Accurate Variant Calling Research Articles

Fast and accurate DNASeq variant calling workflow composed of LUSH toolkit

BackgroundWhole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patient's disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs.ResultsHere, we propose a fast and accurate DNASeq variant calling workflow that is purely composed of tools from LUSH toolkit. The precision and recall measurements indicate that both the LUSH and GATK pipelines exhibit high levels of consistency, with precision and recall rates exceeding 99% on the 30x NA12878 dataset. In terms of processing speed, the LUSH pipeline outperforms the GATK pipeline, completing 30x WGS data analysis in just 1.6 h, which is approximately 17 times faster than GATK. Notably, the LUSH_HC tool completes the processing from BAM to VCF in just 12 min, which is around 76 times faster than GATK.ConclusionThese findings suggest that the LUSH pipeline is a highly promising alternative to the GATK pipeline for WGS data analysis, with the potential to significantly improve bedside analysis of acutely ill patients, large-scale cohort data analysis, and high-throughput variant calling in crop breeding programs. Furthermore, the LUSH pipeline is highly scalable and easily deployable, allowing it to be readily applied to various scenarios such as clinical diagnosis and genomic research.

Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data.

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT's super-high accuracy model. ONT's superior performance is attributed to its ability to overcome Illumina's errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT's super-high accuracy data mitigates ONT's traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.

MiniSNV: accurate and fast single nucleotide variant calling from nanopore sequencing data.

Nanopore sequence technology has demonstrated a longer read length and enabled to potentially address the limitations of short-read sequencing including long-range haplotype phasing and accurate variant calling. However, there is still room for improvement in terms of the performance of single nucleotide variant (SNV) identification and computing resource usage for the state-of-the-art approaches. In this work, we introduce miniSNV, a lightweight SNV calling algorithm that simultaneously achieves high performance and yield. miniSNV utilizes known common variants in populations as variation backgrounds and leverages read pileup, read-based phasing, and consensus generation to identify and genotype SNVs for Oxford Nanopore Technologies (ONT) long reads. Benchmarks on real and simulated ONT data under various error profiles demonstrate that miniSNV has superior sensitivity and comparable accuracy on SNV detection and runs faster with outstanding scalability and lower memory than most state-of-the-art variant callers. miniSNV is available from https://github.com/CuiMiao-HIT/miniSNV.

Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection.

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole genome analysis by short read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing, and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare disease cohort of 98 samples, including 41 probands and some family members, using nanopore sequencing, achieving per sample ∼36x average coverage and 32 kilobase (kb) read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including SVs and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants, and could be used to distinguish postzygotic mosaic variants from prezygotic de novos . Eleven probands were solved, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing

Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k-mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. Moreover, VolcanoSV is robust to various evaluation parameters and accurately identifies breakpoints and SV sequences.

ConsensuSV-ONT - a modern method for accurate structural variant calling.

The improvements in sequencing technology make the development of new tools for the detection of structural variance more and more common. However, the tools available for the long-read Oxford Nanopore sequencing are limited, and it is hard to choose one, which is the best. That is why there is a need to create a tool based on consensus that combines existing work in order to discover a set of high-quality, reliable structural variants that can be used for further downstream analysis. The field has also been subject to revolution in machine learning techniques, especially deep learning. In the spirit of the aforementioned need and developments, we propose a novel, fully automated ConsensuSV-ONT algorithm. The method uses six independent, state-of-the-art structural variant callers for long-read sequencing along with a convolutional neural network for filtering high-quality variants. We provide a runtime environment in the form of a docker image, wrapping a nextflow pipeline for efficient processing using parallel computing. The solution is complete in its form and is ready to use not only by computer scientists but accessible and easy to use for everyone working with Oxford Nanopore long-read sequencing data.

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.

Precision genome profiling for pediatric leukemia with a software-controlled enrichment method using nanopore sequencer.

