Next-generation Sequencing Pipeline Research Articles

Methods and feasibility study for exome sequencing as a universal second-tier test in newborn screening

Newborn screening disorders increasingly require genetic variant analysis as part of second-tier or confirmatory testing. Sanger sequencing and gene-specific next-generation sequencing (NGS)-based tests, the current methods of choice, are costly and lack scalability when expanding to new conditions. We describe a scalable, exome sequencing-based NGS pipeline with a priori analysis restriction that can be universally applied to any NBS disorder. De-identified abnormal newborn screening specimens representing severe combined immune deficiency (SCID), cystic fibrosis (CF), VLCAD deficiency, metachromatic leukodystrophy (MLD), and in silico sequence read data sets were used to validate the pipeline. To support interpretation and clinical decision-making within the bioinformatics pipeline, variants from multiple databases were curated and validated. CFTR variant panel analysis correctly identified all variants. Concordance compared with diagnostic testing results for targeted gene analysis was between 78.6% and 100%. Validation of the bioinformatics pipeline with in silico data sets revealed a 100% detection rate. Varying degrees of overlap were observed between ClinVar and other databases ranging from 3% to 65%. Data normalization revealed that 11% of variants across the databases required manual curation. This pipeline allows for restriction of analysis to variants within a single gene or multiple genes, and can be readily expanded to full exome analysis if clinically indicated and parental consent is granted.

Sharing genetic variants with the NGS pipeline is essential for effective genomic data sharing and reproducibility in health information exchange

Genetic variants causing underlying pharmacogenetic and disease phenotypes have been used as the basis for clinical decision-making. However, due to the lack of standards for next-generation sequencing (NGS) pipelines, reproducing genetic variants among institutions is still difficult. The aim of this study is to show how many important variants for clinical decisions can be individually detected using different pipelines. Genetic variants were derived from 105 breast cancer patient target DNA sequences via three different variant-calling pipelines. HaplotypeCaller, Mutect2 tumor-only mode in the Genome Analysis ToolKit (GATK), and VarScan were used in variant calling from the sequence read data processed by the same NGS preprocessing tools using Variant Effect Predictor. GATK HaplotypeCaller, VarScan, and MuTect2 found 25,130, 16,972, and 4232 variants, comprising 1491, 1400, and 321 annotated variants with ClinVar significance, respectively. The average number of ClinVar significant variants in the patients was 769.43, 16.50% of the variants were detected by only one variant caller. Despite variants with significant impact on clinical decision-making, the detected variants are different for each algorithm. To utilize genetic variants in the clinical field, a strict standard for NGS pipelines is essential.

The clinical utility of exome sequencing and extended bioinformatic analyses in adolescents and adults with a broad range of neurological phenotypes: an Australian perspective

Currently there is no secured ongoing funding in Australia for next generation sequencing (NGS) such as exome sequencing (ES) for adult neurological disorders. Studies have focused on paediatric populations in research or highly specialised settings, utilised standard NGS pipelines focusing only on small insertions, deletions and single nucleotide variants, and not explored impacts on management in detail.This prospective multi-site study performed ES and an extended bioinformatics repeat expansion analysis pipeline, on patients with broad phenotypes (ataxia, dementia, dystonia, spastic paraparesis, motor neuron disease, Parkinson's disease and complex/not-otherwise-specified), with symptom onset between 2 and 60 years. Genomic data analysis was phenotype-driven, using virtual gene panels, reported according to American College of Medical Genetics and Genomics guidelines.One-hundred-and-sixty patients (51% female) were included, median age 52 years (range 14–79) and median 9 years of symptoms. 34/160 (21%) patients received a genetic diagnosis. Highest diagnostic rates were in spastic paraparesis (10/25, 40%), complex/not-otherwise-specified (10/38, 26%) and ataxia (7/28, 25%) groups. Findings were considered ‘possible/uncertain’ in 21/160 patients. Repeat expansion detection identified an unexpected diagnosis of Huntington disease in an ataxic patient with negative ES. Impacts on management, such as more precise and tailored care, were seen in most diagnosed patients (23/34, 68%).ES and a novel bioinformatics analysis pipepline had a substantial diagnostic yield (21%) and management impacts for most diagnosed patients, in heterogeneous, complex, mainly adult-onset neurological disorders in real-world settings in Australia, providing evidence for NGS and complementary multiple, new technologies as valuable diagnostic tools.

