Human mitochondrial DNA (mtDNA) harbors essential mutations linked to aging, neurodegenerative diseases, and complex muscle disorders. Due to its uniparental and haploid inheritance, mtDNA captures matrilineal evolutionary trajectories, playing a crucial role in population and medical genetics. However, critical questions about the genomic diversity patterns, inheritance models, and evolutionary and medical functions of mtDNA remain unresolved or underexplored, particularly in the transition from traditional genotyping to large-scale genomic analyses. This review summarizes recent advancements in data-driven genomic research and technological innovations that address these questions and clarify the biological impact of nuclear-mitochondrial segments (NUMTs) and mtDNA variants on human health, disease, and evolution. We propose a streamlined pipeline to comprehensively identify mtDNA and NUMT genomic diversity using advanced sequencing and computational technologies. Haplotype-resolved mtDNA sequencing and assembly can distinguish authentic mtDNA variants from NUMTs, reduce diagnostic inaccuracies, and provide clearer insights into heteroplasmy patterns and the authenticity of paternal inheritance. This review emphasizes the need for integrative multi-omics approaches and emerging long-read sequencing technologies to gain new insights into mutation mechanisms, the influence of heteroplasmy and paternal inheritance on mtDNA diversity and disease susceptibility, and the detailed functions of NUMTs.

Irpex lacteus is an edible and medicinal macrofungus with significant biological activity and broad pharmaceutical prospects that has received increasing attention in recent years. Although it is an important resource for macrofungi, knowledge of it remains limited. In this study, we sequenced, de novo assembled, and annotated the whole genome of I. lacteus using a PacBio Sequel II sequencer. The assembled 41.83 Mb genome contains 13,135 predicted protein-coding genes, 83.44% of which have searchable sequence similarity to other genes available in public databases. Using genome-based bioinformatics analysis, we identified 556 enzymes involved in carbohydrate metabolism and 103 cytochrome P450 proteins. Genome annotation revealed genes for key enzymes responsible for the biosynthesis of secondary metabolites, such as terpenoids and polyketides. Among them, we identified 14 terpene synthases, 8 NRPS-like enzymes, and 4 polyketide synthases (PKS), as well as 2 clusters of biosynthetic genes presumably related to terpene synthesis in I. lacteus. Gene family analysis revealed that the MYB transcription factor gene family plays an important role in the growth and development of I. lacteus. This study further enriches the genomic content of I. lacteus, provides genomic information for further research on the molecular mechanism of I. lacteus, and promotes the development of I. lacteus in the fields of drug research and functional food production.

The Echiura worm Urechis unicinctus refers to a common benthic invertebrate found in the intertidal zone of Huanghai as well as Bohai Bay. U. unicinctus is known to contain various physiologically active substances, making it highly valuable in terms of its edibility, medicinal properties, and economic potential. Nonetheless, the limited study on the immune system of U. unicinctus poses difficulties for its aquaculture and artificial reproduction. Marine invertebrates, including shellfish and U. unicinctus, are thought to primarily depend on their innate immune system for disease protection, owing to the severalinnate immune molecules they possess. Herein, we employed PacBio single-molecule real-time (SMRT) sequencing technology to perform the full-length transcriptome analysis of U. unicinctus individuals under five different conditions (room temperature (RT), low temperature (LT), high temperature (HT), without water (DRY), ultraviolet irradiation (UV)). Concequently, we identified 59,371 unigenes that had a 2,779 bp average length, 2,613 long non-coding RNAs (lncRNAs), 59,190 coding sequences (CDSs), 35,166 simple sequence repeats (SSRs), and 1,733 transcription factors (TFs), successfully annotating 90.58 % (53,778) of the unigenes. Subsequently, key factors associated with immune-related processes, such as non-self-recognition, cellular immune defenses, and humoral immune defenses, were searched. Our study also identified pattern recognition receptors (PRRs) that included 17 peptidoglycan recognition proteins (PGRPs), 13 Gram-negative binding proteins (GNBPs), 18 scavenger receptors (SRs), 74 toll-like receptors (TLRs), and 89 C-type lectins (CLTs). Altogether, the high-quality transcriptome obtained data will offer valuable insights for further investigations into U. unicinctus innate immune response, laying the foundation for subsequent molecular biology studies and aquaculture.

