Challenges in the Setup of Large-scale Next-Generation Sequencing Analysis Workflows

Applications of the powerful next-generation sequencing tool for detecting parasitic infections: towards a smart laboratory platform

Parasitic infections are a group of infectious diseases that are mainly caused by helminths and protozoa. Recently, emerging diagnostic techniques have been developed to not only elevate the chance of parasite detection but also increase the accurate diagnosis. Next-generation sequencing (NGS) technologies are highlighted among different genomic-based methods due to their wide-spectrum application. NGS presents an opportunity for clinical use in detecting parasitic infections. Also, it allows for the characterization of parasites, as well as the identification of drug resistance. Incorporating NGS into the diagnostic process has the potential to create innovative laboratory platforms that transform the field of parasitology and enhance patient care. The NGS technique has been used in understanding genetic interrelationships among parasites and the detection of parasites in various samples. It is anticipated that these approaches will be changed to the point of care diagnostic testing due to their accuracy and rapid action. Moreover, their efficiency in diagnosing multiple organisms with one sampling, changes these methods into pioneers in diagnostic tests. Here, the applications of NGS as a powerful tool in diagnosing clinically important parasites and how this tool is developing parasitic disease diagnostics are discussed.

Knowledge of the genetics of human pain gained over the last decade from next-generation sequencing

Next-generation sequencing (NGS) technologies have revolutionized pain research by providing comprehensive insights into genetic variation across the genome. Recent studies have expanded the known spectrum of mutations in genes such as SCN9A and NTRK1, which are commonly mutated in hereditary sensory neuropathies. NGS has uncovered critical alternative splicing events and facilitated single-cell transcriptomics, revealing cellular heterogeneity within tissues. An NGS-based classifier predicted extremely high opioid requirements with 80 % accuracy, highlighting the importance of tailoring opioid therapy based on genetic profiles. Key genes such as GDF5, COL11A1, and TRPV1 have been linked to osteoarthritis risk and pain sensitivity, while HLA-DRB1, TNF, and P2X7 play critical roles in inflammation and pain modulation in rheumatoid arthritis. Innovative tools, such as an atlas of the somatosensory system in neuropathic pain, have been developed based on NGS data, focusing on the dorsal root and trigeminal ganglia. This approach allows the analysis of cellular changes during the development of chronic pain. In the study of rare variants, NGS outperforms single nucleotide variant candidate studies and classical genome-wide association approaches. The complex data generated by NGS enables integrated multi-omics approaches, allowing deeper exploration of the molecular and cellular basis of pain perception. In addition, the characterization of non-coding RNAs has opened new therapeutic avenues. NGS-based pain research faces challenges related to complex data analysis and interpretation of rare genetic variants with unknown biological functions. Nevertheless, NGS offers significant potential for improving personalized pain management and highlights the need for interdisciplinary collaboration to translate findings into clinical practice.

Sherlock-Genome: an R Shiny application for genomic analysis and visualization

MOTIVATION

Next-generation sequencing technologies, such as whole genome sequencing (WGS), have become prominent in cancer genomics. However, managing, visualizing, and integratively analyzing WGS results across various bioinformatic pipelines remains challenging, particularly for non-bioinformaticians, hindering the usability of WGS data for biological discovery.

RESULTS

We developed Sherlock-Genome, an R Shiny app for data harmonization, visualization, and integrative analysis of WGS-based cancer genomics studies. Following FAIR principles, Sherlock-Genome provides a platform and guidelines for managing and sharing finalized sample-level WGS analysis results, enabling users to upload results, inspect analyses locally, and perform integrative analyses. It includes modules for major cancer genomic analyses, allowing interactive data visualizations and integrative analyses with other data types. Sherlock-Genome supports both local and cloud deployment, facilitating the sharing of results for related publications. This tool has the potential to be widely adopted in cancer genomics, significantly enhancing the accessibility and usability of sample-level WGS analysis results for comprehensive biological discovery and research advancements.

AVAILABILITY AND IMPLEMENTATION

The source code and installation instructions for Sherlock-Genome can be accessed via Github https://github.com/xtmgah/Sherlock-Genome . Documentation and data requirements for user project data can also be found on the same GitHub page.

Molecular and modular intricacies of precision oncology

Precision medicine is revolutionizing the world in combating different disease modalities, including cancer. The concept of personalized treatments is not new, but modeling it into a reality has faced various limitations. The last decade has seen significant improvements in incorporating several novel tools, scientific innovations and governmental support in precision oncology. However, the socio-economic factors and risk-benefit analyses are important considerations. This mini review includes a summary of some commendable milestones, which are not just a series of successes, but also a cautious outlook to the challenges and practical implications of the advancing techno-medical era.

