Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

Developmental validation of the ForenSeq® Kintelligence kit, MiSeq FGx® sequencing system and ForenSeq Universal Analysis Software

Forensic Investigative Genetic Genealogy, a recent sub discipline of forensic genomics, leverages the high throughput and sensitivity of detection of next generation sequencing and established genetic and genealogical approaches to support the identification of human remains from missing persons investigations and investigative lead generation in violent crimes. To facilitate forensic DNA evidence analysis, the ForenSeq® Kintelligence multiplex, consisting of 10,230 SNPs, was developed. Design of the ForenSeq Kintelligence Kit, the MiSeq FGx® Sequencing System and the ForenSeq Universal Analysis Software is described. Developmental validation in accordance with SWGDAM guidelines and forensic quality assurance standards, using single source samples, is reported for the end-to-end workflow from library preparation to data interpretation. Performance metrics support the conclusion that more genetic information can be obtained from challenging samples compared to other commercially available forensic targeted DNA assays developed for capillary electrophoresis (CE) or other current next generation sequencing (NGS) kits due to the higher number of markers, the overall shorter amplicon sizes (97.8% <150 bp), and kit design. Data indicate that the multiplex is robust and fit for purpose for a wide range of quantity and quality samples. The ForenSeq Kintelligence Kit and the Universal Analysis Software allow transfer of the genetic component of forensic investigative genetic genealogy to the operational forensic laboratory.

SARS-CoV-2 genomic surveillance and reliability of PCR single point mutation assay (SNPsig® SARS-CoV-2 EscapePLEX CE) for the rapid detection of variants of concern in Cameroon

Background

Surveillance of SARS-CoV-2 variants of concern (VOCs) and lineages is crucial for decision-making. Our objective was to study the SARS-CoV-2 clade dynamics across epidemiological waves and evaluate the reliability of SNPsig® SARS-CoV-2 EscapePLEX CE in detecting VOCs in Cameroon.

Material and methods

A laboratory-based study was conducted on SARS-CoV-2 positive nasopharyngeal specimens cycle threshold (Ct)≤30 at the Chantal BIYA International Reference Centre in Yaoundé-Cameroon, between April-2020 to August-2022. Samples were analyzed in parallel with Sanger sequencing and (SNPsig® SARS-CoV-2 EscapePLEX CE), and performance characteristics were evaluated by Cohen's coefficient and McNemar test.

Results

Of the 130 sequences generated, SARS-CoV-2 clades during wave-1 (April-November 2020) showed 97 % (30/31) wild-type lineages and 3 % (1/31) Gamma-variant; wave-2 (December-2020 to May-2021), 25 % (4/16) Alpha-variant, 25 % (4/16) Beta-variant, 44 % (7/16) wild-type and 6 % (1/16) mu; wave-3 (June-October 2021), 94 % (27/29) Delta-variant, 3 % (1/29) Alpha-variant, 3 % (1/29) wild-type; wave-4 (November-2021 to August-2022), 98 % (53/54) Omicron-variant and 2 % (1/54) Delta-variant. Omicron sub-variants were BA.1 (47 %), BA.5 (34 %), BA.2 (13 %) and BA.4 (6 %). Globally, the two genotyping methods accurately identified the SARS-CoV-2 VOCs (P = 0.17, McNemar test; Ka = 0.67).

Conclusion

Genomic surveillance reveals a rapid dynamic in SARS-CoV-2 strains between epidemiological waves in Cameroon. For wide-spread variant surveillance in resource-limited settings, SNPsig® SARS-CoV-2 EscapePLEX CEkit represents a suitable tool, pending upgrading for distinguishing Omicron sub-lineages.

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.

Molecular characterization of Campylobacter spp. isolates obtained from commercial broilers and native chickens in Southern Thailand using whole genome sequencing

Chickens are the primary reservoirs of Campylobacter spp., mainly C. jejuni and C. coli, that cause human bacterial gastrointestinal infections. However, genomic characteristics and antimicrobial resistance of Campylobacter spp. in low- to middle-income countries need more comprehensive exploration. This study aimed to characterize 21 C. jejuni and 5 C. coli isolates from commercial broilers and native chickens using whole genome sequencing and compare them to 28 reference Campylobacter sequences. Among the 26 isolates, 13 sequence types (ST) were identified in C. jejuni and 5 ST in C. coli. The prominent ST was ST 2274 (5 isolates, 19.2%), followed by ST 51, 460, 2409, and 6455 (2 isolates in each ST, 7.7%), while all remaining ST (464, 536, 595, 2083, 6736, 6964, 8096, 10437, 828, 872, 900, 8237, and 13540) had 1 isolate per ST (3.8%). Six types of antimicrobial resistance genes (ant(6)-Ia, aph(3')-III, blaOXA, cat, erm(B), and tet(O)) and one point mutations in the gyrA gene (Threonine-86-Isoleucine) and another in the rpsL gene (Lysine-43-Arginine) were detected. The blaOXA resistance gene was present in all isolates, the gyrA mutations was in 95.2% of C. jejuni and 80.0% of C. coli, and the tet(O) resistance gene in 76.2% of C. jejuni and 80.0% of C. coli. Additionally, 203 virulence-associated genes linked to 16 virulence factors were identified. In terms of phenotypic resistance, the C. jejuni isolates were all resistant to ciprofloxacin, enrofloxacin, and nalidixic acid, with lower levels of resistance to tetracycline (76.2%), tylosin (52.3%), erythromycin (23.8%), azithromycin (22.2%), and gentamicin (11.1%). Most C. coli isolates were resistant to all tested antimicrobials, while 1 C. coli was pan-susceptible except for tylosin. Single-nucleotide polymorphisms concordance varied widely, with differences of up to 13,375 single-nucleotide polymorphisms compared to the reference Campylobacter isolates, highlighting genetic divergence among comparative genomes. This study contributes to a deeper understanding of the molecular epidemiology of Campylobacter spp. in Thai chicken production systems.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Circulating Nucleic Acids in Colorectal Cancer: Diagnostic and Prognostic Value