In forensics, genetic human identification is generally achieved by nuclear STR DNA typing. However, forensic samples often yield DNA in exiguous quantity and low quality, impairing the generation of conclusive DNA profiles by STR typing. In such cases, mitochondrial DNA (mtDNA) can be used as an alternative solution in forensic human identification. The high copy number, small circular DNA, high mutation rate, maternal inheritance, and absence of recombination are mtDNA's key features in forensics. In this work, we review mtDNA characteristics, forensic applications, sequencing methodologies and present some relevant examples in the forensic science literature.

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

Drug resistance surveillance is a major integral part of malaria control programs. Molecular methods play a pivotal role in drug resistance detection and related molecular research. This study aimed to develop a rapid and accurate detection method for drug resistance of Plasmodium falciparum (P. falciparum). A quantitative real-time PCR (qPCR) assay has been developed that identifies the mutation at locus A256T in the P.falciparum multi-drug resistance(pfmdr1) gene producing amino acid change at position 86. The results of 198 samples detected by qPCR were consistent with nested PCR and sequencing, giving an accuracy of 94.3%. The sensitivity, specificity, positive and negative predictive value of qPCR were 85.7%, 97.6%, 90.0% and 96.4%, respectively. The results of qPCR are basically consistent with the nested PCR, which is expected to replace the nested PCR as a new molecular biological method for drug resistance detection, providing reliable technical support for global malaria prevention and control.

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need. This review explores a broad spectrum of progress and accomplishments in the field of DNA sequencing, focusing mainly on electrical detection methods. The review delves deep into both the theoretical and experimental demonstrations of the ionic blockade and transverse tunneling current methods across a broad range of device architectures, nanopore, nanogap, nanochannel, and hybrid/heterostructures. Additionally, various aspects of each architecture are explored along with their strengths and weaknesses, scrutinizing their potential applications for ultrafast DNA sequencing. Finally, an overview of existing challenges and future directions is provided to expedite the emergence of high-precision and ultrafast DNA sequencing with ionic and transverse current approaches.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

The best environment for plant growth and development contains certain essential metabolites. A broad category of metabolites known as "plant biostimulants" (PBs) includes biomolecules such as proteins, carbohydrates, lipids, and other secondary metabolites related to groups of terpenes, specific nitrogen-containing compounds, and benzene ring-conjugated compounds. The formation of biomolecules depends on both biotic and abiotic factors, such as the release of PB by plants, animals, and microorganisms, or it can result from the control of temperature, humidity, and pressure in the atmosphere, in the case of humic substances (HSs). Understanding the genomic outputs of the concerned organism (may be plants or others than them) becomes crucial for identifying the underlying behaviors that lead to the synthesis of these complex compounds. For the purposes of achieving the objectives of sustainable agriculture, detailed research on PBs is essential because they aid in increasing yield and other growth patterns of agro-economic crops. The regulation of homeostasis in the plant-soil-microbe system for the survival of humans and other animals is mediated by the action of plant biostimulants, as considered essential for the growth of plants. The genomic size and gene operons for functional and regulation control have so far been revealed through technological implementations, but important gene annotations are still lacking, causing a delay in revealing the information. Next-generation sequencing techniques, such as nanopore, nanoball, and Illumina, are essential in troubleshooting the information gaps. These technical advancements have greatly expanded the candidate gene openings. The secondary metabolites being important precursors need to be studied in a much wider scale for accurate calculations of biochemical reactions, taking place inside and outside the synthesized living cell. The present review highlights the sequencing techniques to provide a foundation of opportunity generation for agricultural sustainability.

Various methods have been developed so far for detecting N 6-methyladenosine (m6A). The total m6A level or the m6A status at individual positions on mRNA can be detected and quantified through some sequencing-independent biochemical methods, such as LC/MS, SCARLET, SELECT, and m6A-ELISA. However, the m6A-detection techniques relying on high-throughput sequencing have more effectively advanced the understanding about biological significance of m6A-containing mRNA and m6A pathway at a transcriptomic level over the past decade. Various SGS-based (Second Generation Sequencing-based) methods with different detection principles have been widely employed for this purpose. These principles include m6A-enrichment using antibodies, discrimination of m6A from unmodified A-base by nucleases, a fusion protein strategy relying on RNA-editing enzymes, and marking m6A with chemical/biochemical reactions. Recently, TGS-based (Third Generation Sequencing-based) methods have brought a new trend by direct m6A-detection. This review first gives a brief introduction of current knowledge about m6A biogenesis and function, and then comprehensively describes m6A-profiling strategies including their principles, procedures, and features. This will guide users to pick appropriate methods according to research goals, give insights for developing novel techniques in varying areas, and continue to expand our boundary of knowledge on m6A.

