The impact of next-generation sequencing on genomics

Comprehensive genome analysis and variant detection at scale using DRAGEN

Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform.

Genetic testing for diffuse lung diseases in children

Newly developing genomic technologies are an increasingly important part of clinical care and thus, it is not only important to understand the technologies and their limitations, but to also interpret the findings in an actionable fashion. Clinical geneticists and genetic counselors are now an integral part of the clinical team and are able to bridge the complexities of this rapidly changing science between the bedside clinicians and patients. This manuscript reviews the terminology, the current technology, some of the known genetic disorders that result in lung disease, and indications for genetic testing with associated caveats. Because this field is evolving quickly, we also provide links to websites that provide continuously updated information important for integrating genomic technology results into clinical decision-making.

Unlocking biological mechanisms with integrative functional genomics approaches

Reverse genetics offers precise functional insights into genes through the targeted manipulation of gene expression followed by phenotypic assessment. While these approaches have proven effective in model organisms such as Saccharomyces cerevisiae, large-scale genetic manipulations in human cells were historically unfeasible due to methodological limitations. However, recent advancements in functional genomics, particularly clustered regularly interspaced short palindromic repeats (CRISPR)-based screening technologies and next-generation sequencing platforms, have enabled pooled screening technologies that allow massively parallel, unbiased assessments of biological phenomena in human cells. This review provides a comprehensive overview of cutting-edge functional genomic screening technologies applicable to human cells, ranging from short hairpin RNA screens to modern CRISPR screens. Additionally, we explore the integration of CRISPR platforms with single-cell approaches to monitor gene expression, chromatin accessibility, epigenetic regulation, and chromatin architecture following genetic perturbations at the omics level. By offering an in-depth understanding of these genomic screening methods, this review aims to provide insights into more targeted and effective strategies for genomic research and personalized medicine.

Study of tree shrew biology and models: A booming and prosperous field for biomedical research

The tree shrew ( Tupaia belangeri) has long been proposed as a suitable alternative to non-human primates (NHPs) in biomedical and laboratory research due to its close evolutionary relationship with primates. In recent years, significant advances have facilitated tree shrew studies, including the determination of the tree shrew genome, genetic manipulation using spermatogonial stem cells, viral vector-mediated gene delivery, and mapping of the tree shrew brain atlas. However, the limited availability of tree shrews globally remains a substantial challenge in the field. Additionally, determining the key questions best answered using tree shrews constitutes another difficulty. Tree shrew models have historically been used to study hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, myopia, and psychosocial stress-induced depression, with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases. Despite these efforts, the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research. This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model. We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies. The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models, meeting the increasing demands of life science and biomedical research.

Tidings from the Tides-De novo transcriptome assembly of the endemic estuarine bivalve Villorita cyprinoides

The Indian black clam Villorita cyprinoides Gray, 1825, is an economically valuable estuarine bivalve that faces challenges from multiple stressors and anthropogenic pressures. However, limited genomic resources have hindered molecular investigations into the impact of these stressors on clam populations. Here, we have generated the first transcriptomic reference datasets for V. cyprinoides to address this knowledge gap. A total of 25,040,592 and 22,486,217 million Illumina paired-end reads generated from two individuals were assembled using Trinity and rnaSPAdes. From the 47,607 transcripts identified as Coding Domain Sequences, 37,487 returned positive BLAST hits against six different databases. Additionally, a total of 14,063 Single Sequence Repeats were identified using GMATA. This study significantly enhances the genetic understanding of V. cyprinoides, a potential candidate for aquaculture that supports the livelihoods of many people dependent on small-scale fisheries. The data generated provides insights into broader genealogical connections within the family Cyrenidae through comparative transcriptomics. Furthermore, this transcriptional profile serves as baseline data for future studies in toxicological and conservation genetics.

Transcriptomic Insights and the Development of Microsatellite Markers to Assess Genetic Diversity in the Broodstock Management of Litopenaeus stylirostris

The Pacific blue shrimp (Litopenaeus stylirostris) is a premium product in the international seafood market. However, intensified farming has increased disease incidence and reduced genetic diversity. In this study, we developed a transcriptome database for L. stylirostris and mined microsatellite markers to analyze their genetic diversity. Using the Illumina HiSeq 4000 platform, we identified 53,263 unigenes from muscle, hepatopancreas, the intestine, and lymphoid tissues. Microsatellite analysis identified 36,415 markers from 18,657 unigenes, predominantly dinucleotide repeats. Functional annotation highlighted key disease resistance pathways and enriched categories. The screening and PCR testing of 42 transcriptome-based and 58 literature-based markers identified 40 with successful amplification. The genotyping of 200 broodstock samples revealed that Na, Ho, He, PIC, and FIS values were 3, 0.54 ± 0.05, 0.43 ± 0.09, 0.41 ± 0.22, and 0.17 ± 0.27, respectively, indicating moderate genetic variability and significant inbreeding. Four universal microsatellite markers (CL1472.Contig13, CL517.Contig2, Unigene5692, and Unigene7147) were identified for precise diversity analysis in Pacific blue, Pacific white (Litopenaeus vannamei), and black tiger shrimps (Penaeus monodon). The transcriptome database supports the development of markers and functional gene analysis for selective breeding programs. Our findings underscore the need for an appropriate genetic management system to mitigate inbreeding depression, reduce disease susceptibility, and preserve genetic diversity in farmed shrimp populations.

