Method For Detection Of Variants Research Articles

Generative haplotype prediction outperforms statistical methods for small variant detection in next-generation sequencing data.

Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false-positive detections in a typical human whole genome, creating a significant manual review burden. We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern large language models. We train our model on 37 whole genome sequences from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3, and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants, our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested. Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/.

Detection and Evaluation of Procalcitonin Variants As Diagnostic Tools in Systemic Inflammation

BACKGROUND: Procalcitonin is an indicator of systemic inflammation associated with major surgery or sepsis. Procalcitonin exists in a full-length and truncated variant as a result of dipeptidylpeptidase-4 (DPP4)-cleavage. We recently identified differential biological activity of both variants. Here, we present an immunoassay-based method for the separate detection of procalcitonin variants and correlation to clinical data in patients with severe systemic inflammation. METHODS: Rabbits were immunized with peptides of N-terminal sequences of both human procalcitonin variants and polyclonal antibodies purified from rabbit plasma. Antibodies were used for the detection of procalcitonin variants in an indirect sandwich enzyme-linked immunosorbent assay (ELISA) using a commercially available monoclonal anti-procalcitonin antibody as capture. Serum was collected from 19 septic patients exhibiting hyperprocalcitonemia as part of a cross-sectional study; clinical data were analyzed and correlated with procalcitonin variant measurements. DPP4 activity was determined by a DPP4 activity assay. RESULTS: Purified antibodies allowed for the separate detection of both procalcitonin variants in all patients. Levels of truncated procalcitonin (truncPCT) correlated with DPP4-activity (Pearson’s R = 0.85, P < .001) and negatively correlated with patients’ Sequential Organ Failure Score (SOFA) scores (Pearson’s R = –0.56, P = .013). In contrast, the correlation between full-length procalcitonin (fullPCT) and SOFA scores was positive (Pearson’s R = 0.56, P = .013). Separation of the patient collective into groups with higher amounts of fullPCT versus truncPCT revealed higher SOFA scores in patients with fullPCT > truncPCT (mean ± standard error of the mean; 11. 3 ± 0.8 vs 6. 1 ± 1.5, P = .003). Patients with fullPCT > truncPCT showed a tendency towards higher doses of vasopressor (0. 2 ± 0.1 vs 0. 1 ± 0.03 µg/kg/min norepinephrine within the first 24 hours after sepsis diagnosis, P = .062) and exhibited higher creatinine (2. 0 ± 0.2 vs 1. 4 ± 0.3mg/dL, P = .019) and leukocyte levels (31. 0 ± 5.4 vs 12. 8 ± 1.9cells/µL, P = .012). In addition, patients with fullPCT > truncPCT were more often subjected to treatment with hydrocortisone (49.0 vs 0%, P = .018). CONCLUSIONS: Polyclonal antibodies generated using procalcitonin N-terminal variant peptides as immunogens are suitable for procalcitonin variant assessment. The separate detection of procalcitonin variants may offer additional diagnostic value and can be correlated with organ dysfunction and clinical outcomes in patients with systemic inflammation.

Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data

BackgroundStructural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection.ResultsThis study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines’ detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking.ConclusionsThis study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

GWAS determined genetic loci associated with callus induction in oil palm tissue culture.

GWAS identified six loci at 25kb downstream of WAK2, a crucial gene for cell wall and callus formation, enabling development of a SNP marker for enhanced callus induction potential. Efficient callus induction is vital for successful oil palm tissue culture, yet identifying genomic loci and markers for early detection of genotypes with high potential of callus induction remains unclear. In this study, immature male inflorescences from 198 oil palm accessions (dura, tenera and pisifera) were used as explants for tissue culture. Callus induction rates were collected at one-, two- and three-months after inoculation (C1, C2 and C3) as phenotypes. Resequencing generated 11,475,258 high quality single nucleotide polymorphisms (SNPs) as genotypes. GWAS was then performed, and correlation analysis revealed a positive association of C1 with both C2 (R = 0.81) and C3 (R = 0.50), indicating that C1 could be used as the major phenotype for callus induction rate. Therefore, only significant SNPs (P ≤ 0.05) in C1 were identified to develop markers for screening individuals with high potential of callus induction. Among 21 significant SNPs in C1, LD block analysis revealed six SNPs on chromosome 12 (Chr12) potentially linked to callus formation. Subsequently, 13 SNP markers were identified from these loci and electrophoresis results showed that marker C-12 at locus Chr12_12704856 can be used effectively to distinguish the GG allele, which showed the highest probability (69%) of callus induction. Furthermore, a rapid SNP variant detection method without electrophoresis was established via qPCR-based melting curve analysis. Our findings facilitated marker-assisted selection for specific palms with high potential of callus induction using immature male inflorescence as explant, aiding ortet palm selection in oil palm tissue culture.

Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data

Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.

Comparison of Optical Genome Mapping With Conventional Diagnostic Methods for Structural Variant Detection in Hematologic Malignancies.

Structural variants (SVs) are currently analyzed using a combination of conventional methods; however, this approach has limitations. Optical genome mapping (OGM), an emerging technology for detecting SVs using a single-molecule strategy, has the potential to replace conventional methods. We compared OGM with conventional diagnostic methods for detecting SVs in various hematologic malignancies. Residual bone marrow aspirates from 27 patients with hematologic malignancies in whom SVs were observed using conventional methods (chromosomal banding analysis, FISH, an RNA fusion panel, and reverse transcription PCR) were analyzed using OGM. The concordance between the OGM and conventional method results was evaluated. OGM showed concordance in 63% (17/27) and partial concordance in 37% (10/27) of samples. OGM detected 76% (52/68) of the total SVs correctly (concordance rate for each type of SVs: aneuploidies, 83% [15/18]; balanced translocation, 80% [12/15] unbalanced translocation, 54% [7/13] deletions, 81% [13/16]; duplications, 100% [2/2] inversion 100% [1/1]; insertion, 100% [1/1]; marker chromosome, 0% [0/1]; isochromosome, 100% [1/1]). Sixteen discordant results were attributed to the involvement of centromeric/telomeric regions, detection sensitivity, and a low mapping rate and coverage. OGM identified additional SVs, including submicroscopic SVs and novel fusions, in five cases. OGM shows a high level of concordance with conventional diagnostic methods for the detection of SVs and can identify novel variants, suggesting its potential utility in enabling more comprehensive SV analysis in routine diagnostics of hematologic malignancies, although further studies and improvements are required.

A comprehensive review of deep learning-based variant calling methods.

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

A simple and rapid method for detection of single nucleotide variants using tailed primer and HRM analysis

A simple and rapid method for detection of single nucleotide variants using tailed primer and HRM analysis

Plasmonic digital PCR for discriminative detection of SARS-CoV-2 variants

We developed a novel strategy for discriminative detection of SARS-CoV-2 variants based on the plasmonic photothermal effect of gold nanofilms and digital polymerase chain reaction (dPCR) technology. This method consists of the gold nanofilm-based dPCR chip fabrication for ultrafast heating and cooling cycles by the plasmonic photothermal effect, the LED quencher immobilization through the interfacing compound on the surface of the gold nanofilm to prevent photoquenching of PCR signaling dye, and the discriminative detection of the variant viruses from the COVID-19 clinical samples by photothermal cycles with fabricated dPCR chips and a portable plasmonic PCR device. Compared to conventional sequencing or RT-qPCR-based variant detection methods, this technology can be effectively applied to point-of-care testing by enabling ultrafast quantitative analysis with a small device. With this method, we successfully detected the delta variant and the omicron variant with a high sensitivity of 10 copies from COVID-19 patients’ clinical samples within 25 min, including reverse transcription. This method can be applied universally to rapid and accurate point-of-care testing for various pandemic viruses as well as the coronavirus.

