Next-generation Sequencing In Research Research Articles

Abstract 42: High-throughput next-generation sequencing research solutions for detection of oncology variants, gene fusion events, and key oncology endpoints

Abstract Introduction: The Ion Torrent Genexus System has redefined the genomic profiling paradigm as the first fully-integrated, next-generation sequencing (NGS) research platform to provide an automated sample-to-report workflow with results in a single day. With a purification instrument, an enhanced chip architecture, and downstream reporting, the Genexus System provides a convenient solution to enable oncology research. Here we highlight the oncology research applications and high-throughput NGS capabilities of the Genexus System with Oncomine Comprehensive Assay Plus (OCA Plus), an oncology research panel that can detect variants, fusions, and evaluate key oncology research endpoints. We demonstrate the ability of OCA Plus on Genexus to evaluate tumor mutational burden (TMB), microsatellite instability (MSI), loss of heterozygosity (LOH), and homologous recombination repair deficiency (HRD). Methods: The high-throughput capabilities of the Genexus System enable it to support large oncology research panels such as OCA Plus, which comprises over 13,000 amplicons. The extensive per sample coverage allows for comprehensive DNA and RNA genomic profiling of relevant cancer biomarkers in over 500 genes including detection of over 1,300 fusion isoforms. We utilized high-molecular weight and FFPE samples, reference controls, and orthogonally tested research samples to evaluate DNA variant calling, RNA fusion calling, and key oncology research endpoints, including MSI, LOH, TMB, and HRD. Results: Commercially sourced reference control and research samples were sequenced using OCA Plus on the Genexus System. Sequencing data metrics showed ≥24 million reads per sample, with four samples supported per run. The high-throughput capacity of the Genexus chip architecture results in >95% of amplicons achieving a minimum of 500X coverage with an average coverage uniformity of ≥95% when evaluated across all >13,000 amplicons. Variant calling was assessed using the AcroMetrix Oncology Hotspot Control which has 328 hotspot variants covered by OCA Plus. Variant calling performance showed a hotspot and de novo variant sensitivity and PPV >95%. MSI score and status were assessed using known MSI-High and microsatellite stable (MSS) research samples and FFPE samples of interest. Results show high concordance with data from OCA Plus on GeneStudio. Conclusions: The increased throughput of the Genexus System combined with minimal touchpoints and a rapid turnaround time enables comprehensive genomic profiling for research assays such as OCA Plus where an increased number of sequencing reads leads to greater sensitivity for detecting rare variants and low-level fusion transcripts. Further, accurate characterization of key oncology research endpoints, such as TMB, MSI, LOH, and HRD, allow the Genexus System to accelerate research in the field of oncology. Citation Format: Kayla Zochowski, Dinesh Cyanam, Geoffrey Lowman, Jennifer Kilzer, Sameh El-Difrawy, Yan Zhu, Tanaya Puranik, Alex Phan, Derek Wong, Edith Kwong, Coleen Nemes, Iris Casuga, Frances Chan, Eun Ji Kim, Jianjun Guo, Vinay Mittal, Emily Norris, Shrutii Sarda, Mohit Gupta, Fan Shen, Steven Roman, Gabriel Vargas, Ying Jin, Annie Kraltcheva, Paul Williams, Amneet Gulati, Justin Rigby, Christopher Hoff, Richard Meade, Elaine Wong-Ho, Andrew Wong, Jamsheed Ghadiri, David Garcia-Viramontes, Scott Myrand, Seth Sadis. High-throughput next-generation sequencing research solutions for detection of oncology variants, gene fusion events, and key oncology endpoints [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 42.

Non-Coding RNAs Are Brokers in Breast Cancer Interactome Networks and Add Discrimination Power between Subtypes.

