Variant Calling Research Articles

Different control of resistance to two Colletotrichum orbiculare pathogenic races 0 and 1 in cucumber (Cucumis sativus L.).

Quantitative trait locus analysis identified independent novel loci in cucumbers responsible for resistance to races 0 and 1 of the anthracnose fungal pathogen Colletotrichum orbiculare. Cucumbers have been reported to be vulnerable to Colletotrichum orbiculare, causing anthracnose disease with significant yield loss under favorable conditions. The deployment of a single recessive Cssgr gene in cucumber breeding for anthracnose resistance was effective until a recent report on high-virulent strains infecting cucumbers in Japan conquering the resistance. QTL mapping was conducted to identify the resistance loci in the cucumber accession Ban Kyuri (G100) against C. orbiculare strains 104-T and CcM-1 of pathogenic races 0 and 1, respectively. A single dominant locus An5 was detected in the disease resistance hotspot on chromosome 5 for resistance to 104-T. Resistance to CcM-1 was governed by three loci with additive effects located on chromosomes 2 (An2) and 1 (An1.1 and An1.2). Molecular markers were developed based on variant calling between the corresponding QTL regions in the de novo assembly of the G100 genome and the publicly available cucumber genomes. Multiple backcrossed populations were deployed to fine-map An5 locus and narrow the region to approximately 222 kbp. Accumulation of An2 and An1.1 alleles displayed an adequate resistance to CcM-1 strain. This study provides functional molecular markers for pyramiding resistance loci that confer sufficient resistance against anthracnose in cucumbers.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking

BackgroundHigh-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling.ResultsOur approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants.ConclusionsOur findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

MtDNA-Server 2: advancing mitochondrial DNA analysis through highly parallelized data processing and interactive analytics.

Over the past decade, mtDNA-Server established itself as one of the most widely used variant calling web-services for human mitochondrial genomes. The service accepts sequencing data in BAM format and returns an annotated variant analysis report for both homoplasmic and heteroplasmic variants. In this work we present mtDNA-Server 2, which includes several new features highly requested by the community. Most importantly, it includes (a) the integration of a novel variant calling mode that accurately call insertions, deletions and single nucleotide variants at once, (b) the integration of additional quality control and input validation modules, (c) a method to estimate the required coverage to minimize false positives and (d) an interactive analytics dashboard. Furthermore, we migrated the complete analysis workflow to the Nextflow workflow manager for improved parallelization, reproducibility and local execution. Recognizing the importance of insertions and deletions as well as offering novel quality control, validation and reporting features, mtDNA-Server 2 provides researchers and clinicians a new state-of-the-art analysis platform for interpreting mitochondrial genomes. mtDNA-Server 2 is available via mitoverse, our analysis platform that offers a centralized place for mtDNA analysis in the cloud. The web-service, source code and its documentation are freely accessible at https://mitoverse.i-med.ac.at.

SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants.

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Exome Sequencing for the Diagnostics of Osteogenesis Imperfecta in Six Russian Patients.

Osteogenesis imperfecta (OI) is a group of inherited disorders of connective tissue that cause significant deformities and fragility in bones. Most cases of OI are associated with pathogenic variants in collagen type I genes and are characterized by pronounced polymorphisms in clinical manifestations and the absence of clear phenotype-genotype correlation. The objective of this study was to conduct a comprehensive molecular-genetic and clinical analysis to verify the diagnosis of OI in six Russian patients with genetic variants in the COL1A1 and COL1A2 genes. Clinical and laboratory data were obtained from six OI patients who were observed at the Medical Genetics Center in Saint Petersburg from 2016 to 2023. Next-generation sequencing on MGISEQ G400 (MGI, China) was used for DNA analysis. The GATK bioinformatic software (version 4.5.0.0) was used for variant calling and hard filtering. Genetic variants were verified by the direct automatic sequencing of PCR products using the ABI 3500X sequencer. We identified six genetic variants, as follows pathogenic c.3505G>A (p. Gly1169Ser), c.769G>A (p.Gly257Arg), VUS c.4123G>A (p.Ala1375Thr), and c.4114A>T (p.Asn1372Tyr) in COL1A1; and likely pathogenic c.2035G>A (p.Gly679Ser) and c.739-2A>T in COL1A2. In addition, clinical cases are presented due to the presence of the c.4114A>T variant in the COL1A2 gene. Molecular genetics is essential for determining different OI types due to the high similarity across various types of the disease and the failure of unambiguous diagnosis based on clinical manifestations alone. Considering the variable approaches to OI classification, an integrated strategy is required for optimal patient management.