SMN1 copy-number and sequence variant analysis from next-generation sequencing data.

Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy‐number by next‐generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy‐number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.

CARE: context-aware sequencing read error correction.

Error correction is a fundamental pre-processing step in many Next-Generation Sequencing (NGS) pipelines, in particular for de novo genome assembly. However, existing error correction methods either suffer from high false-positive rates since they break reads into independent k-mers or do not scale efficiently to large amounts of sequencing reads and complex genomes. We present CARE-an alignment-based scalable error correction algorithm for Illumina data using the concept of minhashing. Minhashing allows for efficient similarity search within large sequencing read collections which enables fast computation of high-quality multiple alignments. Sequencing errors are corrected by detailed inspection of the corresponding alignments. Our performance evaluation shows that CARE generates significantly fewer false-positive corrections than state-of-the-art tools (Musket, SGA, BFC, Lighter, Bcool, Karect) while maintaining a competitive number of true positives. When used prior to assembly it can achieve superior de novo assembly results for a number of real datasets. CARE is also the first multiple sequence alignment-based error corrector that is able to process a human genome Illumina NGS dataset in only 4 h on a single workstation using GPU acceleration. CARE is open-source software written in C++ (CPU version) and in CUDA/C++ (GPU version). It is licensed under GPLv3 and can be downloaded at https://github.com/fkallen/CARE. Supplementary data are available at Bioinformatics online.

Abstract 3218: CKB: a Clinical Knowledge Base for interpreting correlations among cancer, biomarker and drug

Abstract Background: The field of cancer diagnosis and therapy is being empowered by Next Generation Sequencing (NGS) technology in recent years. The correlations among cancer, biomarker and drug are usually analyzed and used to guide the personalized drug recommendation for patients. However, the accurate correlation analysis faces two major challenges. First, large amount of existing variants carry the high variability in pathogenicity and clinical relevance. Second, the knowledge concerning the correlations is not easily accessible in the case of rare variants. The quantity of currently known variants, and dramatic increase of newly identified variants have showed the need for building up a knowledge base, for interpreting correlations among cancer, biomarker and drug. Method: To make the knowledge base accurately maintainable and friendly usable, we designed knowledge structures (or ontologies), and implemented an ontology based Clinical Knowledge Base (CKB). CKB consists of evidences from three different aspects, including cancer (i.e. disease), biomarker (genes and their corresponding variants), and drug. The inclusion of Gene Ontology (GO), Sequence Ontology (SO), Disease Ontology (DO) and KEGG Pathway Ontology (PO) not only describes the individual entities and the relationships between them, but also facilitates the discovery of correlations for potential biomarkers through ontology searching mechanism. CKB was implemented in Java and MySQL, and it provided a web based UI to input, analyze, relate and retrieve the data. Results: CKB supports effective retrieval of comprehensive information of cancer, biomarker and drug, and it highlights the sophisticated relationship among the respective entities. By inputting a biomarker of interest, evidences associated with the relevant genes/variants and relevant cancer types will be displayed. Evidences will be sorted by the significance levels. The levels include FDA/NMPA approved drugs, NCCN guidelines, clinical trials, clinical case report, pre-clinical evidence, and others. Furthermore, for the advanced users, the correlations between biomarker and cancer type can be explored for the research perspective of umbrella or basket clinical trials. CKB is open to public for research use at ckb.sodayun.com. Conclusions: Ontology is a powerful modeling methodology for medical knowledge. To manage the exponentially increasing NGS variants and their correlation with cancer and drug, ontology based knowledge base like CKB will play a significant role to help provide accurate correlation interpretation. It will be valuable for researchers and clinicians to better understand the correlations, in order to design new diagnostic assay or prescribe therapeutic regimens for their patients. For the more usage of CKB, more research is required, for example, how to embed CKB ontologies into a NGS pipeline. Citation Format: Jing Ma, Yizhou Ye, Hua Dong, Bingru Sun, Bolong He, Hank Yang. CKB: a Clinical Knowledge Base for interpreting correlations among cancer, biomarker and drug [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3218.

