Eight new variants of HLA-DQB1*03 identified by next-generation sequencing.

Genomic sequence of HLA-DQB1*03:01:01:60, -DQB1*03:01:01:61, -DQB1*03:01:01:62, -DQB1*03:01:01:63, -DQB1*03:02:01:23, -DQB1*03:02:01:24, -DQB1*03:02:01:25 and -DQB1*03:03:02:14 alleles in Spanish individuals.

Identification of the novel HLA-DRB3*03:69 allele by next-generation sequencing.

The new HLA-DRB3*03:69 allele differs from DRB3*03:01:01:03 by change of C → G in exon 3.

Investigation of Targeted Genes and Identification of Novel Variants with Next Generation Sequencing Method in Hearing Loss.

Hearing loss is a widespread condition throughout the world. It may affect patients from newborns to the elderly. There are too many reasons for hearing loss, including congenital hearing loss, virus infections, age-related situations, and traumatic situations, which may be related to the immune-mediated system. Fifty percent of hearing loss is related to genetic mutations and defects; genetic causes are highly heterogeneous, so the analysis of new variants are important for diagnosis. We aimed to describe the importance of detected gene variations by using targeted gene panels in the Next-Generation-Sequencing (NGS) platform. Eighty-one hearing loss targeted genes were investigated using Illumina NextSeq550 technology in 100 participants with hearing loss between 2017 and 2022 in our Genetic Diseases Evaluation Center. Targeted genes were performed on 100 patients with hearing loss diagnosis. The total number of detected variants was 77. Forty-seven cases have likely pathogenic/pathogenic variants. Thirty of them have uncertain clinical significance variants, and from the detected variants, 8 are novel. In this research, we highlighted that earlier detection of hearing loss using molecular genetic methods may help us understand the etiology and orient for a better prognosis. Results detected by using the NGS platform can assist and improve the diagnosis. In this study, the diagnostic rate with targeted genes was detected as 35.29%. It has an important role in clinical practice as the recommendation of cochlear implants. Clarifying the genotype and phenotype correlation helps us figure out the etiology of hearing loss and also the worth of genetic counseling in hereditary hearing loss.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Highly accurate single-color fluorogenic DNA decoding sequencing for mutational genotyping.

We proposed a single-color fluorogenic DNA decoding sequencing method designed to improve sequencing accuracy, increase read length and throughput, as well as decrease scanning time. This method involves the incorporation of a mixture of four types of 3'-O-modified nucleotide reversible terminators into each reaction. Among them, two nucleotides are labeled with the same fluorophore, while the remaining two are unlabeled. Only one nucleotide can be extended in each reaction, and an encoding that partially defines base composition can be obtained. Through cyclic interrogation of a template twice with different nucleotide combinations, two sets of encodings are sequentially obtained, enabling the determination of the sequence. We demonstrate the feasibility of this method using established sequencing chemistry, achieving a cycle efficiency of approximately 99.5 %. Notably, this strategy exhibits remarkable efficacy in the detection and correction of sequencing errors, achieving a theoretical error rate of 0.00016 % at a sequencing depth of ×2, which is lower than Sanger sequencing. This method is theoretically compatible with the existing sequencing-by-synthesis (SBS) platforms, and the instrument is simpler, which may facilitate further reductions in sequencing costs, thereby broadening its applications in biology and medicine. Moreover, we demonstrate the capability to detect known mutation sites using information from only a single sequencing run. We validate this approach by accurately identifying a mutation site in the human mitochondrial DNA.

Principle and application of self-transcribing active regulatory region sequencing in enhancer discovery research.

