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Accurate identification of single‐nucleotide variants (SNVs) is paramount for disease diagnosis. Despite the facile design of DNA hybridization probes, their limited specificity poses challenges in clinical applications. Here, a differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network is presented. DRPP leverages differences in reaction intermediate concentrations between SNV and WT groups, directing them into distinct reaction pathways. This generates a strong pulse‐like signal for SNV and a weak unidirectional increase signal for wild‐type (WT). Through the application of machine learning to fluorescence kinetic data analysis, the classification of SNV and WT signals is automated with an accuracy of 99.6%, significantly exceeding the 80.7% accuracy of conventional methods. Additionally, sensitivity for variant allele frequency (VAF) is enhanced down to 0.1%, representing a ten‐fold improvement over conventional approaches. DRPP accurately identified D614G and N501Y SNVs in the S gene of SARS‐CoV‐2 variants in patient swab samples with accuracy over 99% (n = 82). It determined the VAF of ovarian cancer‐related mutations KRAS‐G12R, NRAS‐G12C, and BRAF‐V600E in both tissue and blood samples (n = 77), discriminating cancer patients and healthy individuals with significant difference (p < 0.001). The potential integration of DRPP into clinical diagnostics, along with rapid amplification techniques, holds promise for early disease diagnostics and personalized diagnostics. The differential reaction pathway probe is presented, which utilizes a dynamic DNA reaction network to separate single‐nucleotide variants (SNVs) and wild‐type (WT) into distinct reaction pathways, generating unique fingerprinting kinetics pulse‐like for SNVs and weak unidirectional signals for WT. High accuracy is achieved for SNV detection in clinical samples including SARS‐CoV‐2 patient swab samples and ovarian cancer patient tissue/blood samples.

Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV .

Abstract Context Broad genomic analyses among thyroid histologies have been described from relatively small cohorts. Objective Investigate the molecular findings across a large, real-world cohort of thyroid fine-needle aspiration (FNA) samples. Design Retrospective analysis of RNA sequencing data files. Setting Clinical Laboratory Improvement Amendments laboratory performing Afirma Genomic Sequencing Classifier (GSC) and Xpression Atlas (XA) testing. Participants A total of 50 644 consecutive Bethesda III-VI nodules. Intervention None. Main Outcome Measures Molecular test results. Results Of 48 952 Bethesda III/IV FNAs studied, 66% were benign by Afirma GSC. The prevalence of BRAF V600E was 2% among all Bethesda III/IV FNAs and 76% among Bethesda VI FNAs. Fusions involving NTRK, RET, BRAF, and ALK were most prevalent in Bethesda V (10%), and 130 different gene partners were identified. Among small consecutive Bethesda III/IV sample cohorts with one of these fusions and available surgical pathology excision data, the positive predictive value of an NTRK or RET fusion for carcinoma or noninvasive follicular thyroid neoplasm with papillary-like nuclear features was >95%, whereas for BRAF and ALK fusions it was 81% and 67%, respectively. At least 1 genomic alteration was identified by the expanded Afirma XA panel in 70% of medullary thyroid carcinoma classifier–positive FNAs, 44% of Bethesda III or IV Afirma GSC suspicious FNAs, 64% of Bethesda V FNAs, and 87% of Bethesda VI FNAs. Conclusions This large study demonstrates that almost one-half of Bethesda III/IV Afirma GSC suspicious and most Bethesda V/VI nodules had at least 1 genomic variant or fusion identified, which may optimize personalized treatment decisions.

Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Background: Several variants of the SARS-CoV-2 have been documented globally during the current COVID-19 pandemic. The N501Y, 69-70del, K417N, and E484K SARS-CoV-2 mutations have been documented among the most relevant due to their potential pathogenic biological effects. This study aimed to design, validate, and propose a fast real-time RT-qPCR assay to detect SARS-CoV-2 mutations with possible clinical and epidemiological relevance in the Mexican population.Methods: Targeting spike (S) gene mutations of SARS-CoV-2 (N501Y, 69-70del, K417N, and E484K), specific primers, and probes for three specific quantitative reverse transcription PCR (RT-qPCR) assays were designed, and validated using Sanger sequencing. These assays were applied in clinical samples of 1060 COVID-19 patients from Jalisco Mexico.Results: In silico analyzes showed high specificity of the three assays. Amplicons of samples were confirmed through sequencing. The screening of samples of COVID-19 patients allowed the identification of the E484K mutation in nine individuals and the identification of P.2 Brazilian variant in Mexico.Conclusion: This work provides low-cost RT-qPCR assays for rapid screening and molecular surveillance of mutations with potential clinical impact. This strategy allowed the detection of E484K mutation and P.2 variant for the first time in samples from the Mexican population.

