Long read sequencing technologies provide an efficient approach to generating highly contiguous and informative assemblies. However, higher relative error rates can introduce frameshifts and premature stop codons that pseudogenize genes, hindering downstream analyses. We developed a software tool that detects gene-fragmenting errors in draft assemblies of small genomes through comparison with a curated set of reference genome sequences and raw read information. In our presented example, detected errors represent less than 0.05% of the genome, but when corrected reduced the rate of pseudogenes from 23.3 to 5.6% in example long read assemblies, comparable to the rate of pseudogenes in short read assemblies. We demonstrate that this software can detect assembly errors in long read assemblies generated from small genomes and correct them to de-fragment genes.

Short-read RNA-seq studies of grafted plants have led to the proposal that thousands of messenger RNAs (mRNAs) move over long distances between plant tissues1-7, potentially acting as signals8-12. Transport of mRNAs between cells and tissues has been shown to play a role in several physiological and developmental processes in plants, such as tuberization13, leaf development14 and meristem maintenance15; yet for most mobile mRNAs, the biological relevance of transport remains to be determined16-19. Here we perform a meta-analysis of existing mobile mRNA datasets and examine the associated bioinformatic pipelines. Taking technological noise, biological variation, potential contamination and incomplete genome assemblies into account, we find that a high percentage of currently annotated graft-mobile transcripts are left without statistical support from available RNA-seq data. This meta-analysis challenges the findings of previous studies and current views on mRNA communication.

A simple, rapid, low-cost, and multiplex detection platform is crucial for the diagnosis of infectious diseases, especially for on-site pathogen screening. However, current methods are difficult to satisfy the requirements for minimal instrument and multiplexed point-of-care testing (POCT). Herein, we propose a versatile and easy-to-use platform (FARPA-chip) by combining multiplex FARPA with an autosampling microfluidic chip. A pair of universal recombinase polymerase amplification (RPA) primers introduced during double-stranded cDNA (ds-cDNA) preparation are employed to amplify multiple targets, followed by amplicon-decoding with the chip, indicating no bias in amplifying different targets due to the universal RPA primers. FARPA-chip exhibits that as low as 10 copies of each target RNA in the starting sample can be sensitively detected by 12-plex detection of vector-borne viruses within 45 min and no cross-talk is observed between different targets. The feasibility of this platform is confirmed by designing a 9-plex FARPA-chip to detect 6 kinds of clinically common respiratory viruses from 16 clinical samples of nasopharyngeal swabs, and the results are completely consistent with RT-qPCR. Furthermore, by integrating quick extraction reagent, the turnaround time can be significantly decreased to <50 min, highlighting that our FARPA-chip enables a cost-effective on-site pathogen screening with a relatively high level of multiplexing. Depending on the number of chambers in the chip, the current design is theoretically capable of detecting up to 24 different pathogens, which should fulfill most clinical purposes. We believe that the proposed platform could provide an effective way for a series of healthcare-related applications in resource-limited settings.

Vitiligo is a cutaneous autoimmune disorder characterized by progressive depigmentation due to melanocyte destruction by cytotoxic T cells. Genetic factors predispose patients to the disease and are supported by environmental factors that often initiate new disease episodes. We investigated whether disease outcomes were partially defined by pathogenic microbes that drive nutrient deficiency and inflammation. Our study presents the results of research on the diet and gut microbiome composition of patients with vitiligo and healthy controls from Kazakhstan and the United States. Dietary nutrient intake was assessed using the National Institutes of Health-generated Diet History Questionnaire. Patients with active vitiligo exhibit a limited intake of specific fatty acids, amino acids, fiber, and zinc. Disease activity was further characterized by the abundance of Odoribacter and Escherichia in the gut. Metabolic pathway analysis supported the role of the Escherichia genus in disease activity by limiting energy metabolism and amino acid biosynthetic pathways. Disease activity was also associated with elevated circulating proinflammatory cytokines. These findings suggest that nutritional limitations are not compensated by metabolites from the gut microbiome in active disease, potentially leaving room for inflammation and exacerbating vitiligo. The intricate relationship among diet, gut microbiome composition, and disease progression in vitiligo highlights potential avenues for targeted interventions to reduce autoimmune activity and improve patient outcomes.