PEGR: a management platform for ChIP-based next generation sequencing pipelines.

There has been a rapid development in genome sequencing, including high-throughput next generation sequencing (NGS) technologies, automation in biological experiments, new bioinformatics tools and utilization of high-performance computing and cloud computing. ChIP-based NGS technologies, e.g. ChIP-seq and ChIP-exo, are widely used to detect the binding sites of DNA-interacting proteins in the genome and help us to have a deeper mechanistic understanding of genomic regulation. As sequencing data is generated at an unprecedented pace from the ChIP-based NGS pipelines, there is an urgent need for a metadata management system. To meet this need, we developed the Platform for Eukaryotic Genomic Regulation (PEGR), a web service platform that logs metadata for samples and sequencing experiments, manages the data processing workflows, and provides reporting and visualization. PEGR links together people, samples, protocols, DNA sequencers and bioinformatics computation. With the help of PEGR, scientists can have a more integrated understanding of the sequencing data and better understand the scientific mechanisms of genomic regulation. In this paper, we present the architecture and the major functionalities of PEGR. We also share our experience in developing this application and discuss the future directions.

Novel NGS pipeline for virus discovery from a wide spectrum of hosts and sample types

The study of the microbiome data holds great potential for elucidating the biological and metabolic functioning of living organisms and their role in the environment. Metagenomic analyses have shown that humans, along with for example, domestic animals, wildlife and arthropods, are colonized by an immense community of viruses. The current Coronavirus pandemic (COVID-19) heightens the need to rapidly detect previously unknown viruses in an unbiased way. The increasing availability of metagenomic data in this era of next-generation sequencing (NGS), along with increasingly affordable sequencing technologies, highlight the need for reliable and comprehensive methods to manage such data. In this article, we present a novel bioinformatics pipeline called LAZYPIPE for identifying both previously known and novel viruses in host associated or environmental samples and give examples of virus discovery based on it. LAZYPIPE is a Unix-based pipeline for automated assembling and taxonomic profiling of NGS libraries implemented as a collection of C++, Perl, and R scripts.

Recurrent genetic alterations and biomarker expression in primary and metastatic squamous cell carcinomas of the vulva

Using a comprehensive next-generation sequencing pipeline (143 genes), Oncomine Comprehensive v.2, we analyzed genetic alterations on a set of vulvar squamous cell carcinomas (SCCs) with emphasis on the primary and metastatic samples from the same patient, to identify amenable therapeutic targets. Clinicopathologic features were reported and genomic DNA was extracted from 42 paraffin-embedded tumor tissues of 32 cases. PD-L1 expression was evaluated in 20 tumor tissues (10 cases with paired primary and metastatic tumors). Fifteen (88%) of 17 successfully analyzed HPV-unrelated SCCs harbored TP53 mutations. 2 different TP53 mutations had been detected in the same tumor in 4 of 15 cases. Other recurrent genetic alterations in this group of tumors included CDKN2a mutations (41%), HRAS mutations (12%), NOTCH1 mutations (12%) and BIRC3 (11q22.1-22.2) amplification (12%). Six HPV-related tumors harbored PIK3CA, BAP1, PTEN, KDR, CTNNB1, and BRCA2 mutations, of which, one case also contained TP53 mutation. Six cases showed identical mutations in paired primary site and distant metastatic location and four cases displayed different mutational profiles. PD-L1 expression was seen in 6 of 10 primary tumors and all 6 paired cases showed discordant PD-L1 expression in the primary and metastatic sites. Our results further confirmed the genetic alterations that are amenable to targeted therapy, offering the potential for individualized management strategies for the treatment of these aggressive tumors with different etiology. Discordant PD-L1 expression in the primary and metastatic vulvar SCCs highlights the importance of evaluation of PD-L1 expression in different locations to avoid false negative information provided for immunotherapy.

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations.

BackgroundDepth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next-generation sequencing pipelines, including the analysis of RNA-sequencing data, detection of copy number variants, or quality control procedures.ResultsBuilding upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides efficient depth of coverage calculations, reaching >100× speedup over the state-of-the-art tools. The performance and scalability of our solution allow for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface.ConclusionsSeQuiLa-cov provides significant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines.