Analysis and benchmarking of small and large genomic variants across tandem repeats.

Tandem repeats (TRs) are highly polymorphic in the human genome, have thousands of associated molecular traits and are linked to over 60 disease phenotypes. However, they are often excluded from at-scale studies because of challenges with variant calling and representation, as well as a lack of a genome-wide standard. Here, to promote the development of TR methods, we created a catalog of TR regions and explored TR properties across 86 haplotype-resolved long-read human assemblies. We curated variants from the Genome in a Bottle (GIAB) HG002 individual to create a TR dataset to benchmark existing and future TR analysis methods. We also present an improved variant comparison method that handles variants greater than 4 bp in length and varying allelic representation. The 8.1% of the genome covered by the TR catalog holds ~24.9% of variants per individual, including 124,728 small and 17,988 large variants for the GIAB HG002 'truth-set' TR benchmark. We demonstrate the utility of this pipeline across short-read and long-read technologies.

RNA-Seq Bulked Segregant Analysis of an Exotic B. napus ssp. napobrassica (Rutabaga) F2 Population Reveals Novel QTLs for Breeding Clubroot-Resistant Canola.

In this study, a rutabaga (Brassica napus ssp. napobrassica) donor parent FGRA106, which exhibited broad-spectrum resistance to 17 isolates representing 16 pathotypes of Plasmodiophora brassicae, was used in genetic crosses with the susceptible spring-type canola (B. napus ssp. napus) accession FG769. The F2 plants derived from a clubroot-resistant F1 plant were screened against three P. brassicae isolates representing pathotypes 3A, 3D, and 3H. Chi-square (χ2) goodness-of-fit tests indicated that the F2 plants inherited two major clubroot resistance genes from the CR donor FGRA106. The total RNA from plants resistant (R) and susceptible (S) to each pathotype were pooled and subjected to bulked segregant RNA-sequencing (BSR-Seq). The analysis of gene expression profiles identified 431, 67, and 98 differentially expressed genes (DEGs) between the R and S bulks. The variant calling method indicated a total of 12 (7 major + 5 minor) QTLs across seven chromosomes. The seven major QTLs included: BnaA5P3A.CRX1.1, BnaC1P3H.CRX1.2, and BnaC7P3A.CRX1.1 on chromosomes A05, C01, and C07, respectively; and BnaA8P3D.CRX1.1, BnaA8P3D.RCr91.2/BnaA8P3H.RCr91.2, BnaA8P3H.Crr11.3/BnaA8P3D.Crr11.3, and BnaA8P3D.qBrCR381.4 on chromosome A08. A total of 16 of the DEGs were located in the major QTL regions, 13 of which were on chromosome C07. The molecular data suggested that clubroot resistance in FGRA106 may be controlled by major and minor genes on both the A and C genomes, which are deployed in different combinations to confer resistance to the different isolates. This study provides valuable germplasm for the breeding of clubroot-resistant B. napus cultivars in Western Canada.

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes

BackgroundDrug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options.ResultsWe developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8–96.7%) to 79.6% for pyrazinamide (95% CI 76.9–82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1–100.0%) to 74.9% for capreomycin (95% CI 69.3–80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8–99.3%) to 79.5% for ethionamide (95% CI 76.4–82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC.ConclusionsGenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org.

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.

Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data

Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.