10027 Background: The real-time nature of nanopore sequencing allows for simultaneous basecalling of DNA sequences during sequencing. This unique capability enables adaptive sampling (AS), a software-controlled targeted sequencing method. AS is notable for its ability to rapidly and flexibly enrich multiple genes with long fragment reads and detect various modalities of genetic aberrations in a single run. However, the feasibility and significance of AS for cancer have not been previously reported. Methods: We performed AS on a GridION sequencer using samples from 28 pediatric leukemia patients (10 acute myeloid leukemia, 13 B-cell acute lymphoblastic leukemia, and 5 T-cell acute lymphoblastic leukemia). Target regions were comprised of 466 genes associated with hematologic malignancies and included 30-kilo base pairs of flanking regions. After 3 days of sequencing, single-nucleotide variants (SNVs), structural variations (SVs), and copy number variations (CNVs) were determined. In 21 samples, variant calling accuracy was evaluated using short-read-based whole genome sequencing (WGS). Results: In the on-target regions, mean depth was 21.0× and N50 was 11,191 bps at the median. Of the 13 samples with genetic alterations previously detected through clinical testing for diagnostic classifications, all were reconfirmed by AS. Detecting DUX4 rearrangement needed an additional analysis pipeline because of its structural complexity. Among the 15 remaining samples whose genetic alterations had not been determined by clinical diagnostic testing, AS identified putative driver aberrations in 14 samples. All of these genomic abnormalities were structural variations (SVs) or copy number variations (CNVs), most of which were not encompassed within the genes or regions targeted in clinical diagnostic testing. Detected SVs were described with accurate genomic breakpoints, enabling the efficient detection of gene rearrangements and deletions of the entire region of an exon or a gene. Regarding CNVs, both chromosomal-level CNVs, such as high-hyperdiploid, and focal CNVs, such as CDKN2A deletion, were detectable using genome-wide low-coverage reads in the off-target regions. In the other case with acute myeloid leukemia in which genetic abnormalities were not detected either by clinical testing or AS, WGS identified an NPM1 frameshift deletion located outside of known hotspots. Among variants identified by WGS, AS detected 60.9% of the SNVs, 17.6% of the small indels, and 89.2% of the SVs, with a tendency of poor detection efficiency for variants with low variant allele frequency. Conclusions: AS provides a rapid and precise description of the genomic profile of pediatric leukemia, particularly advantageous for identifying SVs and CNVs, which are difficult to detect by capture-based short-read sequencing.

Clinical validation of the Ion Torrent Oncomine Myeloid Assay GX v2 on the Genexus Integrated Sequencer as a stand-alone assay for single-nucleotide variants, insertions/deletions, and fusion genes: Challenges, performance, and perspectives.

Myeloid neoplasms require comprehensive characterization of genetic abnormalities, including single-nucleotide variants, small insertions and deletions, and fusions and translocations for management. The Oncomine Myeloid Assay GX v2 (Thermo Fisher Scientific) analyzes 17 full genes, 28 hotspot genes, 30 fusion driver genes, and 5 expression genes. The validation set included 192 DNA samples, 28 RNA samples, and 9 cell lines and contrived controls. The DNA and RNA were extracted from both peripheral blood and bone marrow. Library preparation, templating, and sequencing was performed on the fully automated Genexus Integrated Sequencer (Thermo Fisher Scientific). The sequencing data were analyzed by manual curation, default Oncomine filters and the Oncomine Reporter (Thermo Fisher Scientific). Of the 600 reference pathogenic DNA variants targeted by the assay, concordance was seen in 98.3% of unfiltered variant call format files. Precision and reproducibility were 100%, and the lower limit of detection was 2% variant allele frequency for DNA. Inability to detect variants in long homopolymer regions intrinsic to the Ion Torrent chemistry led to 7 missed variants; 100% concordance was seen with reference RNA samples. This extensive clinical validation of the Oncomine Myeloid Assay GX v2 on the Genexus Integrated Sequencer with its built-in bioinformatics pipeline and Ion Torrent Oncomine Reporter shows robust performance in terms of variant calling accuracy, precision, and reproducibility, with the advantage of a rapid turnaround time of 2 days. The greatest limitation is the inability to detect variants in long homopolymer regions.

Negligible effects of read trimming on the accuracy of germline short variant calling in the human genome