Self-transcribing active regulatory region sequencing (STARR-seq) is a high-throughput sequencing method capable of simultaneously discovering and validating all enhancers within the genome. In this method, candidate sequences are inserted into plasmid vectors and electroporated into cells. Acting as both enhancers and target genes, the self-transcription of these sequences will also be enhanced by themselves. By sequencing the transcriptome and comparing the results with the non-inserted control, the locations and activity of enhancers can be determined. In traditional enhancer discovery strategies, the chromatin open regions and transcription active regions were sequenced and predicted as enhancers. However, the activity of these putative enhancers could only be validated one by one without a high-throughput method. STARR-seq solved this limitation, allowing simultaneous enhancers discovery and activity validation in a high-throughput manner. Since the introduction of STARR-seq, it has been widely used to discover enhancers and validate enhancer activity in a number of organisms and cells. In this review, we present the traditional enhancer prediction methods and the basic principles, development history, specific applications of STARR-seq, and its future prospects, aiming to provide a reference for researchers in related fields conducting enhancer studies.

Validated DNA isolation method ensuring successful long-read sequencing of cattle semen genome.

Obtaining high-quality DNA suitable for long-read sequencing can be difficult for many types of tissues and cells, and it is a key step in current genomic studies. The challenge is even greater when it comes to isolating genomic DNA from mammalian spermatozoa, as DNA is tightly packed into a cell with a robust membrane rich in disulfide bonds. Here we describe a method for isolating high molecular weight DNA from Bovine commercial semen straws. This protocol includes a cleaning step to remove diluents and preservatives used for the long-term storage of the semen, which may affect long read sequencing. It is based on a simple salting-out method and avoid the use of spin columns, strong mixing or intensive centrifugation, in order to limit DNA fragmentation. However, we have adapted this protocol to facilitate the disruption of cell membranes and disulfide bonds with strong chaotropic and reducing agents. The average size of the fragments produced was approximately 49 kb, ranging from 25 to 85 kb, according to the femto pulse profiles.This method was used to isolate DNA from semen straws, more than 80 of them were successfully sequenced using the Continuous Long-Read (CLR) sequencing mode on the PacBio SequelII platform to study genome diversity and notably to detect large structural variations within genomes.

Maptcha: an efficient parallel workflow for hybrid genome scaffolding.

BACKGROUND: Genome assembly, which involves reconstructing a target genome, relies on scaffolding methods to organize and link partially assembled fragments. The rapid evolution of long read sequencing technologies toward more accurate long reads, coupled with the continued use of short read technologies, has created a unique need for hybrid assembly workflows. The construction of accurate genomic scaffolds in hybrid workflows is complicated due to scale, sequencing technology diversity (e.g., short vs. long reads, contigs or partial assemblies), and repetitive regions within a target genome. RESULTS: In this paper, we present a new parallel workflow for hybrid genome scaffolding that would allow combining pre-constructed partial assemblies with newly sequenced long reads toward an improved assembly. More specifically, the workflow, called Maptcha, is aimed at generating long scaffolds of a target genome, from two sets of input sequences-an already constructed partial assembly of contigs, and a set of newly sequenced long reads. Our scaffolding approach internally uses an alignment-free mapping step to build a ⟨ contig,contig ⟩ graph using long reads as linking information. Subsequently, this graph is used to generate scaffolds. We present and evaluate a graph-theoretic "wiring" heuristic to perform this scaffolding step. To enable efficient workload management in a parallel setting, we use a batching technique that partitions the scaffolding tasks so that the more expensive alignment-based assembly step at the end can be efficiently parallelized. This step also allows the use of any standalone assembler for generating the final scaffolds. CONCLUSIONS: Our experiments with Maptcha on a variety of input genomes, and comparison against two state-of-the-art hybrid scaffolders demonstrate that Maptcha is able to generate longer and more accurate scaffolds substantially faster. In almost all cases, the scaffolds produced by Maptcha are at least an order of magnitude longer (in some cases two orders) than the scaffolds produced by state-of-the-art tools. Maptcha runs significantly faster too, reducing time-to-solution from hours to minutes for most input cases. We also performed a coverage experiment by varying the sequencing coverage depth for long reads, which demonstrated the potential of Maptcha to generate significantly longer scaffolds in low coverage settings ( 1 × - 10 × ).

A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer.

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC.