A Two-color Single-molecule Sequencing Platform and Its Clinical Applications

DNA sequencers have become increasingly important research and diagnostic tools over the past 20 years. In this study, we developed a single-molecule desktop sequencer, GenoCare 1600 (GenoCare), which utilizes amplification-free library preparation and two-color sequencing-by-synthesis chemistry, making it more user-friendly compared with previous single-molecule sequencing platforms for clinical use. Using the GenoCare platform, we sequenced an Escherichia coli standard sample and achieved a consensus accuracy exceeding 99.99%. We also evaluated the sequencing performance of this platform in microbial mixtures and coronavirus disease 2019 (COVID-19) samples from throat swabs. Our findings indicate that the GenoCare platform allows for microbial quantitation, sensitive identification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and accurate detection of virus mutations, as confirmed by Sanger sequencing, demonstrating its remarkable potential in clinical application.

A Review of Transcriptomics and Metabolomics in Plant Quality and Environmental Response: From Bibliometric Analysis to Science Mapping and Future Trends

Transcriptomics and metabolomics offer distinct advantages in investigating the differentially expressed genes and cellular entities that have the greatest influence on end-phenotype, making them crucial techniques for studying plant quality and environmental responses. While numerous relevant articles have been published, a comprehensive summary is currently lacking. This review aimed to understand the global and longitudinal research trends of transcriptomics and metabolomics in plant quality and environmental response (TMPQE). Utilizing bibliometric methods, we presented a comprehensive science mapping of the social structure, conceptual framework, and intellectual foundation of TMPQE. We uncovered that TMPQE research has been categorized into three distinct stages since 2020. A citation analysis of the 29 most cited articles, coupled with a content analysis of recent works (2020-2023), highlight five potential research streams in plant quality and environmental responses: (1) biosynthetic pathways, (2) abiotic stress, (3) biotic stress, (4) development and ripening, and (5) methodologies and tools. Current trends and future directions are shaped by technological advancements, species diversity, evolving research themes, and an environmental ecology focus. Overall, this review provides a novel and comprehensive perspective to understand the longitudinal trend on TMPQE.

Next-Generation Sequencing Trends among Adult Patients with Select Advanced Tumor Types: A Real-World Evidence Evaluation

There are limited data on the prevalence of next-generation sequencing (NGS) in the United States, especially in light of the increasing importance of identifying actionable oncogenic variants due to molecular biomarker-based therapy approvals. This retrospective study of adult patients with select metastatic solid tumors and central nervous system tumors from the Optum Clinformatics Data Mart US health care claims database (January 1, 2014, to June 30, 2021; N = 63,209) examined NGS use trends over time. A modest increase in NGS was observed across tumor types from 2015 (0.0% to 1.5%) to 2021 (2.1% to 17.4%). A similar increase in NGS rates was also observed across key periods; however, rates in the final key period remained <10% for patients with breast, colorectal, head and neck, soft tissue sarcoma, and thyroid cancers, as well as central nervous system tumors. The median time to NGS from diagnosis was shortest among patients with non-small-cell lung cancer and longest for patients with breast cancer. Predictors of NGS varied by tumor type; test rates for minorities in select tumor types appeared comparable to the White population. Despite improving payer policies to expand coverage of NGS and molecular biomarker-based therapy approvals, NGS rates remained low across tumor types. Given the potential for improved patient outcomes with molecular biomarker-based therapy, further efforts to improve NGS rates are warranted.

A whole-body transcriptome assembly of the annelid worm Hediste diversicolor

The Annelida phylum is composed of a myriad of species exhibiting key phenotypic adaptations. They occupy key ecological niches in a variety of marine, freshwater and terrestrial ecosystems. Importantly, the increment of omic resources is rapidly modifying the taxonomic landscape and knowledge of species belonging to this phylum. Here, we comprehensively characterised and annotated a transcriptome of the common ragworm, Hediste diversicolor (OF Müller). This species belongs to the family Nereididae and inhabits estuarine and lagoon areas on the Atlantic coasts of Europe and North America. Ecologically, H. diversicolor plays an important role in benthic food webs. Given its commercial value, H. diversicolor is a promising candidate for aquaculture development and production in farming facilities, under a circular economy framework. We used Illumina next-generation sequencing technology, to produce a total of 105 million (M) paired-end (PE) raw reads and generate the first whole-body transcriptome assembly of H. diversicolor species. This high-quality transcriptome contains 69,335 transcripts with an N50 transcript length of 2313 bp and achieved a BUSCO gene completeness of 97.7% and 96% in Eukaryota and Metazoa lineage-specific profile libraries. Our findings offer a valuable resource for multiple biological applications using this species.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Circulating MicroRNA-19 and cardiovascular risk reduction in response to weight-loss diets

OBJECTIVE

MicroRNA-19 (miR-19) plays a critical role in cardiac development and cardiovascular disease (CVD). We examined whether change in circulating miR-19 was associated with change in CVD risk during weight loss.

METHODS

This study included 509 participants with overweight or obesity from the 24-month weight-loss diet intervention study (the POUNDS Lost trial) and with available data on circulating miR-19a-3p and miR-19b-3p at baseline and 6 months. The primary outcome for this analysis was the change in atherosclerotic CVD (ASCVD) risk at 6 and 24 months, which estimates the 10-year probability of hard ASCVD events. Secondary outcomes were the changes in ASCVD risk score components.