Antibody repertoire sequencing and display library screening are powerful approaches for antibody discovery and engineering that can connect DNA sequence with antibody function. Antibody display and screening studies have made a tremendous impact on immunology and biotechnology over the last decade, accelerated by technological advances in high-throughput DNA sequencing techniques. Indeed, bioinformatic analysis of antibody DNA library data has now taken a central role in modern antibody drug discovery, and is also critical for many ongoing studies of human immune development. Here, we describe current trends in antibody DNA library screening and analysis, and introduce a selection of protocols describing fundamental bioinformatic techniques to enable scientists to efficiently study antibody DNA libraries.

The revolutionary progress in development of next-generation sequencing (NGS) technologies has made it possible to deliver accurate genomic information in a timely manner. Over the past several years, NGS has transformed biomedical and clinical research and found its application in the field of personalized medicine. Here we discuss the rise of personalized medicine and the history of NGS. We discuss current applications and uses of NGS in medicine, including infectious diseases, oncology, genomic medicine, and dermatology. We provide a brief discussion of selected studies where NGS was used to respond to wide variety of questions in biomedical research and clinical medicine. Finally, we discuss the challenges of implementing NGS into routine clinical use.

Osteogenesis imperfecta (OI) consists of a group of phenotypically and genetically heterogeneous connective tissue disorders that share similar skeletal anomalies causing bone fragility and deformation. The aim was to investigate the molecular genetic etiology and determine the relationship between genotype and phenotype in OI patients using targeted next-generation sequencing (NGS).

A targeted NGS analysis panel (Illumina TruSight One) containing genes involved in collagen/bone synthesis was performed on the Illumina Nextseq550 platform in patients with a confirmed diagnosis of OI.

Fifty-six patients (female/male: 25/31) from 46 different families were included. Consanguinity was noted in 15 (32.6%) families. Based on Sillence classification 18 (33.1%) were type 1 OI, 1 (1.7%) type 2, 26 (46.4%) type 3 and 11 (19.6%) type 4. Median body weight was -1.1 (-6.8, - 2.5) standard deviation scores (SDS), and height was -2.3 (-7.6, - 1.2) SDS. Bone deformity affected 30 (53.5%), while 31 (55.4%) were evaluated as mobile. Thirty-six (60.7%) had blue sclera, 13 (23.2%) had scoliosis, 12 (21.4%) had dentinogenesis imperfecta (DI), and 2 (3.6%) had hearing loss. Disease-causing variants in COL1A1 and COL1A2 were found in 24 (52.1%) and 6 (13%) families, respectively. In 8 (17.3%) of the remaining 16 (34.7%) families, the NGS panel revealed disease-causing variants in three different genes (FKBP10, SERPINF1, and P3H1). Nine (23.6%) of the variants detected by NGS panel had not previously been reported and were also classified as pathogenic based on American College of Medical Genetics guidelines pathogenity scores. In ten (21.7%) families, a disease-related variant was not found in any of the 13 OI genes on the panel.

Genetic etiology was found in 38 (82.6%) of 46 families by targeted NGS analysis. Furthermore, nine new variants were identified in known OI genes which were classified as pathogenic by standard guidelines.

Oral squamous cell carcinoma (OSCC) is one of the most common cancers worldwide. Despite advances in diagnosis and treatment, the incidence of OSCC is increasing, and the mortality rate remains high. This systematic review aims to examine the potential association between the composition of the oral microbiota and OSCC.

This study's protocol was developed according to the PRISMA guidelines. Several search engines, including Medline-PubMed, Scopus (via Elsevier), and Google Scholar, were used to identify original studies that analyzed differences in the oral microbiome between OSCC patients and controls. Twenty-seven studies were identified that reported significant differences in microbial abundance between OSCC and controls.

The systematic review highlights a complex relationship between the oral microbiome and the pathogenesis of OSCC. Significant changes in the microbial composition were identified, with a predominance of phyla such as Bacteroidetes and Fusobacteria, which are associated with inflammatory mechanisms facilitating tumor progression. A remarkable variability in microbial profiles emerged based on the different stages of the disease and the types of samples analyzed, demonstrating the complexity of the oral microbial ecosystem.