Liquid biopsy has recently emerged as an important tool in clinical practice particularly for lung cancer patients. We retrospectively evaluated cell-free DNA analyses performed at our Institution by next generation sequencing methodology detecting the major classes of genetic alterations. Starting from the graphical representation of chromosomal alterations provided by the analysis software, we developed a support vector machine classifier to automatically classify chromosomal profiles as stable (SCP) or unstable (UCP). High concordance was found between our binary classification and tumor fraction evaluation performed using shallow whole genome sequencing. Among clinical features, UCP patients were more likely to have ≥ 3 metastatic sites and liver metastases. Longitudinal assessment of chromosomal profiles in 33 patients with lung cancer receiving immune checkpoint inhibitors (ICIs) showed that only patients that experienced early death or hyperprogressive disease retained or acquired an UCP within 3 weeks from the beginning of ICIs. UCP was not observed following ICIs among patients that experienced progressive disease or clinical benefit. In conclusion, our binary classification, applied to whole copy number alteration profiles, could be useful for clinical risk stratification during systemic treatment for non-small cell lung cancer patients.

Hereditary breast cancer next-generation sequencing panel evaluation in the south region of Brazil: A novel BRCA2 candidate pathogenic variant is reported.

BACKGROUND: In this article, we delineate a loosely selected cohort comprising patients with a history of early-onset breast cancer and/or a familial occurrence of cancer. The aim of this study was to gain insights into the presence of breast cancer-related gene variants in a population from a micro-region in southern Brazil, specifically the Metropolitan Region of Curitiba. This area exhibits a highly genetically mixed population, mirroring the general characteristics of the Brazilian people. METHODS: Comprehensive next-generation sequencing (NGS) multigene panel testing was conducted on 12 patients from the region, utilizing three different library preparation methods. RESULTS: Two pathogenic variants and one candidate pathogenic variant were identified: BRCA2 c.8878C>T, p.Gln2960Ter; CHEK2 c.1100del, p.Thr367Metfs15, and BRCA2 c.3482dup, p.Asp1161Glufs3. CONCLUSION: BRCA2 c.3482dup, a novel candidate pathogenic variant, previously unpublished, is reported. The prevalence of pathogenic variants in this small cohort is similar to that described in the literature. All different library preparation methods were equally proficient in enabling the detection of these variants.

Utility analyses of AVITI sequencing chemistry.

BACKGROUND: DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. RESULTS: Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. CONCLUSION: These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Museomics allows comparative analyses of mitochondrial genomes in the family Gryllidae (Insecta, Orthoptera) and confirms its phylogenetic relationships.

Background: Next-generation sequencing technology can now be used to sequence historical specimens from natural history collections, an approach referred to as museomics. The museomics allows obtaining molecular data from old museum-preserved specimens, a resource of biomolecules largely underexploited despite the fact that these specimens are often unique samples of nomenclatural types that can be crucial for resolving scientific questions. Despite recent technical progress, cricket mitogenomes are still scarce in the databases, with only a handful of new ones generated each year from freshly collected material. Methods: In this study, we used the genome skimming method to sequence and assemble three new complete mitogenomes representing two tribes of the cricket subfamily Eneopterinae: two were obtained from old, historical type material of Xenogryllus lamottei (68 years old) and X. maniema (80 years old), the third one from a freshly collected specimen of Nisitrus vittatus. We compared their genome organization and base composition, and reconstructed the molecular phylogeny of the family Gryllidae. Results: Our study not only confirmed that the genome skimming method used by next generation sequencing allows us to efficiently obtain the whole mitogenome from dry-pinned historical specimens, but we also confirmed how promising it is for large-scale comparative studies of mitogenomes using resources from natural history collections. Used in a phylogenetic context the new mitogenomes attest that the mitogenomic data contain valuable information and also strongly support phylogenetic relationships at multiple time scales.

[A consistency comparison between next-generation sequencing and the FISH method for gene rearrangement detection in B-cell lymphomas].