Background Next generation sequencing (NGS) has become a standard tool in the molecular diagnostics of Mendelian disease, and the precision of such diagnostics is greatly affected by the accuracy of variant calling from sequencing data. Recently, we have comprehensively evaluated the performance of multiple variant calling pipelines. However, no systematic analysis of the effects of read trimming on variant discovery with modern variant calling software has yet been performed. Methods In this work, we systematically evaluated the effects of adapters on the performance of 8 variant calling and filtering methods using 14 standard reference Genome-in-a-Bottle (GIAB) samples. Variant calls were compared to the ground truth variant sets, and the effect of adapter trimming with different tools was assessed using major performance metrics (precision, recall, and F1 score). Results We show that adapter trimming has no effect on the accuracy of the best-performing variant callers (e.g., DeepVariant) on whole-genome sequencing (WGS) data. For whole-exome sequencing (WES) datasets subtle improvement of accuracy was observed in some of the samples. In high-coverage WES data (~200x mean coverage), adapter removal allowed for discovery of 2-4 additional true positive variants in only two out of seven datasets tested. Moreover, this effect was not dependent on the median insert size and proportion of adapter sequences in reads. Surprisingly, the effect of trimming on variant calling was reversed when moderate coverage (~80-100x) WES data was used. Finally, we show that some of the recently developed machine learning-based variant callers demonstrate greater dependence on the presence of adapters in reads. Conclusions Taken together, our results indicate that adapter removal is unnecessary when calling germline variants, but suggest that preprocessing methods should be carefully chosen when developing and using machine learning-based variant analysis methods.

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES).

Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions. A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed. The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations. This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients' response to immunotherapy.

Long-read sequencing and optical mapping generates near T2T assemblies that resolves a centromeric translocation

Long-read genome sequencing (lrGS) is a promising method in genetic diagnostics. Here we investigate the potential of lrGS to detect a disease-associated chromosomal translocation between 17p13 and the 19 centromere. We constructed two sets of phased and non-phased de novo assemblies; (i) based on lrGS only and (ii) hybrid assemblies combining lrGS with optical mapping using lrGS reads with a median coverage of 34X. Variant calling detected both structural variants (SVs) and small variants and the accuracy of the small variant calling was compared with those called with short-read genome sequencing (srGS). The de novo and hybrid assemblies had high quality and contiguity with N50 of 62.85 Mb, enabling a near telomere to telomere assembly with less than a 100 contigs per haplotype. Notably, we successfully identified the centromeric breakpoint of the translocation. A concordance of 92% was observed when comparing small variant calling between srGS and lrGS. In summary, our findings underscore the remarkable potential of lrGS as a comprehensive and accurate solution for the analysis of SVs and small variants. Thus, lrGS could replace a large battery of genetic tests that were used for the diagnosis of a single symptomatic translocation carrier, highlighting the potential of lrGS in the realm of digital karyotyping.

MitoH3: Mitochondrial Haplogroup and Homoplasmic/Heteroplasmic Variant Calling Pipeline for Alzheimer's Disease Sequencing Project.

Mitochondrial DNA (mtDNA) is a double-stranded circular DNA and has multiple copies in each cell. Excess heteroplasmy, the coexistence of distinct variants in copies of mtDNA within a cell, may lead to mitochondrial impairments. Accurate determination of heteroplasmy in whole-genome sequencing (WGS) data has posed a significant challenge because mitochondria carrying heteroplasmic variants cannot be distinguished during library preparation. Moreover, sequencing errors, contamination, and nuclear mtDNA segments can reduce the accuracy of heteroplasmic variant calling. To efficiently and accurately call mtDNA homoplasmic and heteroplasmic variants from the large-scale WGS data generated from the Alzheimer's Disease Sequencing Project (ADSP), and test their association with Alzheimer's disease (AD). In this study, we present MitoH3-a comprehensive computational pipeline for calling mtDNA homoplasmic and heteroplasmic variants and inferring haplogroups in the ADSP WGS data. We first applied MitoH3 to 45 technical replicates from 6 subjects to define a threshold for detecting heteroplasmic variants. Then using the threshold of 5% ≤variant allele fraction≤95%, we further applied MitoH3 to call heteroplasmic variants from a total of 16,113 DNA samples with 6,742 samples from cognitively normal controls and 6,183 from AD cases. This pipeline is available through the Singularity container engine. For 4,311 heteroplasmic variants identified from 16,113 samples, no significant variant count difference was observed between AD cases and controls. Our streamlined pipeline, MitoH3, enables computationally efficient and accurate analysis of a large number of samples.

Clinical Validation of a Targeted Next-Generation Sequencing Panel for Lymphoid Malignancies

Lymphoid malignancies are a heterogeneous group of hematological disorders characterized by a diverse range of morphologic, immunophenotypic, and clinical features. Next-generation sequencing (NGS) is increasingly being applied to delineate the complex nature of these malignancies and identify high-value biomarkers with diagnostic, prognostic, or therapeutic benefit. However, there are various challenges in using NGS routinely to characterize lymphoid malignancies, including pre-analytic issues, such as sequencing DNA from formalin-fixed, paraffin-embedded tissue, and optimizing the bioinformatic workflow for accurate variant calling and filtering. This study reports the clinical validation of a custom capture-based NGS panel to test for molecular markers in a range of lymphoproliferative diseases and histiocytic neoplasms. The fully validated clinical assay represents an accurate and sensitive tool for detection of single-nucleotide variants and small insertion/deletion events to facilitate the characterization and management of patients with hematologic cancers specifically of lymphoid origin.