Colorectal cancer (CRC) is the third most prevalent cancer in the world and the fourth leading cause of cancer-related mortality. DNA (cfDNA/ctDNA) and RNA (cfRNA/ctRNA) in the blood are promising noninvasive biomarkers for molecular profiling, screening, diagnosis, treatment management, and prognosis of CRC. Technological advancements that enable precise detection of both genetic and epigenetic abnormalities, even in minute quantities in circulation, can overcome some of these challenges. This review focuses on testing for circulating nucleic acids in the circulation as a noninvasive method for CRC detection, monitoring, detection of minimal residual disease, and patient management. In addition, the benefits and drawbacks of various diagnostic techniques and associated bioinformatics tools have been detailed.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Long-read sequencing improves diagnostic rate in neuromuscular disorders

Massive parallel sequencing methods, such as exome, genome, and targeted DNA sequencing, have aided molecular diagnosis of genetic diseases in the last 20 years. However, short-read sequencing methods still have several limitations, such inaccurate genome assembly, the inability to detect large structural variants, and variants located in hard-to-sequence regions like highly repetitive areas. The recently emerged PacBio single-molecule real-time (SMRT) and Oxford nanopore technology (ONT) long-read sequencing (LRS) methods have been shown to overcome most of these technical issues, leading to an increase in diagnostic rate. LRS methods are contributing to the detection of repeat expansions in novel disease-causing genes (e.g., ABCD3, NOTCH2NLC and RILPL1 causing an Oculopharyngodistal myopathy or PLIN4 causing a Myopathy with rimmed ubiquitin-positive autophagic vacuolation), of structural variants (e.g., in DMD), and of single nucleotide variants in repetitive regions (TTN and NEB). Moreover, these methods have simplified the characterization of the D4Z4 repeats in DUX4, facilitating the diagnosis of Facioscapulohumeral muscular dystrophy (FSHD). We review recent studies that have used either ONT or PacBio SMRT sequencing methods and discuss different types of variants that have been detected using these approaches in individuals with neuromuscular disorders.

Computational approaches for modeling and structural design of biological systems: A comprehensive review

The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

Relevance of next generation sequencing (NGS) data re-analysis in the diagnosis of monogenic diseases leading to organ failure

BACKGROUND

In 2018, our center started a program to offer genetic diagnosis to patients with kidney and liver monogenic rare conditions, potentially eligible for organ transplantation. We exploited a clinical exome sequencing approach, followed by analyses of in silico gene panels tailored to clinical suspicions, obtaining detection rates in line with what reported in literature. However, a percentage of patients remains without a definitive genetic diagnosis. This work aims to evaluate the utility of NGS data re-analysis for those patients with an inconclusive or negative genetic test at the time of first analysis considering that (i) the advance of alignment and variant calling processes progressively improve the detection rate, limiting false positives and false negatives; (ii) gene panels are periodically updated and (iii) variant annotation may change over time.

METHODS

114 patients, recruited between 2018 and 2020, with an inconclusive or negative NGS report at the time of first analysis, were included in the study. Re-alignment and variant calling of previously generated sequencing raw data were performed using the GenomSys Variant Analyzer software.

RESULTS

21 previously not reported potentially causative variants were identified in 20 patients. In most cases (n = 19), causal variants were retrieved out of the re-classification from likely benign to variants of unknown significance (VUS). In one case, the variant was included because of inclusion in the analysis of a newly disease-associated gene, not present in the original gene panel, and in another one due to the improved data alignment process. Whenever possible, variants were validated with Sanger sequencing and family segregation studies. As of now, 16 out of 20 patients have been analyzed and variants confirmed in 8 patients. Specifically, in two pediatric patients, causative variants were de novo mutations while in the others, the variant was present also in other affected relatives. In the remaining patients, variants were present also in non-affected parents, raising questions on their re-classification.

CONCLUSIONS

Overall, these data indicate that periodic and systematic re-analysis of negative or inconclusive NGS data reports can lead to new variant identification or reclassification in a small but significant proportion of cases, with benefits for patients' management.

Mastering DNA chromatogram analysis in Sanger sequencing for reliable clinical analysis

BACKGROUND

Sanger dideoxy sequencing is vital in clinical analysis due to its accuracy, ability to analyze genetic markers like SNPs and STRs, capability to generate reliable DNA profiles, and its role in resolving complex clinical cases. The precision and robustness of Sanger sequencing contribute significantly to the scientific basis of clinical investigations. Though the reading of chromatograms seems to be a routine step, many errors conducted in PCR may lead to consequent limitations in the readings of AGCT peaks. These errors are possibly associated with improper DNA amplification and its subsequent interpretation of DNA sequencing files, such as noisy peaks, artifacts, and confusion between double-peak technical errors, heterozygosity, and double infection potentials. Thus, it is not feasible to read nucleic acid sequences without giving serious attention to these technical problems. To ensure the accuracy of DNA sequencing outcomes, it is also imperative to detect and rectify technical challenges that may lead to misinterpretation of the DNA sequence, resulting in errors and incongruities in subsequent analyses.