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

Microorganisms in rivers indeed play a crucial role in nutrient cycling within aquatic ecosystems. Understanding the assembly mechanisms of bacterial communities in river networks is essential for predicting their special composition and functional characteristics in natural rivers. This study employed 16S rRNA gene amplicon sequence variation (ASVs) to scrutinize the bacterial community within the uniquely topographical Ili River network. The bacterial community composition varied across the three tributaries with distinct sources and the mainstream. The confluence of various sources diminished the diversity of the bacterial community and altered the functionality of within mainstream. We suggest that strong dispersal limitation predominantly shaped the community at the regional scale (46.6 %), underscoring the significant contribution of headwater sites to bacterial community composition. Contrary to expectation, the bacterial resources in the mainstream were not enriched by the higher diversity in three tributaries. Instead, confluence disturbance potentially increased the undominated processes (36.7 %) and alter the bacterial community composition at the local scale of the mainstream. The intricate coalescence at the confluence could potentially be an intriguing causative factor. Our research indicates that the composition of bacterial communities within intricate river networks exhibits biogeographic patterns, simultaneously influenced by river confluence and geographical features, necessitating multi-scale analysis.

It has become increasingly evident that the structures RNAs adopt are conformationally dynamic; the various structured states that RNAs sample govern their interactions with other nucleic acids, proteins, and ligands to regulate a myriad of biological processes. Although several biophysical approaches have been developed and used to study the dynamic landscape of structured RNAs, technical limitations have limited their application to all classes of RNA due to variable size and flexibility. Recent advances combining chemical probing experiments with next-generation- and direct sequencing have emerged as an alternative approach to exploring the conformational dynamics of RNA. In this review, we provide a methodological overview of the sequencing-based techniques used to study RNA conformational dynamics. We discuss how different techniques have enabled us to better understand the propensity of RNAs from a variety of different classes to sample multiple conformational states. Finally, we present examples of the ways these techniques have reshaped how we think about RNA structure.

Next-Generation Sequencing (NGS) techniques have revolutionized veterinary medicine for cats and dogs, offering insights across various domains. In veterinary parasitology, NGS enables comprehensive profiling of parasite populations, aiding in understanding transmission dynamics and drug resistance mechanisms. In infectious diseases, NGS facilitates rapid pathogen identification, characterization of virulence factors, and tracking of outbreaks. Moreover, NGS sheds light on metabolic processes by elucidating gene expression patterns and metabolic pathways, essential for diagnosing metabolic disorders and designing tailored treatments. In autoimmune diseases, NGS helps identify genetic predispositions and molecular mechanisms underlying immune dysregulation. Veterinary oncology benefits from NGS through personalized tumor profiling, mutation analysis, and identification of therapeutic targets, fostering precision medicine approaches. Additionally, NGS plays a pivotal role in veterinary genetics, unraveling the genetic basis of inherited diseases and facilitating breeding programs for healthier animals. Physiological investigations leverage NGS to explore complex biological systems, unraveling gene-environment interactions and molecular pathways governing health and disease. Application of NGS in treatment planning enhances precision and efficacy by enabling personalized therapeutic strategies tailored to individual animals and their diseases, ultimately advancing veterinary care for companion animals.

Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction tools. So far, these two types of tools are separately investigated, and their interplay remains understudied. Here, we integrate hybrid and self-correction methods for high-quality error correction. Our procedure leverages the inter-similarity between long-read data and high-accuracy information from short reads. We compare the performance of our method and state-of-the-art error correction tools on Escherichia coli and Arabidopsis thaliana datasets. The result shows that the integration approach outperformed the existing error correction methods and holds promise for improving the quality of downstream analyses in genomic research.