Lessons learned: overcoming common challenges in reconstructing the SARS-CoV-2 genome from short-read sequencing data via CoVpipe2

Background Accurate genome sequences form the basis for genomic surveillance programs, the added value of which was impressively demonstrated during the COVID-19 pandemic by tracing transmission chains, discovering new viral lineages and mutations, and assessing them for infectiousness and resistance to available treatments. Amplicon strategies employing Illumina sequencing have become widely established for variant detection and reference-based reconstruction of SARS-CoV-2 genomes, and are routine bioinformatics tasks. Yet, specific challenges arise when analyzing amplicon data, for example, when crucial and even lineage-determining mutations occur near primer sites. Methods We present CoVpipe2, a bioinformatics workflow developed at the Public Health Institute of Germany to reconstruct SARS-CoV-2 genomes based on short-read sequencing data accurately. The decisive factor here is the reliable, accurate, and rapid reconstruction of genomes, considering the specifics of the used sequencing protocol. Besides fundamental tasks like quality control, mapping, variant calling, and consensus generation, we also implemented additional features to ease the detection of mixed samples and recombinants. Results We highlight common pitfalls in primer clipping, detecting heterozygote variants, and dealing with low-coverage regions and deletions. We introduce CoVpipe2 to address the above challenges and have compared and successfully validated the pipeline against selected publicly available benchmark datasets. CoVpipe2 features high usability, reproducibility, and a modular design that specifically addresses the characteristics of short-read amplicon protocols but can also be used for whole-genome short-read sequencing data. Conclusions CoVpipe2 has seen multiple improvement cycles and is continuously maintained alongside frequently updated primer schemes and new developments in the scientific community. Our pipeline is easy to set up and use and can serve as a blueprint for other pathogens in the future due to its flexibility and modularity, providing a long-term perspective for continuous support. CoVpipe2 is written in Nextflow and is freely accessible from \href{https://github.com/rki-mf1/CoVpipe2}{github.com/rki-mf1/CoVpipe2} under the GPL3 license.

Abstract P19: Comprehensive Benchmark of Somatic Mutation Callers on Cell-Free DNA Next-Generation Sequencing Data in Cancer Liquid Biopsy

Abstract Circulating tumour DNA (ctDNA) has emerged as a rich source of biomarkers in precision oncology. However, ctDNA sequence data is often noisier than that derived from standard tissue biopsies, posing significant challenges for accurate cancer mutation detection. Besides, there is a lack of standardised and unbiased benchmark datasets to evaluate and compare performance of variant callers on ctDNA. To address this issue, we present a novel benchmarking approach and comprehensive benchmark of cancer somatic mutation callers on cell-free DNA (cfDNA) Next Generation Sequencing (NGS) data. To create a large-scale benchmark dataset, we developed a new methodology using real colorectal cancer patients plasma samples exhibiting either ultra-high and an ultra-low levels of ctDNA. This enabled the generation of dilution series of deep Whole Genome Sequencing (WGS, 150x) and the creation of high-quality consensus ground truth calls. We evaluated the performance of 9 SNV callers and 7 short INDEL callers frequently used in academic research, including 2 callers that were specifically designed for cfDNA. We further validated our findings at higher depth of coverage using deep Whole Exome Sequencing (WES, >2,000x) data. We further investigated each caller's features to develop practical guidelines for users depending on their application setting: unguided analysis focusing on overall accuracy vs. guided analysis focusing on sensitivity. Citation Format: Hanaé Carrié, Ngak Leng Sim, Pui Mun Wong, Anna Gan, Yi Ting Lau, Polly Poon, Saranya Thangaraju, Iain Tan, Yoon Sim Yap, Limsoon Wong, Anders Skanderup. Comprehensive Benchmark of Somatic Mutation Callers on Cell-Free DNA Next-Generation Sequencing Data in Cancer Liquid Biopsy [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P19.