Excitotoxicity, Oxytosis/Ferroptosis, and Neurodegeneration: Emerging Insights into Mitochondrial Mechanisms

Mitochondrial dysfunction plays a pivotal role in the development of age-related diseases, particularly neurodegenerative disorders. The etiology of mitochondrial dysfunction involves a multitude of factors that remain elusive. This review centers on elucidating the role(s) of excitotoxicity, oxytosis/ferroptosis and neurodegeneration within the context of mitochondrial bioenergetics, biogenesis, mitophagy and oxidative stress and explores their intricate interplay in the pathogenesis of neurodegenerative diseases. The effective coordination of mitochondrial turnover processes, notably mitophagy and biogenesis, is assumed to be critically important for cellular resilience and longevity. However, the age-associated decrease in mitophagy impedes the elimination of dysfunctional mitochondria, consequently impairing mitochondrial biogenesis. This deleterious cascade results in the accumulation of damaged mitochondria and deterioration of cellular functions. Both excitotoxicity and oxytosis/ferroptosis have been demonstrated to contribute significantly to the pathophysiology of neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's Disease (HD), Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). Excitotoxicity, characterized by excessive glutamate signaling, initiates a cascade of events involving calcium dysregulation, energy depletion, and oxidative stress and is intricately linked to mitochondrial dysfunction. Furthermore, emerging concepts surrounding oxytosis/ferroptosis underscore the importance of iron-dependent lipid peroxidation and mitochondrial engagement in the pathogenesis of neurodegeneration. This review not only discusses the individual contributions of excitotoxicity and ferroptosis but also emphasizes their convergence with mitochondrial dysfunction, a key driver of neurodegenerative diseases. Understanding the intricate crosstalk between excitotoxicity, oxytosis/ferroptosis, and mitochondrial dysfunction holds potential to pave the way for mitochondrion-targeted therapeutic strategies. Such strategies, with a focus on bioenergetics, biogenesis, mitophagy, and oxidative stress, emerge as promising avenues for therapeutic intervention.

300 million years apart: the extreme case of macromorphological skeletal convergence between deltocyathids and a turbinoliid coral (Anthozoa, Scleractinia)

The integration of morphological and molecular lines of evidence has enabled the family Deltocyathidae to be erected to accommodate Deltocyathus species that were previously ascribed to the family Caryophylliidae. However, although displaying the same morphological characteristics as other species of Deltocyathus , molecular data suggested that D. magnificus was phylogenetically distant from Deltocyathidae, falling within the family Turbinoliidae instead. To elucidate the enigmatic evolutionary history of this species and skeletal microstructural features, the phylogenetic relationships of Deltocyathidae and Turbinoliidae were investigated using nuclear ultraconserved and exon loci and complete mitochondrial genomes. Both nuclear and mitochondrial phylogenomic reconstructions confirmed the position of D. magnificus within turbinolids. Furthermore, a novel mitochondrial gene order was uncovered for Deltocyathidae species. This gene order was not present in Turbinoliidae or in D. magnificus that both have the scleractinian canonical gene order, further indicating the taxonomic utility of mitochondrial gene order. D. magnificus is therefore formally moved to the family Turbinoliidae and accommodated in a new genus (Dennantotrochus Kitahara, Vaga & Stolarski, gen. nov.). Surprisingly, turbinolids and deltocyathids do not differ in microstructural organisation of the skeleton that consists of densely packed, individualised rapid accretion deposits and thickening deposits composed of fibres perpendicular to the skeleton surface. Therefore, although both families are clearly evolutionarily divergent, macromorphological features indicate a case of skeletal convergence while these may still share conservative biomineralisation mechanisms. ZooBank: urn:lsid:zoobank.org:pub:5F1C0E25-3CC6-4D1F-B1F0-CD9D0014678E.