Although alterations in the oral cavity microbiome composition are evident in patients with OSCC, identifying a specific pattern remains challenging. However, the integration of advanced analytical techniques, such as artificial intelligence, could overcome this problem, allowing the identification of crucial biomarkers and improving the understanding of the role of the microbiome in carcinogenesis. This approach could transform microbiome analysis into a useful tool for screening and monitoring patients with OSCC.

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

Rectal cancer poses challenges in preoperative treatment response, with up to 30% achieving a complete response (CR). Personalized treatment relies on accurate identification of responders at diagnosis. This study aimed to unravel CR determinants, overall survival (OS), and time to recurrence (TTR) using clinical and targeted sequencing data. Analyzing 402 patients undergoing preoperative treatment, tumor stage, size, and treatment emerged as robust response predictors. CR rates were higher in smaller, early-stage, and intensively treated tumors. Targeted sequencing analyzed 216 cases, while 120 patients provided hotspot mutation data. KRAS mutation dramatically reduced CR odds by over 50% (odds ratio [OR] = 0.3 in the targeted sequencing and OR = 0.4 hotspot cohorts, respectively). In contrast, SMAD4 and SYNE1 mutations were associated with higher CR rates (OR = 6.0 and 6.8, respectively). Favorable OS was linked to younger age, CR, and low baseline carcinoembryonic antigen levels. Notably, CR and an APC mutation increased TTR, while a BRAF mutation negatively affected TTR. Beyond tumor burden, SMAD4 and SYNE1 mutations significantly influenced CR. KRAS mutations independently correlated with radiotherapy resistance, and BRAF mutations heightened recurrence risk. Intriguingly, non-responding tumors with initially small sizes carried a higher risk of recurrence. The findings, even if limited in addition to the imperfect clinical factors, offer insights into rectal cancer treatment response, guiding personalized therapeutic strategies. By uncovering factors impacting CR, OS, and TTR, this study underscores the importance of tailored approaches for rectal cancer patients. These findings, based on extensive analysis and mutation data, pave the way for personalized interventions, optimizing outcomes in the challenges of rectal cancer preoperative treatment.

Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPIs) represent a standard of care for the clinical management of high-grade serous ovarian cancer (HGSOC). The recognition of homologous recombination deficiency (HRD) has emerged as a predictive biomarker of response for first-line PARPIs treatment in patients with HGOSC. On the other hand, this test is extremely complex and therefore it is often externalised. Regrettably, the reliability of outsourced HRD testing can be troubled by inconclusive results and high rejection rates. In this methodological study, we assessed the technical feasibility, interassay and interlaboratory reproducibility of in-house HRD testing using three different commercially available next-generation sequencing assays.

A total of n=20 epithelial ovarian cancer samples previously analysed with MyChoice CDx were subjected to HRD retesting using three different platforms in three different major pathology laboratories, that is, SOPHiA DDM HRD Solution, HRD focus and Oncomine homologous recombination repair pathway predesigned panel. Concordance was calculated by Cohen's (dual) and Fleiss (triple) κ coefficients.

In-house BRCA1/2 molecular testing yielded a concordance rate >90.0% among all participating centres. HRD scores were successfully calculated by each institution with a concordance rate of 76.5%. Concerning the external gold standard test, the overall percentage of agreement ranged from 80.0% to 90.0% with a positive percentage agreement ranging from 75.0% to 80.0% and a negative percentage agreement ranging from 80.0% to 100%.

In-house testing for HRD can be reliably performed with commercially available next-generation sequencing assays.

Infectious diseases still cause a significant burden of morbidity and mortality among children in low- and middle-income countries (LMICs). There are ample opportunities for innovation in surveillance, prevention, and management, with the ultimate goal of improving survival.

This review discusses the current status in the use and development of innovative strategies for pediatric infectious diseases in LMICs by focusing on surveillance, diagnosis, prevention, and management. Topics covered are: Minimally Invasive Tissue Sampling as a technique to accurately ascertain the cause of death; Genetic Surveillance to trace the pathogen genomic diversity and emergence of resistance; Artificial Intelligence as a multidisciplinary tool; Portable noninvasive imaging methods; and Prognostic Biomarkers to triage and risk stratify pediatric patients.