RESULTS

Circulating miR-19a-3p and miR-19b-3p levels significantly decreased during the initial 6-month dietary intervention period (P = 0.008, 0.0004, respectively). We found that a greater decrease in miR-19a-3p or miR-19b-3p was related to a greater reduction in ASCVD risk (β[SE] = 0.33 [0.13], P = 0.01 for miR-19a-3p; β[SE] = 0.3 [0.12], P = 0.017 for miR-19b-3p) over 6 months, independent of concurrent weight loss. Moreover, we found significant interactions between change in miR-19 and sleep disturbance on change in ASCVD risk over 24 months of intervention (P interaction = 0.01 and 0.008 for miR-19a-3p and miR-19b-3p, respectively). Participants with a greater decrease in miR-19 without sleep disturbance had a greater reduction of ASCVD risk than those with slight/moderate/great amounts of sleep disturbance. In addition, change in physical activity significantly modified the associations between change in miR-19 and change in ASCVD risk over 24 months (P interaction = 0.006 and 0.004 for miR-19a-3p and miR-19b-3p, respectively). A greater decrease in miR-19 was significantly associated with a greater reduction in ASCVD risk among participants with an increase in physical activity, while non-significant inverse associations were observed among those without an increase in physical activity.

CONCLUSIONS

In conclusion, decreased circulating miR-19 levels during dietary weight-loss interventions were related to a significant reduction in ASCVD risk, and these associations were more evident in people with no sleep disturbance or increase in physical activity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00072995.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies

Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.

Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms

Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size (e.g., SNV/SV) or location (e.g., repeats). Here we present DRAGEN that utilizes novel methods based on multigenomes, hardware acceleration, and machine learning based variant detection to provide novel insights into individual genomes with ~30min computation time (from raw reads to variant detection). DRAGEN outperforms all other state-of-the-art methods in speed and accuracy across all variant types (SNV, indel, STR, SV, CNV) and further incorporates specialized methods to obtain key insights in medically relevant genes (e.g., HLA, SMN, GBA). We showcase DRAGEN across 3,202 genomes and demonstrate its scalability, accuracy, and innovations to further advance the integration of comprehensive genomics for research and medical applications.

A comparative investigation of single nucleotide variant calling for a personal non-Caucasian sequencing sample

BACKGROUND

Dropping cost and increasing clinical application of whole genome sequencing (WGS) lead a necessity of efficient (accurate and rapid) variant calling procedures from a personal WGS data (n = 1). A number of variant calling pipelines have been introduced utilizing the human genome reference GRCh38 as a reference and a benchmark dataset called 'NA12878', which are both 'standard' but limited ethnic origin. Considering the nature of variant calling algorithms and recent updates in sequencing protocol, however, it is necessary to revisit the efficiency of the current best pipelines for a personal WGS data from diverse ethnicity.

OBJECTIVE

We discuss the most efficient practices for variant calling of a personal WGS reads, with a particular emphasis on whether (1) ethnic match or mismatch between the reference genome and a WGS data produces a distinct result and more importantly (2) there is an ethnic-specific optimal workflow.

METHODS

Here, we generate an appropriate WGS data, DNA array, and sufficient number of Sanger validated variants from a single Korean subject to perform such a comprehensive comparison. We applied this WGS reads and the 'NA12878' reads to 8 different variant calling pipelines with 2 different reference genomes (GRCh38 and KOREF, a Korean reference genome) to which the WGS reads from different ethnic origins are aligned.

RESULTS

We evaluated the performance of the pipelines with the matched array genotype data and Sanger sequencing validation and demonstrated that: regardless to the ethnic match/mismatch (1) Novoalign-GATK4 showed the most efficient performance with the exceptional calls in MHC region; (2) the overall performance was better with GRCh38, while a significant difference in recall was observed. In addition, we found it is largely reduced computing cost maintaining performance to remove 'markduplication' step with PCR-free WGS data.

CONCLUSION

For variant calling of a personal PCR-free WGS data, regardless of ethnicity consideration, we recommend the use of the Novoalign + GATK4 with GRCh38 and without 'markduplication'.

Challenges for Plant Growth Promoting Microorganism Transfer from Science to Industry: A Case Study from Chile

Research on the plant growth promoting microorganisms (PGPM) is increasing strongly due to the biotechnological potential for the agricultural, forestry, and food industry. The benefits of using PGPM in crop production are well proven; however, their incorporation in agricultural management is still limited. Therefore, we wanted to explore the gaps and challenges for the transfer of biotechnological innovations based on PGPM to the agricultural sector. Our systematic review of the state of the art of PGPM research and knowledge transfer takes Chile as an example. Several transfer limiting aspects are identified and discussed. Our two main conclusions are: neither academia nor industry can meet unfounded expectations during technology transfer, but mutually clarifying their needs, capabilities, and limitations is the starting point for successful collaborations; the generation of a collaborative innovation environment, where academia as well as public and private stakeholders (including the local community) take part, is crucial to enhance the acceptance and integration of PGPM on the way to sustainable agriculture.

A comparative investigation of variant calling and genotyping for a single non-Caucasian whole genome

Most genome benchmark studies utilize hg38 as a reference genome (based on Caucasian and African samples) and 'NA12878' (a Caucasian sequencing read) for comparison. Here, we aimed to elucidate whether 1) ethnic match or mismatch between the reference genome and sequencing reads produces a distinct result; 2) there is an optimal work flow for single genome data. We assessed the performance of variant calling pipelines using hg38 and a Korean genome (reference genomes) and two whole-genome sequencing (WGS) reads from different ethnic origins: Caucasian (NA12878) and Korean. The pipelines used BWA-mem and Novoalign as mapping tools and GATK4, Strelka2, DeepVariant, and Samtools as variant callers. Using hg38 led to better performance (based on precision and recall), regardless of the ethnic origin of the WGS reads. Novoalign + GATK4 demonstrated best performance when using both WGS data. We assessed pipeline efficiency by removing the markduplicate process, and all pipelines, except Novoalign + DeepVariant, maintained their performance. Novoalign identified more variants overall and in MHC of chr6 when combined with GATK4. No evidence suggested improved variant calling performance from single WGS reads with a different ethnic reference, re-validating hg38 utility. We recommend using Novoalign + GATK4 without markduplication for single PCR-free WGS data.

