New mutation of the TP53 gene associated with the hereditary breast cancer in a young Tuvinian woman

Background. The identification of the ethnospecific mutations associated with hereditary breast cancer remains challenging. Next generation sequencing (Ngs) technology fully enables the compilation of germline variants associated with the risk for inherited diseases. Despite the success of the Ngs, up to 20 % of molecular tests report genetic variant of unknown significance (Vus) or novel variants that have never been previously described and their clinical significances are unknown. To obtain extended information about the variants of the unknown significance, it is necessary to use an alternative approach for the analysis of the Ngs data. To obtain extended characteristic about the unknown significance variants, it is necessary to search for additional tools for the analysis of the Ngs data. Material and methods. We reclassified the mutation of the unknown significance using the activedrivedb database that assessed the effect of mutations on sites of post-translational modifications, and the proteinpaint tool that complemented the existing cancer genome portals and provided a comprehensive and intuitive view of cancer genomic data. Results. In this study, we report a 44-year-old tuvinian woman with a family history of breast cancer. Based on the Ngs data, mutational analysis revealed the presence of the lrg_321t1: c.80c>t heterozygous variant in exon 2, which led to the proline to leucine change at codon 27 of the protein. In the dbpubmed database, this mutation was determined as unknown significance due to data limitation. According to the data of the activedriverdb tool, this mutation is located distally at the site of post-translational protein modification, which is responsible for binding to kinases that regulate genes of the cell cycle, etc. (atm, chek2, cdk, mapk). In accordance with proteinpaint tool, the lrg_321t1: c.80c>t mutation is located in functionally specialized transactivation domains and codon of the tp53 gene, where the pathogenic mutation associated with li-Fraumeni syndrome has been earlier described. Conclusion. This report is the first to describe a new variant in the tp53 gene (rs1555526933), which is likely to be associated with hereditary cancer-predisposing syndrome, including li-Fraumeni syndrome, in a tuvinian Bc patient with young-onset and familial Bc.

Rapid and Accurate Identification of SARS-CoV-2 Omicron Variants Using Droplet Digital PCR (RT-ddPCR)

Background Mutations in the receptor binding domain of the SARS-CoV-2 Spike protein are associated with increased transmission or substantial reductions in vaccine efficacy, including in the recently described Omicron variant. The changing frequencies of these mutations combined with their differing susceptibility to available therapies have posed significant problems for clinicians and public health professionals. Objective To develop an assay capable of rapidly and accurately identifying variants including Omicron in clinical specimens to enable case tracking and/or selection of appropriate clinical treatment. Study Design Using three duplex RT-ddPCR reactions targeting four amino acids, we tested 419 positive clinical specimens from February to December 2021 during a period of rapidly shifting variant prevalences and compared genotyping results to genome sequences for each sample, determining the sensitivity and specificity of the assay for each variant. Results Mutation determinations for 99.7% of detected samples agree with NGS data for those samples, and are accurate despite wide variation in RNA concentration and potential confounding factors like transport medium, presence of additional respiratory viruses, and additional mutations in primer and probe sequences. The assay accurately identified the first 15 Omicron variants in our laboratory including the first Omicron in Washington State and discriminated against S-gene dropout Delta specimen. Conclusion We describe an accurate, precise, and specific RT-ddPCR assay for variant detection that remains robust despite being designed prior the emergence of Delta and Omicron variants. The assay can quickly identify mutations in current and past SARS-CoV-2 variants, and can be adapted to future mutations.

Analysis of the cells isolated from epithelial cell rests of Malassez through single-cell limiting dilution

The epithelial cell rests of Malassez (ERM) are essential in preventing ankylosis between the alveolar bone and the tooth (dentoalveolar ankylosis). Despite extensive research, the mechanism by which ERM cells suppress ankylosis remains uncertain; perhaps its varied population is to reason. Therefore, in this study, eighteen unique clones of ERM (CRUDE) were isolated using the single-cell limiting dilution and designated as ERM 1–18. qRT-PCR, ELISA, and western blot analyses revealed that ERM-2 and -3 had the highest and lowest amelogenin expression, respectively. Mineralization of human periodontal ligament fibroblasts (HPDLF) was reduced in vitro co-culture with CRUDE ERM, ERM-2, and -3 cells, but recovered when an anti-amelogenin antibody was introduced. Transplanted rat molars grown in ERM-2 cell supernatants produced substantially less bone than those cultured in other cell supernatants; inhibition was rescued when an anti-amelogenin antibody was added to the supernatants. Anti-Osterix antibody staining was used to confirm the development of new bones. In addition, next-generation sequencing (NGS) data were analysed to discover genes related to the distinct roles of CRUDE ERM, ERM-2, and ERM-3. According to this study, amelogenin produced by ERM cells helps to prevent dentoalveolar ankylosis and maintain periodontal ligament (PDL) space, depending on their clonal diversity.