To overcome the specific hurdles in child health for LMICs, some innovative strategies appear at the forefront of research. If the development of these next-generation tools remains focused on accessibility, sustainability and capacity building, reshaping epidemiological surveillance, diagnosis, and treatment in LMICs, can become a reality and result in a significant public health impact. Their integration with existing healthcare infrastructures may revolutionize disease detection and surveillance, and improve child health and survival.

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Understanding the relationship between plant diversity and invasibility is essential in invasion ecology. Species-rich communities are hypothesized to be more resistant to invasions than species-poor communities. However, while soil microorganisms play a crucial role in regulating this diversity-invasibility relationship, the effects of plant competition mode and soil nutrient status on their role remain unclear. To address this, we conducted a two-stage greenhouse experiment. Soils were first conditioned by growing nine native species separately in them for 1 year, then mixed in various configurations with soils conditioned using one, three, or six species, respectively. Next, we inoculated the mixed soil into sterilized substrate soil and planted the alien species Rhus typhina and native species Ailanthus altissima as test plants. We set up two competition modes (intraspecific and interspecific) and two nutrient levels (fertilization using slow-release fertilizer and nonfertilization). Under intraspecific competition, regardless of fertilization, the biomass of the alien species was higher in soil conditioned by six native species. By contrast, under interspecific competition, the biomass increased without fertilization but remained stable with fertilization in soil conditioned by six native species. Analysis of soil microbes suggests that pathogens and symbiotic fungi in diverse plant communities influenced R. typhina growth, which varied with competition mode and nutrient status. Our findings suggest that the soil microbiome is pivotal in mediating the diversity-invasibility relationship, and this influence varies according to competition mode and nutrient status.

Balamuthia amoebic encephalitis (BAE), caused by Balamuthia mandrillaris, is a rare and life-threatening infectious disease with no specific and effective treatments available. The diagnosis of BAE at an early stage is difficult because of the non-specific clinical manifestations and neuroimaging.

A 52-year-old male patient, who had no previous history of skin lesions, presented to the emergency department with an acute headache, walking difficulties, and disturbance of consciousness. The patient underwent a series of examinations, including regular cerebrospinal fluid (CSF) studies and magnetic resonance imaging, and tuberculous meningoencephalitis was suspected. Despite being treated with anti-TB drugs, no clinical improvement was observed in the patient. Following corticosteroid therapy, the patient developed a rapid deterioration in consciousness with dilated pupils. Metagenomic next-generation sequencing (mNGS) revealed an unexpected central nervous system (CNS) amoebic infection, and the patient died soon after the confirmed diagnosis.

This study highlights the application of mNGS for the diagnosis of patients with suspected encephalitis or meningitis, especially those caused by rare opportunistic infections.

The skin of fish contains a diverse microbiota that has symbiotic functions with the host, facilitating pathogen exclusion, immune system priming, and nutrient degradation. The composition of fish skin microbiomes varies across species and in response to a variety of stressors, however, there has been no systematic analysis across these studies to evaluate how these factors shape fish skin microbiomes. Here, we examined 1922 fish skin microbiomes from 36 studies that included 98 species and nine rearing conditions to investigate associations between fish skin microbiome, fish species, and water physiochemical factors. Proteobacteria, particularly the class Gammaproteobacteria, were present in all marine and freshwater fish skin microbiomes. Acinetobacter, Aeromonas, Ralstonia, Sphingomonas and Flavobacterium were the most abundant genera within freshwater fish skin microbiomes, and Alteromonas, Photobacterium, Pseudoalteromonas, Psychrobacter and Vibrio were the most abundant in saltwater fish. Our results show that different culturing (rearing) environments have a small but significant effect on the skin bacterial community compositions. Water temperature, pH, dissolved oxygen concentration, and salinity significantly correlated with differences in beta-diversity but not necessarily alpha-diversity. To improve study comparability on fish skin microbiomes, we provide recommendations for approaches to the analyses of sequencing data and improve study reproducibility.

