widely-used open-source tools. We used ICR142 Benchmarker with VCF files outputted by GATK, OpEx and DeepVariant to create a benchmark for variant calling performance. This evaluation revealed pipeline-specific differences and shared challenges in variant calling, for example in detecting indels in short repeating sequence motifs. We next used ICR142 Benchmarker to perform regression testing with DeepVariant versions 0.5.2 and 0.6.1. This showed that v0.6.1 improves variant calling performance, but there was evidence of minor changes in indel calling behaviour that may benefit from attention. The data also allowed us to evaluate filters to optimise DeepVariant calling, and we recommend using 30 as the QUAL threshold for base substitution calls when using DeepVariant v0.6.1. Finally, we used ICR142 Benchmarker with VCF files from two commercial variant calling providers to facilitate optimisation of their in-house pipelines and to provide transparent benchmarking of their performance. ICR142 Benchmarker consistently and transparently analyses variant calling performance based on the ICR142 NGS validation series, using the standard VCF input and outputting informative metrics to enable user understanding of pipeline performance. ICR142 Benchmarker is freely available at https://github.com/RahmanTeamDevelopment/ICR142_Benchmarker/releases.
| S-EPMC6234721 | biostudies-literature
Project description:The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM and PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs the most difficult exon CNV to detect. The full dataset includes FASTQ files, target BED file, and MLPA results.
| EGAS00001002428 | EGA
Project description:BackgroundEmergency laparotomy is a high risk procedure which is demonstrated by high morbidity and mortality. However, the problem is tremendous in resource limited settings and there is limited data on patient outcome. We aimed to assess postoperative patient outcome after emergency laparotomy and associated factors.MethodsAn observational study was conducted in our hospital from March 11- June 30, 2015 using emergency laparotomy network tool. All consecutive surgical patients who underwent emergency laparotomy were included. Binary and multiple logistic regressions were employed using adjusted odds ratios and 95% CI, and P-value < 0.05 was considered to be statistically significant.ResultA total of 260 patients were included in the study. The majority of patients had late presentation (>6hrs) to the hospital after the onset of symptoms of the diseases and surgical intervention after hospital admission. The incidences of postoperative morbidity and mortality were 39.2% and 3.5% respectively. Factors associated with postoperative morbidity were preoperative co-morbidity (AOR?=?0.383, CI?=?0.156-0.939) and bowel resection (AOR?=?0.232, CI?=?0.091-0.591). Factors associated with postoperative mortality were anesthetists' preoperative opinion on postoperative patient outcome (AOR?=?0.067, CI?=?0.008-0.564), level of consciousness during recovery from anaesthesia (AOR?=?0.114, CI?=?0.021-10.628) and any re-intervention within 30 days after primary operation (AOR?=?0.083, CI?=?0.009-0.750).Conclusion and recommendationThe incidence of postoperative morbidity and mortality after emergency laparotomy were high. We recommend preoperative optimization, early surgical intervention, and involvement of senior professionals during operation in these risky surgical patients. Also, we recommend the use of WHO or equivalent Surgical Safety Checklist and establishment of perioperative patient care bundle including surgical ICU and radiology investigation modalities such as CT scan.
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Project description:SummaryHuman immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms.Availability and implementationHIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org.
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