Next-generation Sequencing Pipeline Research Articles

CoverView: a sequence quality evaluation tool for next generation sequencing data.

Quality assurance and quality control are essential for robust next generation sequencing (NGS). Here we present CoverView, a fast, flexible, user-friendly quality evaluation tool for NGS data. CoverView processes mapped sequencing reads and user-specified regions to report depth of coverage, base and mapping quality metrics with increasing levels of detail from a chromosome-level summary to per-base profiles. CoverView can flag regions that do not fulfil user-specified quality requirements, allowing suboptimal data to be systematically and automatically presented for review. It also provides an interactive graphical user interface (GUI) that can be opened in a web browser and allows intuitive exploration of results. We have integrated CoverView into our accredited clinical cancer predisposition gene testing laboratory that uses the TruSight Cancer Panel (TSCP). CoverView has been invaluable for optimisation and quality control of our testing pipeline, providing transparent, consistent quality metric information and automatic flagging of regions that fall below quality thresholds. We demonstrate this utility with TSCP data from the Genome in a Bottle reference sample, which CoverView analysed in 13 seconds. CoverView uses data routinely generated by NGS pipelines, reads standard input formats, and rapidly creates easy-to-parse output text (.txt) files that are customised by a simple configuration file. CoverView can therefore be easily integrated into any NGS pipeline. CoverView and detailed documentation for its use are freely available at github.com/RahmanTeamDevelopment/CoverView/releases and www.icr.ac.uk/CoverView.

The Quality Sequencing Minimum (QSM): providing comprehensive, consistent, transparent next generation sequencing data quality assurance.

Next generation sequencing (NGS) is routinely used in clinical genetic testing. Quality management of NGS testing is essential to ensure performance is consistently and rigorously evaluated. Three primary metrics are used in NGS quality evaluation: depth of coverage, base quality and mapping quality. To provide consistency and transparency in the utilisation of these metrics we present the Quality Sequencing Minimum (QSM). The QSM defines the minimum quality requirement a laboratory has selected for depth of coverage (C), base quality (B) and mapping quality (M) and can be applied per base, exon, gene or other genomic region, as appropriate. The QSM format is CX_BY(P Y)_MZ(P Z). X is the parameter threshold for C, Y the parameter threshold for B, P Y the percentage of reads that must reach Y, Z the parameter threshold for M, P Z the percentage of reads that must reach Z. The data underlying the QSM is in the BAM file, so a QSM can be easily and automatically calculated in any NGS pipeline. We used the QSM to optimise cancer predisposition gene testing using the TruSight Cancer Panel (TSCP). We set the QSM as C50_B10(85)_M20(95). Test regions falling below the QSM were automatically flagged for review, with 100/1471 test regions QSM-flagged in multiple individuals. Supplementing these regions with 132 additional probes improved performance in 85/100. We also used the QSM to optimise testing of genes with pseudogenes such as PTEN and PMS2. In TSCP data from 960 individuals the median number of regions that passed QSM per sample was 1429 (97%). Importantly, the QSM can be used at an individual report level to provide succinct, comprehensive quality assurance information about individual test performance. We believe many laboratories would find the QSM useful. Furthermore, widespread adoption of the QSM would facilitate consistent, transparent reporting of genetic test performance by different laboratories.

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

A comprehensive BRCA1/2 NGS pipeline for an immediate Copy Number Variation (CNV) detection in breast and ovarian cancer molecular diagnosis

BackgroundThe aim of this study was to assess the ability of a Next Generation Sequencing (NGS)-based approach in the detection of BRCA1/2 Large Genomic Rearrangements (LGRs). MethodsWe investigated 287 consecutive unrelated Italian women affected by breast or ovarian cancer. A NGS pipeline with a reliable Copy number Variation (CNV) prediction algorithm was applied. In addition, all samples were investigated using MAQ (Multiplex Amplicon Quantification) assay, while the MLPA (Multiplex Ligation-dependent Probe Amplification) technique was used as alternative method if necessary. ResultsSmall pathogenic variants were identified in 80 samples. During NGS analysis, 11 samples were identified by Amplicon Suite Software (SmartSeq, Novara, Italy) as positive for large deletions or duplications. However, MAQ assay provided positive results in 19 patients. In particular, BRCA2 exon 25 deletion, not revealed by NGS CNV prediction algorithm, was detected in 8 unrelated women as false positive result. Sequencing analysis showed IVS24-113T/G (c.9257-113T>G) variant in 6 of these 8 patients, while the novel IVS24-129G>A (c.9257-129G>A) variant was detected in only two samples. DiscussionThe prevalence of large rearrangements in BRCA1/2 genes was the 12% of all disease-causing mutations detected in our patients. In particular, BRCA1 rearrangements were the 14.5% of all BRCA1 causing variants identified. Differently, BRCA2 large deletions were only the 6.9% of all mutations occurring in this gene. While MAQ assay showed 2,8% of false positive results, our integrative NGS-based approach fully satisfied the sensitivity and specificity parameters required on the BRCA1/2 LGRs detection. The workflow represents a robust and easy-to-use method for full BRCA1/2 screening, which can be easily implemented in routine diagnostic testing.