The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

The role of 16S rRNA has been and largely remains crucial for the identification of microbial organisms. Although 16S rRNA could certainly be described as one of the most studied sequences ever, the current view of it remains somewhat ambiguous. While some consider 16S rRNA to be a variable marker with resolution power down to the strain level, others consider them to be living fossils that carry information about the origin of domains of cellular life. We show that 16S rRNA is clearly an evolutionarily very rigid sequence, making it a largely unique and irreplaceable marker, but its applicability beyond the genus level is highly limited. Interestingly, it seems that the evolutionary rigidity is not driven by functional constraints of the sequence (RNA-protein interactions), but rather results from the characteristics of the host organism. Our results suggest that, at least in some lineages, Horizontal Gene Transfer (HGT) within genera plays an important role for the evolutionary non-dynamics (stasis) of 16S rRNA. Such genera exhibit an apparent lack of diversification at the 16S rRNA level in comparison to the rest of a genome. However, why it is limited specifically and solely to 16S rRNA remains enigmatic.

Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. The CRISPR‒Cas system has emerged as a pivotal tool for genome engineering, enabling the precise incorporation of specific genetic variations, including VUSs, into DNA to facilitate their functional characterization. Additionally, the integration of CRISPR‒Cas technology with sequencing tools allows the high-throughput evaluation of mutations, transforming uncertain genetic data into actionable insights. This allows researchers to comprehensively study the functional consequences of point mutations, paving the way for enhanced understanding and increasing application to precision medicine. This review summarizes the current genome editing tools utilizing CRISPR‒Cas systems and their combination with sequencing tools for functional genomics, with a focus on point mutations.

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Elucidating the intricate interactions between viral pathogens and host cellular machinery during infection is paramount for understanding pathogenic mechanisms and identifying potential therapeutic targets. The RNA modification N6-methyladenosine (m6A) has emerged as a significant factor influencing the trajectory of viral infections. Hence, the precise and quantitative mapping of m6A modifications in both host and viral RNA is pivotal to understanding its role during viral infection. With the rapid advancement of sequencing technologies, scientists are able to detect m6A modifications with various quantitative, high-resolution, transcriptome approaches. These technological strides have reignited research interest in m6A, underscoring its significance and prompting a deeper investigation into its dynamics during viral infections. This review provides a comprehensive overview of the historical evolution of m6A epitranscriptome sequencing technologies, highlights the latest developments in transcriptome-wide m6A mapping, and emphasizes the innovative technologies for detecting m6A modification. We further discuss the implications of these technologies for future research into the role of m6A in viral infections.

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

Climate change poses a major threat to global food security, significantly reducing crop yields as cause of abiotic stresses, and for boosting the spread of new and old pathogens and pests. Sustainable crop management as a route to mitigation poses the challenge of recruiting an array of solutions and tools for the new aims. Among these, the deployment of positive interactions between the micro-biotic components of agroecosystems and plants can play a highly significant role, as part of the agro-ecological revolution. Endophytic microorganisms have emerged as a promising solution to tackle this challenge. Among these, Arbuscular Mycorrhizal Fungi (AMF) and endophytic bacteria and fungi have demonstrated their potential to alleviate abiotic stresses such as drought and heat stress, as well as the impacts of biotic stresses. They can enhance crop yields in a sustainable way also by other mechanisms, such as improving the nutrient uptake, or by direct effects on plant physiology. In this review we summarize and update on the main types of endophytes, we highlight several studies that demonstrate their efficacy in improving sustainable yields and explore possible avenues for implementing crop-microbiota interactions. The mechanisms underlying these interactions are highly complex and require a comprehensive understanding. For this reason, omic technologies such as genomics, transcriptomics, proteomics, and metabolomics have been employed to unravel, by a higher level of information, the complex network of interactions between plants and microorganisms. Therefore, we also discuss the various omic approaches and techniques that have been used so far to study plant-endophyte interactions.

The early detection of upcoming disease outbreaks is essential to avoid both health and economic damage. The last four years of COVID-19 pandemic have proven wastewater-based epidemiology is a reliable system for monitoring the spread of SARS-CoV-2, a causative agent of COVID-19, in an urban population. As this monitoring enables the identification of the prevalence of spreading variants of SARS-CoV-2, it could provide a critical tool in the fight against this viral disease. In this study, we evaluated the presence of variants and subvariants of SARS-CoV-2 in Prague wastewater using nanopore-based sequencing. During August 2021, the data clearly showed that the number of identified SARS-CoV-2 RNA copies increased in the wastewater earlier than in clinical samples indicating the upcoming wave of the Delta variant. New SARS-CoV-2 variants consistently prevailed in wastewater samples around a month after they already prevailed in clinical samples. We also analyzed wastewater samples from smaller sub-sewersheds of Prague and detected significant differences in SARS-CoV-2 lineage progression dynamics among individual localities studied, e.g., suggesting faster prevalence of new variants among the sites with highest population density and mobility.