Full-Length Transcriptome of the Great Himalayan Leaf-Nosed Bats (Hipposideros armiger) Optimized Genome Annotation and Revealed the Expression of Novel Genes

The Great Himalayan Leaf-nosed bat (Hipposideros armiger) is one of the most representative species of all echolocating bats and is an ideal model for studying the echolocation system of bats. An incomplete reference genome and limited availability of full-length cDNAs have hindered the identification of alternatively spliced transcripts, which slowed down related basic studies on bats' echolocation and evolution. In this study, we analyzed five organs from H. armiger for the first time using PacBio single-molecule real-time sequencing (SMRT). There were 120 GB of subreads generated, including 1,472,058 full-length non-chimeric (FLNC) sequences. A total of 34,611 alternative splicing (AS) events and 66,010 Alternative Polyadenylation (APA) sites were detected by transcriptome structural analysis. Moreover, a total of 110,611 isoforms were identified, consisting of 52% new isoforms of known genes and 5% of novel gene loci, as well as 2112 novel genes that have not been annotated before in the current reference genome of H. armiger. Furthermore, several key novel genes, including Pol, RAS, NFKB1, and CAMK4, were identified as being associated with nervous, signal transduction, and immune system processes, which may be involved in regulating the auditory nervous perception and immune system that helps bats to regulate in echolocation. In conclusion, the full-length transcriptome results optimized and replenished existing H. armiger genome annotation in multiple ways and offer advantages for newly discovered or previously unrecognized protein-coding genes and isoforms, which can be used as a reference resource.

Transcriptome Analysis of Differentially Expressed Genes Associated with Salt Stress in Cowpea (Vigna unguiculata L.) during the Early Vegetative Stage

Cowpea (Vigna unguiculata (L.), 2n = 22) is a tropical crop grown in arid and semiarid regions that is tolerant to abiotic stresses such as heat and drought. However, in these regions, salt in the soil is generally not eluted by rainwater, leading to salt stress for a variety of plant species. This study was conducted to identify genes related to salt stress using the comparative transcriptome analysis of cowpea germplasms with contrasting salt tolerance. Using the Illumina Novaseq 6000 platform, 1.1 billion high-quality short reads, with a total length of over 98.6 billion bp, were obtained from four cowpea germplasms. Of the differentially expressed genes identified for each salt tolerance type following RNA sequencing, 27 were shown to exhibit significant expression levels. These candidate genes were subsequently narrowed down using reference-sequencing analysis, and two salt stress-related genes (Vigun_02G076100 and Vigun_08G125100) with single-nucleotide polymorphism (SNP) variation were selected. Of the five SNPs identified in Vigun_02G076100, one that caused significant amino acid variation was identified, while all nucleotide variations in Vigun_08G125100 was classified as missing in the salt-resistant germplasms. The candidate genes and their variation, identified in this study provide, useful information for the development of molecular markers for cowpea breeding programs.

Genome-wide assessment of the population structure and genetic diversity of four Portuguese native sheep breeds

As the effects of global warming become increasingly complex and difficult to manage, the conservation and sustainable use of locally adapted sheep breeds are gaining ground. Portuguese native sheep breeds are important reservoirs of genetic diversity, highly adapted to harsh environments and reared in low input production systems. Genomic data that would describe the breeds in detail and accelerate the selection of more resilient animals to be able to cope with climatic challenges are still lacking. Here, we sequenced the genomes of 37 animals from four Portuguese native sheep breeds (Campaniça, Bordaleira Serra da Estrela, Merino Branco and Merino Preto) and 19 crossbred sheep to make inferences on their genomic diversity and population structure. Mean genomic diversities were very similar across these breeds (.30 ≤ H_o ≤ .34; .30 ≤ H_e ≤ .35; 1.7 × 10^-3 ≤ π ≤ 3.1 × 10^-3) and the levels of inbreeding were negligible (.005 ≤ F_IS ≤ .038). The Principal Components, Bayesian clustering and Treemix analyses split the Portuguese breeds in two main groups which are consistent with historical records: one comprising Campaniça and Serra da Estrela together with other European and transboundary dairy breeds; and another of the well-differentiated multi-purpose Merino and Merino-related breeds. Runs of homozygosity analyses yielded 1,690 ROH segments covering an average of 2.27 Gb across the genome in all individuals. The overall genome covered by ROH segments varied from 27,75 Mb in Serra da Estrela to 61,29 Mb in Campaniça. The phylogenetic analysis of sheep mitogenomes grouped the Portuguese native breeds within sub-haplogroup B1a along with two animals of the Akkaraman breed from Turkey. This result provides additional support to a direct influence of Southwest Asian sheep in local breeds from the Iberian Peninsula. Our study is a first step pertaining to the genomic characterization of Portuguese sheep breeds and the results emphasize the potential of genomic data as a valid tool to guide conservation efforts in locally adapted sheep breeds. In addition, the genomic data we generated can be used to identify markers for breed assignment and traceability of certified breed-products.

RNA-Seq and genetic diversity analysis of faba bean (Vicia faba L.) varieties in China

Background

Faba bean (Vicia faba L) is one of the most important legumes in the world. However, there is relatively little genomic information available for this species owing to its large genome. The lack of data impedes the discovery of molecular markers and subsequent genetic research in faba bean. The objective of this study was to analyze the faba bean transcriptome, and to develop simple sequence repeat (SSR) markers to determine the genetic diversity of 226 faba bean varieties derived from different regions in China.