Assessment of Capture and Amplicon-Based Approaches for the Development of a Targeted Next-Generation Sequencing Pipeline to Personalize Lymphoma Management

Targeted next-generation sequencing panels are increasingly used to assess the value of gene mutations for clinical diagnostic purposes. For assay development, amplicon-based methods have been preferentially used on the basis of short preparation time and small DNA input amounts. However, capture sequencing has emerged as an alternative approach because of high testing accuracy. We compared capture hybridization and amplicon sequencing approaches using fresh-frozen and formalin-fixed, paraffin-embedded tumor samples from eight lymphoma patients. Next, we developed a targeted sequencing pipeline using a 32-gene panel for accurate detection of actionable mutations in formalin-fixed, paraffin-embedded tumor samples of the most common lymphocytic malignancies: chronic lymphocytic leukemia, diffuse large B-cell lymphoma, and follicular lymphoma. We show that hybrid capture is superior to amplicon sequencing by providing deep more uniform coverage and yielding higher sensitivity for variant calling. Sanger sequencing of 588 variants identified specificity limits of thresholds for mutation calling, and orthogonal validation on 66 cases indicated 93% concordance with whole-genome sequencing. The developed pipeline and assay identified at least one actionable mutation in 91% of tumors from 219 lymphoma patients and revealed subtype-specific mutation patterns and frequencies consistent with the literature. This pipeline is an accurate and sensitive method for identifying actionable gene mutations in routinely acquired biopsy materials, suggesting further assessment of capture-based assays in the context of personalized lymphoma management.

MutaNET: a tool for automated analysis of genomic mutations in gene regulatory networks.

SummaryMutations in genomic key elements can influence gene expression and function in various ways, and hence greatly contribute to the phenotype. We developed MutaNET to score the impact of individual mutations on gene regulation and function of a given genome. MutaNET performs statistical analyses of mutations in different genomic regions. The tool also incorporates the mutations in a provided gene regulatory network to estimate their global impact. The integration of a next-generation sequencing pipeline enables calling mutations prior to the analyses. As application example, we used MutaNET to analyze the impact of mutations in antibiotic resistance (AR) genes and their potential effect on AR of bacterial strains.Availability and implementation MutaNET is freely available at https://sourceforge.net/projects/mutanet/. It is implemented in Python and supported on Mac OS X, Linux and MS Windows. Step-by-step instructions are available at http://service.bioinformatik.uni-saarland.de/mutanet/.Supplementary information Supplementary data are available at Bioinformatics online.

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N = 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome.

P.336 - MNDcap gene panel as a diagnostic tool in motor neuron disorders

Motor neuron disorders (MNDs) are a heterogeneous group of diseases that result from degeneration of motor neurons. Clinical and genetic overlap of MNDs is significant, however, and therefore diagnostics is challenging. MNDcap is a gene panel targeted to the exons of 275 genes that are known or predicted to cause motor neuron disease, hereditary neuropathy, spastic paraplegia or myasthenic syndrome. In our study, we have used MNDcap as a diagnostic tool for 157 MND patients whose diagnose had remained unsolved despite extensive studies. The sequencing data was processed using an in-house developed next-generation sequencing pipeline and the variants found were compared to the clinical findings. The results were classified in five categories: 1) diagnostic finding; 2) likely diagnostic finding; 3) possibly disease-related finding; 4) unlikely disease-related finding, and 5) no findings. In our patient cohort, the final diagnosis was reached in 20 patients (13 %), and in ten patients (6 %) the finding was likely diagnostic. Twenty-five patients (16 %) had a possibly disease-related finding, whereas 25 patients (16 %) had a finding that was unlikely disease-related, and 77 patients (49 %) did not have any clinically relevant findings. Altogether 13 different diagnoses were made, which reflects the complexity of the disease group. In this regard, MNDcap seems to be an effective diagnostic tool. However, the analysis method used does not detect repeat expansion mutations. Also potentially pathogenic deletions, insertions and inversions may not be detected, depending on their size. Therefore, it must be kept in mind, that quite common causes of MNDs, like the repeat expansion mutation in C9orf72 or the duplication of PMP22, cannot be detected by this method.