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

The demand for accurate, faster, and inexpensive sequencing of deoxyribonucleic acid (DNA) is increasing and is driving the emergence of next-generation sequencing (NGS) technologies. NGS can provide useful insights to help researchers and clinicians to develop the right treatment options. NGS has wide applications in novel fields in biology and medicine. These technologies are of great aid to decode mysteries of life, to improve the quality of crops to detect the pathogens, and also useful in improving life qualities. Thousands to millions of molecules can be sequenced simultaneously in parallel using various NGS methods. NGS can identify and characterize the microbial species more comprehensively than culture-based methods. Recently, the NGS approach has been used for oral microbial analysis.

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

This review highlights operational principles, features, and modern aspects of the development of third-generation sequencing technology of biopolymers focusing on the nucleic acids analysis, namely the nanopore sequencing system. Basics of the method and technical solutions used for its realization are considered, from the first works showing the possibility of creation of these systems to the easy-to-handle procedure developed by Oxford Nanopore Technologies company. Moreover, this review focuses on applications, which were developed and realized using equipment developed by the Oxford Nanopore Technologies, including assembly of whole genomes, methagenomics, direct analysis of the presence of modified bases.

PacBio long-read sequencing is a third-generation technology that generates long reads up to 20 kilobases (kb), unlike short-read sequencing instruments that produce up to 600 bases. Long-read sequencing is particularly advantageous in higher organisms, such as humans and plants, where repetitive regions in the genome are more abundant. The PacBio long-read sequencing uses a single molecule, real-time approach where the SMRT cells contain several zero-mode waveguides (ZMWs). Each ZMW contains a single DNA molecule bound by a DNA polymerase. All ZMWs are flushed with deoxy nucleotides with a fluorophore specific to each nucleotide. As the sequencing proceeds, the detector detects the wavelength of the fluorescence and the nucleotides are read in real-time. This chapter describes the sample and library preparation for PacBio long-read sequencing for grapevine.

To achieve the enormous potential of gene-editing technology in clinical therapies, one needs to evaluate both the on-target efficiency and unintended editing consequences comprehensively. However, there is a lack of a pipelined, large-scale, and economical workflow for detecting genome editing outcomes, in particular insertion or deletion of a large fragment. Here, we describe an approach for efficient and accurate detection of multiple genetic changes after CRISPR/Cas9 editing by pooled nanopore sequencing of barcoded long-range PCR products. Recognizing the high error rates of Oxford nanopore sequencing, we developed a novel pipeline to capture the barcoded sequences by grepping reads of nanopore amplicon sequencing (GREPore-seq). GREPore-seq can assess nonhomologous end-joining (NHEJ)-mediated double-stranded oligodeoxynucleotide (dsODN) insertions with comparable accuracy to Illumina next-generation sequencing (NGS). GREPore-seq also reveals a full spectrum of homology-directed repair (HDR)-mediated large gene knock-in, correlating well with the fluorescence-activated cell sorting (FACS) analysis results. Of note, we discovered low-level fragmented and full-length plasmid backbone insertion at the CRISPR cutting site. Therefore, we have established a practical workflow to evaluate various genetic changes, including quantifying insertions of short dsODNs, knock-ins of long pieces, plasmid insertions, and large fragment deletions after CRISPR/Cas9-mediated editing. GREPore-seq is freely available at GitHub (https://github.com/lisiang/GREPore-seq) and the National Genomics Data Center (NGDC) BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007293).