Objective: To compare the consistency of lymphoma multigene detection panels based on next-generation sequencing (NGS) with FISH detection of B-cell lymphoma gene rearrangement. Methods: From January 2019 to May 2023, fusion genes detected by lymphoma-related 413 genes that targeted capture sequencing of 489 B-cell lymphoma tissues embedded in paraffin were collected from Henan Cancer Hospital, and the results were compared with simultaneous FISH detection of four break/fusion genes: BCL2, BCL6, MYC, and CCND1. Consistency was defined as both methods yielding positive or negative results for the same sample. The relationship between fusion mutation abundance in NGS and the positivity rate of cells in FISH was also analyzed. Results: Kappa consistency analysis revealed high consistency between NGS and FISH in detecting the four B-cell lymphoma-related gene rearrangement (P<0.001 for all) ; however, the detection rates of positive individuals differed for the four genes. Compared with FISH, NGS demonstrated a higher detection rate for BCL2 rearrangement, a lower detection rate for BCL6 and MYC rearrangement, and a similar detection rate for CCND1 rearrangement. No correlation was found between fusion mutation abundance in NGS and the positivity rate of cells in FISH. Conclusions: NGS and FISH detection of B-cell lymphoma gene rearrangement demonstrate overall good consistency. NGS is superior to FISH in detecting BCL2 rearrangement, inferior in detecting MYC rearrangement, and comparable in detecting CCND1 rearrangement.

Novel method for detecting frequent TERT promoter hot spot mutations in bladder cancer samples.

Telomerase reverse transcriptase promoter (TERTp) mutations are frequently targeted tumor markers, however, they reside in regions with high GC content, which poses challenges when examined with simple molecular techniques or even with next-generation sequencing (NGS). In bladder cancer (BC), TERTp mutations are particularly frequent, however, none of the available tools have demonstrated efficacy in detecting TERTp mutations via a simple noninvasive technique. Therefore, we developed a novel PCR-based method for the detection of the two most common TERTp mutations and demonstrated its use for the analysis of BC samples. The developed SHARD-PCR TERTp mutation detection technique requires PCR and restriction digestion steps that are easily implementable even in less well-equipped laboratories. Cell lines with known mutational status were utilized for method development. Matching urine and tumor tissue samples from BC patients were analyzed, and the results were validated by next-generation sequencing. Analysis of eighteen urine and corresponding tumor tissue samples by SHARD-PCR revealed perfect matches in sample pairs, which paralleled the corresponding NGS results: fourteen samples exhibited mutations at the -124 position, two samples showed mutations at the -146 position, and no mutations were detected in two samples. Our study serves as a proof-of-concept and is limited by its small sample size, nonetheless, it demonstrates that SHARD-PCR is a simple, economic and highly reliable method for detecting TERTp mutations, which are common in different cancer types. For bladder cancer, SHARD-PCR can be performed with the use of noninvasive samples and could replace or complement currently used techniques.

[Feasibility study on the use of peripheral blood differentially expressed genes for objective classification of chronic subjective tinnitus: a case study on high-frequency tinnitus].

Objective: To explore the feasibility of constructing an objective tinnitus subtype model based on peripheral blood differentially expressed genes (DEGs) using a combination of Weighted Gene Co-expression Network Analysis (WGCNA) and Random Forest algorithm (RF). Methods: From October 2019 to June 2020, peripheral blood DEGs were obtained from 37 patients (from the Third Affiliated Hospital of Sun Yat-sen University)with chronic subjective high-frequency tinnitus (21 unbothersome type, 16 bothersome type) and 20 healthy volunteers through high-throughput sequencing. WGCNA was used to construct gene modules with different expression patterns and analyze their relationships with tinnitus characteristics. Subsequently, RF was employed to build subtype models, which were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, and F1-score. Results: A total of 12 351 intergroup DEGs were divided into 9 gene modules. Among them, MEblue, MEgreen, and MEbrown showed significant negative correlations with the healthy volunteer group, while MEpink showed a significant positive correlation with the tinnitus distress group. The "Tinnitus vs. Normal" and "Compensatory vs. Decompensatory" subtype models, based on MEblue and MEpink respectively, both had AUCs greater than 0.80, accuracies above 90%, and F1-scores above 0.90, indicating good performance. Conclusions: Peripheral blood DEGs are potential biological indicators for objective classification of subjective tinnitus. The combined application of WGCNA and the Random Forest algorithm should be a viable approach to constructing an objective tinnitus subtype model. However, further exploration and refinement are needed to validate the model's generalizability, cross-dataset performance, and algorithm optimization.