Despite the power of high-throughput genomics, most non-coding RNA (ncRNA) biotypes remain hard to identify, characterize, and validate. This is a clear indication that intensive next-generation sequencing research has led to great efficiency and accuracy in detecting ncRNAs, but not in their functionalization. Computational scientists continue to support the discovery process by spotting significant data features (expression or mutational profiles), elucidating phenotype uncertainty, and delineating complex regulation landscapes for biological pathways and pathophysiological processes. With reference to transcriptome regulation dynamics in cancer, this work introduces a novel network-driven inference approach designed to reveal the potential role of computationally identified ncRNAs in discriminating between breast cancer (BC) subtypes beyond the traditional gene expression signatures. As heterogeneity cast in the subtypes is a characteristic of most cancers, the proposed approach is generalizable beyond BC. Expression profiles of a wide transcriptome spectrum were obtained for a number of BC patients (and controls) listed in TCGA and processed with RNA-Seq. The well-known PAM50 subtype signature was available for the samples and used to move from differentially expressed transcript profiles to subtype-specific biclusters associating gene patterns with patients. Co-expressed gene networks were then generated and annotations were provided, focusing on the biclusters with basal and luminal signatures. These were used to build template maps, i.e., networks in which to embed the ncRNAs and contextually functionalize them based on their interactors. This inference approach is able to assess the influence of ncRNAs at the level of BC subtype. Network topology was considered through the brokerage measure to account for disruptiveness effects induced by the removal of nodes corresponding to ncRNAs. Equivalently, it is shown that ncRNAs can act as brokers of network interactome dynamics, and removing them allows the refinement of subtype-related characteristics previously obtained by gene signatures only. The results of the study elucidate the role of pseudogenes in two major BC subtypes, considering the contextual annotations. Put into a wider perspective, ncRNA brokers may help predictive functionalization studies targeted to new disease phenotypes, for instance those linked to the tumor microenvironment or metabolism, or those specifically involving metastasis. Overall, the approach may represent an in silico prioritization strategy toward the systems identification of new diagnostic and prognostic biomarkers.

Calling small variants using universality with Bayes-factor-adjusted odds ratios.

The application of next-generation sequencing in research and particularly in clinical routine requires highly accurate variant calling. Here we describe UVC, a method for calling small variants of germline or somatic origin. By unifying opposite assumptions with sublation, we discovered the following two empirical laws to improve variant calling: allele fraction at high sequencing depth is inversely proportional to the cubic root of variant-calling error rate, and odds ratios adjusted with Bayes factors can model various sequencing biases. UVC outperformed other variant callers on the GIAB germline truth sets, 192 scenarios of in silico mixtures simulating 192 combinations of tumor/normal sequencing depths and tumor/normal purities, the GIAB somatic truth sets derived from physical mixture, and the SEQC2 somatic reference sets derived from the breast-cancer cell-line HCC1395. UVC achieved 100% concordance with the manual review conducted by multiple independent researchers on a Qiagen 71-gene-panel dataset derived from 16 patients with colon adenoma. UVC outperformed other unique molecular identifier (UMI)-aware variant callers on the datasets used for publishing these variant callers. Performance was measured with sensitivity-specificity trade off for called variants. The improved variant calls generated by UVC from previously published UMI-based sequencing data provided additional insight about DNA damage repair. UVC is open-sourced under the BSD 3-Clause license at https://github.com/genetronhealth/uvc and quay.io/genetronhealth/gcc-6-3-0-uvc-0-6-0-441a694.

Appreci8: a pipeline for precise variant calling integrating 8 tools.

MotivationThe application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.ResultsWe developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8’s performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach.Availability and implementationAppreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411).Supplementary information Supplementary data are available at Bioinformatics online.

Large-Scale Low-Cost NGS Library Preparation Using a Robust Tn5 Purification and Tagmentation Protocol.

Efficient preparation of high-quality sequencing libraries that well represent the biological sample is a key step for using next-generation sequencing in research. Tn5 enables fast, robust, and highly efficient processing of limited input material while scaling to the parallel processing of hundreds of samples. Here, we present a robust Tn5 transposase purification strategy based on an N-terminal His6-Sumo3 tag. We demonstrate that libraries prepared with our in-house Tn5 are of the same quality as those processed with a commercially available kit (Nextera XT), while they dramatically reduce the cost of large-scale experiments. We introduce improved purification strategies for two versions of the Tn5 enzyme. The first version carries the previously reported point mutations E54K and L372P, and stably produces libraries of constant fragment size distribution, even if the Tn5-to-input molecule ratio varies. The second Tn5 construct carries an additional point mutation (R27S) in the DNA-binding domain. This construct allows for adjustment of the fragment size distribution based on enzyme concentration during tagmentation, a feature that opens new opportunities for use of Tn5 in customized experimental designs. We demonstrate the versatility of our Tn5 enzymes in different experimental settings, including a novel single-cell polyadenylation site mapping protocol as well as ultralow input DNA sequencing.

VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering.

BackgroundThe low concordance between different variant calling methods still poses a challenge for the wide-spread application of next-generation sequencing in research and clinical practice. A wide range of variant annotations can be used for filtering call sets in order to improve the precision of the variant calls, but the choice of the appropriate filtering thresholds is not straightforward. Variant quality score recalibration provides an alternative solution to hard filtering, but it requires large-scale, genomic data.ResultsWe evaluated germline variant calling pipelines based on BWA and Bowtie 2 aligners in combination with GATK UnifiedGenotyper, GATK HaplotypeCaller, FreeBayes and SAMtools variant callers, using simulated and real benchmark sequencing data (NA12878 with Illumina Platinum Genomes). We argue that these pipelines are not merely discordant, but they extract complementary useful information.We introduce VariantMetaCaller to test the hypothesis that the automated fusion of measurement related information allows better performance than the recommended hard-filtering settings or recalibration and the fusion of the individual call sets without using annotations. VariantMetaCaller uses Support Vector Machines to combine multiple information sources generated by variant calling pipelines and estimates probabilities of variants.This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, ranging from a few hundred kilobases to whole exomes. We also demonstrated that VariantMetaCaller supports a quantitative, precision based filtering of variants under wider conditions. Specifically, the computed probabilities of the variants can be used to order the variants, and for a given threshold, probabilities can be used to estimate precision. Precision then can be directly translated to the number of true called variants, or equivalently, to the number of false calls, which allows finding problem-specific balance between sensitivity and precision.ConclusionsVariantMetaCaller can be applied to small target regions and whole exomes as well, and it can be used in cases of organisms for which highly accurate variant call sets are not yet available, therefore it can be a viable alternative to hard filtering in cases where variant quality score recalibration cannot be used. VariantMetaCaller is freely available at http://bioinformatics.mit.bme.hu/VariantMetaCaller.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2050-y) contains supplementary material, which is available to authorized users.

Next-generation sequencing for research and diagnostics in kidney disease.

The advent of next-generation sequencing technologies has enabled genetic nephrology research to move beyond single gene analysis to the simultaneous investigation of hundreds of genes and entire pathways. These new sequencing approaches have been used to identify and characterize causal factors that underlie inherited heterogeneous kidney diseases such as nephronophthisis and congenital anomalies of the kidney and urinary tract. In this Review, we describe the development of next-generation sequencing in basic and clinical research and discuss the implementation of this novel technology in routine patient management. Widespread use of targeted and nontargeted approaches for gene identification in clinical practice will require consistent phenotyping, appropriate disease modelling and collaborative efforts to combine and integrate data analyses. Next-generation sequencing is an exceptionally promising technique that has the potential to improve the management of patients with inherited kidney diseases. However, identifying the molecular mechanisms that lead to renal developmental disorders and ciliopathies is difficult. A major challenge in the near future will be how best to integrate data obtained using next-generation sequencing with personalized medicine, including use of high-throughput disease modelling as a tool to support the clinical diagnosis of kidney diseases.

Genetic Counseling Practice in Next Generation Sequencing Research: Implications for the Ethical Oversight of the Informed Consent Process

The potential for next generation sequencing research (NGS) to generate individual genetic results could have implications for the informed consent process and the provision of genetic counseling. We undertook a content analysis of informed consent templates and guidelines produced by Canadian institutional review boards, purposively sampling documents used by researchers to obtain consent from participants in genetics studies. Our goal was to examine the extent to which the informed consent documents addressed genetic counseling and the return of individual genetic results. Our analysis reveals that the majority of informed consent documents did not mention genetic counseling while several did not mention the return of results. We found differences in the ways in which documents addressed availability of counseling, eligibility criteria for referral to a genetic counselor, genetic counselor involvement, provision of services to family members of participants and incidental findings. From an ethical standpoint, consent documents should provide appropriate information so that participants may make an informed decision about their participation in research. The need to ensure adequate counseling for study populations in an NGS research context will necessarily involve adapting values that underlie care in genetic counseling practice. If the interests of research participants are to be truly promoted, the drafting and review of informed consent documents should give proper due to genetic counseling.