Genome-wide characterization of the mutational landscape of proliferative verrucous leukoplakia

ObjectiveProliferative verrucous leukoplakia (PVL) is a rare but highly aggressive variant of oral leukoplakia that almost inevitably progresses to oral squamous cell carcinoma (OSCC). The aims of this study were to perform whole exome sequencing of a cohort of patients diagnosed with PVL and identify potential mutational profiles and pathways in this disorder. Study DesignA total of 12 oral cavity mucosal biopsies from 6 patients with oral lesions clinically compatible with PVL were used. Of these, 9 were diagnosed as dysplasia, 1 OSCC and 2 hyperkeratosis/hyperplasia. Exome sequencing used the Ion AmpliSeq Exome platform. Ion Reporter software was used for variant calling, annotation and filtering. Analysis and visualization of somatic mutations was carried out using the MAFtools R package. ResultsFollowing exome sequencing and mutational profiling we analyzed the profiles for cancer associated genes and signatures. Genes previously associated with OSCC including HYDIN, MUC16, MAML3, CDKN2A, FAT1 and CASP8 were mutated in multiple samples. Several DNA damage repair genes including PARP1 were mutated in PVL samples. NOTCH and Hippo pathways were the most frequently impacted by mutation. ConclusionThis genome wide characterization of premalignant PVL identifies both known and potentially novel oncogenic mechanisms in this disorder.

New investigation of encoding secondary metabolites gene by genome mining of a marine bacterium, Pseudoalteromonas viridis BBR56

Pseudoalteromonas viridis strain BBR56 was isolated from seawater at Dutungan Island, South Sulawesi, Indonesia. Bacterial DNA was isolated using Promega Genomic DNA TM050. DNA purity and quantity were assessed using NanoDrop spectrophotometers and Qubit fluorometers. The DNA library and sequencing were prepared using Oxford Nanopore Technology GridION MinKNOW 20.06.9 with long read, direct, and comprehensive analysis. High accuracy base calling was assessed with Guppy version 4.0.11. Filtlong and NanoPlot were used for filtering and visualizing the FASTQ data. Flye (2.8.1) was used for de novo assembly analysis. Variant calls and consensus sequences were created using Medaka. The annotation of the genome was elaborated by DFAST. The assembled genome and annotation were tested using Busco and CheckM. Herein, we found that the highest similarity of the BBR56 isolate was 98.37% with the 16 S rRNA gene sequence of P. viridis G-1387. The genome size was 5.5 Mb and included chromosome 1 (4.2 Mbp) and chromosome 2 (1.3 Mbp), which encoded 61 pseudogenes, 4 noncoding RNAs, 113 tRNAs, 31 rRNAs, 4,505 coding DNA sequences, 4 clustered regularly interspaced short palindromic repeats, 4,444 coding genes, and a GC content of 49.5%. The sequence of the whole genome of P. viridis BBR56 was uploaded to GenBank under the accession numbers CP072425–CP072426, biosample number SAMN18435505, and bioproject number PRJNA716373. The sequence read archive (SRR14179986) was successfully obtained from NCBI for BBR56 raw sequencing reads. Digital DNA–DNA hybridization results showed that the genome of BBR56 had the potential to be a new species because no other bacterial genomes were similar to the sample. Biosynthetic gene clusters (BGCs) were assessed using BAGEL4 and the antiSMASH bacterial version. The genome harbored diverse BGCs, including genes that encoded polyketide synthase, nonribosomal peptide synthase, RiPP-like, NRP-metallophore, hydrogen cyanide, betalactone, thioamide-NRP, Lant class I, sactipeptide, and prodigiosin. Thus, BBR56 has considerable potential for further exploration regarding the use of its secondary metabolite products in the human and fisheries sectors.

Chromosome 20p11.2 deletions cause congenital hyperinsulinism via the loss of FOXA2 or its regulatory elements.