Background SARS-CoV-2 related research has increased in importance worldwide since December 2019. Several new variants of SARS-CoV-2 have emerged globally, of which the most notable and concerning currently are the UK variant B.1.1.7, the South African variant B1.351 and the Brazilian variant P.1. Detecting and monitoring novel variants is essential in SARS-CoV-2 surveillance. While there are several tools for assembling virus genomes and performing lineage analyses to investigate SARS-CoV-2, each is limited to performing singular or a few functions separately. Results Due to the lack of publicly available pipelines, which could perform fast reference-based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect variants of concern, we have developed an open source bioinformatic pipeline called HAVoC (Helsinki university Analyzer for Variants of Concern). HAVoC can reference assemble raw sequence reads and assign the corresponding lineages to SARS-CoV-2 sequences. Conclusions HAVoC is a pipeline utilizing several bioinformatic tools to perform multiple necessary analyses for investigating genetic variance among SARS-CoV-2 samples. The pipeline is particularly useful for those who need a more accessible and fast tool to detect and monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. HAVoC is currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. HAVoC user manual and source code are available at https://www.helsinki.fi/en/projects/havoc and https://bitbucket.org/auto_cov_pipeline/havoc , respectively.

Background miRNAs play a key role in normal physiology and various diseases. miRNA profiling through next generation sequencing (miRNA-seq) has become the main platform for biological research and biomarker discovery. However, analyzing miRNA sequencing data is challenging as it needs significant amount of computational resources and bioinformatics expertise. Several web based analytical tools have been developed but they are limited to processing one or a pair of samples at time and are not suitable for a large scale study. Lack of flexibility and reliability of these web applications are also common issues. Results We developed a Comprehensive Analysis Pipeline for microRNA Sequencing data (CAP-miRSeq) that integrates read pre-processing, alignment, mature/precursor/novel miRNA detection and quantification, data visualization, variant detection in miRNA coding region, and more flexible differential expression analysis between experimental conditions. According to computational infrastructure, users can install the package locally or deploy it in Amazon Cloud to run samples sequentially or in parallel for a large number of samples for speedy analyses. In either case, summary and expression reports for all samples are generated for easier quality assessment and downstream analyses. Using well characterized data, we demonstrated the pipeline’s superior performances, flexibility, and practical use in research and biomarker discovery. Conclusions CAP-miRSeq is a powerful and flexible tool for users to process and analyze miRNA-seq data scalable from a few to hundreds of samples. The results are presented in the convenient way for investigators or analysts to conduct further investigation and discovery.

Circulating tumor DNA (ctDNA) is a biomarker that could potentially improve the survival rate of ovarian cancer (OC), e.g., by monitoring treatment response and early relapse detection. However, an optimal method for ctDNA analysis in OC remains to be established. We developed a method for tumor-informed single-nucleotide variant detection of ctDNA in OC using whole-genome sequencing. Tumor and plasma samples obtained at the time of diagnosis from 10 patients with OC were included. The tested method involved applying basic filters with different cut-offs of read depth, allelic depth, and variant allele frequency of tumor and normal DNA. In addition, we applied a new filtering approach using plasma samples from the other included OC patients (the plasma pool) for specific removal of artefacts. The basic filters with varying cut-offs showed minor improvement in signal-to-noise ratio (S2N). However, the addition of the plasma pool filter resulted in a considerable ctDNA signal improvement, indicated by both S2N and z-score. This study demonstrates a promising method for ctDNA detection in OC patients using a tumor-informed approach for whole-genome sequencing. Despite the limited number of patients involved, the results suggest a significant potential of the method for ctDNA signal detection in patients with OC.

Background To diagnose the full spectrum of hereditary and congenital diseases, genetic laboratories use many different workflows, ranging from karyotyping to exome sequencing. A single generic high-throughput workflow would greatly increase efficiency. We assessed whether genome sequencing (GS) can replace these existing workflows aimed at germline genetic diagnosis for rare disease. Methods We performed short-read GS (NovaSeq™6000; 150 bp paired-end reads, 37 × mean coverage) on 1000 cases with 1271 known clinically relevant variants, identified across different workflows, representative of our tertiary diagnostic centers. Variants were categorized into small variants (single nucleotide variants and indels < 50 bp), large variants (copy number variants and short tandem repeats) and other variants (structural variants and aneuploidies). Variant calling format files were queried per variant, from which workflow-specific true positive rates (TPRs) for detection were determined. A TPR of ≥ 98% was considered the threshold for transition to GS. A GS-first scenario was generated for our laboratory, using diagnostic efficacy and predicted false negative as primary outcome measures. As input, we modeled the diagnostic path for all 24,570 individuals referred in 2022, combining the clinical referral, the transition of the underlying workflow(s) to GS, and the variant type(s) to be detected. Results Overall, 95% (1206/1271) of variants were detected. Detection rates differed per variant category: small variants in 96% (826/860), large variants in 93% (341/366), and other variants in 87% (39/45). TPRs varied between workflows (79–100%), with 7/10 being replaceable by GS. Models for our laboratory indicate that a GS-first strategy would be feasible for 84.9% of clinical referrals (750/883), translating to 71% of all individuals (17,444/24,570) receiving GS as their primary test. An estimated false negative rate of 0.3% could be expected. Conclusions GS can capture clinically relevant germline variants in a ‘GS-first strategy’ for the majority of clinical indications in a genetics diagnostic lab.