A Fast, Reproducible, High-throughput Variant Calling Workflow for Population Genomics

The increasing availability of genomic resequencing data sets and high-quality reference genomes across the tree of life present exciting opportunities for comparative population genomic studies. However, substantial challenges prevent the simple reuse of data across different studies and species, arising from variability in variant calling pipelines, data quality, and the need for computationally intensive reanalysis. Here, we present snpArcher, a flexible and highly efficient workflow designed for the analysis of genomic resequencing data in nonmodel organisms. snpArcher provides a standardized variant calling pipeline and includes modules for variant quality control, data visualization, variant filtering, and other downstream analyses. Implemented in Snakemake, snpArcher is user-friendly, reproducible, and designed to be compatible with high-performance computing clusters and cloud environments. To demonstrate the flexibility of this pipeline, we applied snpArcher to 26 public resequencing data sets from nonmammalian vertebrates. These variant data sets are hosted publicly to enable future comparative population genomic analyses. With its extensibility and the availability of public data sets, snpArcher will contribute to a broader understanding of genetic variation across species by facilitating the rapid use and reuse of large genomic data sets.

How to make DNA data storage more applicable

The storage of digital data is becoming a worldwide problem. DNA has been recognized as a biological solution due to its ability to store genetic information without alteration over long periods. The first demonstrations of high-capacity long-lasting DNA digital data storage have been shown. However, high storage costs and slow retrieval of the data must be overcome to make DNA data storage more applicable and marketable. Herein, we discuss the issues and recent advances in DNA data storage methods and highlight pathways to make this technology more applicable to real-world digital data storage. We envision that a combination of molecular biology, nanotechnology, novel polymers, electronics, and automation with systematic development will allow DNA data storage sufficient for everyday use.

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.

Khairuddin RF, Mahmood Y. Genome assembly composition of the String "ACGT" array: a review of data structure accuracy and performance challenges

Background

The development of sequencing technology increases the number of genomes being sequenced. However, obtaining a quality genome sequence remains a challenge in genome assembly by assembling a massive number of short strings (reads) with the presence of repetitive sequences (repeats). Computer algorithms for genome assembly construct the entire genome from reads in two approaches. The de novo approach concatenates the reads based on the exact match between their suffix-prefix (overlapping). Reference-guided approach orders the reads based on their offsets in a well-known reference genome (reads alignment). The presence of repeats extends the technical ambiguity, making the algorithm unable to distinguish the reads resulting in misassembly and affecting the assembly approach accuracy. On the other hand, the massive number of reads causes a big assembly performance challenge.

Method

The repeat identification method was introduced for misassembly by prior identification of repetitive sequences, creating a repeat knowledge base to reduce ambiguity during the assembly process, thus enhancing the accuracy of the assembled genome. Also, hybridization between assembly approaches resulted in a lower misassembly degree with the aid of the reference genome. The assembly performance is optimized through data structure indexing and parallelization. This article's primary aim and contribution are to support the researchers through an extensive review to ease other researchers' search for genome assembly studies. The study also, highlighted the most recent developments and limitations in genome assembly accuracy and performance optimization.

Results

Our findings show the limitations of the repeat identification methods available, which only allow to detect of specific lengths of the repeat, and may not perform well when various types of repeats are present in a genome. We also found that most of the hybrid assembly approaches, either starting with de novo or reference-guided, have some limitations in handling repetitive sequences as it is more computationally costly and time intensive. Although the hybrid approach was found to outperform individual assembly approaches, optimizing its performance remains a challenge. Also, the usage of parallelization in overlapping and reads alignment for genome assembly is yet to be fully implemented in the hybrid assembly approach.

Conclusion

We suggest combining multiple repeat identification methods to enhance the accuracy of identifying the repeats as an initial step to the hybrid assembly approach and combining genome indexing with parallelization for better optimization of its performance.

GeniePool: genomic database with corresponding annotated samples based on a cloud data lake architecture

In recent years, there are a huge influx of genomic data and a growing need for its phenotypic correlations, yet existing genomic databases do not allow easy storage and accessibility to the combined phenotypic-genotypic information. Freely accessible allele frequency (AF) databases, such as gnomAD, are crucial for evaluating variants but lack correlated phenotype data. The Sequence Read Archive (SRA) accumulates hundreds of thousands of next-generation sequencing (NGS) samples tagged by their submitters and various attributes. However, samples are stored in large raw format files, inaccessible for a common user. To make thousands of NGS samples and their corresponding additional attributes easily available to clinicians and researchers, we generated a pipeline that continuously downloads raw human NGS data uploaded to SRA using SRAtoolkit and preprocesses them using GATK pipeline. Data are then stored efficiently in a cloud data lake and can be accessed via a representational state transfer application programming interface (REST API) and a user-friendly website. We thus generated GeniePool, a simple and intuitive web service and API for querying NGS data from SRA with direct access to information related to each sample and related studies, providing significant advantages over existing databases for both clinical and research usages. Utilizing data lake infrastructure, we were able to generate a multi-purpose tool that can serve many clinical and research use cases. We expect users to explore the meta-data served via GeniePool both in daily clinical practice and in versatile research endeavours. Database URL https://geniepool.link.