Corynebacterium pseudotuberculosis is a zoonotic Gram-positive bacterial pathogen known to cause different diseases in many mammals, including lymph node abscesses in camels. Strains from biovars equi and ovis of C. pseudotuberculosis can infect camels. Comparative genomics could help to identify features related to host adaptation, and currently strain Cp162 from biovar equi is the only one from camel with a sequenced genome.

In this work, we compared the quality of three genome assemblies of strain Cp162 that used data from the DNA sequencing platforms SOLiD v3 Plus, IonTorrent PGM, and Illumina HiSeq 2500 with an optical map and investigate the unique features of this strain. For this purpose, we applied comparative genomic analysis on the different Cp162 genome assembly versions and included other 129 genomes from the same species.

Since the first version of the genome, there was an increase of 88 Kbp and 121 protein-coding sequences, a decrease of pseudogenes from 139 to 53, and two inversions and one rearrangement corrected. We identified 30 virulence genes, none associated to the camel host, and the genes rpob2 and rbpA predicted to confer resistance to rifampin. In comparison to 129 genomes of the same species, strain Cp162 has four genes exclusively present, two of them code transposases and two truncated proteins, and the three exclusively absent genes lysG, NUDIX domain protein, and Hypothetical protein. All 130 genomes had the rifampin resistance genes rpob2 and rbpA. Our results found no unique gene that could be associated with tropism to camel host, and further studies should include more genomes and genome-wide association studies testing for genes and SNPs.

We present avidity sequencing, a sequencing chemistry that separately optimizes the processes of stepping along a DNA template and that of identifying each nucleotide within the template. Nucleotide identification uses multivalent nucleotide ligands on dye-labeled cores to form polymerase-polymer-nucleotide complexes bound to clonal copies of DNA targets. These polymer-nucleotide substrates, termed avidites, decrease the required concentration of reporting nucleotides from micromolar to nanomolar and yield negligible dissociation rates. Avidity sequencing achieves high accuracy, with 96.2% and 85.4% of base calls having an average of one error per 1,000 and 10,000 base pairs, respectively. We show that the average error rate of avidity sequencing remained stable following a long homopolymer.

AmpliSAS and AmpliHLA are tools for automatic genotyping of MHC genes from high-throughput sequencing data. AmpliSAS is designed specifically to analyze amplicon sequencing data from non-model species and it is able to perform de novo genotyping without any previous knowledge of the reference alleles. AmpliHLA is a human specific version; it performs HLA typing by comparing sequenced variants against human reference alleles from the IMGT/HLA database. Both tools are available in AmpliSAT web-server as well as scripts for local/server installation. Here we describe the installation and deployment of AmpliSAS and AmpliHLA Perl scripts and dependencies on a local or a server computer. We will show how to run them in the command line using as examples four genotyping protocols: the first two use amplicon sequencing data to genotype the MHC genes of a passerine bird and human respectively; the third and fourth present the HLA typing of a human cell line starting from RNA and exome sequencing data respectively.

The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet.

The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process.

Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme. RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/. To use RecallME, users must obtain a license for ANNOVAR by themselves.

Nanopore metagenomics has been used for infectious disease diagnosis for bacterial pathogens. However, this technology currently lacks comprehensive performance studies in clinical settings for simultaneous detection of bacteria, fungi, and viruses.

We developed a dual-process of Nanopore sequencing for one sample, with unbiased metagenomics in Meta process and target enrichment in Panel process (Nanopore Meta-Panel process, NanoMP) and prospectively enrolled 450 respiratory specimens from multiple centers. The filter system of pathogen detection was established with machine learning and receiver operator characteristic (ROC) curve to optimize the detection accuracy based on orthogonal test of 21 species. Antimicrobial resistance (AMR) genes were identified based on the Comprehensive Antibiotic Resistance Database (CARD) and single-nucleotide polymorphism matrix.

Our approach showed high sensitivity in Meta process, with 82.9%, 88.7%, and 75.0% for bacteria, fungi (except Aspergillus), and Mycobacterium tuberculosis groups, respectively. Moreover, target amplification improved the sensitivity of virus (>80.0% vs. 39.4%) and Aspergillus (81.8% vs. 42.3%) groups in Panel process compared with Meta process. Overall, NanoMP achieved 80.2% sensitivity and 98.8% specificity compared with the composite reference standard, and we were able to accurately detect AMR genes including blaKPC-2, blaOXA-23 and mecA and distinguish their parent organisms in patients with mixed infections.