Abstract 5023: Analytical validation of the Oncomine™ Dx Express Test using liquid biopsy samples as an IUO assay in a CAP/CLIA lab

Abstract Introduction: The Oncomine™ Dx Express Test (ODxET) is a next-generation sequencing (NGS) test. It can be used as an investigational use only (IUO**) test to detect somatic DNA and RNA alterations in human cell-free total nucleic acid (cfTNA) isolated from plasma samples. This study outlines the analytical validation of the ODxET for relevant single nucleotide variants (SNV) and insertion/deletions (INDEL) in liquid biopsy specimens. The Ion Torrent Genexus™ platform has a short turnaround time with minimal operator input. This study was performed in a College of American Pathologists (CAP™) accredited and Clinical Laboratory Improvement Act (CLIA) licensed laboratory. Methods: Forty-seven unique samples (39 clinical, 3 derived, 2 reference and 3 commercially available DNA controls) were analyzed. All experiments began with sample extraction, except the lower input limit of detection (LLOD). We evaluated limit of detection, variant calling accuracy, and precision. Two control NIST™ samples were sequenced with 3 technical replicates each for INDEL and SNVs analytical accuracy. The LOD was evaluated at both the target input of 2ng/µl (40ng), and the minimum input,0.33ng/µl (6.6ng) with allele frequency (AF) levels at 1%, 0.5%, and 0.25%. The 39 clinical plasma samples were used for variant calling accuracy. Inter-assay reproducibility was assessed by sequencing DNA controls across four runs by different operators over multiple days/systems. Intra-assay repeatability was assessed by sequencing 4 samples in replicate on the same run by one operator on the same day/system. Results: The analytical accuracy and NPV was 100% for SNV/INDEL variants. The 40ng input LOD sensitivity was 96% for SNVs at 0.5% AF and 100% for INDELs at 0.25% Minimum AF. The lower input LOD sensitivity was 89% for SNVs and 100% for INDEL at 1% AF. Variant calling accuracy on samples with pre-characterized data was 100% for the 13 positive samples. Specificity was &amp;gt;99% for all samples including both healthy donor and positive samples. Repeatability was 100% for INDELs and &amp;gt;96% for SNVs, reproducibility was 100% for both variant classes. Conclusion: This validation study to estimate analytical accuracy, limit of detection, variant calling accuracy and precision has demonstrated that the ODxET assay is highly robust in detecting SNV and INDEL mutations in plasma samples and is suitable for use in a CAP/CLIA laboratory as an IUO assay.**=In Vitro Diagnostic use only. Not available in all regions including North America. Citation Format: Juan C. Espinoza, Rajendra Ramsamooj, Akemi Yuki, John Williamson, Diarra Hassell, Madhu Jasti, Suzanne Salazar, David Ginzinger. Analytical validation of the Oncomine™ Dx Express Test using liquid biopsy samples as an IUO assay in a CAP/CLIA lab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5023.

Abstract 326: Matched fresh frozen and FFPE patient tissues reveal the enhanced sensitivity and data quality of a novel DNA library prep method