Uncovering the true features of dystrophin gene rearrangement and improving the molecular diagnosis of Duchenne and Becker muscular dystrophies

Duchenne and Becker muscular dystrophies (DMD/BMD) are caused by complex mutations in the dystrophin gene (DMD). Currently, there is no integrative method for the precise detection of all potential DMD variants, a gap which we aimed to address using long-read sequencing. The captured long-read sequencing panel developed in this study was applied to 129 subjects, including 11 who had previously unsolved cases. The results showed that this method accurately detected DMD mutations, ranging from single-nucleotide variations to structural variations. Furthermore, our findings revealed that continuous exon duplication/deletion in the DMD/BMD cohort may be attributed to complex segmental rearrangements and that noncontiguous duplication/deletion is generally attributed to intragenic inversion or interchromosome translocation. Mutations in the deep introns were confirmed to produce a pseudoexon. Moreover, variations in female carriers were precisely identified. The integrated and precise DMD gene screening method proposed in this study could improve the molecular diagnosis of DMD/BMD.

SVsearcher: A more accurate structural variation detection method in long read data

Structural variations (SVs) represent genomic rearrangements (such as deletions, insertions, and inversions) whose sizes are larger than 50bp. They play important roles in genetic diseases and evolution mechanism. Due to the advance of long-read sequencing (i.e. PacBio long-read sequencing and Oxford Nanopore (ONT) long-read sequencing), we can call SVs accurately. However, for ONT long reads, we observe that existing long read SV callers miss a lot of true SVs and call a lot of false SVs in repetitive regions and in regions with multi-allelic SVs. Those errors are caused by messy alignments of ONT reads due to their high error rate. Hence, we propose a novel method, SVsearcher, to solve these issues. We run SVsearcher and other callers in three real datasets and find that SVsearcher improves the F1 score by approximately 10% for high coverage (50×) datasets and more than 25% for low coverage (10×) datasets. More importantly, SVsearcher can identify 81.7%–91.8% multi-allelic SVs while existing methods only identify 13.2% (Sniffles)–54.0% (nanoSV) of them. SVsearcher is available at https://github.com/kensung-lab/SVsearcher.

CnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network

BackgroundGenomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting multi-type structural variants.ResultsIn this paper, we propose a method called cnnLSV to obtain detection results with higher quality by eliminating false positives in the detection results merged from the callsets of existing methods. We design an encoding strategy for four types of structural variants to represent long-read alignment information around structural variants into images, input the images into a constructed convolutional neural network to train a filter model, and load the trained model to remove the false positives to improve the detection performance. We also eliminate mislabeled training samples in the training model phase by using principal component analysis algorithm and unsupervised clustering algorithm k-means. Experimental results on both simulated and real datasets show that our proposed method outperforms existing methods overall in detecting insertions, deletions, inversions, and duplications. The program of cnnLSV is available at https://github.com/mhuidong/cnnLSV.ConclusionsThe proposed cnnLSV can detect structural variants by using long-read alignment information and convolutional neural network to achieve overall higher performance, and effectively eliminate incorrectly labeled samples by using the principal component analysis and k-means algorithms in training model stage.

Research on Android-odometry based on the estimation of GNSS temporal ambiguity variation with Xiaomi MI8

Following extant studies on temporal relative localization (TRL) methods, this study uses a doppler velocity constraint-based temporal ambiguity variation (TAV) detection method and a two-step TAV resolution method to obtain the high-precisiontemporal relative position variations over time based on raw GNSS observations of Android smartphones, commonly plagued by frequent half-cycle slips and lock-loss. Results show that the proportion of the detectable satellites over 0.65 reaches the 95.57% level and the fix rate of global TAV resolution reaches 82.93%. We then propose the Android-odometry method which is a real-time high-precision TRL method with time-differenced carrier-phase observations of Android smartphones. The experimental results show that the method achieves centimeter-level temporal relative position accuracy during the 13-min dynamic test with Xiaomi MI8, showing a dramatic improvement of over 90% compared to the traditional RTK method.

Hairpin Structure Facilitates Multiplex High-Fidelity DNA Amplification in Real-Time Polymerase Chain Reaction.

