Sequencing technologies — the next generation

Transcriptomic and Metabolomic Analyses Reveal That Fullerol Improves Drought Tolerance in Brassica napus L

Carbon nanoparticles have potential threats to plant growth and stress tolerance. The polyhydroxy fullerene-fullerol (one of the carbon nanoparticles) could increase biomass accumulation in several plants subjected to drought; however, the underlying molecular and metabolic mechanisms governed by fullerol in improving drought tolerance in Brassica napus remain unclear. In the present study, exogenous fullerol was applied to the leaves of B. napus seedlings under drought conditions. The results of transcriptomic and metabolomic analyses revealed changes in the molecular and metabolic profiles of B. napus. The differentially expressed genes and the differentially accumulated metabolites, induced by drought or fullerol treatment, were mainly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to carbohydrate metabolism (e.g., "carbon metabolism" and "galactose metabolism"), amino acid metabolism (e.g., "biosynthesis of amino acids" and "arginine and proline metabolism"), and secondary metabolite metabolism (e.g., "biosynthesis of secondary metabolites"). For carbohydrate metabolism, the accumulation of oligosaccharides (e.g., sucrose) was decreased, whereas that of monosaccharides (e.g., mannose and myo-inositol) was increased by drought. With regard to amino acid metabolism, under drought stress, the accumulation of amino acids such as phenylalanine and tryptophan decreased, whereas that of glutamate and proline increased. Further, for secondary metabolite metabolism, B. napus subjected to soil drying showed a reduction in phenolics and flavonoids, such as hyperoside and trans-3-coumaric acid. However, the accumulation of carbohydrates was almost unchanged in fullerol-treated B. napus subjected to drought. When exposed to water shortage, the accumulation of amino acids, such as proline, was decreased upon fullerol treatment. However, that of phenolics and flavonoids, such as luteolin and trans-3-coumaric acid, was enhanced. Our findings suggest that fullerol can alleviate the inhibitory effects of drought on phenolics and flavonoids to enhance drought tolerance in B. napus.

CMIC: an efficient quality score compressor with random access functionality

Background

Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access.

Results

In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores.

Conclusions

CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04837-1.

Multimodal Genotype and Phenotype Data Integration to Improve Partial Data-Based Longitudinal Prediction

Multimodal data analysis has attracted ever-increasing attention in computational biology and bioinformatics community recently. However, existing multimodal learning approaches need all data modalities available at both training and prediction stages, thus they cannot be applied to many real-world biomedical applications, which often have a missing modality problem as the collection of all modalities is prohibitively costly. Meanwhile, two diagnosis-related pieces of information are of main interest during the examination of a subject regarding a chronic disease (with longitudinal progression): their current status (diagnosis) and how it will change before next visit (longitudinal outcome). Correct responses to these queries can identify susceptible individuals and provide the means of early interventions for them. In this article, we develop a novel adversarial mutual learning framework for longitudinal disease progression prediction, allowing us to leverage multiple data modalities available for training to train a performant model that uses a single modality for prediction. Specifically, in our framework, a single-modal model (which utilizes the main modality) learns from a pretrained multimodal model (which accepts both main and auxiliary modalities as input) in a mutual learning manner to (1) infer outcome-related representations of the auxiliary modalities based on its own representations for the main modality during adversarial training and (2) successfully combine them to predict the longitudinal outcome. We apply our method to analyze the retinal imaging genetics for the early diagnosis of age-related macular degeneration (AMD) disease, that is, simultaneous assessment of the severity of AMD at the time of the current visit and the prognosis of the condition at the subsequent visit. Our experiments using the Age-Related Eye Disease Study dataset show that our method is more effective than baselines at classifying patients' current and forecasting their future AMD severity.

Effect of mutation allele frequency on the risk stratification of myelodysplastic syndrome patients

Myelodysplastic syndrome (MDS) is a heterogeneous group of clonal myeloid malignancies. Though several recurrent mutations are closely correlated with clinical outcomes, data concerning the association between mutation variant allele frequencies (VAF) and prognosis are limited. In this study, we performed comprehensive VAF analyses of relevant myeloid-malignancy related mutations in 698 MDS patients and correlated the results with their prognosis. Mutation VAF in DNMT3A, TET2, ASXL1, EZH2, SETBP1, BCOR, SFSF2, ZRSR2, and TP53 mutations correlated with outcomes. In multivariable analysis, DNMT3A and ZRSR2 mutations with high VAF and mutant IDH2, CBL, U2AF1, and TP53 were independent poor prognostic factors for overall survival. A substantial portion of patients in each revised International Prognostic Scoring System (IPSS-R) risk group could be adjusted to different prognostic groups based on the integrated VAF and mutational profiles. Patients with these unfavorable mutations in each IPSS-R risk subgroup had survivals worse than other patients of the same risk but similar to those in the next higher-risk subgroup. Furthermore, patients harboring U2AF1 mutation might benefit from hypomethylating agents. This study demonstrated the critical role of VAF of mutations for risk stratification in MDS patients and may be incorporated in novel scoring systems.