41. Comparison of third-party NGS pipelines using control material

Background: With the development of new biomarkers and therapeutic targets in solid tumor oncology there comes a need for expanded Next Generation Sequencing (NGS) panels. Laboratories with limited bioinformatics resources can mitigate this complexity by using third party bioinformatics platforms. This study assesses the accuracy of Pierian CGW and SOPHiA DDM (SOPHiA GENETICS) variant calling using the Horizon HD789 Control material sequenced using the llumina TruSight Tumor 170 (TS170) panel.

Validation of a hepatitis B virus (HBV) Next Generation Sequencing (NGS) pipeline at Barts Health NHS Trust

At Barts Health NHS Trust, historically HBV genotyping and resistance testing is performed using nested PCR followed by Sanger sequencing. With the development of NGS assays for HIV and HCV, in order to harmonise workflows an NGS HBV pipeline was developed. Primers were designed for the whole genome amplification of HBV. Plasma samples were extracted for nucleic acid using a Qiagen Qiasymphony. Whole genomes of HBV were amplified in a single round PCR and quantified using a Qubit fluorometer. Libraries were prepared using the Nextera XT library preparation kit and sequencing was performed on an Illumina Miseq. Sequencing reads were assembled into consensus sequences using a Linex-based pipeline. Resistances and genotypes were determined using the HBV Grade website. Minority variants were variants <20 % of the overall population. Pan-genotypic PCR primers were designed in the nick region of the partially double stranded HBV genome. In-silico analysis allowed primers to be designed to bind multiple genotypes. Sensitivity analysis of primers was investigated for the most common circulating genotypes. A panel of HBV positive and external quality assurance samples showed good comparability to Sanger sequencing results. Inter- and intra-assay variability showed the assay was robust and fit for purpose. No resistance associated minority variants were observed. The development an NGS pipeline for analysis of HBV allowed the harmonisation of diagnostic pathways within the virology sequencing laboratory. Introduction of this test would allow data to be gathered to assess the importance of minority variants in HBV infection.

Different next-generation sequencing pipelines based detection of tumor DNA in cerebrospinal fluid of lung adenocarcinoma cancer patients with leptomeningeal metastases

BackgroundThe nucleic acid mutation status in intracranial metastasis is markedly significant clinically. The goal of the current study was to explore whether the tumor-associated mutations can be detected by different next-generation sequencing (NGS) pipelines in paired cerebrospinal fluid (CSF) and plasma samples from lung adenocarcinoma (LAC) patients with leptomeningeal metastases (LM).MethodsPaired CSF cell free DNA (cfDNA), CSF cells, plasma and formalin-fixed and paraffin-embedded (FFPE) samples of primary tumors were collected from 29 LAC patients with LM to detect the mutations by different NGS pipelines.ResultsDNA libraries were generated successfully for 79 various samples in total for NGS sequencing, of which mutations were detected in 7 plasma samples (24.14%), 12 CSF cfDNA samples (66.67%), and 10 CSF cells (76.9%) samples. For the 26 patients with detected mutations, 8/26(30.77%) had mutations in plasma, which was significantly lower than that those from CSF cfDNA (12/15, 80.00%), CSF cells (10/11, 90.91%) and FFPE samples (13/17, 76.47%). When the input DNA of CSF cells was less than 20 ng, the cHOPE pipeline of NGS identified the most mutations for epidermal growth factor receptor (EGFR).ConclusionsNGS-based detection of mutations in cfDNA or cells from CSF provided more information than from plasma samples from LAC patients with LM. In addition, the cHOPE pipeline performed better than the other three NGS pipelines when input DNA from CSF cells was low.

Comparing Ease of Programming in C++, Go, and Java for Implementing a Next-Generation Sequencing Tool.

elPrep is an extensible multithreaded software framework for efficiently processing Sequence Alignment/Map (SAM)/Binary Alignment/Map (BAM) files in next-generation sequencing pipelines. Similar to other SAM/BAM tools, a key challenge in elPrep is memory management, as such programs need to manipulate large amounts of data. We therefore investigated 3 programming languages with support for assisted or automated memory management for implementing elPrep, namely C++, Go, and Java. We implemented a nontrivial subset of elPrep in all 3 programming languages and compared them by benchmarking their runtime performance and memory use to determine the best language in terms of computational performance. In a previous article, we motivated why, based on these results, we eventually selected Go as our implementation language. In this article, we discuss the difficulty of achieving the best performance in each language in terms of programming language constructs and standard library support. While benchmarks are easy to objectively measure and evaluate, this is less obvious for assessing ease of programming. However, because we expect elPrep to be regularly modified and extended, this is an equally important aspect. We illustrate representative examples of challenges in all 3 languages, and give our opinion why we think that Go is a reasonable choice also in this light.