SHORT CONCLUSION

This overview sheds light on prominent technical concerns that can emerge prior to and during the interpretation of DNA chromatograms in Sanger sequencing, along with offering strategies to address them effectively. The significance of identifying and tackling these technical limitations during the chromatogram analysis is underscored in this review. Recognizing these concerns can aid in enhancing the quality of downstream analyses for Sanger sequencing results, which holds notable improvement in accuracy, reliability, and ability to provide crucial genetic information in clinical analysis.

A rapid, multiplex digital PCR assay to detect gene variants and fusions in non-small cell lung cancer

Digital PCR (dPCR) is emerging as an ideal platform for the detection and tracking of genomic variants in cancer due to its high sensitivity and simple workflow. The growing number of clinically actionable cancer biomarkers creates a need for fast, accessible methods that allow for dense information content and high accuracy. Here, we describe a proof-of-concept amplitude modulation-based multiplex dPCR assay capable of detecting 12 single-nucleotide and insertion/deletion (indel) variants in EGFR, KRAS, BRAF, and ERBB2, 14 gene fusions in ALK, RET, ROS1, and NTRK1, and MET exon 14 skipping present in non-small cell lung cancer (NSCLC). We also demonstrate the use of multi-spectral target-signal encoding to improve the specificity of variant detection by reducing background noise by up to an order of magnitude. The assay reported an overall 100% positive percent agreement (PPA) and 98.5% negative percent agreement (NPA) compared with a sequencing-based assay in a cohort of 62 human formalin-fixed paraffin-embedded (FFPE) samples. In addition, the dPCR assay rescued actionable information in 10 samples that failed to sequence, highlighting the utility of a multiplexed dPCR assay as a potential reflex solution for challenging NSCLC samples.

Next-Generation sequencing transforming clinical practice and precision medicine

Next-generation sequencing (NGS) has revolutionized the field of genomics and is rapidly transforming clinical diagnosis and precision medicine. This advanced sequencing technology enables the rapid and cost-effective analysis of large-scale genomic data, allowing comprehensive exploration of the genetic landscape of diseases. In clinical diagnosis, NGS has proven to be a powerful tool for identifying disease-causing variants, enabling accurate and early detection of genetic disorders. Additionally, NGS facilitates the identification of novel disease-associated genes and variants, aiding in the development of targeted therapies and personalized treatment strategies. NGS greatly benefits precision medicine by enhancing our understanding of disease mechanisms and enabling the identification of specific molecular markers for disease subtypes, thus enabling tailored medical interventions based on individual characteristics. Furthermore, NGS contributes to the development of non-invasive diagnostic approaches, such as liquid biopsies, which can monitor disease progression and treatment response. The potential of NGS in clinical diagnosis and precision medicine is vast, yet challenges persist in data analysis, interpretation, and protocol standardization. This review highlights NGS applications in disease diagnosis, prognosis, and personalized treatment strategies, while also addressing challenges and future prospects in fully harnessing genomic potential within clinical practice.

Hypervirulent Klebsiella pneumoniae detection methods: a minireview

Hypervirulent Klebsiella pneumoniae (hvKp), characterized by high virulence and epidemic potential, has become a global public health challenge. Therefore, improving the identification of hvKp and enabling earlier and faster detection in the community to support subsequent effective treatment and prevention of hvKp are an urgent issue. To address these issues, a number of assays have emerged, such as String test, Galleria mellonella infection test, PCR, isothermal exponential amplification, and so on. In this paper, we have collected articles on the detection methods of hvKp and conducted a retrospective review based on two aspects: traditional detection technology and biomarker-based detection technology. We summarize the advantages and limitations of these detection methods and discuss the challenges as well as future directions, hoping to provide new insights and references for the rapid detection of hvKp in the future. The aim of this study is to focus on the research papers related to Hypervirulent Klebsiella pneumoniae involving the period from 2012 to 2022. We conducted searches using the keywords "Hypervirulent Klebsiella pneumoniae, biomarkers, detection techniques" on ScienceDirect and Google Scholar. Additionally, we also searched on PubMed, using MeSH terms associated with the keywords (such as Klebsiella pneumoniae, Klebsiella Infections, Virulence, Biomarkers, diagnosis, etc.).

