High-throughput sequencing techniques have significantly increased the molecular diagnosis rate for patients with monogenic disorders. This is primarily due to a substantially increased identification rate of disease mutations in the coding sequence, primarily SNVs and indels. Further progress is hampered by difficulties in the detection of structural variants and the interpretation of variants outside the coding sequence. In this review, we provide an overview about how novel sequencing techniques and state-of-the-art algorithms can be used to discover small and structural variants across the whole genome and introduce bioinformatic tools for the prediction of effects variants may have in the non-coding part of the genome.

Inborn hemolytic anemia requiring frequent blood transfusions can be a life-threatening disease. Treatment, besides blood transfusion, includes iron chelation for prevention of iron accumulation due to frequent blood transfusions. We present the results of a clinical investigation where the proband was diagnosed with severe hemolytic anemia of unknown origin soon after birth. Transfusion was required every 4-6 weeks. After whole exome sequencing of the proband and his parents as well as a healthy sibling, we established that the proband had a compound heterozygous state carrying two rare variants in the erythrocytic spectrin gene, SPTA1. The maternal allele was a stop mutation (rs755630903) and the paternal allele was a missense mutation (rs375506528). The healthy sibling had the paternal variant but not the maternal variant. These rare variants of SPTA1 most likely account for the hemolytic anemia. A severely reduced osmotic resistance in the erythrocytes from the proband was demonstrated. Splenectomy considerably improved the hemolytic anemia and obviated the need for blood transfusion despite the severe clinical presentation.

Genetic mutations are critical factors leading to congenital surgical diseases and can be identified through genomic analysis. Early and accurate identification of genetic mutations underlying these conditions is vital for clinical diagnosis and effective treatment. In recent years, artificial intelligence (AI) has been widely applied for analyzing genomic data in various clinical settings, including congenital surgical diseases. This review paper summarizes current state-of-the-art AI-based approaches used in genomic analysis and highlighted some successful applications that deepen our understanding of the etiology of several congenital surgical diseases. We focus on the AI methods designed for the detection of different variant types and the prioritization of deleterious variants located in different genomic regions, aiming to uncover susceptibility genomic mutations contributed to congenital surgical disorders.

Coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a considerable threat to global public health. This study developed and evaluated a rapid, low-cost, expandable, and sequencing-free high-resolution melting (HRM) assay for the direct detection of SARS-CoV-2 variants. A panel of 64 common bacterial and viral pathogens that can cause respiratory tract infections was employed to evaluate our method\'s specificity. Serial dilutions of viral isolates determined the sensitivity of the method. Finally, the assay\'s clinical performance was assessed using 324 clinical samples with potential SARS-CoV-2 infection. Multiplex HRM analysis accurately identified SARS-CoV-2 (as confirmed with parallel reverse transcription-quantitative PCR [qRT-PCR] tests), differentiating between mutations at each marker site within approximately 2 h. For each target, the limit of detection (LOD) was lower than 10 copies/reaction (the LOD of N, G142D, R158G, Y505H, V213G, G446S, S413R, F486V, and S704L was 7.38, 9.72, 9.96, 9.96, 9.50, 7.80, 9.33, 8.25, and 8.25 copies/reaction, respectively). No cross-reactivity occurred with organisms of the specificity testing panel. In terms of variant detection, our results had a 97.9% (47/48) rate of agreement with standard Sanger sequencing. The multiplex HRM assay therefore offers a rapid and simple procedure for detecting SARS-CoV-2 variants. IMPORTANCE In the face of the current severe situation of increasing SARS-CoV-2 variants, we developed an upgraded multiplex HRM method for the predominant SARS-CoV-2 variants based on our original research. This method not only could identify the variants but also could be utilized in subsequent detection of novel variants since the assay has great performance in terms of flexibility. In summary, the upgraded multiplex HRM assay is a rapid, reliable, and economical detection method, which could better screen prevalent virus strains, monitor the epidemic situation, and help to develop measures for the prevention and control of SARS-CoV-2.

Tuberous sclerosis complex (TSC) is a neurogenetic disorder due to loss-of-function TSC1 or TSC2 variants, characterized by tumors affecting multiple organs, including skin, brain, heart, lung, and kidney. Mosaicism for TSC1 or TSC2 variants occurs in 10%-15% of individuals diagnosed with TSC. Here, we report comprehensive characterization of TSC mosaicism by using massively parallel sequencing (MPS) of 330 TSC samples from a variety of tissues and fluids from a cohort of 95 individuals with mosaic TSC. TSC1 variants in individuals with mosaic TSC are much less common (9%) than in germline TSC overall (26%) (p < 0.0001). The mosaic variant allele frequency (VAF) is significantly higher in TSC1 than in TSC2, in both blood and saliva (median VAF: TSC1, 4.91%; TSC2, 1.93%; p = 0.036) and facial angiofibromas (median VAF: TSC1, 7.7%; TSC2 3.7%; p = 0.004), while the number of TSC clinical features in individuals with TSC1 and TSC2 mosaicism was similar. The distribution of mosaic variants across TSC1 and TSC2 is similar to that for pathogenic germline variants in general TSC. The systemic mosaic variant was not present in blood in 14 of 76 (18%) individuals with TSC, highlighting the value of analysis of multiple samples from each individual. A detailed comparison revealed that nearly all TSC clinical features are less common in individuals with mosaic versus germline TSC. A large number of previously unreported TSC1 and TSC2 variants, including intronic and large rearrangements (n = 11), were also identified.