We combined metagenomic and enriched Nanopore sequencing for one sample in parallel. Our NanoMP approach simultaneously covered bacteria, viruses and fungi in respiratory specimens and demonstrated good diagnostic performance in real clinical settings.

National Key Research and Development Program of China and National Natural Science Foundation of China.

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods (RDMs) have been developed over the past two decades; however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed eight RDMs: PhiPack (Profile), 3SEQ, GENECONV, recombination detection program (RDP) (OpenRDP), MaxChi (OpenRDP), Chimaera (OpenRDP), UCHIME (VSEARCH), and gmos; to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Furthermore, we provide a practical example for the analysis and validation of empirical data. We find that RDMs need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.

The main guidelines and recommendations for the implementation of the BRCA1/2 somatic test do not focus on the clinical application of predictive testing on bone metastases, a frequent condition in metastatic prostate cancer, by analyzing the critical issues encountered by laboratory practice. Our goal is to produce a document (protocol) deriving from a multidisciplinary team approach to obtain high quality nucleic acids from biopsy of bone metastases. This document aims to compose an operational check-list of three phases: the pre-analytical phase concerns tumor cellularity, tissue processing, sample preservation (blood/FFPE), fixation and staining, but above all the decalcification process, the most critical phase because of its key role in allowing the extraction of somatic DNA with a good yield and high quality. The analytical phase involves the preparation of the libraries that can be analyzed in various NGS genetic sequencing platforms and with various bioinformatics software for the interpretation of sequence variants. Finally, the post-analytical phase that allows to report the variants of the BRCA1/2 genes in a clear and usable way to the clinician who will use these data to manage cancer therapy with PARP Inhibitors.

Since 1990, numerous methods for aptamer selection have been developed, although a quantitative comparison of their sequence enrichment is lacking. In this study, we compared the enrichment factors of three library-immobilization SELEX methods (capture-SELEX, GO-SELEX, and gold-SELEX). We used a spiked library that contained multiple DNA aptamers with different affinities for adenosine. The aptamer separation efficiency was measured using qPCR, and all of the three methods showed a very low DNA release (<1%) in the presence of 100 μM adenosine. Among these, barely any DNA was released from the gold nanoparticles. Deep sequencing was used to compare the enrichment of three aptamers: Ade1301, Ade1304, and the classical aptamer. Enrichment up to 30 to 50-fold was observed only for the capture-SELEX method, whereas the other two methods showed enrichment factors below 1. By blocking the primer-binding regions of the library, GO-SELEX reached up to 14% enrichment. Finally, the enrichment of aptamers based on nonspecific release and target-induced release was discussed, and the advantages of capture-SELEX were rationalized. Taken together, these results indicate that capture-SELEX is a much more efficient method for enriching aptamers.

The soil fungal community plays an important role in forest ecosystems and is crucially influenced by forest secondary succession. However, the driving factors of fungal community and function during temperate forest succession and their potential impact on succession processes remain poorly understood. In this study, we investigated the dynamics of the soil fungal community in three temperate forest secondary successional stages (shrublands, coniferous forests, and deciduous broad-leaved forests) using high-throughput DNA sequencing coupled with functional prediction via the FUNGuild database. We found that fungal community richness, α-diversity, and evenness decreased significantly during the succession process. Soil available phosphorus and nitrate nitrogen decreased significantly after initial succession occurred, and redundancy analysis showed that both were significant predictors of soil fungal community structure. Among functional groups, fungal saprotrophs and pathotrophs represented by plant pathogens were significantly enriched in the early-successional stage, while fungal symbiotrophs represented by ectomycorrhiza were significantly increased in the late-successional stage. The abundance of both saprotroph and pathotroph fungal guilds was positively correlated with soil nitrate nitrogen and available phosphorus content. Ectomycorrhizal fungi were negatively correlated with nitrate nitrogen and available phosphorus content and positively correlated with ammonium nitrogen content. These results indicate that the dynamics of fungal community and function reflected the changes in nitrogen and phosphorus availability caused by the secondary succession in temperate forests. The fungal plant pathogen accumulated in the early-successional stage and ectomycorrhizal fungi accumulated in the late-successional stage may have a potential role in promoting forest succession. These findings contribute to a better understanding of the response of soil fungal communities to secondary forest succession and highlight the importance of fungal communities during the successional process.