De novo drug design based on patient gene expression profiles via deep learning

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

Advances in bread wheat production through CRISPR/Cas9 technology: a comprehensive review of quality and other aspects

MAIN CONCLUSION

This review provides a comprehensive overview of the CRISPR/Cas9 technique and the research areas of this gene editing tool in improving wheat quality. Wheat (Triticum aestivum L.), the basic nutrition for most of the human population, contributes 20% of the daily energy needed because of its, carbohydrate, essential amino acids, minerals, protein, and vitamin content. Wheat varieties that produce high yields and have enhanced nutritional quality will be required to fulfill future demands. Hexaploid wheat has A, B, and D genomes and includes three like but not identical copies of genes that influence important yield and quality. CRISPR/Cas9, which allows multiplex genome editing provides major opportunities in genome editing studies of plants, especially complicated genomes such as wheat. In this overview, we discuss the CRISPR/Cas9 technique, which is credited with bringing about a paradigm shift in genome editing studies. We also provide a summary of recent research utilizing CRISPR/Cas9 to investigate yield, quality, resistance to biotic/abiotic stress, and hybrid seed production. In addition, we provide a synopsis of the laboratory experience-based solution alternatives as well as the potential obstacles for wheat CRISPR studies. Although wheat's extensive genome and complicated polyploid structure previously slowed wheat genetic engineering and breeding progress, effective CRISPR/Cas9 systems are now successfully used to boost wheat development.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

Translational Bioinformatics Applied to the Study of Complex Diseases

Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.

MALDI Mass Spectrometry Imaging: A Potential Game-Changer in a Modern Microbiology

Nowadays, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is routinely implemented as the reference method for the swift and straightforward identification of microorganisms. However, this method is not flawless and there is a need to upgrade the current methodology in order to free the routine lab from incubation time and shift from a culture-dependent to an even faster independent culture system. Over the last two decades, mass spectrometry imaging (MSI) gained tremendous popularity in life sciences, including microbiology, due to its ability to simultaneously detect biomolecules, as well as their spatial distribution, in complex samples. Through this literature review, we summarize the latest applications of MALDI-MSI in microbiology. In addition, we discuss the challenges and avenues of exploration for applying MSI to solve current MALDI-TOF MS limits in routine and research laboratories.

Pharmacogenomics in treatment of depression and psychosis: an update

Genetic factors can, to a certain extent, successfully predict the therapeutic effects, metabolism, and adverse reactions of drugs. This research field, pharmacogenomics, is well developed in oncology and is currently expanding in psychiatry. Here, we summarize the latest development in pharmacogenomic psychiatry, where results of several recent large studies indicate a true benefit and cost-effectiveness of pre-emptive genotyping for more successful psychotherapy. However, it is apparent that we still lack knowledge of many additional heritable genetic factors of importance for explanation of the interindividual differences in response to psychiatric drugs. Thus, more effort to further develop pharmacogenomic psychiatry should be invested to achieve a broader clinical implementation.

Application of second-generation sequencing in congenital pulmonary airway malformations

To investigate the differential expression of genes in whole transcripts of congenital pulmonary airway malformation (CPAM) using second-generation sequencing (also known as next-generation sequencing, NGS) technology. Children with CPAM were strictly screened after setting the criteria, and grouped by taking CPAM parietal tissue and CPAM lesion tissue respectively, and RNA-Seq libraries were established separately using second-generation sequencing technology, followed by differential expression analysis and GO (gene ontology) functional enrichment analysis, KEGG (Kyoto encyclopedia of genes and genomes, a database) pathway analysis and GSEA (Gene Set Enrichment Analysis) analysis. Five cases were screened from 36 children with CPAM, and high-throughput sequencing was performed to obtain 10 whole transcripts of samples with acceptable sequence quality and balanced gene coverage. One aberrantly expressed sample (3b) was found by analysis of principal components, which was excluded and then subjected to differential expression analysis, and 860 up-regulated genes and 203 down-regulated genes. GO functional enrichment analysis of differentially expressed genes demonstrates the functional class and cellular localization of target genes. The whole transcript of CPAM shows obvious gene up and down-regulation, differentially expressed genes are located in specific cells and belong to different functional categories, and NGS can provide an effective means to study the transcriptional regulation of CPAM from the overall transcriptional level.

Editorial: Advances in genomic and genetic tools, and their applications for understanding embryonic development and human diseases

Significant advances have been recently made in the development of the genetic and genomic platforms. This has greatly contributed to a better understanding of gene expression and regulation machinery. Consequently, this led to considerable progress in unraveling evidence of the genotype-phenotype correlation between normal/abnormal embryonic development and human disease complexity. For example, advanced genomic tools such as next-generation sequencing, and microarray-based CGH have substantially helped in the identification of gene and copy number variants associated with diseases as well as in the discovery of causal gene mutations. In addition, bioinformatic analysis tools of genome annotation and comparison have greatly aided in data analysis for the interpretation of the genetic variants at the individual level. This has unlocked potential possibilities for real advances toward new therapies in personalized medicine for the targeted treatment of human diseases. However, each of these genomic and bioinformatics tools has its limitations and hence further efforts are required to implement novel approaches to overcome these limitations. It could be possible that the use of more than one platform for genotype-phenotype deep analysis is an effective approach to disentangling the cause and treatment of the disease complexities. Our research topic aimed at deciphering these complexities by shedding some light on the recent applications of the basic and advanced genetic/genomic and bioinformatics approaches. These include studying gene-gene, protein-protein, and gene-environment interactions. We, in addition, aimed at a better understanding of the link between normal/abnormal embryonic development and the cause of human disease induction.

Quantum computing algorithms: getting closer to critical problems in computational biology

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

CTLA-4 Insufficiency due to a Novel CTLA-4 Deletion, Identified through Copy Number Variation Analysis

BACKGROUND

The diagnostic yield of next-generation sequencing (NGS) technologies in the diagnosis of monogenic inborn errors of immunity (IEI) remains limited, rarely exceeding 30%. Monoallelic pathogenic germline variants in cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) result in variable immunodeficiency and immune dysregulation. The genetic diagnosis of CTLA-4 insufficiency can affect follow-up procedures and may lead to consideration of treatment with CTLA-4-Ig.