Pathogenic variants in both mitochondrial and nuclear genes contribute to the clinical and genetic heterogeneity of mitochondrial diseases. There are now pathogenic variants in over 300 nuclear genes linked to human mitochondrial diseases. Nonetheless, diagnosing mitochondrial disease with a genetic outcome remains challenging. However, there are now many strategies that help us to pinpoint causative variants in patients with mitochondrial disease. This chapter describes some of the approaches and recent advancements in gene/variant prioritization using whole-exome sequencing (WES).

In keeping with the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the COVID-19 causative agent, PCR assays have been developed to rapidly detect SARS-CoV-2 variants, which have emerged since the first (Alpha) variant was identified. Based on specific assortment of SARS-CoV-2 spike-protein mutations (ΔH69/V70, E484K, N501Y, W152C, L452R, K417N, and K417T) among the major variants known to date, Seegene Allplex SARS-CoV-2 Variants I and Variants II assays have been available since a few months before the last (Omicron) variant became predominant. Using S gene next-generation sequencing (NGS) as the SARS-CoV-2 variant identification reference method, we assessed the results of SARS-CoV-2-positive nasopharyngeal swab samples from two testing periods, before (n = 288, using only Variants I) and after (n = 77, using both Variants I and Variants II) the appearance of Omicron. The Variants I assay allowed correct identification for Alpha (37/37), Beta/Gamma (28/30), or Delta (220/221) variant-positive samples. The combination of the Variants I and Variants II assays allowed correct identification for 61/77 Omicron variant-positive samples. While 16 samples had the K417N mutation undetected with the Variants II assay, 74/77 samples had both ΔH69/V70 and N501Y mutations detected with the Variants I assay. If considering only the results by the Variants I assay, 6 (2 Beta variant positive, 1 Delta variant positive, and 3 Omicron variant positive) of 365 samples tested in total provided incorrect identification. We showed that the Variants I assay alone might be more suitable than both the Variants I and Variants II assays to identify currently circulating SARS-CoV-2 variants. Inclusion of additional variant-specific mutations should be expected in the development of future assays. IMPORTANCE Omicron variants of SARS-CoV-2 pose more important public health concerns than the previously circulating Alpha or Delta variants, particularly regarding the efficacy of anti-SARS-CoV-2 vaccines and therapeutics. Precise identification of these variants highly requires performant PCR-based assays that allow us to reduce the reliance on NGS-based assays, which remain the reference method in this topic. While the current epidemiological SARS-CoV-2 pandemic context suggests that PCR assays such as the Seegene Variants II may be dispensable, we took advantage of NGS data obtained in this study to show that the array of SARS-CoV-2 spike protein mutations in the Seegene Variants II assay may be suboptimal. This reinforces the concept that initially developed PCR assays for SARS-CoV-2 variant detection could be no longer helpful if the SARS-CoV-2 pandemic evolves to newly emerging variants.

Despite entering an endemic phase, SARS-CoV-2 remains a significant burden to public health across the global community. Wastewater sampling has consistently proven utility to understanding SARS-CoV-2 prevalence trends and genetic variation as it represents a less biased assessment of the corresponding communities. Here, we report that ongoing monitoring of SARS-CoV-2 genetic variation in samples obtained from the wastewatersheds of the city of Louisville in Jefferson county Kentucky has revealed the periodic reemergence of the Delta strain in the presence of the presumed dominant Omicron strain. Unlike previous SARS-CoV-2 waves/emergence events, the Delta reemergence events were geographically restricted in the community and failed to spread into other areas as determined by wastewater analyses. Moreover, the reemergence of the Delta strain did not correlate with vaccination rates as communities with lower relative vaccination have been, to date, not affected. Importantly, Delta reemergence events correlate with increased public health burdens, as indicated by increased daily case rates and mortality relative to non-Delta wastewatershed communities. While the underlying reasons for the reemergence of the Delta variant remain unclear, these data reaffirm the ongoing importance of wastewater genomic analyses towards understanding SARS-CoV-2 as it enters the endemic phase.

The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a major health crisis worldwide with its continuously emerging new strains, resulting in new viral variants that drive \"waves\" of infection. PCR or antigen detection assays have been routinely used to detect clinical infections; however, the emergence of these newer strains has presented challenges in detection. One of the alternatives has been to detect and characterize variant-specific peptide sequences from viral proteins using mass spectrometry (MS)-based methods. MS methods can potentially help in both diagnostics and vaccine development by understanding the dynamic changes in the viral proteome associated with specific strains and infection waves. In this study, we developed an accessible, flexible, and shareable bioinformatics workflow that was implemented in the Galaxy Platform to detect variant-specific peptide sequences from MS data derived from the clinical samples. We demonstrated the utility of the workflow by characterizing published clinical data from across the world during various pandemic waves. Our analysis identified six SARS-CoV-2 variant-specific peptides suitable for confident detection by MS in commonly collected clinical samples.

Detecting SNV at very low read depths helps to reduce sequencing requirements, lowers sequencing costs, and aids in the early screening, diagnosis, and treatment of cancer. However, the accuracy of SNV detection is significantly reduced at read depths below ×34 due to the lack of a sufficient number of read pairs to help filter out false positives. Many recent studies have revealed the potential of mutational signature (MS) in detecting true SNV, understanding the mutational processes that lead to the development of human cancers, and analyzing the endogenous and exogenous causes. Here, we present DETexT, an SNV detection method better suited to low read depths, which classifies false positive variants by combining MS with deep learning algorithms to mine correlation information around bases in individual reads without relying on the support of duplicate read pairs. We have validated the effectiveness of DETexT on simulated and real datasets and conducted comparative experiments. The source code has been uploaded to https://github.com/TrinaZ/extra-lowRD for academic use only.