Accelerating next generation sequencing data analysis with system level optimizations

Next generation sequencing (NGS) data analysis is highly compute intensive. In-memory computing, vectorization, bulk data transfer, CPU frequency scaling are some of the hardware features in the modern computing architectures. To get the best execution time and utilize these hardware features, it is necessary to tune the system level parameters before running the application. We studied the GATK-HaplotypeCaller which is part of common NGS workflows, that consume more than 43% of the total execution time. Multiple GATK 3.x versions were benchmarked and the execution time of HaplotypeCaller was optimized by various system level parameters which included: (i) tuning the parallel garbage collection and kernel shared memory to simulate in-memory computing, (ii) architecture-specific tuning in the PairHMM library for vectorization, (iii) including Java 1.8 features through GATK source code compilation and building a runtime environment for parallel sorting and bulk data transfer (iv) the default ’on-demand’ mode of CPU frequency is over-clocked by using ’performance-mode’ to accelerate the Java multi-threads. As a result, the HaplotypeCaller execution time was reduced by 82.66% in GATK 3.3 and 42.61% in GATK 3.7. Overall, the execution time of NGS pipeline was reduced to 70.60% and 34.14% for GATK 3.3 and GATK 3.7 respectively.

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines.

Processing of next-generation sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized postanalysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers toward seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. We present an approach for abstracting the complex data operations of multistep, bioinformatics pipelines for NGS data analysis. As examples, we have deployed 2 pipelines for RNA sequencing and chromatin immunoprecipitation sequencing, preconfigured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simply as running a single bioinformatics tool. This is achieved using a “meta-script” that automatically starts the Bio-Docklets and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is postprocessed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. Our goal is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts on any computing environment, whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

Targeted sequencing of both DNA strands barcoded and captured individually by RNA probes to identify genome-wide ultra-rare mutations

Next Generation Sequencing (NGS) has been widely implemented in biological research and has made a profound impact on patient care. One of the essential NGS applications is to identify disease-causing sequence variants, where high coverage and accuracy are needed. Here, we reported a novel NGS pipeline, termed a Sequencing System of Digitalized Barcode Encrypted Single-stranded Library from Extremely Low (quality and quantity) DNA Input with Probe-based DNA Enrichment by RNA probes targeting DNA duplex (DEEPER-Seq). This method combines an ultra-sensitive single-stranded library construction with barcoding error correction, termed DEEPER-Library; and a DNA capture approach using RNA probes targeting both DNA strands, termed DEEPER-Capture. DEEPER-Seq can create NGS libraries from as little as 20 pg DNA with PCR error correcting capabilities, and capture target sequences at an average ratio of 29.2% by targeting both DNA strands simultaneously with an over 98.6% coverage. Our method tags and sequences each of the two strands of a DNA duplex independently and only scores mutations that are found at the same position in both strands, which allows us to identify mutations with allelic fractions down to 0.03% in a whole exome sequencing (WES) study with a background error rate of one artificial error per 4.8 × 109 nucleotides.

Erratum to: A metagenomics study for the identification of respiratory viruses in mixed clinical specimens: an application of the iterative mapping approach.