OBJECTIVES

The aim of the study was to identify the genetic cause of familial immunodeficiency and immune dysregulation in cases where single nucleotide variant analysis of short-read NGS data yielded no diagnostic result.

METHODS

Analysis of copy number variants (CNVs) was applied on short-read NGS data.

RESULTS

We identified a novel monoallelic deletion-insertion variant in CTLA-4 (c.445_568-544delinsTTTGCGATTG) resulting in familial autoimmunity. This is the second larger scale variant in CTLA-4, which despite consistently reduced expression of CTLA-4 displayed variable expressivity, ranging from typical juvenile idiopathic arthritis to common variable immunodeficiency-like immunodeficiency.

CONCLUSIONS

Our report suggests the significance of integration of CNV analysis in routine evaluation of NGS, which may increase its diagnostic yield in IEI.

A retrospective cohort analysis of the Yale pediatric genomics discovery program

The Pediatric Genomics Discovery Program (PGDP) at Yale uses next-generation sequencing (NGS) and translational research to evaluate complex patients with a wide range of phenotypes suspected to have rare genetic diseases. We conducted a retrospective cohort analysis of 356 PGDP probands evaluated between June 2015 and July 2020, querying our database for participant demographics, clinical characteristics, NGS results, and diagnostic and research findings. The three most common phenotypes among the entire studied cohort (n = 356) were immune system abnormalities (n = 105, 29%), syndromic or multisystem disease (n = 103, 29%), and cardiovascular system abnormalities (n = 62, 17%). Of 216 patients with final classifications, 77 (36%) received new diagnoses and 139 (64%) were undiagnosed; the remaining 140 patients were still actively being investigated. Monogenetic diagnoses were found in 67 (89%); the largest group had variants in known disease genes but with new contributions such as novel variants (n = 31, 40%) or expanded phenotypes (n = 14, 18%). Finally, five PGDP diagnoses (8%) were suggestive of novel gene-to-phenotype relationships. A broad range of patients can benefit from single subject studies combining NGS and functional molecular analyses. All pediatric providers should consider further genetics evaluations for patients lacking precise molecular diagnoses.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Transcriptome Profiling of Different State Callus Induced from Immature Embryo in Maize

Embryogenic and regenerable tissue cultures are widely used in plant transformation. To dissect the molecular mechanism of embryogenesis, we used inbred line A188 as the material; the immature embryo of kernels (15 day after pollination, 15DAP) was isolated and cultured in inducing medium and subjected to RNA-Seq. The results revealed that 5,076 differentially expressed genes (DEGs) were involved in morphological and histological changes and endogenous indole-3-acetic acid (IAA) alteration. Functional analysis showed that the DEGs were related to metabolic pathways and biosynthesis of secondary metabolites. In particular, ARF16 and ARF8 genes of auxin response factors (ARF) were upregulated from EC to IDC and EC to IRC. Meanwhile, BBM2, SERK1, and SERK2 genes of the embryogenic pathway were upregulated, and WIP2 and ESR genes of the wound-inducible were upregulated from EC to IDC and EC to IRC. These changes can improve conversion efficiency from EC to IRC, which is important for elucidating the underlying molecular mechanisms of callus formation.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Design and Analysis of Hospital Throughput Maximization Algorithm under COVID-19 Pandemic

Under the global pandemic of COVID-19, public health facilities, such as hospitals, are required to readjust, design, and plan a safe movement flow of people to meet the social distance rules and quarantine COVID-19 and the non-COVID-19 patients to prevent cross-infection. However, readjustments to separate patients have significantly reduced the maximum throughput of public health facilities, worsening already scarce public health resources. Therefore, this paper proposes throughput maximization algorithms based on the one-way street problem which meets the requirements of social distance rules. First, the floor plan of a hospital is transformed into a graph, each node is traversed by breadth-first search. Then, this paper considers patients' node pair sets as different set unions, the direction of edges, and the color of links based on DFS-XOR algorithm are designed to distinguish the paths of COVID-19 and non-COVID-19 patients. Finally, this paper utilizes minimum shared link algorithms to determine the minimized sharing links between paths linking different set unions and components. The throughput is maximized by reducing the number of shared links and alternating links. The results indicate that compared with the brute force algorithms, the algorithms proposed in this paper significantly improve the maximum throughput.

Neuromuscular disorders: finding the missing genetic diagnoses

Neuromuscular disorders (NMDs) are a wide-ranging group of diseases that seriously affect the quality of life of affected individuals. The development of next-generation sequencing revolutionized the diagnosis of NMD, enabling the discovery of hundreds of NMD genes and many more pathogenic variants. However, the diagnostic yield of genetic testing in NMD cohorts remains incomplete, indicating a large number of genetic diagnoses are not identified through current methods. Fortunately, recent advancements in sequencing technologies, analytical tools, and high-throughput functional screening provide an opportunity to circumvent current challenges. Here, we discuss reasons for missing genetic diagnoses in NMD, how emerging technologies and tools can overcome these hurdles, and examine future approaches to improving diagnostic yields in NMD.

Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps

In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume in biological databases and their continuous exponential increase, high-speed data processing is necessary. Therefore, this work proposes a parallel hardware design for the SW algorithm with a systolic array structure to accelerate the forward and backtracking steps. For this purpose, the architecture calculates and stores the paths in the forward stage for pre-organizing the alignment, which reduces the complexity of the backtracking stage. The backtracking starts from the maximum score position in the matrix and generates the optimal SW sequence alignment path. The architecture was validated on Field-Programmable Gate Array (FPGA), and synthesis analyses have shown that the proposed design reaches up to 79.5 Giga Cell Updates per Second (GCPUS).

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Viral Eco-Genomic Tools: Development and Implementation for Aquatic Biomonitoring

Enteric viruses (EVs) occurrence within aquatic environments varies and leads to significant risk on public health of humans, animals, and diversity of aquatic taxa. Early and efficacious recognition of cultivable and fastidious EVs in aquatic systems are important to ensure the sanitary level of aquatic water and implement required treatment strategies. Herein, we provided a comprehensive overview of the conventional and up-to-date eco-genomic tools for aquatic biomonitoring of EVs, aiming to develop better water pollution monitoring tools. In combination with bioinformatics techniques, genetic tools including cloning sequencing analysis, DNA microarray, next-generation sequencing (NGS), and metagenomic sequencing technologies are implemented to make informed decisions about the global burden of waterborne EVs-associated diseases. The data presented in this review are helpful to recommend that: (1) Each viral pollution detection method has its own merits and demerits; therefore, it would be advantageous for viral pollution evaluation to be integrated as a complementary platform. (2) The total viral genome pool extracted from aquatic environmental samples is a real reflection of pollution status of the aquatic eco-systems; therefore, it is recommended to conduct regular sampling through the year to establish an updated monitoring system for EVs, and quantify viral peak concentrations, viral typing, and genotyping. (3) Despite that conventional detection methods are cheaper, it is highly recommended to implement molecular-based technologies to complement aquatic ecosystems biomonitoring due to numerous advantages including high-throughput capability. (4) Continuous implementation of the eco-genetic detection tools for monitoring the EVs in aquatic ecosystems is recommended.

Demystifying the Discussion of Sequencing Panel Size in Oncology Genetic Testing

Clinical laboratories worldwide are implementing next-generation sequencing (NGS) to identify cancer genomic variants and ultimately improve patient outcomes. The ability to massively sequence the entire genome or exome of tumour cells has been critical to elucidating many complex biological questions. However, the depth of information obtained by these methods is strenuous to process in the clinical setting, making them currently unfeasible for broader adoption. Instead, targeted sequencing, usually on a selection of clinically relevant genes, represents the predominant approach that best balances accurate identification of genomic variants with high sensitivity and a good cost-effectiveness ratio. The information obtained from targeted sequencing can support diagnostic classification, guide therapeutic decisions, and provide prognostic insights. The use of targeted gene panels expedites sample processing, including data analysis, results interpretation, and medical reports generation, directly affecting patient management. The key decision factors for selecting sequencing methods and panel size in routine testing should include diagnostic yield and clinical utility, sample availability, and processing turnaround time. Profiling by default all patients with late-stage cancer with large panels is not affordable for most healthcare systems and does not provide substantial clinical benefit at present. Balancing between understanding cancer biology, including patients in clinical trials, maximising testing, and ensuring a sustainable financial burden for society requires thorough consideration. This review provides an overview of the advantages and drawbacks of different sizes NGS panels for tumour molecular profiling and their clinical applicability.

Identification of DNAH17 Variants in Han-Chinese Patients With Left–Right Asymmetry Disorders

The formation of left–right asymmetry of the visceral organs is a conserved feature of the human body, and the asymmetry specification of structure and function is precisely orchestrated by multiple regulatory mechanisms. The abnormal results of organ positioning situs arise from defective cilia structure or function during embryogenesis in humans. In this study, we recruited two unrelated Han-Chinese families with left–right asymmetry disorders. The combination of whole-exome sequencing and Sanger sequencing identified two compound heterozygous variants: c.4109C>T and c.9776C>T, and c.612C>G and c.8764C>T in the dynein axonemal heavy chain 17 gene (DNAH17) in two probands with left–right asymmetry disorders. We report for the first time a possible association between DNAH17 gene variants and left–right asymmetry disorders, which is known as a causal gene for asthenozoospermia. Altogether, the findings of our study may enlarge the DNAH17 gene variant spectrum in human left–right asymmetry disorders, pave a way to illustrate the potential pathogenesis of ciliary/flagellar disorders, and provide supplementary explanation for genetic counseling.