Genome sequencing is widely recognized as a fundamental pillar in genetic research and legal studies of biological phenomena, providing essential insights for genetic investigations and legal analyses of biological events. The field of genome sequencing has experienced significant progress due to rapid improvements in scientific and technological developments. These advancements encompass not only significant improvements in the speed and quality of sequencing but also provide an unparalleled opportunity to explore the subtle complexities of genomes, particularly in the context of rare species. Such a wide range of possibilities has successfully supported the validation of plant gene functions and the refinement of precision breeding methodologies. This expanded scope now includes a comprehensive exploration of the current state and conservation efforts of gymnosperm gene sequencing, offering invaluable insights into their genomic landscapes. This comprehensive review elucidates the trajectory of development and the diverse applications of genome sequencing. It encompasses various domains, including crop breeding, responses to abiotic stress, species evolutionary dynamics, biodiversity, and the unique challenges faced in the conservation and utilization of gymnosperms. It highlights both ongoing challenges and the unveiling of forthcoming developmental trajectories.

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

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.

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

Pseudomonas aeruginosa (P.aeruginosa) is an important opportunistic pathogen with broad environmental adaptability and complex drug resistance. Single-molecule real-time (SMRT) sequencing technique has longer read-length sequences, more accuracy, and the ability to identify epigenetic DNA alterations.

This study applied SMRT technology to sequence a clinical strain P. aeruginosa PA3 to obtain its genome sequence and methylation modification information. Genomic, comparative, pan-genomic, and epigenetic analyses of PA3 were conducted.

General genome annotations of PA3 were discovered, as well as information about virulence factors, regulatory proteins (RPs), secreted proteins, type II toxin-antitoxin (TA) pairs, and genomic islands. A genome-wide comparison revealed that PA3 was comparable to other P. aeruginosa strains in terms of identity, but varied in areas of horizontal gene transfer (HGT). Phylogenetic analysis showed that PA3 was closely related to P. aeruginosa 60503 and P. aeruginosa 8380. P. aeruginosa's pan-genome consists of a core genome of roughly 4,300 genes and an accessory genome of at least 5,500 genes. The results of the epigenetic analysis identified one main methylation sites, N6-methyladenosine (m6A) and 1 motif (CATNNNNNNNTCCT/AGGANNNNNNNATG). 16 meaningful methylated sites were picked. Among these, purH, phaZ, and lexA are of great significance playing an important role in the drug resistance and biological environment adaptability of PA3, and the targeting of these genes may benefit further antibacterial studies.

This study provided a detailed visualization and DNA methylation information of the PA3 genome and set a foundation for subsequent research into the molecular mechanism of DNA methyltransferase-controlled P. aeruginosa pathogenicity.

Saliva is a versatile biofluid that contains a wide variety of biomarkers reflecting both physiologic and pathophysiologic states. Saliva collection is noninvasive and highly applicable for tests requiring serial sampling. Furthermore, advances in test accuracy, sensitivity and precision for saliva has improved diagnostic performance as well as the identification of novel markers especially in oral disease processes. These include dental caries, periodontitis, oral squamous cell carcinoma (OSCC) and Sjögren's syndrome (SS). Numerous growth factors, enzymes, interleukins and cytokines have been identified and are the subject of much research investigation. This review highlights current procedures for successful determination of saliva biomarkers including preanalytical factors associated with sampling, storage and pretreatment as well as subsequent analysis. Moreover, it provides an overview of the diagnostic applications of these salivary biomarkers in common oral diseases.

Batocera horsfieldi (Hope) (Coleoptera: Cerambycidae) is an important forest pest in China that mainly infests timber and economic forests. This pest primarily causes plant tissue to necrotize, rot, and eventually die by feeding on the woody parts of tree trunks. To gain a deeper understanding of the genetic mechanism of B. horsfieldi, this study employed single-molecule real-time sequencing (SMRT) and Illumina RNA-seq technologies to conduct full-length transcriptome sequencing of the insect. Total RNA extracted from male and female adults was mixed and subjected to SMRT sequencing, generating a complete transcriptome. Transcriptome analysis, prediction of long non-coding RNA (lncRNA), coding sequences (CDs), analysis of simple sequence repeats (SSR), prediction of transcription factors, and functional annotation of transcripts were performed in this study. The collective 20,356,793 subreads (38.26 G, clean reads) were generated, including 432,091 circular consensus sequences and 395,851 full-length non-chimera reads. The full-length non-chimera reads (FLNC) were clustered and redundancies were removed, resulting in 39,912 consensus reads. SSR and ANGEL software v3.0 were used for predicting SSR and CDs. In addition, four tools were used for annotating 6058 lncRNAs, identifying 636 transcription factors. Furthermore, a total of 84,650 transcripts were functionally annotated in seven different databases. This is the first time that the full-length transcriptome of B. horsfieldi has been obtained using SMRT sequencing. This provides an important foundation for investigating the gene regulation underlying the interaction between B. horsfieldi and its host plants through gene editing in the future and provides a scientific basis for the prevention and control of B. horsfieldi.