Chromosomal fragment deletion in APRR2-repeated locus modulates the dark stem color in Cucurbita pepo

Cp4.1LG15g03420 (CpDsc-1), which encodes a two-component response regulator-like protein (APRR2) in the nucleus, influences dark green stem formation in Cucurbita pepo by regulating the chlorophyll content. Stem color is an important agronomic trait in zucchini (Cucurbita pepo) for robust seeding and high yield. However, the gene controlling the stem color has not been characterized. In this study, we identified a single locus accounting for the dark green stem color of C. pepo (CpDsc-1). Genetic analysis of this trait in segregated populations derived from two parental lines (line 296 with dark green stems and line 274 with light green stems) revealed that stem color was controlled by a single dominant gene (dark green vs. light green). In bulked segregant analysis, CpDsc-1 was mapped to a 2.09-Mb interval on chromosome 15. This region was further narrowed to 65.2 kb using linkage analysis of the F₂ population. Sequencing analysis revealed a 14 kb deletion between Cp4.1LG15g03420 and Cp4.1LG15g03360; these two genes both encoded a two-component response regulator-like protein (APRR2). The incomplete structures of the two APRR2 genes and abnormal chloroplasts in line 274 might be the main cause of the light green phenotype. Gene expression pattern analysis showed that only Cp4.1LG15g03420 was upregulated in line 296. Subcellular localization analysis indicated that Cp4.1LG15g03420 was a nuclear gene. Furthermore, a co-dominant marker, G4563 (93% accuracy rate), and a co-segregation marker, Fra3, were established in 111 diverse germplasms; both of these markers were tightly linked with the color trait. This study provided insights into chlorophyll regulation mechanisms and revealed the markers valuable for marker-assisted selection in future zucchini breeding.

Community-led risk analysis of direct-to-consumer whole-genome sequencing

Direct-to-consumer (DTC) genetic testing is cheaper and more accessible than ever before; however, the intention to combine, reuse, and resell this genetic information as powerful data sets is generally hidden from the consumer. This financial gain is creating a competitive DTC market, reducing the price of whole-genome sequencing (WGS) to under 300 USD. Entering this transition from single-nucleotide polymorphism-based DTC testing to WGS DTC testing, individuals looking for access to their whole-genomic information face new privacy and security risks. Differences between WGS and other methods of consumer genetic tests are left unexplored by regulation, leading to the application of legal data anonymization methods on whole-genome data, and questionable consent methods. Large representative genomic data sets are important for research and improve the standard of medicine and personalized care. However, these data can also be used by market players, law enforcement, and governments for surveillance, population analyses, marketing purposes, and discrimination. Here, we present a summary of the state of WGS DTC genetic testing and its current regulation, through a community-based lens to expose dual-use risks in consumer-facing biotechnologies.

Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Advancements and future prospective of DNA barcodes in the herbal drug industry

Ethnopharmacological relevance: The past couple of decades have witnessed the global resurgence of medicinal plants in the field of herbal-based health care. Increased consumption of medicinal plants and their derivative products is the major cause of the adulteration issues in herbal industries. As a result, the quality of herbal products is affected by spurious and unauthorized raw materials. Recent development in molecular plant identification using DNA barcodes has become a robust methodology to identify and authenticate the adulterants in herbal samples. Hence, rapid and accurate identification of medicinal plants is the key to success for the herbal industry. Aim of the study: This paper provides a comprehensive review of the application of DNA barcoding and advanced technologies that have emerged over the past 10 years related to medicinal plant identification and authentication and the future prospects of this technology. Materials and methods: Information on DNA barcodes was compiled from scientific databases (Google Scholar, Web of Science, SciFinder and PubMed). Additional information was obtained from books, Ph.D. thesis and MSc. Dissertations. Results: Working out an appropriate DNA barcode for plants is challenging; the single locus-based DNA barcodes (rbcL, ITS, ITS2, matK, rpoB, rpoC, trnH-psbA) to multi-locus DNA barcodes have become the successful species-level identification among herbal plants. Additionally, multi-loci have become efficient in the authentication of herbal products. Emerging advances in DNA barcoding and related technologies such as next-generation sequencing, high-resolution melting curve analysis, meta barcodes and mini barcodes have paved the way for successful herbal plant/samples identification. Conclusion: DNA barcoding needs to be employed together with other techniques to check and rationally and effectively quality control the herbal drugs. It is suggested that DNA barcoding techniques combined with metabolomics, transcriptomics, and proteomics could authenticate the herbal products. The invention of simple, cost-effective and improved DNA barcoding techniques to identify herbal drugs and their associated products of medicinal value in a fool-proof manner will be the future thrust of Pharmacopoeial monograph development for herbal drugs.

A Novel ssDNA Aptamer Targeting Carcinoembryonic Antigen: Selection and Characterization

One of the major causes of a drastically shorter life expectancy and one of the most prevalent diseases in the world today is cancer. Given the data on the rise in cancer cases throughout the world, it is obvious that, despite the diagnostic techniques currently being used, there is a pressing need to develop precise and sensitive techniques for early diagnosis of the disease. A high degree of affinity and specificity towards particular targets is maintained by the short nucleic acid molecules known as aptamers. Aptamers outperform antibodies due to their unique benefits, such as their simplicity in synthesis and modification, lack of toxicity, and long-term stability. Utilizing an accurate recognition element and a robust signal transduction mechanism, molecular diagnostics can be extremely sensitive and specific. In this study, development of new single-stranded DNA aptamers against CEA for use in cancer diagnostics was accomplished using SELEX and NGS methods. As a result of 12 iterative SELEX rounds, nine aptamer candidates against CEA were developed. NGS comparative analysis revealed that round twelve had an enriched number of aptamers that were specifically bound, as opposed to round eight. Among the selected nine sequences characterized by bioinformatics analysis and ELONA, an aptamer sequence with the highest specificity and affinity for the target protein was identified and further examined. Aptamer sequence (6) was screened in a concentration-dependent assay, specificity analysis was performed, and its potential secondary and tertiary structures were predicted, which enabled us to test one of the possible putative interactions with CEA. Finally, aptamer sequence (6) labelled with a Cy5 fluorescent tag was used in confocal microscopy to observe its binding towards the CEA expressed in HT-29 human colon adenocarcinoma cell line.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data

Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the "cut and paste" mechanism, retrotransposons mobilize via the "copy and paste" method. They have been an essential research topic because some of the active elements, such as Long interspersed element 1 (LINE-1), Alu, and SVA elements, have contributed to the genetic diversity of primates beyond humans. In addition, they can cause genetic disorders by altering gene expression and generating structural variations (SVs). The development and rapid technological advances in next-generation sequencing (NGS) have led to new perspectives on detecting retrotransposon-mediated SVs, especially insertions. Moreover, various computational methods have been developed based on NGS data to precisely detect the insertions and deletions in the human genome. Therefore, this review discusses details about the recently studied and utilized NGS technologies and the effective computational approaches for discovering retrotransposons through it. The final part covers a diverse range of computational methods for detecting retrotransposon insertions with human NGS data. This review will give researchers insights into understanding the TEs and how to investigate them and find connections with research interests.

In the context of the triple burden of malnutrition: A systematic review of gene-diet interactions and nutritional status

Genetic background interacts with dietary components to modulate nutritional health status. This study aimed to review the evidence for gene-diet interactions in all forms of malnutrition. A comprehensive systematic literature search was conducted through April 2021 to identify observational and intervention studies reporting the effects of gene-diet interactions in over-nutrition, under-nutrition and micronutrient status. Risk of publication bias was assessed using the Quality Criteria Checklist and a tool specifically designed for gene-diet interaction research. 167 studies from 27 populations were included. The majority of studies investigated single nucleotide polymorphisms (SNPs) in overnutrition (n = 158). Diets rich in whole grains, vegetables, fruits and low in total and saturated fats, such as Mediterranean and DASH diets, showed promising effects for reducing obesity risk among individuals who had higher genetic risk scores for obesity, particularly the risk alleles carriers of FTO rs9939609, rs1121980 and rs1421085. Other SNPs in MC4R, PPARG and APOA5 genes were also commonly studied for interaction with diet on overnutrition though findings were inconclusive. Only limited data were found related to undernutrition (n = 1) and micronutrient status (n = 9). The findings on gene-diet interactions in this review highlight the importance of personalized nutrition, and more research on undernutrition and micronutrient status is warranted.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Cancer Genetics and Clinical Research

Understanding how complex diseases as well as cancers arise is one of the great challenges of modern medicine [...].

iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning

Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position's reactivity and exhibits interesting behavior on neighboring bases in the sequence.

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

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

A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Background

Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies.

Objective

The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now.

Results

The proposed study, based on previous studies and manufacturers' descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position.

Conclusion

The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

Can multi-cropping affect soil microbial stoichiometry and functional diversity, decreasing potential soil-borne pathogens? A study on European organic vegetable cropping systems

Crop diversification in spatial and temporal patterns can optimize the synchronization of nutrients plant demand and availability in soils, as plant diversity and soil microbial communities are the main drivers of biogeochemical C and nutrient cycling. The introduction of multi-cropping in organic vegetable production can represent a key strategy to ensure efficient complementation mediated by soil microbiota, including beneficial mycorrhizal fungi. This study shows the effect of the introduction of multi-cropping in five European organic vegetable systems (South-West: Italy; North-West: Denmark and Belgium; North-East: Finland and Latvia) on: (i) soil physicochemical parameters; (ii) soil microbial biomass stoichiometry; (iii) crop root mycorrhization; (iv) bacterial and fungal diversity and composition in crop rhizosphere; (v) relative abundance of selected fungal pathogens species. In each site, three cropping systems were considered: (1) crop 1-monocropping; (2) crop 2-monocropping; (3) crop 1-crop 2-intercropping or strip cropping. Results showed that, just before harvest, multi-cropping can increase soil microbial biomass amount and shape microbial community toward a predominance of some bacteria or fungi phyla, in the function of soil nutrient availability. We mainly observed a selection effect of crop type on rhizosphere microbiota. Particularly, Bacteroidetes and Mortierellomycota relative abundances in rhizosphere soil resulted in suitable ecological indicators of the positive effect of plant diversity in field, the first ones attesting an improved C and P cycles in soil and the second ones a reduced soil pathogens' pressure. Plant diversity also increased the root mycorrhizal colonization between the intercropped crops that, when properly selected, can also reduce the relative abundance of potential soil-borne pathogens, with a positive effect on crop productivity in long term.

Genome-wide association studies of seedling quantitative trait loci against salt tolerance in wheat

Salinity is one of the significant factors in decreasing wheat yield and quality. To counter this, it is necessary to develop salt-tolerant wheat varieties through conventional and advanced molecular techniques. The current study identified quantitative trait loci in response to salt stress among worldwide landraces and improved varieties of wheat at the seedling stage. A total of 125 landraces and wheat varieties were subjected to salt treatment (50, 100, and 150 mM) with control. Morphological seedling traits, i.e., shoot length, root length, and fresh and dry shoot and root weights for salinity tolerance were observed to assess salt tolerance and genetic analysis using SNP data through DArT-seq. The results showed that, at the seedling stage, 150 mM NaCl treatment decreased shoot length, root length, and fresh and dry weights of the shoot and root. The root length and dry root weight were the most affected traits at the seedling stage. Effective 4417 SNPs encompassing all the chromosomes of the wheat genome with marker density, i.e., 37%, fall in genome B, genome D (32%), and genome A (31%). Five loci were found on four chromosomes 6B, 6D, 7A, and 7D, showing strong associations with the root length, fresh shoot weight, fresh root weight, and dry root weight at the p < 0.03 significance level. The positive correlation was found among all morphological traits under study.