Dynamic variations of the gastric microbiota: Key therapeutic points in the reversal of Correa's cascade

Correa's cascade is a dynamic process in the development of intestinal-type gastric cancer (GC), and its pathological features, gastric microbiota and interactions between microorganisms and their hosts vary at different developmental stages. The characteristics of cells, tissues and gastric microbiota before or after key therapeutic points are critical for monitoring malignant transformation and early tumour reversal. This review summarises the pathological features of gastric mucosa, characteristics of gastric microbiota, specific microbial markers, microbe-microbe interactions and microbe-host interactions at different stages in Correa's cascade. The markers related to each Correa's cascade point were analysed in detail. We attempted to identify key therapeutic points for early cancer reversal and provide a novel approach to reduce the incidence of GC and improve precise treatment.

Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases

The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

A gene-based capture assay for surveying patterns of genetic diversity and insecticide resistance in a worldwide group of invasive mosquitoes

Understanding patterns of diversification, genetic exchange, and pesticide resistance in arthropod disease vectors is necessary for effective population management. With the availability of next-generation sequencing technologies, one of the best approaches for surveying such patterns involves the simultaneous genotyping of many samples for a large number of genetic markers. To this end, the targeting of gene sequences of known function can be a cost-effective strategy. One insect group of substantial health concern are the mosquito taxa that make up the Culex pipiens complex. Members of this complex transmit damaging arboviruses and filariae worms to humans, as well as other pathogens such as avian malaria parasites that are detrimental to birds. Here we describe the development of a targeted, gene-based assay for surveying genetic diversity and population structure in this mosquito complex. To test the utility of this assay, we sequenced samples from several members of the complex, as well as from distinct populations of the relatively under-studied Culex quinquefasciatus. The data generated was then used to examine taxonomic divergence and population clustering between and within these mosquitoes. We also used this data to investigate genetic variants present in our samples that had previously been shown to correlate with insecticide-resistance. Broadly, our gene capture approach successfully enriched the genomic regions of interest, and proved effective for facilitating examinations of taxonomic divergence and geographic clustering within the Cx. pipiens complex. It also allowed us to successfully survey genetic variation associated with insecticide resistance in Culex mosquitoes. This enrichment protocol will be useful for future studies that aim to understand the genetic mechanisms underlying the evolution of these ubiquitous and increasingly damaging disease vectors.

The mosquito taxa that make up the Culex pipiens complex are important vectors of the agents of several human diseases such as West Nile and St. Louis encephalitides, and lymphatic filariasis. They are also important vectors of avian malaria, which impacts livestock and wildlife. The development of effective strategies for the control of these mosquitoes requires knowledge of their origins, distribution, dispersal patterns, and the extent to which discreet taxonomic entities within the complex interbreed. To achieve these objectives, it is necessary to compare patterns of genetic diversity across many mosquito samples, which can be cost-prohibitive. To address this limitation, we developed a targeted, gene-based assay that allowed us to cost-effectively genotype a large number of genetic variants from a representative global sampling of individual Cx. pipiens complex mosquitoes. We show that this assay is a powerful tool for examining genetic structure and hybridization among populations. We also explore its utility for surveying alleles previously shown to be associated with insecticide resistance. Future use of this enrichment assay and the bioinformatics methods described here will allow researchers to study evolutionary patterns across the Cx. pipiens complex as well as monitor the presence of genetic variation that could affect control efforts.

Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach

Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId's workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId's workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6-17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId's workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId's workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId's conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders

Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the β-oxidation of fatty acids, and the Krebs's cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.