Abstract In cancer genomics, a common source of DNA is formalin-fixed, paraffin-embedded (FFPE) tissue from patient surgical samples, where in most cases high quality fresh or frozen tissue samples are not available. FFPE DNA poses many notable challenges for preparing NGS libraries, including low input amounts and highly variable damage from fixation, storage, and extraction methods. It is difficult to obtain libraries with sufficient coverage and the sequencing artifacts arising from damaged DNA bases confound somatic variant detection. Additionally, many laboratories process FFPE tumor samples alongside matched, high quality, normal DNA and many library prep workflows are not readily compatible with both sample types. We developed a novel NGS library prep method compatible with both high quality and very low quality FFPE DNA samples, employing a novel enzymatic DNA repair mix, enzymatic fragmentation mix, and PCR master mix. To validate this workflow on real patient sample sets, we obtained DNA extracted from matched tumor and normal tissue of various tissue types preserved by both fresh frozen and FFPE methods. The FFPE DNA samples ranged in quality from DNA integrity number (DIN) 1.5 to 6.8, including samples stored for nearly 3 decades. We prepared libraries using this method compared against other library prep workflows and sequenced them by WGS and target capture. We assessed the performance of these workflows by library yield, library quality metrics, depth and evenness of coverage, and somatic variant calling with fresh frozen-extracted DNA providing the gold standard for library quality and mutation content. This new enzymatic fragmentation-based library prep workflow not only reduced the false positive rate in somatic variant detection by repairing damage-derived mutations in FFPE DNA samples but also improved the library yield, library quality metrics (including mapping, chimeras, and properly-paired reads), library complexity, coverage depth and uniformity, and hybrid capture library quality metrics. Comparing the variant calls from matched FFPE and frozen tissues revealed an improved sensitivity and accuracy of variant calling using this library prep method compared to mechanical shearing and other enzymatic fragmentation library prep approaches. This new suite of enzyme mixes allows even highly damaged FFPE samples to achieve high quality libraries with a greater sensitivity for somatic variant identification. The workflow is robust and flexible, compatible with both FFPE DNA and matched high quality DNA samples as well as automation-friendly for convenience in sample processing. Citation Format: Margaret R. Heider, Jian Sun, Brittany Sexton, Bradley W. Langhorst, Laura Blum, Pingfang Liu. Matched fresh frozen and FFPE patient tissues reveal the enhanced sensitivity and data quality of a novel DNA library prep method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 326.

Abstract 4926: Advancements in somatic variant calling from UG100 whole genome and whole exome sequencing data

Abstract Somatic variant calling involves the identification of genomic alterations that occur in somatic cells, requiring deep coverage to enable high sensitivity for low-frequency variants. Characterizing somatic variants across the entire genome therefore benefits from novel cost-efficient sequencing platforms, such as UG100. Here, we present optimization of variant calling tools for short and structural variants on WGS and WES data from UG100. For calling short variants, we optimized DeepVariant (DV) for somatic calling using data from matched tumor-normal sample pairs, improving both variant calling accuracy and pipeline running time (up to 10-fold). We defined the task of somatic variant calling as deciding if the pileup image containing reads from the tumor and normal samples represents a true somatic variant (vs a germline variant or artifact). The challenge of robust variant calling using deep learning models is exacerbated in somatic calling, where sequencing depth and coverage variability are typically high. Our optimized DV overcomes these challenges by several data sampling strategies. First, allele-frequency preserving down-sampling reduces randomness of read sub-sampling in high coverage regions. Second, alternative allele prioritization samples alt-allele supporting reads first allowing to call variants at very high coverage loci without sacrificing sensitivity and computational efficiency. Finally, a Panel-of-Normals based on targeted WES data provides an additional improvement of precision for this assay type. We used these strategies to train two models, one for tumor characterization using WGS (T/N coverage: 40x-150x/40x-100x), and one for deep WES (T/N coverage: &amp;gt;500x/&amp;gt;120x). We called variants on simulated tumors using the WGS model. For VAF&amp;gt;10% the model showed SNV recall &amp;gt;98% and indel recall &amp;gt;95% with false-positive rate of 0.2/Mb. For VAF range of 5-10%, indel recall was 67% and SNV recall was 86%. To demonstrate the utility of our somatic variant calling, we applied the models to call somatic variants from well characterized cancer cell lines: COLO829, HCC1395 and HCC1143. Results showed F1&amp;gt;90% for variants with VAF&amp;gt;10%. The WES model was used to reliably call variants at VAF&amp;gt;5% on simulated tumors with average SNV recall of 99% with precision &amp;gt;99% and indel recall &amp;gt;86% with precision &amp;gt;94%. To analyze structural and copy-number variations, we optimized the assembly engine of GRIDSS to enable fast calling of structural variations and demonstrate that Control-FREEC can be used to call copy number variants. SV calling on COLO829/COLO829BL achieved sensitivity &amp;gt;95%. In conclusion, our research highlights the utility of UG100 within the field of oncology, demonstrating its capacity for comprehensive and precise somatic variant detection, both on WGS and WES data. Citation Format: Doron Shem-Tov, Maya Levy, Gil Hornung, Ilya Soifer, Hila Benjamin, Ariel Jaimovich, Adam Blattler, William Brandler, Robert Sugar, Isaac Kinde, Omer Barad, Doron Lipson. Advancements in somatic variant calling from UG100 whole genome and whole exome sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4926.

ArCH: improving the performance of clonal hematopoiesis variant calling and interpretation.