MicroRNA (miRNA) are constituted of approximately 22 nucleotides and play an important role in the regulation of many physiological functions and diseases. In the last 10 years, an increasing interest has been recorded in studying the expression profile of miRNAs in cancer. Real time-quantitative polymerase chain reaction (RT-qPCR), microarrays, and small RNA sequencing represent the gold standard techniques used in the last 30 years as detection methods. The advent of nanotechnology has allowed the fabrication of nanostructured biosensors which are widely exploited in the diagnostic field. Nanostructured biosensors offer many advantages: (i) their small size allows the construction of portable, wearable, and low-cost products; (ii) the large surface-volume ratio enables the loading of a great number of biorecognition elements (e.g., probes, receptors); and (iii) direct contact of the recognition element with the analyte increases the sensitivity and specificity inducing low limits of detection (LOD). In this review, the role of nanostructured biosensors in miRNA detection is explored, focusing on electrochemical and optical sensing. In particular, four types of nanomaterials (metallic nanoparticles, graphene oxide, quantum dots, and nanostructured polymers) are reported for both detection strategies with the aim to show their distinct properties and applications.

The fungal pathogen Colletotrichum graminicola causes the anthracnose of maize (Zea mays) and is responsible for significant yield losses worldwide. The genome of C. graminicola was sequenced in 2012 using Sanger sequencing, 454 pyrosequencing, and an optical map to obtain an assembly of 13 pseudochromosomes. We re-sequenced the genome using a combination of short-read (Illumina) and long-read (PacBio) technologies to obtain a chromosome-level assembly. The new version of the genome sequence has 13 chromosomes with a total length of 57.43 Mb. We detected 66 (23.62 Mb) structural rearrangements in the new assembly with respect to the previous version, consisting of 61 (21.98 Mb) translocations, 1 (1.41 Mb) inversion, and 4 (221 Kb) duplications. We annotated the genome and obtained 15,118 predicted genes and 3,614 new gene models compared to the previous version of the assembly. We show that 25.88% of the new assembly is composed of repetitive DNA elements (13.68% more than the previous assembly version), which are mostly found in gene-sparse regions. We describe genomic compartmentalization consisting of repeat-rich and gene-poor regions vs. repeat-poor and gene-rich regions. A total of 1,140 secreted proteins were found mainly in repeat-rich regions. We also found that ~75% of the three smallest chromosomes (minichromosomes, between 730 and 551 Kb) are strongly affected by repeat-induced point mutation (RIP) compared with 28% of the larger chromosomes. The gene content of the minichromosomes (MCs) comprises 121 genes, of which 83.6% are hypothetical proteins with no predicted function, while the mean percentage of Chr1-Chr10 is 36.5%. No predicted secreted proteins are present in the MCs. Interestingly, only 2% of the genes in Chr11 have homologs in other strains of C. graminicola, while Chr12 and 13 have 58 and 57%, respectively, raising the question as to whether Chrs12 and 13 are dispensable. The core chromosomes (Chr1-Chr10) are very different with respect to the MCs (Chr11-Chr13) in terms of the content and sequence features. We hypothesize that the higher density of repetitive elements and RIPs in the MCs may be linked to the adaptation and/or host co-evolution of this pathogenic fungus.