Metagenomic approaches to detect viral genomes and variants in clinical samples have various challenges, including low viral titers and bacterial and human genome contamination. To address these limitations, we examined a next-generation sequencing (NGS) and iterative mapping approach for virus detection in clinical samples. We analyzed 40 clinical specimens from hospitalized children diagnosed with acute bronchiolitis, croup, or respiratory tract infections in which virus identification by viral culture or polymerase chain reaction (PCR) was unsuccessful. For our NGS data analysis pipeline, clinical samples were pooled into two NGS groups to reduce sequencing costs, and the depth and coverage of assembled contigs were effectively increased using an iterative mapping approach. PCR was individually performed for each specimen according to the NGS-predicted viral type. We successfully detected previously unidentified respiratory viruses in 26 of 40 specimens using our proposed NGS pipeline. Two dominant populations within the detected viruses were human rhinoviruses (HRVs; n = 14) and human coronavirus NL63 (n = 8), followed by human parainfluenza virus (HPIV), human parechovirus, influenza A virus, respiratory syncytial virus (RSV), and human metapneumovirus. This is the first study reporting the complete genome sequences of HRV-A101, HRV-C3, HPIV-4a, and RSV, as well as an analysis of their genetic variants, in Taiwan. These results demonstrate that this NGS pipeline allows to detect viruses which were not identified by routine diagnostic assays, directly from clinical samples.

Validation of a Next-Generation Sequencing Pipeline for the Molecular Diagnosis of Multiple Inherited Cancer Predisposing Syndromes

Despite the growing knowledge of the genetic background behind the cancers that occur in a context of hereditary predisposition, personal or family cancer history may not be clear enough to support directional gene testing. Defined targeted next-generation sequencing gene panels allow identification of the causative disease mutations of multigene syndromes and differential diagnosis for syndromes with phenotypically overlapping characteristics. Herein, we established a next-generation sequencing analysis pipeline for the molecular diagnosis of multiple inherited cancer predisposing syndromes using the commercially available target sequencing panel TruSight Cancer. To establish the analysis pipeline, we included 22 control samples with deleterious mutations covering all genes currently analyzed at our institution by standard Sanger sequencing. We tested the pipeline using 51 samples from patients with a clinical diagnosis of neurofibromatosis type 1 (NF1), 10 of which without previous molecular characterization of the causative NF1 mutations. We propose a thoroughly validated analysis pipeline that combines Isaac Enrichment, Burrows-Wheeler Aligner Enrichment, and NextGENe for the alignment and variant calling, and GeneticistAssistant for variant annotation and prioritization. This pipeline allowed the identification of disease-causing mutations in all 73 patients, including a large duplication of 37 bp in NF1. We show that high sensitivity and specificity can be achieved by using multiple bioinformatic tools for alignment and variant calling and careful variant filtering, having in mind the clinical question.

From Vineyard Soil to Wine Fermentation: Microbiome Approximations to Explain the "terroir" Concept.

Wine originally emerged as a serendipitous mix of chemistry and biology, where microorganisms played a decisive role. From these ancient fermentations to the current monitored industrial processes, winegrowers and winemakers have been continuously changing their practices according to scientific knowledge and advances. A new enology direction is emerging and aiming to blend the complexity of spontaneous fermentations with industrial safety of monitored fermentations. In this context, wines with distinctive autochthonous peculiarities have a great acceptance among consumers, causing important economic returns. The concept of terroir, far from being a rural term, conceals a wide range of analytical parameters that are the basis of the knowledge-based enology trend. In this sense, the biological aspect of soils has been underestimated for years, when actually it contains a great microbial diversity. This soil-associated microbiota has been described as determinant, not only for the chemistry and nutritional properties of soils, but also for health, yield, and quality of the grapevine. Additionally, recent works describe the soil microbiome as the reservoir of the grapevine associated microbiota, and as a contributor to the final sensory properties of wines. To understand the crucial roles of microorganisms on the entire wine making process, we must understand their ecological niches, population dynamics, and relationships between ‘microbiome- vine health’ and ‘microbiome-wine metabolome.’ These are critical steps for designing precision enology practices. For that purpose, current metagenomic techniques are expanding from laboratories, to the food industry. This review focuses on the current knowledge about vine and wine microbiomes, with emphasis on their biological roles and the technical basis of next-generation sequencing pipelines. An overview of molecular and informatics tools is included and new directions are proposed, highlighting the importance of –omics technologies in wine research and industry.

A metagenomics study for the identification of respiratory viruses in mixed clinical specimens: an application of the iterative mapping approach.