Three MPS platforms are being used in forensic genetic analysis, i.e., MiSeq FGx, Ion S5 XL, and MGISEQ-2000. However, few studies compared their performance. In this study, we sequenced 83 common SNPs of 71 samples using the ForenSeq™ DNA Signature Prep Kit on MiSeq FGx, the Precision ID Identity Panel on Ion S5 XL, and the MGIEasy Signature Identification Library Prep Kit on MGISEQ-2000 and then the performance was compared. Results showed that the MiSeq FGx had the highest sequence quality but the lowest sequencing depth and allele balance. Discordant genotypes were observed at six SNPs, which may be caused by variants at primer binding regions, indel errors, or misalignments. Besides, two kinds of background noises, allele-specific miscalled reads (ASMR) and allele-nonspecific miscalled reads (ANMR), were characterized. MGISEQ-2000 showed the highest level of ASMR while Ion S5 XL had the highest level of ANMR. Site- and genotype-dependent miscalled patterns were observed at several SNPs on Ion S5 XL and MGISEQ-2000, but few on MiSeq FGx. In conclusion, the three MPS platforms perform differently with respect to sequencing quality, sequencing depth, allele balance, concordance, and background noise. These findings may be useful for data comparison, mixture deconvolution, and heteroplasmy analysis in forensic genetics.

Brahmi is one of the important nootropic botanicals, widely sold in the market, with the name "Brahmi'' being used to describe both Bacopa monnieri and Centella asiatica species. The Brahmi herbal products market is expanding; hence, economically motivated adulteration is highly prevalent.

This study aimed to develop DNA-based methods, including SCAR marker-based PCR and metabarcoding, to authenticate Brahmi herbal products and compare these methods with HPLC. These methods have been validated using mock controls (in-house blended formulations). All targeted plant species in mock controls were detected successfully with all three methods, whereas, in market samples, only 22.2%, 55.6%, and 50.0% were found positive for Brahmi by PCR assay, DNA metabarcoding, and HPLC, respectively. Metabarcoding can detect the presence of non-labeled plants together with targeted species, which is an advantage over PCR assay or HPLC.

SCAR marker-based PCR is a rapid and cost-effective method for detecting the presence of B. monnieri and C. asiatica. However, in this study, the success rate of PCR amplification was relatively low because the primers targeted either RAPD or ITS-based SCAR markers. HPLC assay, although an alternative, was unable to detect the presence of other botanicals, just like the SCAR marker-based PCR assay. On the other hand, metabarcoding can be utilized to identify the target plants, even in very small quantities, while also providing simulated identification of other botanicals. This study successfully addressed the need for quality control of Brahmi herbal products and provided the first-time report of DNA metabarcoding for such products.

Marine and freshwater biodiversity is under threat from both natural and manmade causes. Biological monitoring is currently a top priority for biodiversity protection. Given present limitations, traditional biological monitoring methods may not achieve the proposed monitoring aims. Environmental DNA metabarcoding technology reflects species information by capturing and extracting DNA from environmental samples, using molecular biology techniques to sequence and analyze the DNA, and comparing the obtained information with existing reference libraries to obtain species identification. However, its practical application has highlighted several limitations. This paper summarizes the main steps in the environmental application of eDNA metabarcoding technology in aquatic ecosystems, including the discovery of unknown species, the detection of invasive species, and evaluations of biodiversity. At present, with the rapid development of big data and artificial intelligence, certain advanced technologies and devices can be combined with environmental DNA metabarcoding technology to promote further development of aquatic species monitoring and management.