Antibody phage display is a key technology for the discovery and development of target-specific monoclonal antibodies (mAbs) for use in research, diagnostics, and therapy. The construction of a high-quality antibody library, with larger and more diverse antibody repertoires, is essential for the successful development of phage display-derived mAbs. In this study, a large human combinatorial single-chain variable fragment library (1.5 × 10¹¹ colonies) was constructed from Epstein-Barr virus-infected human peripheral blood mononuclear cells stimulated with a combination of two of the activators of human B cells, the Toll-like receptor 7/8 agonist R848 and interleukin-2. Next-generation sequencing analysis with approximately 1.9 × 10⁶ and 2.7 × 10⁶ full-length sequences of heavy chain variable (VH) and κ light chain variable (Vκ) domains, respectively, revealed that the library consists of unique VH (approximately 94%) and Vκ (approximately 91%) sequences with greater diversity than germline sequences. Lastly, multiple unique mAbs with high affinity and broad cross-species reactivity could be isolated from the library against two therapeutically relevant target antigens, validating the library quality. These findings suggest that the novel antibody library we have developed may be useful for the rapid development of target-specific phage display-derived recombinant human mAbs for use in therapeutic and diagnostic applications.

Third-generation sequencing can be used in human cancer genomics and epigenomic research. Oxford Nanopore Technologies (ONT) recently released R10.4 flow cell, which claimed an improved read accuracy compared to R9.4.1 flow cell. To evaluate the benefits and defects of R10.4 flow cell for cancer cell profiling on MinION devices, we used the human non-small-cell lung-carcinoma cell line HCC78 to construct libraries for both single-cell whole-genome amplification (scWGA) and whole-genome shotgun sequencing. The R10.4 and R9.4.1 reads were benchmarked in terms of read accuracy, variant detection, modification calling, genome recovery rate and compared with the next generation sequencing (NGS) reads. The results highlighted that the R10.4 outperforms R9.4.1 reads, achieving a higher modal read accuracy of over 99.1%, superior variation detection, lower false-discovery rate (FDR) in methylation calling, and comparable genome recovery rate. To achieve high yields scWGA sequencing in the ONT platform as NGS, we recommended multiple displacement amplification with a modified T7 endonuclease Ⅰ cutting procedure as a promising method. In addition, we provided a possible solution to filter the likely false positive sites among the whole genome region with R10.4 by using scWGA sequencing result as a negative control. Our study is the first benchmark of whole genome single-cell sequencing using ONT R10.4 and R9.4.1 MinION flow cells by clarifying the capacity of genomic and epigenomic profiling within a single flow cell. A promising method for scWGA sequencing together with the methylation calling results can benefit researchers who work on cancer cell genomic and epigenomic profiling using third-generation sequencing.

High concentrations of heavy metals in the environment will cause serious harm to ecosystems and human health. It is urgent to develop effective methods to control soil heavy metal pollution. Phytoremediation has advantages and potential for soil heavy metal pollution control. However, the current hyperaccumulators have the disadvantages of poor environmental adaptability, single enrichment species and small biomass. Based on the concept of modularity, synthetic biology makes it possible to design a wide range of organisms. In this paper, a comprehensive strategy of "microbial biosensor detection - phytoremediation - heavy metal recovery" for soil heavy metal pollution control was proposed, and the required steps were modified by using synthetic biology methods. This paper summarizes the new experimental methods that promote the discovery of synthetic biological elements and the construction of circuits, and combs the methods of producing transgenic plants to facilitate the transformation of constructed synthetic biological vectors. Finally, the problems that should be paid more attention to in the remediation of soil heavy metal pollution based on synthetic biology were discussed.

Sequencing technology is the most commonly used technology in molecular biology research and an essential pillar for the development and applications of molecular biology. Since 1977, when the first generation of sequencing technology opened the door to interpreting the genetic code, sequencing technology has been developing for three generations. It has applications in all aspects of life and scientific research, such as disease diagnosis, drug target discovery, pathological research, species protection, and SARS-CoV-2 detection. However, the first- and second-generation sequencing technology relied on fluorescence detection systems and DNA polymerization enzyme systems, which increased the cost of sequencing technology and limited its scope of applications. The third-generation sequencing technology performs PCR-free and single-molecule sequencing, but it still depends on the fluorescence detection device. To break through these limitations, researchers have made arduous efforts to develop a new advanced portable sequencing technology represented by nanopore sequencing. Nanopore technology has the advantages of small size and convenient portability, independent of biochemical reagents, and direct reading using physical methods. This paper reviews the research and development process of nanopore sequencing technology (NST) from the laboratory to commercially viable tools; discusses the main types of nanopore sequencing technologies and their various applications in solving a wide range of real-world problems. In addition, the paper collates the analysis tools necessary for performing different processing tasks in nanopore sequencing. Finally, we highlight the challenges of NST and its future research and application directions.