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

Genomics in Egypt: Current Status and Future Aspects

Egypt is the third most densely inhabited African country. Due to the economic burden and healthcare costs of overpopulation, genomic and genetic testing is a huge challenge. However, in the era of precision medicine, Egypt is taking a shift in approach from “one-size-fits all” to more personalized healthcare via advancing the practice of medical genetics and genomics across the country. This shift necessitates concrete knowledge of the Egyptian genome and related diseases to direct effective preventive, diagnostic and counseling services of prevalent genetic diseases in Egypt. Understanding disease molecular mechanisms will enhance the capacity for personalized interventions. From this perspective, we highlight research efforts and available services for rare genetic diseases, communicable diseases including the coronavirus 2019 disease (COVID19), and cancer. The current state of genetic services in Egypt including availability and access to genetic services is described. Drivers for applying genomics in Egypt are illustrated with a SWOT analysis of the current genetic/genomic services. Barriers to genetic service development in Egypt, whether economic, geographic, cultural or educational are discussed as well. The sensitive topic of communicating genomic results and its ethical considerations is also tackled. To understand disease pathogenesis, much can be gained through the advancement and integration of genomic technologies via clinical applications and research efforts in Egypt. Three main pillars of multidisciplinary collaboration for advancing genomics in Egypt are envisaged: resources, infrastructure and training. Finally, we highlight the recent national plan to establish a genome center that will aim to prepare a map of the Egyptian human genome to discover and accurately determine the genetic characteristics of various diseases. The Reference Genome Project for Egyptians and Ancient Egyptians will initialize a new genomics era in Egypt. We propose a multidisciplinary governance system in Egypt to support genomic medicine research efforts and integrate into the healthcare system whilst ensuring ethical conduct of data.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Fast, Ungapped Reads Mapping Using Squid

Advances in Next Generation Sequencing technologies allow us to inspect and unlock the genome to a level of detail that was unimaginable only a few decades ago. Omics-based studies are casting a light on the patterns and determinants of disease conditions in populations, as well as on the influence of microbial communities on human health, just to name a few. Through increasing volumes of sequencing information, for example, it is possible to compare genomic features and analyze the modulation of the transcriptome under different environmental stimuli. Although protocols for NGS preparation are intended to leave little to no space for contamination of any kind, a noticeable fraction of sequencing reads still may not uniquely represent what was intended to be sequenced in the first place. If a natural consequence of a sequencing sample is to assess the presence of features of interest by mapping the obtained reads to a genome of reference, sometimes it is useful to determine the fraction of those that do not map, or that map discordantly, and store this information to a new file for subsequent analyses. Here we propose a new mapper, which we called Squid, that among other accessory functionalities finds and returns sequencing reads that match or do not match to a reference sequence database in any orientation. We encourage the use of Squid prior to any quantification pipeline to assess, for instance, the presence of contaminants, especially in RNA-Seq experiments.

Lessons learned: next-generation sequencing applied to undiagnosed genetic diseases

Rare genetic disorders, when considered together, are relatively common. Despite advancements in genetics and genomics technologies as well as increased understanding of genomic function and dysfunction, many genetic diseases continue to be difficult to diagnose. The goal of this Review is to increase the familiarity of genetic testing strategies for non-genetics providers. As genetic testing is increasingly used in primary care, many subspecialty clinics, and various inpatient settings, it is important that non-genetics providers have a fundamental understanding of the strengths and weaknesses of various genetic testing strategies as well as develop an ability to interpret genetic testing results. We provide background on commonly used genetic testing approaches, give examples of phenotypes in which the various genetic testing approaches are used, describe types of genetic and genomic variations, cover challenges in variant identification, provide examples in which next-generation sequencing (NGS) failed to uncover the variant responsible for a disease, and discuss opportunities for continued improvement in the application of NGS clinically. As genetic testing becomes increasingly a part of all areas of medicine, familiarity with genetic testing approaches and result interpretation is vital to decrease the burden of undiagnosed disease.

Screening of OTULIN gene mutation with targeted next generation sequencing in Turkish populations and in silico analysis of these mutations

BACKGROUND

OTULIN-related autoinflammatory syndrome (ORAS) is an autosomal recessive disease characterized by systemic inflammation, recurrent fever. Due to limited knowledge about the OTULIN DNA variants that cause ORAS, the diagnosis and treatment of this disease is difficult. In this study, we aim to identify OTULIN DNA variants responsible for the genetic pathology of ORAS and observe the effects of these variants on the OTULIN protein structure and the function with different bioinformatics approaches.

METHODS

The present study included 3230 individuals with the suspicion of an autoinflammatory disease who were referred to Ege University Children's Hospital Molecular Medicine Laboratory. OTULIN variants were detected using a panel consisting of 37 different autoinflammatory diseases (AID) genes via targeted Next-Generation Sequencing.

RESULTS

As a result of the study, DNA variants associated with various AID were detected in 65% of the individuals to whom the panel was applied. Among these variants, only three different OTULIN variants (p.Val82Ile, p.Gln115His and p.Leu131_Arg132insLeuCysThrGlu) were detected. The pathogenic effects of the variants detected in the OTULIN gene were determined by using Polyphen2 as "Probably Pathogenic" for the p.Val82Ile and "benign" for the p.Gln115His. At the same time, the effects of these variants on the structure and function of the OTULIN protein were investigated by in silico approaches. Both variants reduce protein stability and binding affinity.

CONCLUSION

The results of the current study suggest that the evaluation of OTULIN variants with in silico approaches will contribute to the development of personalized treatments by diagnosing the disease specific to the variant.

BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: looking for the hidden meaning

Hereditary breast and ovarian cancer syndrome is caused by germline mutations in BRCA1/2 genes. These genes are very large and their mutations are heterogeneous and scattered throughout the coding sequence. In addition to the above-mentioned mutations, variants of uncertain/unknown significance (VUSs) have been identified in BRCA genes, which make more difficult the clinical management of the patient and risk assessment. In the last decades, several laboratories have developed different databases that contain more than 2000 variants for the two genes and integrated strategies which include multifactorial prediction models based on direct and indirect genetic evidence, to classify the VUS and attribute them a clinical significance associated with a deleterious, high-low or neutral risk. This review provides a comprehensive overview of literature studies concerning the VUSs, in order to assess their impact on the population and provide new insight for the appropriate patient management in clinical practice.