A Practical Guide to Design and Assess a Phylogenomic Study

Over the last decade, molecular systematics has undergone a change of paradigm as high-throughput sequencing now makes it possible to reconstruct evolutionary relationships using genome-scale datasets. The advent of "big data" molecular phylogenetics provided a battery of new tools for biologists but simultaneously brought new methodological challenges. The increase in analytical complexity comes at the price of highly specific training in computational biology and molecular phylogenetics, resulting very often in a polarized accumulation of knowledge (technical on one side and biological on the other). Interpreting the robustness of genome-scale phylogenetic studies is not straightforward, particularly as new methodological developments have consistently shown that the general belief of "more genes, more robustness" often does not apply, and because there is a range of systematic errors that plague phylogenomic investigations. This is particularly problematic because phylogenomic studies are highly heterogeneous in their methodology, and best practices are often not clearly defined. The main aim of this article is to present what I consider as the ten most important points to take into consideration when planning a well-thought-out phylogenomic study and while evaluating the quality of published papers. The goal is to provide a practical step-by-step guide that can be easily followed by nonexperts and phylogenomic novices in order to assess the technical robustness of phylogenomic studies or improve the experimental design of a project.

An Ensemble Tf-Idf Based Approach to Protein Function Prediction via Sequence Segmentation

This paper explores the use of variants of tf-idf-based descriptors, namely length-normalized-tf-idf and log-normalized-tf-idf, combined with a segmentation technique, for efficient modeling of variable-length protein sequences. The proposed solution, ProtVecGen-Ensemble, is an ensemble of three models trained on differently segmented datasets constructed from an input dataset containing complete protein sequences. Evaluations using biological process (BP) and molecular function (MF) datasets demonstrate that the proposed feature set is not only superior to its contemporaries but also produces more consistent results with respect to variation in sequence lengths. Improvements of +6.07% (BP) and +7.56% (MF) over state-of-the-art tf-idf-based MLDA feature set were obtained. The best results were achieved when ProtVecGen-Ensemble was combined with ProtVecGen-Plus - the state-of-the-art method for protein function prediction - resulting in improvements of +8.90% (BP) and +11.28% (MF) over MLDA and +1.49% (BP) and +2.07% (MF) over ProtVecGen-Plus+MLDA. To capture the performance consistency with respect to sequence lengths, we have defined a variance-based metric, with lower values indicating better performance. On this metric, the proposed ProtVecGen-Ensemble+ProtVecGen-Plus framework resulted in reductions of 56.85 percent (BP) and 56.08 percent (MF) over MLDA and 10.37 percent (BP) and 26.48 percent (MF) over ProtVecGenPlus+MLDA.

Multiplexed Single-Cell in Situ Protein Profiling

The ability to profile a large number of different proteins in individual cells in their native cellular locations is critical to accelerate our understanding of normal cell physiology and disease pathogenesis. Bulk cell protein quantification masks the cell heterogeneity in complex biological systems, while conventional immunofluorescence or immunohistochemistry are limited by their low multiplexing capacity. Recent technological advances in multiplexed protein imaging approaches allow many distinct proteins to be analyzed in single cells in situ. These methods will bring new insights into various biological and biomedical fields, such as cell type and subtype classification, signaling network regulation, tissue architecture, and disease diagnosis and prognosis, along with treatment monitoring. In this Review, we will describe the recent advances of multiplexed single-cell in situ protein profiling technologies, discuss their unique advantages and limitations, highlight their applications in biology and medicine, present the current challenges, and propose potential solutions.

Advances in Single-Cell Toxicogenomics in Environmental Toxicology

The toxicity evaluation system of environmental pollutants has undergone numerous changes due to the application of new technologies. Single-cell toxicogenomics is rapidly changing our view on environmental toxicology by increasing the resolution of our analysis to the level of a single cell. Applications of this technology in environmental toxicology have begun to emerge and are rapidly expanding the portfolio of existing technologies and applications. Here, we first summarized different methods involved in single-cell isolation and amplification in single-cell sequencing process, compared the advantages and disadvantages of different methods, and analyzed their development trends. Then, we reviewed the main advances of single-cell toxicogenomics in environmental toxicology, emphatically analyzed the application prospects of this technology in identifying the target cells of pollutants in early embryos, clarifying the heterogeneous response of cell subtypes to pollutants, and finding pathogenic bacteria in unknown microbes, and highlighted the unique characteristics of this approach with high resolution, high throughput, and high specificity by examples. We also offered a prediction of the further application of this technology and the revolution it brings in environmental toxicology. Overall, these advances will provide practical solutions for controlling or mitigating exogenous toxicological effects that threaten human and ecosystem health, contribute to improving our understanding of the physiological processes affected by pollutants, and lead to the emergence of new methods of pollution control.

T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer

The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.

B-Cell Receptor Repertoire Sequencing: Deeper Digging into the Mechanisms and Clinical Aspects of Immune-mediated Diseases

B cells play an essential role in adaptive immunity and are intimately correlated with pleiotropic immune-mediated diseases. Each B cell occupies a unique B cell receptor (BCR), and all BCRs throughout our body form “BCR repertoire.” With the development of sequencing technology and coupled bioinformatics, accumulating evidence indicates that BCR repertoire largely varies under physiological and pathological conditions. Therefore, comprehensive grasp of BCR repertoire will provide new insights into the pathogenesis of immune-mediated diseases and help exploit efficient diagnostic and treatment strategies. In this review, we start with an overview of BCR repertoire and related sequencing technologies and summarize their current applications in immune-mediated diseases. We also underscore the challenges of this emerging field and propose promising future directions in advancing BCR repertoire exploration.