Methods

Faba bean varieties with different phenotype were used in transcriptome analysis. The functions of the unigenes were analyzed using various database. SSR markers were developed and the polymorphic markers were selected to conduct genetic diversity analysis.

Results

A total of 92.43 Gb of sequencing data was obtained in this study, and 133,487 unigene sequences with a total length of 178,152,541 bp were assembled. A total of 5,200 SSR markers were developed on the basis of RNA-Seq analysis. Then, 200 SSR markers were used to evaluate polymorphisms. In total, 103 (51.5%) SSR markers showed significant and repeatable bands between different faba bean varieties. Clustering analysis revealed that 226 faba bean materials were divided into five groups. Genetic diversity analysis revealed that the relationship between different faba beans in China was related, especially in the same region. These results provided a valuable data resource for annotating genes to different categories and developing SSR markers.

Decoding the microbial universe with metagenomics: a brief insight

A major part of any biological system on earth involves microorganisms, of which the majority are yet to be cultured. The conventional methods of culturing microbes have given fruitful outcomes yet have limitations. The curiosity for better understanding has led to the development of culture-independent molecular methods that help push aside the roadblocks of earlier methods. Metagenomics unifies the scientific community in search of a better understanding of the functioning of the ecosystem and its component organisms. This approach has opened a new paradigm in advanced research. It has brought to light the vast diversity and novelty among microbial communities and their genomes. This review focuses on the development of this field over time, the techniques and analysis of data generated through sequencing platforms, and its prominent interpretation and representation.

Many Common Pathogens are Present in the Operative Room Air During Surgery

BACKGROUND

The main objective of this study was to assess the sanitary measures of operating theaters using next-generation sequencing.

METHODS

Air was sampled from the operating room for the whole duration of 10 surgical days of "clean" (no infection cases) procedures (6 hip/knee arthroplasty and 4 spine cases). Controls consisted of samples at the beginning of the day (1 hour before the surgery started) and at the end of the day after terminal cleaning. One additional control sample, consisting of a culture swab that was opened and exposed to the air for 5 seconds, was collected at each time point. All samples were sent for next-generation sequencing analysis (16S rRNA sequencing) for bacterial identification.

RESULTS

Overall, 306 samples were collected (159 controls and 147 experimental). Microbial DNA was detected in only 1 control sample, while 18 (12.2%) experimental samples were positive for microbial DNA. The most common organisms retrieved were Escherichia coli (6/18, 30%), Cutibacterium acnes (3/18, 15%), and Pseudomonas aeruginosa (2/18, 11.1%). There was no difference in positive samples between arthroplasty and spine cases (P > .05).

CONCLUSION

Microbial organisms are not uncommonly present in the operating room air during hip and knee arthroplasties and spine procedures.

A sensitive and inexpensive high-resolution melting-based testing algorithm for diagnosis of transient abnormal myelopoiesis and myeloid leukemia of Down syndrome

Patients with Down syndrome (DS) are commonly affected by a pre-leukemic disorder known as transient abnormal myelopoiesis (TAM). This condition usually undergoes spontaneous remission within the first 2 months after birth; however, in children under 5, 20%-30% of cases evolve to myeloid leukemia of Down syndrome (ML-DS). TAM and ML-DS are caused by co-operation between trisomy 21 and acquired mutations in the GATA1 gene. Currently, only next-generation sequencing (NGS)-based methodologies are sufficiently sensitive for diagnosis in samples with small GATA1 mutant clones (≤10% blasts). Alternatively, this study presents research on a new, fast, sensitive, and inexpensive high-resolution melting (HRM)-based diagnostic approach that allows the detection of most cases of GATA1 mutations, including silent TAM. The algorithm first uses flow cytometry for blast count, followed by HRM and Sanger sequencing to search for mutations on exons 2 and 3 of GATA1. We analyzed 138 samples of DS patients: 110 of asymptomatic neonates, 10 suspected of having TAM, and 18 suspected of having ML-DS. Our algorithm enabled the identification of 33 mutant samples, among them five cases of silent TAM (5/110) and seven cases of ML-DS (7/18) with blast count ≤10%, in which GATA1 alterations were easily detected by HRM. Depending on the type of genetic variation and its location, our methodology reached sensitivity similar to that obtained by NGS (0.3%) at a considerably reduced time and cost, thus making it accessible worldwide.

iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features

BACKGROUND

Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec - an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets.

RESULTS

The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency.

CONCLUSIONS

iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec .

Next-generation sequencing in the biodiversity conservation of endangered medicinal plants

Medicinal plants have been used as traditional herbal medicines in the treatment of various types of diseases. However, the increased demand for these plants highlights the importance of conservation specifically for endangered species. Significant advancements in next-generation sequencing (NGS) technologies have accelerated medicinal plant research while reducing costs and time demands. NGS systems enable high-throughput whole genome sequencing as well as direct RNA sequencing and transcriptome analysis. The sequence data sets created can be used in a variety of areas of study, including biodiversity conservation, comparative genomics, transcriptomic analysis, single cell mining, metagenomics, epigenetics, molecular marker discovery, multi genome sequencing, and so on. Commercial sequencing service providers are constantly working to improve technologies to address bioinformatics problems in NGS data analysis. Several genome sequencing projects on medicinal plants have been completed recently and a few more are in the works. In some medicinal plants, massive NGS-based data has been developed. In the present review, we have attempted to briefly discuss advancements in NGS technology on medicinally essential plants in India. The review will also provide ideas for applying NGS technologies for exploring genomes of various endangered medicinal plants whose genome sequences are not normally available and thus provides valuable insights for the conservation of these vulnerable species.