Metagenomic approaches to detect viral genomes and variants in clinical samples have various challenges, including low viral titers and bacterial and human genome contamination. To address these limitations, we examined a next-generation sequencing (NGS) and iterative mapping approach for virus detection in clinical samples. We analyzed 40 clinical specimens from hospitalized children diagnosed with acute bronchiolitis, croup, or respiratory tract infections in which virus identification by viral culture or polymerase chain reaction (PCR) was unsuccessful. For our NGS data analysis pipeline, clinical samples were pooled into two NGS groups to reduce sequencing costs, and the depth and coverage of assembled contigs were effectively increased using an iterative mapping approach. PCR was individually performed for each specimen according to the NGS-predicted viral type. We successfully detected previously unidentified respiratory viruses in 26 of 40 specimens using our proposed NGS pipeline. Two dominant populations within the detected viruses were human rhinoviruses (HRVs; n = 14) and human coronavirus NL63 (n = 8), followed by human parainfluenza virus (HPIV), human parechovirus, influenza A virus, respiratory syncytial virus (RSV), and human metapneumovirus. This is the first study reporting the complete genome sequences of HRV-A101, HRV-C3, HPIV-4a, and RSV, as well as an analysis of their genetic variants, in Taiwan. These results demonstrate that this NGS pipeline allows to detect viruses which were not identified by routine diagnostic assays, directly from clinical samples.Electronic supplementary materialThe online version of this article (doi:10.1007/s00705-017-3367-4) contains supplementary material, which is available to authorized users.

Comparing mutation calls in fixed tumour samples between the affymetrix OncoScan\xae array and PCR based next-generation sequencing

BackgroundThe importance of accurate and affordable mutation calling in fixed pathology samples is becoming increasingly important as we move into the era of personalised medicine. The Affymetrix OncoScan® Array platform is designed to produce actionable mutation calls in archival material.MethodsWe compared calls made using the OncoScan platform with calls made using a custom designed PCR panel followed by next-generation sequencing (NGS), in order to benchmark the sensitivity and specificity of the OncoScan calls in a large cohort of fixed tumour samples. 392 fixed, clinical samples were sequenced, encompassing 641 PCR regions, 403 putative positive calls and 1528 putative negative calls.ResultsA small number of mutations could not be validated, either due to large indels or pseudogenes impairing parts of the NGS pipeline. For the remainder, if calls were filtered according to simple quality metrics, both sensitivity and specificity for the OncoScan platform were over 98%. This applied even to samples with poorer sample quality and lower variant allele frequency (5–10%) than product claims indicated.ConclusionsThis benchmarking study will be useful to users and potential users of this platform, who wish to compare technologies or interpret their own results.

Evaluating the Calling Performance of a Rare Disease NGS Panel for Single Nucleotide and Copy Number Variants.

Variant detection protocols for clinical next-generation sequencing (NGS) need application-specific optimization. Our aim was to analyze the performance of single nucleotide variant (SNV) and copy number (CNV) detection programs on an NGS panel for a rare disease. Thirty genes were sequenced in 83 patients with hereditary spastic paraplegia. The variant calls obtained with LifeScope, GATK UnifiedGenotyper and GATK HaplotypeCaller were compared with Sanger sequencing. The calling efficiency was evaluated for 187 (56 unique) SNVs and indels. Five multiexon deletions detected by multiple ligation probe assay were assessed from the NGS panel data with ExomeDepth, panelcn.MOPS and CNVPanelizer software. There were 48/51 (94%) SNVs and 1/5 (20%) indels consistently detected by all the calling algorithms. Two SNVs were not detected by any of the callers because of a rare reference allele, and one SNV in a low coverage region was only detected by two algorithms. Regarding CNVs, ExomeDepth detected 5/5 multi-exon deletions, panelcn.MOPs 4/5 and only 3/5 deletions were accurately detected by CNVPanelizer. The calling efficiency of NGS algorithms for SNVs is influenced by variant type and coverage. NGS protocols need to account for the presence of rare variants in the reference sequence as well as for ambiguities in indel calling. CNV detection algorithms can be used to identify large deletions from NGS panel data for diagnostic applications; however, sensitivity depends on coverage, selection of the reference set and deletion size. We recommend the incorporation of several variant callers in the NGS pipeline to maximize variant detection efficiency.

Genome-Wide Analysis of RAS/ERK Signaling Targets.

Identifying gene expression changes mediated by signaling pathways is necessary to determine mechanisms that cause phenotypic change. Recent advances in next-generation sequencing and informatic pipelines have streamlined the ability for laboratories to create and analyze transcriptomic data. Here, we describe the preparation of samples and transcriptomic analysis in order to determine gene expression programs regulated by RAS/ERK signaling.

Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN.

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, 'exon CNVs') in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.