Base editors are genome editing tools that enable site-specific base conversions through the chemical modification of nucleobases in DNA. Adenine base editors (ABEs) convert A ⋅ T to G ⋅ C base pairs in DNA by using an adenosine deaminase enzyme to modify target adenosines to inosine intermediates. Due to the lack of a naturally occurring adenosine deaminase that can modify DNA, ABEs were evolved from a tRNA-deaminating enzyme, TadA. Previous experiments with an ABE comprising a wild-type (wt) TadA showed no detectable activity on DNA, and directed evolution was therefore required to enable this enzyme to accept DNA as a substrate. Here we show that wtTadA can perform base editing in DNA in both bacterial and mammalian cells, with a strict sequence motif requirement of TAC. We leveraged this discovery to optimize a reporter assay to detect base editing levels as low as 0.01 %. Finally, we used this assay along with molecular dynamics simulations of full ABE:DNA complexes to better understand how the sequence recognition of mutant TadA variants change as they accumulate mutations to better edit DNA substrates.

Insertion and deletion polymorphisms (InDels) have considerable potential in the field of forensic genetics because of their low mutation rate and small amplicons. At present, InDel polymorphisms detection based on the technique of capillary electrophoresis is the main technique used in forensic DNA laboratory. However, this method is complicated and time-consuming, and is not suitable for rapid on-site paternity and personal identification. Next-generation sequencing analysis of InDels polymorphisms requires expensive instruments, large upfront reagent and supply costs, computational requirements and complex bioinformatics, increased the time to obtain results. Thus, there is an urgent need to establish a method to provide reliable, rapid, sensitive and economical genotyping for InDels.

A rapid InDels (32 InDels) panel was established using fluorogenic probes-based multiplex real-time PCR with microfluidic test cartridge and portable real-time PCR instrument. Then, we performed several validation studies including concordance, accuracy, sensitivity, stability, species specificity.

It showed that the complete genotypes could be obtained from ≥100 pg of input DNA and from a series of challenging samples with high accuracy and specificity within 90 min.

This method provides a rapid and cost-effective solution for InDels genotyping and personal identification in portable format.

Long non-coding RNAs (lncRNAs) play a crucial role in numbers of biological processes and have received wide attention during the past years. Since the rapid development of high-throughput transcriptome sequencing technologies (RNA-seq) lead to a large amount of RNA data, it is urgent to develop a fast and accurate coding potential predictor. Many computational methods have been proposed to address this issue, they usually exploit information on open reading frame (ORF), protein sequence, k-mer, evolutionary signatures, or homology. Despite the effectiveness of these approaches, there is still much room to improve. Indeed, none of these methods exploit the contextual information of RNA sequence, for example, k-mer features that counts the occurrence frequencies of continuous nucleotides (k-mer) in the whole RNA sequence cannot reflect local contextual information of each k-mer. In view of this shortcoming, here, we present a novel alignment-free method, CPPVec, which exploits the contextual information of RNA sequence for coding potential prediction for the first time, it can be easily implemented by distributed representation (e.g., doc2vec) of protein sequence translated from the longest ORF. The experimental findings demonstrate that CPPVec is an accurate coding potential predictor and significantly outperforms existing state-of-the-art methods.

Tumour DNA sequencing is essential for precision medicine since it guides therapeutic decisions but also fosters the identification of patients who may benefit from germline testing. Notwithstanding, the tumour-to-germline testing workflow presents a few caveats. The low sensitivity for indels at loci with sequences of identical bases (homopolymers) of ion semiconductor-based sequencing techniques represents a well-known limitation, but the prevalence of indels overlooked by these techniques in high-risk populations has not been investigated. In our study, we addressed this issue at the homopolymeric regions of BRCA1/2 in a retrospectively selected cohort of 157 patients affected with high-grade ovarian cancer and negative at tumour testing by ION Torrent sequencing. Variant allele frequency (VAF) of indels at each of the 29 investigated homopolymers was systematically revised with the IGV software. Thresholds to discriminate putative germline variants were defined by scaling the VAF to a normal distribution and calculating the outliers that exceeded the mean + 3 median-adjusted deviations of a control population. Sanger sequencing of the outliers confirmed the occurrence of only one of the five putative indels in both tumour and blood from a patient with a family history of breast cancer. Our results indicated that the prevalence of homopolymeric indels overlooked by ion semiconductor techniques is seemingly low. A careful evaluation of clinical and family history data would further help minimise this technique-bound limitation, highlighting cases in which a deeper look at these regions would be recommended.