Effective Identification of Bacterial Genomes From Short and Long Read Sequencing Data

With the development of sequencing technology, microbiological genome sequencing analysis has attracted extensive attention. For inexperienced users without sufficient bioinformatics skills, making sense of sequencing data for microbial identification, especially for bacterial identification, through reads analysis is still challenging. In order to address the challenge of effectively analyzing genomic information, in this paper, we develop an effective approach and automatic bioinformatics pipeline called PBGI for bacterial genome identification, performing automatedly and customized bioinformatics analysis using short-reads or long-reads sequencing data produced by multiple platforms such as Illumina, PacBio and Oxford Nanopore. An evaluation of the proposed approach on the practical data set is presented, showing that PBGI provides a user-friendly way to perform bacterial identification through short or long reads analysis, and could provide accurate analyzing results. The source code of the PBGI is freely available at https://github.com/lyotvincent/PBGI.

Rh Blood Group D Antigen Genotyping Using a Portable Nanopore-based Sequencing Device: Proof of Principle

BACKGROUND

Nanopore sequencing is direct sequencing of a single-stranded DNA molecule using biological pores. A portable nanopore-based sequencing device from Oxford Nanopore Technologies (MinION) depends on driving a DNA molecule through nanopores embedded in a membrane using a voltage. Changes in current are then measured by a sensor, thousands of times per second and translated to nucleobases.

METHODS

Genomic DNA (gDNA) samples (n = 13) were tested for Rh blood group D antigen (RHD) gene zygosity using droplet digital PCR. The RHD gene was amplified in 6 overlapping amplicons using long-range PCR. Amplicons were purified, and the sequencing library was prepared following the 1D Native barcoding gDNA protocol. Sequencing was carried out with 1D flow cells R9 version. Data analysis included basecalling, aligning to the RHD reference sequence, and calling variants. Variants detected were compared to the results acquired previously by the Ion Personal Genome Machine (Ion PGM).

RESULTS

Up to 500× sequence coverage across the RHD gene allowed accurate variant calling. Exonic changes in the RHD gene allowed RHD allele determination for all samples sequenced except 1 RHD homozygous sample, where 2 heterozygous RHD variant alleles are suspected. There were 3 known variant RHD alleles (RHD*01W.02, RHD*11, and RHD*15) and 6 novel RHD variant alleles, as previously seen in Ion PGM sequencing data for these samples.

CONCLUSIONS

MinION was effective in blood group genotyping, provided enough sequencing data to achieve high coverage of the RHD gene, and enabled confident calling of variants and RHD allele determination.

Integrated driver mutations profile of chinese gastrointestinal-natural killer/T-cell lymphoma

Background

One of the most common nasal external sites in extranodal Natural Killer/T-cell lymphoma (NKTCL) is in the gastrointestinal (GI) system. Despite this, reports on gastrointestinal-Natural Killer/T-cell lymphoma (GI-NKTCL) are very few. To obtain a better understanding of this manifestation of NKTCL, we conducted a retrospective study on GI-NKTCL to analyze its clinical features, genomic changes and immune infiltration.

Methods

We retrospectively collected patients diagnosed with GI-NKTCL in the Sixth Affiliated Hospital of Sun Yat-sen University from 2010 to 2020. From this cohort we obtained mutation data via whole exome sequencing.

Results

Genomic analysis from 15 patients with GI-NKTCL showed that the most common driving mutations were ARID1B(14%, 2/15), ERBB3(14%, 2/15), POT1(14%, 2/15), and TP53(14%, 2/15). In addition, we found the most common gene mutation in patients with GI-NKTCL to be RETSAT(29%, 4/15) and SNRNP70(21%, 3/15), and the most common hallmark pathway mutations to be G2M checkpoint pathway (10/15, 66.7%), E2F targets (8/15, 53.3%), estrogen response late (7/15, 46.7%), estrogen response early (7/15, 46.7%), apoptosis (7/15, 46.7%) and TNFA signaling via NFKB (7/15, 46.7%). In the ICIs-Miao cohort, SNRNP7-wild-type (WT) melanoma patients had significantly prolonged overall survival (OS) time compared with SNRNP7 mutant type (MT) melanoma patients. In the TCGA-UCEC cohort, the patients with RETSAT-MT or SNRNP7-MT had significantly increased expression of immune checkpoint molecules and upregulation of inflammatory immune cells.

Conclusions

In this study, we explored GI-NKTCL by means of genomic analysis, and identified the most common mutant genes (RETSAT and SNRNP70), pathway mutations (G2M checkpoint and E2F targets) in GI-NKTCL patients. Also, we explored the association between the common mutant genes and immune infiltration. Our aim is that our exploration of these genomic changes will aid in the discovery of new biomarkers and therapeutic targets for those with GI-NKTCL, and finally provide a theoretical basis for improving the treatment and prognosis of patients with GI-NKTCL.

The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining the core microbiome is a complex task at several levels. In this review, we consider the current state of core human microbiome research. We consider the knowledge that has been gained, the factors limiting our ability to achieve a reliable description of the core human microbiome, and the fields most likely to improve that ability. DNA sequencing technologies and the methods for analyzing metagenomics and amplicon data will most likely facilitate higher accuracy and resolution in describing the microbiome. However, more effort should be invested in characterizing the microbiome’s interactions with its human host, including the immune system and nutrition. Other components of this holobiontic system should also be emphasized, such as fungi, protists, lower eukaryotes, viruses, and phages. Most importantly, a collaborative effort of experts in microbiology, nutrition, immunology, medicine, systems biology, bioinformatics, and machine learning is probably required to identify the traits of the core human microbiome.