Understanding of the various aspects of gene regulatory networks related to crop improvement

The hierarchical relationship between transcription factors, associated proteins, and their target genes is defined by a gene regulatory network (GRN). GRNs allow us to understand how the genotype and environment of a plant are incorporated to control the downstream physiological responses. During plant growth or environmental acclimatization, GRNs are diverse and can be differently regulated across tissue types and organs. An overview of recent advances in the development of GRN that speed up basic and applied plant research is given here. Furthermore, the overview of genome and transcriptome involving GRN research along with the exciting advancement and application are discussed. In addition, different approaches to GRN predictions were elucidated. In this review, we also describe the role of GRN in crop improvement, crop plant manipulation, stress responses, speed breeding and identifying genetic variations/locus. Finally, the challenges and prospects of GRN in plant biology are discussed.

Unique Molecular Identifier-Based High-Resolution HLA Typing and Transcript Quantitation Using Long-Read Sequencing

HLA typing provides essential results for stem cell and solid organ transplants, as well as providing diagnostic benefits for various rheumatology, gastroenterology, neurology, and infectious diseases. It is becoming increasingly clear that understanding the expression of patient HLA transcripts can provide additional benefits for many of these same patient groups. Our study cohort was evaluated using a long-read RNA sequencing methodology to provide rapid HLA genotyping results and normalized HLA transcript expression. Our assay used NGSEngine to determine the HLA genotyping result and normalized mRNA transcript expression using Athlon2. The assay demonstrated an excellent concordance rate of 99.7%. Similar to previous studies, for the class I loci, patients demonstrated significantly lower expression of HLA-C than HLA-A and -B (Mann-Whitney U, p value = 0.0065 and p value = 0.0154, respectively). In general, the expression of class II transcripts was lower than that of class I transcripts. This study demonstrates a rapid high-resolution HLA typing assay using RNA-Seq that can provide accurate HLA genotyping and HLA allele-specific transcript expression in 7-8 h, a timeline short enough to perform the assay for deceased donors.

Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening

Objective

The aim of this project was to implement long-read sequencing for BCR-ABL1 TKI resistance mutation screening in a clinical setting for patients undergoing treatment for chronic myeloid leukemia.

Materials and Methods

Processes were established for registering and transferring samples from the clinic to an academic sequencing facility for long-read sequencing. An automated analysis pipeline for detecting mutations was established, and an information system was implemented comprising features for data management, analysis and visualization. Clinical validation was performed by identifying BCR-ABL1 TKI resistance mutations by Sanger and long-read sequencing in parallel. The developed software is available as open source via GitHub at https://github.com/pharmbio/clamp.

Results

The information system enabled traceable transfer of samples from the clinic to the sequencing facility, robust and automated analysis of the long-read sequence data, and communication of results from sequence analysis in a reporting format that could be easily interpreted and acted upon by clinical experts. In a validation study, all 17 resistance mutations found by Sanger sequencing were also detected by long-read sequencing. An additional 16 mutations were found only by long-read sequencing, all of them with frequencies below the limit of detection for Sanger sequencing. The clonal distributions of co-existing mutations were automatically resolved through the long-read data analysis. After the implementation and validation, the clinical laboratory switched their routine protocol from using Sanger to long-read sequencing for this application.

Conclusions

Long-read sequencing delivers results with higher sensitivity compared to Sanger sequencing and enables earlier detection of emerging TKI resistance mutations. The developed processes, analysis workflow, and software components lower barriers for adoption and could be extended to other applications.

Thirty Years of Mungbean Genome Research: Where Do We Stand and What Have We Learned?

Mungbean is a socioeconomically important legume crop in Asia that is currently in high demand by consumers and industries both as dried beans and in plant-based protein foods. Marker-assisted and genomics-assisted breeding are promising approaches to efficiently and rapidly develop new cultivars with improved yield, quality, and resistance to biotic and abiotic stresses. Although mungbean was at the forefront of research at the dawn of the plant genomics era 30 years ago, the crop is a "slow runner" in genome research due to limited genomic resources, especially DNA markers. Significant progress in mungbean genome research was achieved only within the last 10 years, notably after the release of the VC1973A draft reference genome constructed using next-generation sequencing technology, which enabled fast and efficient DNA marker development, gene mapping, and identification of candidate genes for complex traits. Resistance to biotic stresses has dominated mungbean genome research to date; however, research is on the rise. In this study, we provide an overview of the past progress and current status of mungbean genomics research. We also discuss and evaluate some research results to provide a better understanding of mungbean genomics.

Microfluidics-Assisted Fluorescence Mapping of DNA Phosphorothioation

As the key player of a new restriction modification system, DNA phosphorothioate (PT) modification, which swaps oxygen for sulfur on the DNA backbone, protects the bacterial host from foreign DNA invasion. The identification of PT sites helps us understand its physiological defense mechanisms, but accurately quantifying this dynamic modification remains a challenge. Herein, we report a simple quantitative analysis method for optical mapping of PT sites in the single bacterial genome. DNA molecules are fully stretched and immobilized in a microfluidic chip by capillary flow and electrostatic interactions, improving the labeling efficiency by maximizing exposure of PT sites on DNA while avoiding DNA loss and damage. After screening 116 candidates, we identified a bifunctional chemical compound, iodoacetyl-polyethylene glycol-biotin, that can noninvasively and selectively biotinylate PT sites, enabling further labeling with streptavidin fluorescent nanoprobes. With this method, PT sites in PT⁺ DNA can be easily detected by fluorescence, while almost no detectable ones were found in PT^- DNA, achieving real-time visualization of PT sites on a single DNA molecule. Collectively, this facile genome-wide PT site detection method directly characterizes the distribution and frequency of DNA modification, facilitating a better understanding of its modification mechanism that can be potentially extended to label DNAs in different species.