Clinically and biologically, it is essential to detect rare DNA-sequence variants for early cancer diagnosis or drug-resistance mutation identification. Some of the common quantitative polymerase chain reaction (qPCR)-based variant detection methods are restricted in the limit of detection (LoD) because the DNA polymerases used for these methods have a high polymerase misincorporation rate; thus, the detection sensitivity is sometimes unsatisfactory. With the proofreading activity, high-fidelity (HiFi) DNA polymerases have a 50- to 250-fold higher fidelity. However, there are currently no proper probe-based designs functioning as the fluorescence indicator allowing multiplexed HiFi qPCR reactions, thus restricting the application of HiFi DNA polymerases like the variant detection. We presented the occlusion system, composed of a 5'-overhanged primer with a fluorophore modification and a probe with a short-stem hairpin and a 3' quencher modification. We demonstrated that the occlusion system allowed multiplexing HiFi qPCR reaction, and it was compatible with the current variant-enrichment method to improve the LoD up to 10-fold. Thus, the occlusion system satisfactorily functioned as an efficient fluorescence indicator in HiFi qPCR reactions and allowed the application of HiFi DNA polymerases in variant detection methods to improve detection sensitivity.

Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage.In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances.Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.

Cost-Efficient Sequence-Based Nonextensible Oligonucleotide in Real-Time PCR and High-Throughput Sequencing.

Molecular detection of disease-associated mutations, especially those with low abundance, is essential for academic research and clinical diagnosis. Certain variant detection methods reach satisfactory sensitivity and specificity in detecting rare mutations based on the introduction of blocking oligos to prevent the amplification of wild-type or unwanted templates, thus selectively amplifying and enriching the mutations. These blocking oligos usually suppress PCR amplification through the 3' chemical modifications, with high price, slow synthesis, and reduced purity. Herein, we introduce chemistry-free designs to block enzymatic extension during PCR by the steric hindrance from the secondary structures attached to the 3' end of the oligos (nonextensible oligonucleotide, NEO). We demonstrated that NEO efficiently prohibited the extension of both Taq and high-fidelity DNA polymerases. By further applying NEO as blockers in blocker displacement amplification (BDA) qPCR, multiplex BDA (mBDA) NGS, and quantitative BDA (QBDA) NGS methods, we showed that NEO blockers had performance comparable with previously validated chemical modifications. Comparison experiments using QBDA with NEO blockers and droplet digital PCR (ddPCR) on clinical formalin-fixed paraffin-embedded (FFPE) samples exhibited 100% concordance. Lastly, the ability of NEO to adjust plex uniformity through changes of PCR amplification efficiency was demonstrated in an 80-plex NGS panel.

Clinical Selection of Prenatal Diagnostic Techniques Following Positive Noninvasive Prenatal Screening Results in Southwest China.

Background: This study aims to evaluate prenatal diagnosis methods following positive noninvasive prenatal screening (NIPS) results. Methods: According to the positive noninvasive prenatal screening results, 926 pregnant women were divided into three groups: main target disease group (high risk for trisomy 21, trisomy 18, or trisomy 13), sex chromosome aneuploidy (SCA) group, and other chromosomal abnormalities group [abnormal Z-scores for chromosomes other than trisomy (T)21/T18/T13 or SCAs]. The verification methods and results were then retrospectively analysed. Results: In the main target disease group, the positive rate of chromosomal abnormalities confirmed by quantitative fluorescence polymerase chain reaction (QF-PCR) was 75.18% (212/282), which was not significantly different from that by karyotyping (79.36%, 173/218) and copy number variation (CNV) detection methods (71.43%, 65/91). The positive rate of additional findings confirmed by karyotyping and copy number variation detection methods in main target disease group was 0.46% (1/218) and 8.79% (8/91), respectively. The positive rate of chromosomal abnormalities confirmed by karyotyping and CNV detection methods were 27.11% (45/166) and 38.46% (95/247) in the SCA group and 4.17% (1/24) and 20% (36/180) in the other chromosomal abnormalities group, respectively. Fetal sex chromosome mosaicism was detected in 16.13% (20/124) of the confirmed SCA cases. There were no significant differences in the detection rates of chromosomal microarray analysis (CMA) and CNV sequencing (CNVseq) among the three groups (p > 0.05). Conclusion: QF-PCR can quickly and accurately identify aneuploidies following NIPS-positive results for common aneuploidy, and in combination with karyotyping and CNV detection techniques can provide more comprehensive results. With the NIPS-positive results for SCA or other abnormalities, CMA and CNVseq may have the same effect on increasing the detection rate. The addition of fluorescence in situ hybridization assay may help to identify true fetal mosaicism.