Novel BRCA1 Large Genomic Rearrangements in Italian Breast/Ovarian Cancer Patients.

In recent years, the number of patients being offered BRCA1/2 testing has changed dramatically. Advances in high-throughput sequencing technology have led many diagnostic laboratories to test next-generation sequencing (NGS)-based platforms as the main technology for clinical testing. As a consequence, the proportion of novel BRCA1/2 variants detected has greatly increased. Here, we describe two novel BRCA1 large deletions detected in Italian patients affected by hereditary breast and ovarian cancer syndrome (HBOC). We applied an NGS pipeline with a reliable copy number variation (CNV) prediction algorithm. Successively, samples were investigated using the Multiplex Amplicon Quantification (MAQ) assay and array comparative genomic hybridization (CGH). In a single case, long-range polymerase chain reaction (PCR) was employed for careful detection of the breakpoint region, while the RepeatMasker program was used to identify Alu sequences at the junction point. A 137.8kb deletion, involving the first six exons of BRCA1 and the full NBR2, BRCA1P1, NBR1, and TMEM106a genes, was detected in an Italian woman diagnosed with high-grade serous ovarian carcinoma. A second rearrangement, involving the deletion of BRCA1 11-14 exons, was detected in a breast cancer patient and was fully characterized and reported according to recommended Human Genome Variation Society (HGVS) nomenclature: NG_005905.2: g.125038_143266del; NM_007294.3: c.2817_4716del; NP_009225: p.Lys862Metfs? Although it was not possible to perform a familial segregation analysis and more direct evidence of the relationship between genotype and phenotype is necessary, both of the novel reported rearrangements cause the loss of crucial functional domains of the BRCA1 protein and this event supports their pathogenicity.

Validating Comprehensive Next-Generation Sequencing Results for Precision Oncology: The NCT/DKTK Molecularly Aided Stratification for Tumor Eradication Research Experience.

Rapidly evolving genomics technologies, in particular comprehensive next-generation sequencing (NGS), have led to exponential growth in the understanding of cancer biology, shifting oncology toward personalized treatment strategies. However, comprehensive NGS approaches, such as whole-exome sequencing, have limitations that are related to the technology itself as well as to the input source. Hence, clinical implementation of comprehensive NGS in a quality-controlled diagnostic workflow requires both the standardization of sequencing procedures and continuous validation of sequencing results by orthogonal methods in an ongoing program to enable the determination of key test parameters and continuous improvement of NGS and bioinformatics pipelines. We present validation data on 220 patients who were enrolled between 2013 and 2016 in a multi-institutional, genomics-guided precision oncology program (Molecularly Aided Stratification for Tumor Eradication Research) of the National Center for Tumor Diseases Heidelberg and the German Cancer Consortium. More than 90% of clinically actionable genomic alterations identified by combined whole-exome sequencing and transcriptome sequencing were successfully validated, with varying frequencies of discordant results across different types of alterations (fusions, 3.7%; single-nucleotide variants, 2.6%; amplifications, 1.1%; overexpression, 0.9%; deletions, 0.6%). The implementation of new computational methods for NGS data analysis led to a substantial improvement of gene fusion calling over time. Collectively, these data demonstrate the value of a rigorous validation program that partners with comprehensive NGS to successfully implement and continuously improve cancer precision medicine in a clinical setting.

ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling performance using the ICR142 NGS validation series.

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.

ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling performance using the ICR142 NGS validation series

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.

ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling using the ICR142 NGS validation series.

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 variant calling performance using the ICR142 NGS validation series, a dataset of 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 versions 0.5.2 and 0.6.1 of DeepVariant. This showed that v0.6.1 improves variant calling performance, but there was evidence of some minor changes in indel calling behaviour that may benefit from attention in future updates. 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.

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