K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data

In the mid-1970s, the first-generation sequencing technique (Sanger) was created. It used Advanced BioSystems sequencing devices and Beckman's GeXP genetic testing technology. The second-generation sequencing (2GS) technique arrived just several years after the first human genome was published in 2003. 2GS devices are very quicker than Sanger sequencing equipment, with considerably cheaper manufacturing costs and far higher throughput in the form of short reads. The third-generation sequencing (3GS) method, initially introduced in 2005, offers further reduced manufacturing costs and higher throughput. Even though sequencing technique has result generations, it is error-prone due to a large number of reads. The study of this massive amount of data will aid in the decoding of life secrets, the detection of infections, the development of improved crops, and the improvement of life quality, among other things. This is a challenging task, which is complicated not just by a large number of reads and by the occurrence of sequencing mistakes. As a result, error correction is a crucial duty in data processing; it entails identifying and correcting read errors. Various k-spectrum-based error correction algorithms' performance can be influenced by a variety of characteristics like coverage depth, read length, and genome size, as demonstrated in this work. As a result, time and effort must be put into selecting acceptable approaches for error correction of certain NGS data.

Simultaneous compression of multiple error-corrected short-read sets for faster data transmission and better de novo assemblies

Next-Generation Sequencing has produced incredible amounts of short-reads sequence data for de novo genome assembly over the last decades. For efficient transmission of these huge datasets, high-performance compression algorithms have been intensively studied. As both the de novo assembly and error correction methods utilize the overlaps between reads data, a concern is that the will the sequencing errors bring up negative effects on genome assemblies also affect the compression of the NGS data. This work addresses two problems: how current error correction algorithms can enable the compression algorithms to make the sequence data much more compact, and whether the sequence-modified reads by the error-correction algorithms will lead to quality improvement for de novo contig assembly. As multiple sets of short reads are often produced by a single biomedical project in practice, we propose a graph-based method to reorder the files in the collection of multiple sets and then compress them simultaneously for a further compression improvement after error correction. We use examples to illustrate that accurate error correction algorithms can significantly reduce the number of mismatched nucleotides in the reference-free compression, hence can greatly improve the compression performance. Extensive test on practical collections of multiple short-read sets does confirm that the compression performance on the error-corrected data (with unchanged size) significantly outperforms that on the original data, and that the file reordering idea contributes furthermore. The error correction on the original reads has also resulted in quality improvements of the genome assemblies, sometimes remarkably. However, it is still an open question that how to combine appropriate error correction methods with an assembly algorithm so that the assembly performance can be always significantly improved.

Recent Advances in Microscale Electroporation

Electroporation (EP) is a commonly used strategy to increase cell permeability for intracellular cargo delivery or irreversible cell membrane disruption using electric fields. In recent years, EP performance has been improved by shrinking electrodes and device structures to the microscale. Integration with microfluidics has led to the design of devices performing static EP, where cells are fixed in a defined region, or continuous EP, where cells constantly pass through the device. Each device type performs superior to conventional, macroscale EP devices while providing additional advantages in precision manipulation (static EP) and increased throughput (continuous EP). Microscale EP is gentle on cells and has enabled more sensitive assaying of cells with novel applications. In this Review, we present the physical principles of microscale EP devices and examine design trends in recent years. In addition, we discuss the use of reversible and irreversible EP in the development of therapeutics and analysis of intracellular contents, among other noteworthy applications. This Review aims to inform and encourage scientists and engineers to expand the use of efficient and versatile microscale EP technologies.

A Pipeline for the Development of Microsatellite Markers using Next Generation Sequencing Data

Background: Also known as Simple Sequence Repetitions (SSRs), microsatellites are profoundly informative molecular markers and powerful tools in genetics and ecology studies on plants. Objective: This research presents a workflow for developing microsatellite markers using genome skimming. Methods: The pipeline was proposed in several stages that must be performed sequentially: obtaining DNA sequences, identifying microsatellite regions, designing primers, and selecting candidate microsatellite regions to develop the markers. Our pipeline efficiency was analyzed using Illumina sequencing data from the non-model tree species Pterodon emarginatus Vog. Results: The pipeline revealed 4,382 microsatellite regions and drew 7,411 pairs of primers for P. emarginatus. However, a much larger number of microsatellite regions with the potential to develop markers were discovered from our pipeline. We selected 50 microsatellite regions with high potential for developing markers and organized 29 microsatellite regions in sets for multiplex PCR. Conclusion: The proposed pipeline is a powerful tool for fast and efficient development of microsatellite markers on a large scale in several species, especially nonmodel plant species.

A case report-application of pericardial effusion cytology and next-generation sequencing technology: quick and secure diagnosis of primary effusion lymphoma

Background

Primary effusion lymphoma (PEL) is an uncommon subtype of non-Hodgkin lymphoma (NHL) that usually involves the pleura, pericardium, and peritoneum without an obvious tumour mass, with multiple plasma effusions as its main clinical feature. We report a case of a massive pericardial effusion in an elderly male with a final diagnosis of PEL.

Case summary

A 70-year-old male patient was admitted to hospital with symptoms of chest tightness, shortness of breath, fatigue, loss of appetite, and cough with phlegm after a pericardial effusion had been found for 5 months. The next-generation sequencing of pericardial effusion found human herpesvirus type 8 (HHV-8) infection, and further cytomorphological and immunohistochemical examination were done. According to the patient's HHV-8 infection, the pathological features of heterogeneous B cells with plasmablastic differentiation and the immunohistochemical characteristics of PEL, the final diagnosis was made as human immunodeficiency virus-negative PEL.