A Review of Circulating Tumor DNA in the Diagnosis and Monitoring of Esophageal Cancer

Circulating tumor DNA (ctDNA) is a type of cell-free DNA released by tumor cells after necrosis and apoptosis, and it can be actively secreted by tumor cells. Since ctDNA is derived from various tumor sites, it can provide far more comprehensive genomic and epigenomic information than a single-site biopsy. Therefore, ctDNA can overcome tumor heterogeneity, which is the major limitation of a traditional tissue biopsy approach. Noninvasive ctDNA assays allow continuous real-time monitoring of the molecular status of cancers. Recently, ctDNA assays have been widely used in clinical practice, including cancer diagnosis, evaluation of therapeutic efficacy and prognosis, and monitoring of relapse and metastasis. Although ctDNA shows a high diagnostic performance in advanced esophageal cancer, it is far from satisfactory for early diagnosis of esophageal cancer. Monitoring the dynamic changes of ctDNA is beneficial for the evaluation of therapeutic efficacy and prediction of early recurrence in esophageal cancer. It is necessary to establish standards for individualized ctDNA detection in the evaluation of treatment response and surveillance of esophageal cancer and to develop clinical practice guideline for the systemic treatment of patients with "ctDNA recurrence." This review aims to provide an update on the role of ctDNA in the diagnosis and monitoring of esophageal cancer.

LABRADOR-A Computational Workflow for Virus Detection in High-Throughput Sequencing Data

High-throughput sequencing (HTS) allows detection of known and unknown viruses in samples of broad origin. This makes HTS a perfect technology to determine whether or not the biological products, such as vaccines are free from the adventitious agents, which could support or replace extensive testing using various in vitro and in vivo assays. Due to bioinformatics complexities, there is a need for standardized and reliable methods to manage HTS generated data in this field. Thus, we developed LABRADOR—an analysis pipeline for adventitious virus detection. The pipeline consists of several third-party programs and is divided into two major parts: (i) direct reads classification based on the comparison of characteristic profiles between reads and sequences deposited in the database supported with alignment of to the best matching reference sequence and (ii) de novo assembly of contigs and their classification on nucleotide and amino acid levels. To meet the requirements published in guidelines for biologicals’ safety we generated a custom nucleotide database with viral sequences. We tested our pipeline on publicly available HTS datasets and showed that LABRADOR can reliably detect viruses in mixtures of model viruses, vaccines and clinical samples.

Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells

DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance.

Sequencing a large region of DNA containing many surplus copies of genes linked to drug resistance in colon cancer cells may illuminate how these genomic rearrangements arise. Such regions of gene amplification are highly repetitive, making them impossible to sequence using ordinary methods, and little is known about how they are generated. Using advanced methods, Jeong-Sun Seo at Seoul National University Bundang Hospital in South Korea and co-workers sequenced a region of gene amplification in colon cancer cells. The amplified region was approximately 20 times the length of that in healthy cells and contained many copies of an eleven-gene segment, including a gene implicated in drug resistance. The region also contained mutations in chromosomal repair genes which would disrupt repair pathways. These results illuminate the genetic changes that lead to gene amplification and drug resistance in cancer cells.

New Approach for Detection of Normal Alternative Splicing Events and Aberrant Spliceogenic Transcripts with Long-Range PCR and Deep RNA Sequencing

RNA sequencing is a promising technique for detecting normal and aberrant RNA isoforms. Here, we present a new single-gene, straightforward 1-day hands-on protocol for detection of splicing alterations with deep RNA sequencing from blood. We have validated our method's accuracy by detecting previously published normal splicing isoforms of STK11 gene. Additionally, the same technique was used to provide the first comprehensive catalogue of naturally occurring alternative splicing events of the NBN gene in blood. Furthermore, we demonstrate that our approach can be used for detection of splicing impairment caused by genetic variants. Therefore, we were able to reclassify three variants of uncertain significance: NBN:c.584G>A, STK11:c.863-5_863-3delCTC and STK11:c.615G>A. Due to the simplicity of our approach, it can be incorporated into any molecular diagnostics laboratory for determination of variant's impact on splicing.

Evaluation of serverless computing for scalable execution of a joint variant calling workflow

Advances in whole-genome sequencing have greatly reduced the cost and time of obtaining raw genetic information, but the computational requirements of analysis remain a challenge. Serverless computing has emerged as an alternative to using dedicated compute resources, but its utility has not been widely evaluated for standardized genomic workflows. In this study, we define and execute a best-practice joint variant calling workflow using the SWEEP workflow management system. We present an analysis of performance and scalability, and discuss the utility of the serverless paradigm for executing workflows in the field of genomics research. The GATK best-practice short germline joint variant calling pipeline was implemented as a SWEEP workflow comprising 18 tasks. The workflow was executed on Illumina paired-end read samples from the European and African super populations of the 1000 Genomes project phase III. Cost and runtime increased linearly with increasing sample size, although runtime was driven primarily by a single task for larger problem sizes. Execution took a minimum of around 3 hours for 2 samples, up to nearly 13 hours for 62 samples, with costs ranging from $2 to $70.