Analysis of genetic variants in myeloproliferative neoplasms using a 22-gene next-generation sequencing panel

Background

The Philadelphia (Ph)-negative myeloproliferative neoplasms (MPNs), namely essential thrombocythaemia (ET), polycythaemia vera (PV) and primary myelofibrosis (PMF), are a group of chronic clonal haematopoietic disorders that have the propensity to advance into bone marrow failure or acute myeloid leukaemia; often resulting in fatality. Although driver mutations have been identified in these MPNs, subtype-specific markers of the disease have yet to be discovered. Next-generation sequencing (NGS) technology can potentially improve the clinical management of MPNs by allowing for the simultaneous screening of many disease-associated genes.

Methods

The performance of a custom, in-house designed 22-gene NGS panel was technically validated using reference standards across two independent replicate runs. The panel was subsequently used to screen a total of 10 clinical MPN samples (ET n = 3, PV n = 3, PMF n = 4). The resulting NGS data was then analysed via a bioinformatics pipeline.

Results

The custom NGS panel had a detection limit of 1% variant allele frequency (VAF). A total of 20 unique variants with VAFs above 5% (4 of which were putatively novel variants with potential biological significance) and one pathogenic variant with a VAF of between 1 and 5% were identified across all of the clinical MPN samples. All single nucleotide variants with VAFs ≥ 15% were confirmed via Sanger sequencing.

Conclusions

The high fidelity of the NGS analysis and the identification of known and novel variants in this study cohort support its potential clinical utility in the management of MPNs. However, further optimisation is needed to avoid false negatives in regions with low sequencing coverage, especially for the detection of driver mutations in MPL.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01145-0.

Role of TlyA in the Biology of Uncultivable Mycobacteria

TlyA proteins are related to distinct functions in a diverse spectrum of bacterial pathogens including mycobacterial spp. There are several annotated proteins function as hemolysin or pore forming molecules that play an important role in the virulence of pathogenic organisms. Many studies reported the dual activity of mycobacterial TlyA as 'hemolysin' and 'S-adenosylmethionine dependent rRNA methylase'. To act as a hemolysin, a sequence must have a signal sequence and transmembrane segment which helps the protein to enter the extracellular environment. Interestingly, the mycobacterial tlyA has neither a traditional signal sequences of general/sec/tat pathways nor any transmembrane segments are present. Still it can reach the extracellular milieu with the help of non-classical signal mechanisms. Also, retention of tlyA in cultivable mycobacterial pathogens (such as Mycobacterium tuberculosis and M. marinum) as well as uncultivated mycobacterial pathogens despite their extreme reductive evolution (such as M. leprae, M. lepromatosis and M. uberis) suggests its crucial role in evolutionary biology of pathogenic mycobacteria. Numerous virulence factors have been characterised from the uncultivable mycobacteria but the information of TlyA protein is still limited in terms of molecular and structural characterisation. The genomic insights offered by comparative analysis of TlyA sequences and its conserved domains reveal its pore forming activity which further confirms its role as a virulence protein, particularly in uncultivable mycobacteria. Therefore, this review presents a comparative analysis of mycobacterial TlyA family by sequence homology and alignment to improve our understanding of this unconventional hemolysin and RNA methyltransferase TlyA of uncultivable mycobacteria.

Trimming and Validation of Illumina Short Reads Using Trimmomatic, Trinity Assembly, and Assessment of RNA-Seq Data

Next-generation sequencing (NGS) technologies can generate billions of reads in a single sequencing run. However, with such high-throughput comes quality issues which have to be addressed before undertaking downstream analysis. Quality control on short reads is usually performed at default settings due to a lack of in-depth understanding of a particular software's parameters and their effect if changed on the output. Here we demonstrate how to optimize read trimming using Trimmomatic. We highlight the benefits of trimming by comparing the quality of transcripts assembled using trimmed and untrimmed reads.

Oral microbiome research - A Beginner's glossary

Oral microbiome plays a key role in the etiology of oral diseases and is linked to many diseases in other parts of the body as well. This makes the oral microbiome an area of interest for researchers globally. A meticulous planning of the research project is the first and most crucial step while conducting an oral microbiome study. For beginners in this field, it is essential to be familiar with the terminologies used in oral microbiome research for a better understanding. The purpose of this article is to familiarize new researchers to the frequently used terms for the field of oral microbiome research.

Applications of Optical Mapping for Plant Genome Assembly and Structural Variation Detection

Optical mapping plays an important role in plant genomics, particularly in plant genome assembly and large-scale structural variation detection. While DNA sequencing provides base-by-base nucleotide information, optical mapping shows the physical locations of selected enzyme restriction sites in a genome. The long single-molecule maps produced by optical mapping make it a useful auxiliary technique to DNA sequencing, which generally cannot span large and complex genomic regions. Although optical mapping, therefore, offers unique advantages to researchers, there are few dedicated tools to assist in optical mapping analyses. In this chapter, we present runBNG2, a successor of runBNG to help optical-mapping data analysis for diverse datasets.