Target Enrichment Approaches for Next-Generation Sequencing Applications in Oncology

Screening for genomic sequence variants in genes of predictive and prognostic significance is an integral part of precision medicine. Next-generation sequencing (NGS) technologies are progressively becoming platforms of choice to facilitate this, owing to their massively parallel sequencing capability, which can be used to simultaneously screen multiple markers in multiple samples for a variety of variants (single nucleotide and multi nucleotide variants, insertions and deletions, gene copy number variations, and fusions). A crucial step in the workflow of targeted NGS is the enrichment of the genomic regions of interest to be sequenced, against the whole genomic background. This ensures that the NGS effort is focused to predominantly screen target regions of interest with minimal off-target sequencing, making it more accurate and economical. Polymerase chain reaction-based (PCR, or amplicon-based) and hybridization capture-based methodologies are the two prominent approaches employed for target enrichment. This review summarizes the basic principles of target enrichment utilized by these methods, their multiple variations that have evolved over time, automation approaches, overall comparison of their advantages and drawbacks, and commercially available choices for these methodologies.

PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model

Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma (σ) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses σ70, σ24, σ32, σ38, σ28, and σ54. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30–90% for training, prediction, and hyperparameter optimization without compromising classification quality.

A model for isoform-level differential expression analysis using RNA-seq data without pre-specifying isoform structure

Motivation

Next generation sequencing (NGS) technology has been widely used in biomedical research, particularly on those genomics-related studies. One of NGS applications is the high-throughput mRNA sequencing (RNA-seq), which is usually applied to evaluate gene expression level (i.e. copies of isoforms), to identify differentially expressed genes, and to discover potential alternative splicing events. Popular tools for differential expression (DE) analysis using RNA-seq data include edgeR and DESeq. These methods tend to identify DE genes at the gene-level, which only allows them to compare the total size of isoforms, that is, sum of an isoform’s copy number times its length over all isoforms. Naturally, these methods may fail to detect DE genes when the total size of isoforms remains similar but isoform-wise expression levels change dramatically. Other tools can perform isoform-level DE analysis only if isoform structures are known but would still fail for many non-model species whose isoform information are missing. To overcome these disadvantages, we developed an isoform-free (without need to pre-specify isoform structures) splicing-graph based negative binomial (SGNB) model for differential expression analysis at isoform level. Our model detects not only the change in the total size of isoforms but also the change in the isoform-wise expression level and hence is more powerful.

Results

We performed extensive simulations to compare our method with edgeR and DESeq. Under various scenarios, our method consistently achieved a higher detection power, while controlling pre-specified type I error. We also applied our method to a real data set to illustrate its applicability in practice.

TADeus2: a web server facilitating the clinical diagnosis by pathogenicity assessment of structural variations disarranging 3D chromatin structure

In recent years great progress has been made in identification of structural variants (SV) in the human genome. However, the interpretation of SVs, especially located in non-coding DNA, remains challenging. One of the reasons stems in the lack of tools exclusively designed for clinical SVs evaluation acknowledging the 3D chromatin architecture. Therefore, we present TADeus2 a web server dedicated for a quick investigation of chromatin conformation changes, providing a visual framework for the interpretation of SVs affecting topologically associating domains (TADs). This tool provides a convenient visual inspection of SVs, both in a continuous genome view as well as from a rearrangement’s breakpoint perspective. Additionally, TADeus2 allows the user to assess the influence of analyzed SVs within flaking coding/non-coding regions based on the Hi-C matrix. Importantly, the SVs pathogenicity is quantified and ranked using TADA, ClassifyCNV tools and sampling-based P-value. TADeus2 is publicly available at https://tadeus2.mimuw.edu.pl.

Spatial Metagenomic Analysis in Understanding the Microbial Diversity of Thar Desert

Simple Summary

We present a systematic investigation of the distribution of microbial communities in arid and semi-arid regions of Thar Desert Rajasthan, India. Their responses in multiple environmental stresses, including surface soil, surface water and underground water were evaluated. We further assess the biotechnological potential of native microorganisms and discover functional species with results providing a detailed understanding of the abundance of microbial communities in these regions, associated with various stress-related biogeochemical and biotechnological processes. We hope our work will facilitate the development of effective future strategies for the use of extremophiles in complex environments.

Abstract

The arid and semi-arid regions of Rajasthan are one of the most extreme biomes of India, possessing diverse microbial communities that exhibit immense biotechnological potential for industries. Herein, we sampled study sites from arid and semi-arid regions of Thar Desert, Rajasthan, India and subjected them to chemical, physical and metagenomics analysis. The microbial diversity was studied using V3–V4 amplicon sequencing of 16S rRNA gene by Illumina MiSeq. Our metagenomic analyses revealed that the sampled sites consist mainly of Proteobacteria (19–31%) followed by unclassified bacteria (5–21%), Actinobacteria (3–25%), Planctomycetes (5–13%), Chloroflexi (2–14%), Bacteroidetes (3–12%), Firmicutes (3–7%), Acidobacteria (1–4%) and Patescibacteria (1–4%). We have found Proteobacteria in abundance which is associated with a range of activities involved in biogeochemical cycles such as carbon, nitrogen, and sulphur. Our study is perhaps the first of its kind to explore soil bacteria from arid and semi-arid regions of Rajasthan, India. We believe that the new microbial candidates found can be further explored for various industrial and biotechnological applications.