Sneathia: an emerging pathogen in female reproductive disease and adverse perinatal outcomes

Sneathia is an emerging pathogen implicated in adverse reproductive and perinatal outcomes. Although scarce, recent data suggest that vaginally residing Sneathia becomes pathogenic following its ascension into the upper urogenital tract, amniotic fluid, placenta, and foetal membranes. The role of Sneathia in women's health and disease is generally underappreciated because the cultivation of these bacteria is limited by their complex nutritional requirements, slow growth patterns, and anaerobic nature. For this reason, molecular methods are typically required for the detection and differential diagnosis of Sneathia infections. Here, we review the laboratory methods used for the diagnosis of Sneathia infections, the molecular mechanisms underlying its virulence, and its sensitivity to antibiotics. We further review the evidence of Sneathia's contributions to the pathogenesis of bacterial vaginosis, chorioamnionitis, preterm prelabour rupture of membranes, spontaneous preterm labour, stillbirth, maternal and neonatal sepsis, HIV infection, and cervical cancer. Collectively, growing evidence indicates that Sneathia represents an important yet underappreciated pathogen affecting the development and progression of several adverse clinical conditions diagnosed in pregnant women and their neonates, as well as in non-pregnant women.

The new microbiology: cultivating the future of microbiome-directed medicine

The discovery of human-associated microscopic life forms has captivated the scientific community since their first documentation in the 17th century. Subsequent isolation and cultivation of microorganisms have spurred great leaps in medicine, including the discovery of antibiotics, identifying pathogens that cause infectious diseases, and vaccine development. The realization that there is a vast discrepancy between the number of microscopic cell counts and how many could thrive in the laboratory motivated the advent of sequencing-based approaches to characterize the uncultured fraction of the microbiota, leading to an unprecedented view into their composition and putative function on all bodily surfaces. It soon became apparent that specific members of the microbiota can be our commensal partners with new implications on various aspects of health, as well as a rich source of therapeutic compounds and tools for biotechnology. Harnessing the immense repertoire of microbial properties, however, inadvertently requires pure cultures for validation and manipulation of candidate genes, proteins, or metabolic pathways, just as mammalian cell culture has become an indispensable tool for mechanistic understanding of host biology. Yet, this renewed interest in growing microorganisms, individually or as a consortium, is stalled by the laborious nature of conventional cultivation methods. Addressing this unmet need through implementation of improved media design and new cultivation techniques is arguably instrumental to future milestones in translational microbiome research.

Isabl Platform, a digital biobank for processing multimodal patient data

BACKGROUND

The widespread adoption of high throughput technologies has democratized data generation. However, data processing in accordance with best practices remains challenging and the data capital often becomes siloed. This presents an opportunity to consolidate data assets into digital biobanks-ecosystems of readily accessible, structured, and annotated datasets that can be dynamically queried and analysed.

RESULTS

We present Isabl, a customizable plug-and-play platform for the processing of multimodal patient-centric data. Isabl's architecture consists of a relational database (Isabl DB), a command line client (Isabl CLI), a RESTful API (Isabl API) and a frontend web application (Isabl Web). Isabl supports automated deployment of user-validated pipelines across the entire data capital. A full audit trail is maintained to secure data provenance, governance and ensuring reproducibility of findings.

CONCLUSIONS

As a digital biobank, Isabl supports continuous data utilization and automated meta analyses at scale, and serves as a catalyst for research innovation, new discoveries, and clinical translation.

Artificial Intelligence in Pathology: A Simple and Practical Guide

Artificial intelligence (AI) is having an increasing impact on the field of pathology, as computation techniques allow computers to perform tasks previously performed by people. Here, we offer a simple and practical guide to AI methods used in pathology, such as digital image analysis, next-generation sequencing, and natural language processing. We not only provide a comprehensive review, but also discuss relevant history and future directions of AI in pathology. We additionally provide a short tabular dictionary of AI terminology which will help practicing pathologists and researchers to understand this field.

Microbiome definition re-visited: old concepts and new challenges

The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term "microbiome." Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role. Video Abstract.