Discussion

The diversity and non-specificity of PEL symptoms, as well as its rarity, make it difficult to diagnose. In this case, we used the next-generation sequencing technology to screen the pathogen of the patient's pericardial effusion and carried out morphological and immunohistochemical examination of the cells in the pericardial effusion, which provided a clinically operable diagnosis for an uncommon disease, enabling us to make a clear diagnosis faster and start treatment in time.

Comparison of High-Throughput Technologies in the Classification of Adult-Onset Still's Disease Patients

A meta-analysis study was conducted to compare high-throughput technologies in the classification of Adult-Onset Still's Disease patients, using differentially expressed genes from independent profiling experiments. We exploited two publicly available datasets from the Gene Expression Omnibus and performed a separate differential expression analysis on each dataset to extract statistically important genes. We then mapped the genes of the two datasets and subsequently we employed well-established machine learning algorithms to evaluate the denoted genes as candidate biomarkers. Using next-generation sequencing data, we managed to achieve the maximum (100%) classification accuracy, sensitivity and specificity with the Gradient Boosting and the Random Forest classifiers, compared to the 83% of the DNA microarray data. Clinical Relevance- When biomarkers derived from one study are applied to the data of another, in many cases the results may diverge significantly. Here we establish that in cross-profiling meta-analysis approaches based on differential expression analysis, next-generation sequencing data provide more accurate results than microarray experiments in the classification of Adult-Onset Still's Disease patients.

Two different aging paths in human blood revealed by integrated analysis of gene Expression, mutation and alternative splicing

Aging is a complex life process that human organs and tissues steadily and continuously decline. Aging has huge heterogeneity, which shows different aging rates among different individuals and in different tissues of the same individual. Many studies of aging are often contradictory and show little common signature. The integrated analysis of these transcriptome datasets will provide an unbiased global view of the aging process. Here, we integrated 8 transcriptome datasets including 757 samples from healthy human blood to study aging from three aspects of gene expression, mutations, and alternative splicing. Surprisingly, we found that transcriptome changes in blood are relatively independent of the chronological age. Further pseudotime analysis revealed two different aging paths (AgingPath1 and AgingPath2) in human blood. The differentially expressed genes (DEGs) along the two paths showed a limited overlap and are enriched in different biological processes. The mutations of DEGs in AgingPath1 are significantly increased in the aging process, while the opposite trend was observed in AgingPath2. Expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) analysis identified 304 important mutations that can affect both gene expression and alternative splicing during aging. Finally, by comparison between aging and Alzheimer's disease, we identified 37 common DEGs in AgingPath1, AgingPath2 and Alzheimer's disease. These genes may contribute to the shift from aging state to Alzheimer's disease. In summary, this study revealed the two aging paths and the related genes and mutations, which provides a new insight into aging and aging-related disease.

scPreGAN, a deep generative model for predicting the response of single-cell expression to perturbation

MOTIVATION

Rapid developments of single-cell RNA sequencing technologies allow study of responses to external perturbations at individual cell level. However, in many cases, it is hard to collect the perturbed cells, such as knowing the response of a cell type to the drug before actual medication to a patient. Prediction in silicon could alleviate the problem and save cost. Although several tools have been developed, their prediction accuracy leaves much room for improvement.

RESULTS

In this article, we propose scPreGAN (Single-Cell data Prediction base on GAN), a deep generative model for predicting the response of single-cell expression to perturbation. ScPreGAN integrates autoencoder and generative adversarial network, the former is to extract common information of the unperturbed data and the perturbed data, the latter is to predict the perturbed data. Experiments on three real datasets show that scPreGAN outperforms three state-of-the-art methods, which can capture the complicated distribution of cell expression and generate the prediction data with the same expression abundance as the real data.

AVAILABILITY AND IMPLEMENTATION

The implementation of scPreGAN is available via https://github.com/JaneJiayiDong/scPreGAN. To reproduce the results of this article, please visit https://github.com/JaneJiayiDong/scPreGAN-reproducibility.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Genomic Loci and Candidate Genes Related to Seed Tocopherol Content in Soybean

Soybean seeds are primary sources of natural tocopherols used by the food and pharmaceutical industries, owing to their beneficial impacts on human health. Selection for higher tocopherol contents in seeds along with other desirable traits is an important goal in soybean breeding. In order to identify the genomic loci and candidate genes controlling tocopherol content in soybean seeds, the bulked-segregant analysis technique was performed using a natural population of soybean consisting of 1525 accessions. We constructed the bulked-segregant analysis based on 98 soybean accessions that showed extreme phenotypic variation for the target trait, consisting of 49 accessions with extremely-high and 49 accessions with extremely-low tocopherol content. A total of 144 variant sites and 109 predicted genes related to tocopherol content were identified, in which a total of 83 genes were annotated by the gene ontology functions. Furthermore, 13 enriched terms (p < 0.05) were detected, with four of them found to be highly enriched: response to lipid, response to abscisic acid, transition metal ion transmembrane transporter activity, and double-stranded DNA binding. Especially, six candidate genes were detected at 41.8−41.9 Mb genomic hotspots on chromosome 5 based on ANNOtate VARiation analysis. Among the genes, only Glyma.05G243400 carried a non-synonymous mutation that encodes a “translation elongation factor EF1A or initiation factor IF2gamma family protein” was identified. The haplotype analysis confirmed that Glyma.05G243400 exhibited highly significant variations in terms of tocopherol content across multiple experimental locations, suggesting that it can be the key candidate gene regulating soybean seed tocopherols. The present findings provide novel gene resources related to seed tocopherols for further validation by genome editing, functional characterization, and genetic improvement targeting enhanced tocopherol composition in soybean molecular breeding.