CNN-Based Malware Variants Detection Method for Internet of Things

Malware has become one of the most serious security threats to the Internet of Things (IoT). Detection of malware variants can inhibit the spread of malicious code from the traditional network to the IoT, and can also inhibit the spread of malicious code within the IoT, which is of great significance to the security detection and defense of the IoT. Since the terminals and the operating systems of IoT are very different from the traditional network, when malicious code is transferred from the traditional network to the IoT platform, the characteristics of the variants may change significantly. As a result, malicious code variant detection methods for traditional platforms cannot be directly applied to the IoT. In this article, a malware variant detection method for the IoT is proposed. First, we propose a feature representation method based on RGB image for IoT to solve the problem of representation difficulty caused by platform difference, which pays more attention to the assembly code and developer information of the malware. The generated image has richer texture information, which can dig out the deep association between the IoT variants and the original malicious code. Moreover, this article improves the convolutional neural network model by combining the self-attention mechanism and spatial pyramid pooling to solve the problem of large differences in the size of IoT malware. Experimental results show that our method can be used in cross-platform to detect malware variants in the IoT effectively.

TU58. COMPARISON OF EKLIPSE AND MITOSALT PIPELINES IN THE DETECTION OF MITOCHONDRIAL DNA REARRANGEMENTS IN SUBJECTS WITH SCHIZOPHRENIA, INTELLECTUAL DISABILITY AND AUTISM SPECTRUM DISORDER

Mitochondrial dysfunction has emerged to be associated with a broad spectrum of diseases, and there is an increasing demand for accurate detection of mitochondrial DNA (mtDNA) variants. Whole genome sequencing (WGS) has been the dominant sequencing approach to identify genetic variants in recent decades, but most studies focus on variants on the nuclear genome. Whole genome sequencing is also costly and time consuming. Sequencing specifically targeted for mtDNA is commonly used in the diagnostic settings and has lower costs. However, there is a lack of pairwise comparisons between these two sequencing approaches for calling mtDNA variants on a population basis. In this study, we compared WGS and mtDNA-targeted sequencing (targeted-seq) in analyzing mitochondrial DNA from 1499 participants recruited into the Severe Asthma Research Program (SARP). Our study reveals that targeted-sequencing and WGS have comparable capacity to determine genotypes and to call haplogroups and homoplasmies on mtDNA. However, there exists a large variability in calling heteroplasmies, especially for low-frequency heteroplasmies, which indicates that investigators should be cautious about heteroplasmies acquired from different sequencing methods. Further research is highly desired to improve variant detection methods for mitochondrial DNA.

RT-PCR based SARS-CoV-2 variant screening assays require careful quality control.

BackgroundDistinctive genotypes of SARS-CoV-2 have emerged that are or may be associated with increased transmission, pathogenicity, and/or antibody escape. In many countries, clinical and diagnostic laboratories are under mandate to identify and report these so-called variants of concern (VOC).ObjectivesWe used an external quality assessment scheme to determine the scope, accuracy, and reliability of laboratories using various molecular diagnostic assays to identify current VOC (03 March 2021).Study designParticipant laboratories were sent the same five patient-derived samples and were asked to provide their variant detection methods, variant detection results and interpretation of results.ResultsTwenty-five laboratories reported a range of RT-qPCR-based assays to identify specific variations in the SARS-CoV-2 spike protein that are characteristic of three VOC lineages. Laboratories that detected VOC-associated nucleotide mutations at four specific sites had the highest ratio of correct classification. Low template copy number and additional variation in target regions resulted in loss of confidence and accuracy in sample classification.ConclusionsMelting-curve-based assays to identify genomic variants are less time-consuming and require less bioinformatic analysis compared to partial or whole genome sequencing. However, our results suggest that correct classification of a given genotype/lineage (e.g., a VOC) relies on the ability to detect more than one variant site, adequate template in the sample (i.e., relatively high viral load/copy number) and results may be unclear in certain samples with additional genetic variations. These initial results suggest that some diagnostic laboratories may require additional training to interpret and report complex genetic information about a dynamic emerging virus.