Performance assessment of computational tools to detect microsatellite instability.

Microsatellite instability (MSI) is a phenomenon seen in several cancer types, which can be used as a biomarker to help guide immune checkpoint inhibitor treatment. To facilitate this, researchers have developed computational tools to categorize samples as having high microsatellite instability, or as being microsatellite stable using next-generation sequencing data. Most of these tools were published with unclear scope and usage, and they have yet to be independently benchmarked. To address these issues, we assessed the performance of eight leading MSI tools across several unique datasets that encompass a wide variety of sequencing methods. While we were able to replicate the original findings of each tool on whole exome sequencing data, most tools had worse receiver operating characteristic and precision-recall area under the curve values on whole genome sequencing data. We also found that they lacked agreement with one another and with commercial MSI software on gene panel data, and that optimal threshold cut-offs vary by sequencing type. Lastly, we tested tools made specifically for RNA sequencing data and found they were outperformed by tools designed for use with DNA sequencing data. Out of all, two tools (MSIsensor2, MANTIS) performed well across nearly all datasets, but when all datasets were combined, their precision decreased. Our results caution that MSI tools can have much lower performance on datasets other than those on which they were originally evaluated, and in the case of RNA sequencing tools, can even perform poorly on the type of data for which they were created.

The value of metagenomic next-generation sequencing with different nucleic acid extracting methods of cell-free DNA or whole-cell DNA in the diagnosis of non-neutropenic pulmonary aspergillosis.

Purpose: Metagenomic next-generation sequencing(mNGS) is a novel molecular diagnostic technique. For nucleic acid extraction methods, both whole-cell DNA (wcDNA) and cell-free DNA (cfDNA) are widely applied with the sample of bronchoalveolar lavage fluid (BALF). We aim to evaluate the clinical value of mNGS with cfDNA and mNGS with wcDNA for the detection of BALF pathogens in non-neutropenic pulmonary aspergillosis. Methods: mNGS with BALF-cfDNA, BALF-wcDNA and conventional microbiological tests (CMTs) were performed in suspected non-neutropenic pulmonary aspergillosis. The diagnostic value of different assays for pulmonary aspergillosis was compared. Results: BALF-mNGS (cfDNA, wcDNA) outperformed CMTs in terms of microorganisms detection. Receiver operating characteristic (ROC) analysis indicated BALF-mNGS (cfDNA, wcDNA) was superior to culture and BALF-GM. Combination diagnosis of either positive for BALF-mNGS (cfDNA, wcDNA) or CMTs is more sensitive than CMTs alone in the diagnosis of pulmonary aspergillosis (BALF-cfDNA+CMTs/BALF-wcDNA+CMTs vs. CMTs: ROC analysis: 0.813 vs.0.66, P=0.0142/0.796 vs.0.66, P=0.0244; Sensitivity: 89.47% vs. 47.37%, P=0.008/84.21% vs. 47.37%, P=0.016). BALF-cfDNA showed a significantly greater reads per million (RPM) than BALF-wcDNA. The area under the ROC curve (AUC) for RPM of Aspergillus detected by BALF-cfDNA, used to predict "True positive" pulmonary aspergillosis patients, was 0.779, with a cut-off value greater than 4.5. Conclusion: We propose that the incorporation of BALF-mNGS (cfDNA, wcDNA) with CMTs improves diagnostic precision in the identification of non-neutropenic pulmonary aspergillosis when compared to CMTs alone. BALF-cfDNA outperforms BALF-wcDNA in clinical value.