Persistent congenital hyperinsulinism (HI) is a rare genetically heterogeneous condition characterised by dysregulated insulin secretion leading to life-threatening hypoglycaemia. For up to 50% of affected individuals screening of the known HI genes does not identify a disease-causing variant. Large deletions have previously been used to identify novel regulatory regions causing HI. Here, we used genome sequencing to search for novel large (>1 Mb) deletions in 180 probands with HI of unknown cause and replicated our findings in a large cohort of 883 genetically unsolved individuals with HI using off-target copy number variant calling from targeted gene panels. We identified overlapping heterozygous deletions in five individuals (range 3-8 Mb) spanning chromosome 20p11.2. The pancreatic beta-cell transcription factor gene, FOXA2, a known cause of HI was deleted in two of the five individuals. In the remaining three, we found a minimal deleted region of 2.4 Mb adjacent to FOXA2 that encompasses multiple non-coding regulatory elements that are in conformational contact with FOXA2. Our data suggests that the deletions in these three children may cause disease through the dysregulation of FOXA2 expression. These findings provide new insights into the regulation of FOXA2 in the beta-cell and confirm an aetiological role for chromosome 20p11.2 deletions in syndromic HI.

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.

Abstract LB095: ASPYRE-Lung addresses critical gaps in NGS-based biomarker testing: Robust variant calling from NGS QC fails

Abstract Advanced NSCLC treatment guidelines recommend testing patients for genomic biomarkers to utilize over 30 FDA-approved targeted therapies. A major challenge with current NGS-based testing methods is that ~45% of patient samples fail NGS quality control (QC), leading to inadequate patient care. The ASPYRE® technology addresses the urgent need for rapid, accessible and affordable diagnostics informing actionable genomic target variants. The ASPYRE-Lung® panel covers 114 variants in 11 genes (ALK, BRAF, EGFR, ERBB2, KRAS, RET, ROS1, MET & NTRK1/2/3) to robustly inform clinical management, based on clinical practice guidelines. The assay concurrently detects single nucleotide variants, insertions, deletions, and gene fusions from NSCLC tissue-derived DNA and RNA. In this study, we investigated an NSCLC patient sample set from Precision for Medicine, including biobanked clinical specimens that had previously failed NGS QC, and for which genomic biomarker data were unavailable. 120 matched DNA and RNA biobanked NSCLC FFPE patient samples previously analyzed by Illumina TSO 500 were tested using ASPYRE-Lung in Biofidelity’s CLIA laboratory, including 94 samples which failed NGS QC parameters for DNA, RNA, or both, and 26 control samples which passed NGS QC. Samples were tested by ASPYRE-Lung using an input of 20 ng DNA and 6 ng RNA, except for 5 patient samples that were run at lower inputs, ranging from 4.25 to 14 ng DNA. Key findings:•Of the 94 patient samples that failed NGS QC, 98% of samples (92/94) passed ASPYRE-Lung QC and could inform patient care.•In the 92 patient samples that passed ASPYRE-Lung QC, 47% (43/92) had a detectable variant identified by ASPYRE-Lung.•All 26 control samples that passed NGS QC also passed ASPYRE-Lung QC.•Of the 5 samples with 4.25 to 14 ng DNA inputs to ASPYRE-Lung, data were generated on 4/5 samples and 2/4 had a detectable variant.•ASPYRE-Lung has the potential to provide actionable clinical information on patient samples deemed to be of insufficient quantity for NGS testing.ASPYRE-Lung has a high success rate, is easily adoptable and cost effective making it suitable as a first-line testing option, or for samples that are either QNS or fail NGS QC, and can provide a large fraction of NSCLC patients with actionable biomarker information, with potentially smaller tissue requirements. ASPYRE-Lung genomic testing is transformative for cancer care management, and allows more patients with NSCLC to benefit from effective and better tolerated therapies. Citation Format: Ryan T. Evans, Elyse Shapiro, Elizabeth Gillon-Zhang, Julia N. Brown, Candace King, Cory Kiser, Mary Beth Rossi, Darren Davis, Michelle L. Taylor, Jennifer Clower, Jian Yang, Cullen A. Taylor, Robert W. Snyder, Wendy J. Levin. ASPYRE-Lung addresses critical gaps in NGS-based biomarker testing: Robust variant calling from NGS QC fails [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB095.

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.

Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery.

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.