Capsid integrity detection of pathogenic viruses in waters: Recent progress and potential future applications

Waterborne diseases caused by pathogenic human viruses are a major public health concern. To control the potential risk of viral infection through contaminated waters, a rapid, reliable tool to assess the infectivity of pathogenic viruses is required. Recently, an advanced approach (i.e., capsid integrity (RT-)qPCR) was developed to discriminate intact viruses (potentially infectious) from inactivated viruses. In this approach, samples were pretreated with capsid integrity reagents (e.g., monoazide dyes or metal compounds) before (RT -)qPCR. These reagents can only penetrate inactivated viruses with compromised capsids to bind to viral genomes and prevent their amplification, but they cannot enter viruses with intact capsids. Therefore, only viral genomes of intact viruses were amplified or detected by (RT-)qPCR after capsid integrity treatment. In this study, we reviewed recent progress in the development and application of capsid integrity (RT-)qPCR to assess the potential infectivity of viruses (including non-enveloped and enveloped viruses with different genome structures [RNA and DNA]) in water. The efficiency of capsid integrity (RT-)qPCR has been shown to depend on various factors, such as conditions of integrity reagent treatment, types of viruses, environmental matrices, and the capsid structure of viruses after disinfection treatments (e.g., UV, heat, and chlorine). For the application of capsid integrity (RT-)qPCR in real-world samples, the use of suitable virus concentration methods and process controls is important to control the efficiency of capsid integrity (RT-)qPCR. In addition, potential future applications of capsid integrity (RT-)qPCR for determining the mechanism of disinfection treatment on viral structure (e.g., capsid or genome) and a combination of capsid integrity treatment and next-generation sequencing (NGS) (capsid integrity NGS) for monitoring the community of intact pathogenic viruses in water are also discussed. This review provides essential information on the application of capsid integrity (RT-)qPCR as an efficient tool for monitoring the presence of pathogenic viruses with intact capsids in water.

A single substitution in Vacuolar protein sorting 4 is responsible for resistance to Watermelon mosaic virus in melon

In plants, introgression of genetic resistance is a proven strategy for developing new resistant lines. While host proteins involved in genome replication and cell to cell movement are widely studied, other cell mechanisms responsible for virus infection remain under investigated. Endosomal sorting complexes required for transport (ESCRT) play a key role in membrane trafficking in plants and are involved in the replication of several plant RNA viruses. In this work, we describe the role of the ESCRT protein CmVPS4 as a new susceptibility factor to the Potyvirus Watermelon mosaic virus (WMV) in melon. Using a worldwide collection of melons, we identified three different alleles carrying non-synonymous substitutions in CmVps4. Two of these alleles were shown to be associated with WMV resistance. Using a complementation approach, we demonstrated that resistance is due to a single non-synonymous substitution in the allele CmVps4P30R. This work opens up new avenues of research on a new family of host factors required for virus infection and new targets for resistance.

WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering

Copy number variation (CNV) detection based on second-generation sequencing technology is the basis of much gene research, but the read depth is affected by mapping errors, repeated reads, and GC bias. The existing methods have low sensitivity to variation regions with a short length and small variation range. Therefore, it is necessary to improve the sensitivity of algorithms to short-variation fragments. This study proposes a new CNV-detection method named WAVECNV to solve this issue. The algorithm uses wavelet clustering to process the read depth and determine the normal cluster and abnormal cluster according to the size of the cluster. Then, according to the distance between genome bins and normal clusters, the outlier of each genome bin is evaluated. Finally, a statistical model is established, and the p-value test is used for calling CNVs. Through this method, the information of the short variation region is retained. WAVECNV was tested and compared with peer methods in terms of simulated data and real cancer-sequencing data. The results show that the sensitivity of WAVECNV is better than the existing methods. It also has high precision in data with low purity and coverage. In real data experiments, WAVECNV can detect more cancer genes than existing methods. Therefore, this method can be regarded as a conventional method in the field of genomic mutation analysis of cancer samples.

Two Genetic Mechanisms in Two Siblings with Intellectual Disability, Autism Spectrum Disorder, and Psychosis

Intellectual disability (ID) and autism spectrum disorder (ASD) are complex neurodevelopmental disorders with high heritability. To search for the genetic deficits in two siblings affected with ID and ASD in a family, we first performed a genome-wide copy number variation (CNV) analysis using chromosomal microarray analysis (CMA). We found a 3.7 Mb microdeletion at 22q13.3 in the younger sister. This de novo microdeletion resulted in the haploinsufficiency of SHANK3 and several nearby genes involved in neurodevelopment disorders. Hence, she was diagnosed with Phelan–McDermid syndrome (PMS, OMIM#606232). We further performed whole-genome sequencing (WGS) analysis in this family. We did not detect pathogenic mutations with significant impacts on the phenotypes of the elder brother. Instead, we identified several rare, likely pathogenic variants in seven genes implicated in neurodevelopmental disorders: KLHL17, TDO2, TRRAP, EIF3F, ATP10A, DICER1, and CDH15. These variants were transmitted from his unaffected parents, indicating these variants have only moderate clinical effects. We propose that these variants worked together and led to the clinical phenotypes in the elder brother. We also suggest that the combination of multiple genes with moderate effects is part of the genetic mechanism of neurodevelopmental disorders.