Multi-omics investigations within the Phylum Mollusca, Class Gastropoda: from ecological application to breakthrough phylogenomic studies

Gastropods are the largest and most diverse class of mollusc and include species that are well studied within the areas of taxonomy, aquaculture, biomineralization, ecology, microbiome and health. Gastropod research has been expanding since the mid-2000s, largely due to large-scale data integration from next-generation sequencing and mass spectrometry in which transcripts, proteins and metabolites can be readily explored systematically. Correspondingly, the huge data added a great deal of complexity for data organization, visualization and interpretation. Here, we reviewed the recent advances involving gastropod omics ('gastropodomics') research from hundreds of publications and online genomics databases. By summarizing the current publicly available data, we present an insight for the design of useful data integrating tools and strategies for comparative omics studies in the future. Additionally, we discuss the future of omics applications in aquaculture, natural pharmaceutical biodiscovery and pest management, as well as to monitor the impact of environmental stressors.

Transcriptome-based target-enrichment baits for stony corals (Cnidaria: Anthozoa: Scleractinia)

Despite the ecological and economic significance of stony corals (Scleractinia), a robust understanding of their phylogeny remains elusive due to patchy taxonomic and genetic sampling, as well as the limited availability of informative markers. To increase the number of genetic loci available for phylogenomic analyses in Scleractinia, we designed 15,919 DNA enrichment baits targeting 605 orthogroups (mean 565 ± SD 366 bp) over 1,139 exon regions. A further 236 and 62 barcoding baits were designed for COI and histone H3 genes respectively for quality and contamination checks. Hybrid capture using these baits was performed on 18 coral species spanning the presently understood scleractinian phylogeny, with two corallimorpharians as outgroup. On average, 74% of all loci targeted were successfully captured for each species. Barcoding baits were matched unambiguously to their respective samples and revealed low levels of cross-contamination in accordance with expectation. We put the data through a series of stringent filtering steps to ensure only scleractinian and phylogenetically informative loci were retained, and the final probe set comprised 13,479 baits, targeting 452 loci (mean 531 ± SD 307 bp) across 865 exon regions. Maximum likelihood, Bayesian and species tree analyses recovered maximally supported, topologically congruent trees consistent with previous phylogenomic reconstructions. The phylogenomic method presented here allows for consistent capture of orthologous loci among divergent coral taxa, facilitating the pooling of data from different studies and increasing the phylogenetic sampling of scleractinians in the future.

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Viruses are evolving at an alarming rate, spreading and inconspicuously adapting to cutting-edge therapies. Therefore, the search for rapid, informative and reliable diagnostic methods is becoming urgent as ever. Conventional clinical tests (PCR, serology, etc.) are being continually optimized, yet provide very limited data. Could high throughput sequencing (HTS) become the future gold standard in molecular diagnostics of viral infections? Compared to conventional clinical tests, HTS is universal and more precise at profiling pathogens. Nevertheless, it has not yet been widely accepted as a diagnostic tool, owing primarily to its high cost and the complexity of sample preparation and data analysis. Those obstacles must be tackled to integrate HTS into daily clinical practice. For this, three objectives are to be achieved: (1) designing and assessing universal protocols for library preparation, (2) assembling purpose-specific pipelines, and (3) building computational infrastructure to suit the needs and financial abilities of modern healthcare centers. Data harvested with HTS could not only augment diagnostics and help to choose the correct therapy, but also facilitate research in epidemiology, genetics and virology. This information, in turn, could significantly aid clinicians in battling viral infections.

Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings

Next-generation sequencing (NGS) technologies have revolutionized biological research by generating genomic data that were once unaffordable by traditional first-generation sequencing technologies. These sequencing methodologies provide an opportunity for in-depth analyses of host and pathogen genomes as they are able to sequence millions of templates at a time. However, these large datasets can only be efficiently explored using bioinformatics analyses requiring huge data storage and computational resources adapted for high-performance processing. High-performance computing allows for efficient handling of large data and tasks that may require multi-threading and prolonged computational times, which is not feasible with ordinary computers. However, high-performance computing resources are costly and therefore not always readily available in low-income settings. We describe the establishment of an affordable high-performance computing bioinformatics cluster consisting of 3 nodes, constructed using ordinary desktop computers and open-source software including Linux Fedora, SLURM Workload Manager, and the Conda package manager. For the analysis of large antibody sequence datasets and for complex viral phylodynamic analyses, the cluster out-performed desktop computers. This has demonstrated that it is possible to construct high-performance computing capacity capable of analyzing large NGS data from relatively low-cost hardware and entirely free (open-source) software, even in resource-limited settings. Such a cluster design has broad utility beyond bioinformatics to other studies that require high-performance computing.