Identification of missed viruses by metagenomic sequencing of clinical respiratory samples from Kenya

Pneumonia remains a major cause of mortality and morbidity. Most molecular diagnoses of viruses rely on polymerase chain reaction (PCR) assays that however can fail due to primer mismatch. We investigated the performance of routine virus diagnostics in Kilifi, Kenya, using random-primed viral next generation sequencing (viral NGS) on respiratory samples which tested negative for the common viral respiratory pathogens by a local standard diagnostic panel. Among 95 hospitalised pneumonia patients and 95 household-cohort individuals, analysis of viral NGS identified at least one respiratory-associated virus in 35 (37%) and 23 (24%) samples, respectively. The majority (66%; 42/64) belonged to the Picornaviridae family. The NGS data analysis identified a number of viruses that were missed by the diagnostic panel (rhinovirus, human metapneumovirus, respiratory syncytial virus and parainfluenza virus), and these failures could be attributed to PCR primer/probe binding site mismatches. Unexpected viruses identified included parvovirus B19, enterovirus D68, coxsackievirus A16 and A24 and rubella virus. The regular application of such viral NGS could help evaluate assay performance, identify molecular causes of missed diagnoses and reveal gaps in the respiratory virus set used for local screening assays. The results can provide actionable information to improve the local pneumonia diagnostics and reveal locally important viral pathogens.

ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data

Background Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. Results ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. Conclusions IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/.

miND pipeline AWS EC2 installation and setup v2

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data

Copy number variation (CNV) is a well-known type of genomic mutation that is associated with the development of human cancer diseases. Detection of CNVs from the human genome is a crucial step for the pipeline of starting from mutation analysis to cancer disease diagnosis and treatment. Next-generation sequencing (NGS) data provides an unprecedented opportunity for CNVs detection at the base-level resolution, and currently, many methods have been developed for CNVs detection using NGS data. However, due to the intrinsic complexity of CNVs structures and NGS data itself, accurate detection of CNVs still faces many challenges. In this paper, we present an alternative method, called KNNCNV (K-Nearest Neighbor based CNV detection), for the detection of CNVs using NGS data. Compared to current methods, KNNCNV has several distinctive features: 1) it assigns an outlier score to each genome segment based solely on its first k nearest-neighbor distances, which is not only easy to extend to other data types but also improves the power of discovering CNVs, especially the local CNVs that are likely to be masked by their surrounding regions; 2) it employs the variational Bayesian Gaussian mixture model (VBGMM) to transform these scores into a series of binary labels without a user-defined threshold. To evaluate the performance of KNNCNV, we conduct both simulation and real sequencing data experiments and make comparisons with peer methods. The experimental results show that KNNCNV could derive better performance than others in terms of F1-score.

sgDI-tector: defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs

Coronavirus RNA-dependent RNA polymerases produce subgenomic RNAs (sgRNAs) that encode viral structural and accessory proteins. User-friendly bioinformatic tools to detect and quantify sgRNA production are urgently needed to study the growing number of next-generation sequencing (NGS) data of SARS-CoV-2. We introduced sgDI-tector to identify and quantify sgRNA in SARS-CoV-2 NGS data. sgDI-tector allowed detection of sgRNA without initial knowledge of the transcription-regulatory sequences. We produced NGS data and successfully detected the nested set of sgRNAs with the ranking M>ORF3a>N>ORF6>ORF7a>ORF8>S>E>ORF7b. We also compared the level of sgRNA production with other types of viral RNA products such as defective interfering viral genomes.

Next-generation Sequencing-based Test for Resectable Colorectal Cancer in Real-world Clinical Practice

Purpose: This study aimed to evaluate the significance of Next-generation sequencing (NGS)-based gene panel testing in resectable colorectal cancers (CRC)s by analyzing real-world data collected prospectively from patients. Methods: Patients with CRC who underwent surgery from July 2018 to February 2020 at our institution were included, and correlations between various NGS data and clinicopathological findings were evaluated. Results: Overall, 107 patients were included in this study. The tumor stage was I in 28 cases (26.2%), II in 40 cases (37.4%), III in 32 cases (29.9%), and IV in 7 cases (6.5%). Actionable gene alterations were found in 97.2% of the cases. Co-alteration analysis suggested that either TP53- or APC-related alterations were more frequently found in early-stage tumors (stage I). The copy number alteration count was significantly lower in right side colon tumors than in tumors in other locations (P < 0.05). Homologous recombination deficiency (HRD) was more often identified in stage IV tumors than in stage I or II tumors (P < 0.05). Moreover, high HRD status was suggested to be useful for identifying high-risk stage II tumors (P < 0.05). Conclusion: In this study, real-world NGS data represented the biological features of CRCs. HRD was identified as a useful result of gene panel testing with novel utility in clinical practice.

miND pipeline AWS EC2 installation and setup v1

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.