The acquisition of somatic mutations in hematopoietic stem and progenitor stem cells with resultant clonal expansion, termed clonal hematopoiesis (CH), is associated with increased risk of hematologic malignancies and other adverse outcomes. CH is generally present at low allelic fractions, but clonal expansion and acquisition of additional mutations leads to hematologic cancers in a small proportion of individuals. With high depth and high sensitivity sequencing, CH can be detected in most adults and its clonal trajectory mapped over time. However, accurate CH variant calling is challenging due to the difficulty in distinguishing low frequency CH mutations from sequencing artifacts. The lack of well-validated bioinformatic pipelines for CH calling may contribute to lack of reproducibility in studies of CH. Here, we developed ArCH, an Artifact filtering Clonal Hematopoiesis variant calling pipeline for detecting single nucleotide variants and short insertions/deletions by combining the output of four variant calling tools and filtering based on variant characteristics and sequencing error rate estimation. ArCH is an end-to-end cloud-based pipeline optimized to accept a variety of inputs with customizable parameters adaptable to multiple sequencing technologies, research questions, and datasets. Using deep targeted sequencing data generated from six acute myeloid leukemia patient tumor: normal dilutions, 31 blood samples with orthogonal validation, and 26 blood samples with technical replicates, we show that ArCH improves the sensitivity and positive predictive value of CH variant detection at low allele frequencies compared to standard application of commonly used variant calling approaches. The code for this workflow is available at: https://github.com/kbolton-lab/ArCH.

A Nanopore Sequencing-based Pharmacogenomic Panel to Personalize Tuberculosis Drug Dosing.

Rationale: Standardized dosing of antitubercular drugs leads to variable plasma drug levels, which are associated with adverse drug reactions, delayed treatment response, and relapse. Mutations in genes affecting drug metabolism explain considerable interindividual pharmacokinetic variability; however, pharmacogenomic assays that predict metabolism of antitubercular drugs have been lacking. Objectives: We sought to develop a Nanopore sequencing panel and validate its performance in patients with active tuberculosis (TB) to personalize treatment dosing. Methods: We developed a Nanopore sequencing panel targeting 15 SNPs in five genes affecting the metabolism of antitubercular drugs. For validation, we sequenced DNA samples (n = 48) from the 1,000 Genomes Project and compared the variant calling accuracy with that of Illumina genome sequencing. We then sequenced DNA samples from patients with active TB (n = 100) from South Africa on a MinION Mk1C and evaluated the relationship between genotypes and pharmacokinetic parameters for isoniazid (INH) and rifampin (RIF). Measurements and Main Results: The pharmacogenomic panel achieved 100% concordance with Illumina sequencing in variant identification for the samples from the 1,000 Genomes Project. In the clinical cohort, coverage was more than 100× for 1,498 of 1,500 (99.8%) amplicons across the 100 samples. Thirty-three percent, 47%, and 20% of participants were identified as slow, intermediate, and rapid INH acetylators, respectively. INH clearance was 2.2 times higher among intermediate acetylators and 3.8 times higher among rapid acetylators, compared with slow acetylators (P < 0.0001). RIF clearance was 17.3% (2.50-29.9) lower in individuals with homozygous AADAC rs1803155 G→A substitutions (P = 0.0015). Conclusions: Targeted sequencing can enable the detection of polymorphisms that influence TB drug metabolism on a low-cost, portable instrument to personalize dosing for TB treatment or prevention.

Flexible and cost-effective genomic surveillance of P. falciparum malaria with targeted nanopore sequencing

Genomic surveillance of Plasmodium falciparum malaria can provide policy-relevant information about antimalarial drug resistance, diagnostic test failure, and the evolution of vaccine targets. Yet the large and low complexity genome of P. falciparum complicates the development of genomic methods, while resource constraints in malaria endemic regions can limit their deployment. Here, we demonstrate an approach for targeted nanopore sequencing of P. falciparum from dried blood spots (DBS) that enables cost-effective genomic surveillance of malaria in low-resource settings. We release software that facilitates flexible design of amplicon sequencing panels and use this software to design two target panels for P. falciparum. The panels generate 3–4 kbp reads for eight and sixteen targets respectively, covering key drug-resistance associated genes, diagnostic test antigens, polymorphic markers and the vaccine target csp. We validate our approach on mock and field samples, demonstrating robust sequencing coverage, accurate variant calls within coding sequences, the ability to explore P. falciparum within-sample diversity and to detect deletions underlying rapid diagnostic test failure.