Copy Number Variation Detection Using Total Variation

Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

Array-CGH increased the diagnostic rate of developmental delay or intellectual disability in Taiwan

BACKGROUND

Unexplained developmental delay or intellectual disability (DD/ID) has an estimated prevalence of about 3%-5% in the general population of Taiwan. Array comparative genomic hybridization (array-CGH) is a high-resolution tool that can detect about 50 Kb chromosome aberrations. A previous study has reported a detection rate of 10%-20% for this array.¹ This study aimed to investigate and compare the diagnosis rate for DD/ID using array-CGH and conventional chromosome study in DD/ID patients in Taiwan.

METHODS

We enrolled 177 patients with DD/ID who underwent array-CGH examination at the MacKay Memory Hospital between June 2010 and September 2017. The copy number variants (CNV) were classified into the following three groups: pathogenic (potential pathologic variant), benign (normal genomic variant), and uncertain clinical significance (variance of uncertain significance, VOUS), according to the ACMG guideline.² RESULTS: Of the 177 enrolled patients, 100 (56.5%) were men and 77 (43.5%) were women. Ages ranged from 3 months to 50 years, with a median age of 5.2 years. Total 32.0% (32/100) male patients had pathogenic CNV, and 32.5% (25/77) female patients had pathogenic CNV. The ratio of pathogenic CNV in male and female patients was not significantly different (p = 0.379). The proportions of pathogenic CNV at <3 years, 3-6 years, 6-12 years, 12-18 years, and >18 years of age were 32.3% (31/96), 19.4% (6/31), 34.8% (8/23), 16.7% (2/12), and 66.7% (10/15), respectively. The overall diagnosed rate of DD/ID with pathogenic CNV was 27.7% (49/177) using array-CGH in this study. There were 105 patients with conventional karyotyping and array-CGH data at the same time. Nineteen (18.1%) patients had visible chromosomal abnormality. Total 32/105 (30.5%) patients could find at least one pathogenic CNVs. The array-CGH had a higher diagnosed rate than the conventional karyotyping in clinical application.

CONCLUSIONS

Although array-CGH could not detect point mutation, balanced translocations, inversions, or low-level mosaicism, the diagnosis rate in clinical application was up to 46.3% and 2.5 times that of conventional karyotyping analysis (18.1%). This study demonstrated that array-CGH is a powerful diagnostic tool and should be the first genetic test instead of conventional karyotyping analysis for patients with unexplained DD/ID.

Tools and Strategies for Long-Read Sequencing and De Novo Assembly of Plant Genomes

The commercial release of third-generation sequencing technologies (TGSTs), giving long and ultra-long sequencing reads, has stimulated the development of new tools for assembling highly contiguous genome sequences with unprecedented accuracy across complex repeat regions. We survey here a wide range of emerging sequencing platforms and analytical tools for de novo assembly, provide background information for each of their steps, and discuss the spectrum of available options. Our decision tree recommends workflows for the generation of a high-quality genome assembly when used in combination with the specific needs and resources of a project.

Laboratory hemostasis: from biology to the bench

Physiological hemostasis is an intricate biological system, where procoagulant and anticoagulant forces interplay and preserves blood fluidity when blood vessels are intact, or trigger clot formation to prevent excessive bleeding when blood vessels are injured. The modern model of hemostasis is divided into two principal phases. The first, defined as primary hemostasis, involves the platelet-vessel interplay, whilst the second, defined as secondary hemostasis, mainly involves coagulation factors, damaged cells and platelet surfaces, where the so-called coagulation cascade rapidly develops. The activation and amplification of the coagulation cascade is finely modulated by the activity of several physiological inhibitors. Once bleeding has been efficiently stopped by blood clot formation, dissolution of the thrombus is essential to restore vessel permeability. This process, known as fibrinolysis, also develops through coordinate action of a vast array of proteins and enzymes. An accurate diagnosis of hemostasis disturbance entails a multifaceted approach, encompassing family and personal history of hemostatic disorders, accurate collection of clinical signs and symptoms, integrated with laboratory hemostasis testing. Regarding laboratory testing, a reasonable approach entails classifying hemostasis testing according to cost, complexity and available clinical information. Laboratory workout may hence initiate with some rapid and inexpensive "screening" tests, characterized by high negative predictive value, then followed by second- or third-line analyses, specifically aimed to clarify the nature and severity of bleeding or thrombotic phenotype. This article aims to provide a general overview of the hemostatic process, and to provide some general suggestions to optimally facilitate laboratory hemostasis testing.

The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.