[Autoinflammation-A clinical and genetic challenge]

In the last two decades clinical rheumatological practice has been confronted with a steadily increasing number of autoinflammatory diseases, the immunological pathomechanisms of which have been elucidated and in part can be clinically well classified. Whereas targeted genetic diagnostics previously served to confirm a clinically suspected diagnosis, genetic sequencing technology has much improved and enables a new diagnostic approach via high-throughput sequencing, e.g., panel sequencing, whole exome and whole genome sequencing. Thus, the decision to make a diagnosis clinically and/or genetically, has become a daily challenge. This article contrasts the clinical, immunological and genetic aspects of autoinflammatory diseases.

Comparative Characterization of Mitogenomes From Five Orders of Cestodes (Eucestoda: Tapeworms)

The recognized potential of using mitogenomics in phylogenetics and the more accessible use of high-throughput sequencing (HTS) offer an opportunity to investigate groups of neglected organisms. Here, we leveraged HTS to execute the most comprehensive documentation of mitogenomes for cestodes based on the number of terminals sequenced. We adopted modern approaches to obtain the complete mitogenome sequences of 86 specimens representing five orders of cestodes (three reported for the first time: Phyllobothriidea, “Tetraphyllidea” and Trypanorhyncha). These complete mitogenomes represent an increase of 41% of the mitogenomes available for cestodes (61–147) and an addition of 33% in the representativeness of the cestode orders. The complete mitochondrial genomes are conserved, circular, encoded in the same strand, and transcribed in the same direction, following the pattern observed previously for tapeworms. Their length varies from 13,369 to 13,795 bp, containing 36 genes in total. Except for the Trypanorhyncha specimen, the gene order of the other four cestode orders sequenced here suggests that it could be a synapomorphy for the acetabulate group (with a reversion for taenids). Our results also suggest that no single gene can tell all the evolutionary history contained in the mitogenome. Therefore, cestodes phylogenies based on a single mitochondrial marker may fail to capture their evolutionary history. We predict that such phylogenies would be improved if conducted under a total evidence framework. The characterization of the new mitochondrial genomes is the first step to provide a valuable resource for future studies on the evolutionary relationships of these groups of parasites.

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.

COVID-19 Pharmacotherapy: Drug Development, Repurposing of Drugs, and the Role of Pharmacogenomics

The SARS-CoV-2 virus has been the subject of intense pharmacological research. Various pharmacotherapeutic approaches including antiviral and immunotherapy are being explored. A pandemic, however, cannot depend on the development of new drugs; the time required for conventional drug discovery and development is far too lengthy. As such, repurposing drugs is being used as a viable approach for identifying pharmacological agents for COVID-19 infections. Evaluation of repurposed drug candidates with pharmacogenomic analysis is being used to identify near-term pharmacological remedies for COVID-19.

Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton (Gossypium spp.)

Cotton genetic resources contain diverse economically important traits that can be used widely in breeding approaches to create of high-yielding elite cultivars with superior fiber quality and adapted to biotic and abiotic stresses. Nevertheless, the creation of new cultivars using conventional breeding methods is limited by the cost and proved to be time consuming process, also requires a space to make field observations and measurements. Decoding genomes of cotton species greatly facilitated generating large-scale high-throughput DNA markers and identification of QTLs that allows confirmation of candidate genes, and use them in marker-assisted selection (MAS)-based breeding programs. With the advances of quantitative trait loci (QTL) mapping and genome-wide-association study approaches, DNA markers associated with valuable traits significantly accelerate breeding processes by replacing the selection with a phenotype to the selection at the DNA or gene level. In this review, we discuss the evolution and genetic diversity of cotton Gossypium genus, molecular markers and their types, genetic mapping and QTL analysis, application, and perspectives of MAS-based approaches in cotton breeding.

Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants

Identifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms because they may result in markedly different clinical phenotypes. Here, we generated an extensive database of documented germline GOF and LOF pathogenic variants by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF variants and applied machine learning and statistical analyses to identify discriminative features. We found that GOF variants were enriched in essential genes, for autosomal-dominant inheritance, and in protein binding and interaction domains, whereas LOF variants were enriched in singleton genes, for protein-truncating variants, and in protein core regions. We developed a user-friendly web-based interface that enables the extraction of selected subsets from the GOF/LOF database by a broad set of annotated features and downloading of up-to-date versions. These results improve our understanding of how variants affect gene/protein function and may ultimately guide future treatment options.

Recent Advancements in the Technologies Detecting Food Spoiling Agents

To match the current life-style, there is a huge demand and market for the processed food whose manufacturing requires multiple steps. The mounting demand increases the pressure on the producers and the regulatory bodies to provide sensitive, facile, and cost-effective methods to safeguard consumers' health. In the multistep process of food processing, there are several chances that the food-spoiling microbes or contaminants could enter the supply chain. In this contest, there is a dire necessity to comprehend, implement, and monitor the levels of contaminants by utilizing various available methods, such as single-cell droplet microfluidic system, DNA biosensor, nanobiosensor, smartphone-based biosensor, aptasensor, and DNA microarray-based methods. The current review focuses on the advancements in these methods for the detection of food-borne contaminants and pathogens.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Enhancing Physical and Thermodynamic Properties of DNA Storage Sets with End-constraint

With the explosion of data, DNA is considered as an ideal carrier for storage due to its high storage density. However, low-quality DNA sets hamper the widespread use of DNA storage. This work proposes a new method to design high-quality DNA storage sets. Firstly, random switch and double-weight offspring strategies are introduced in Double-strategy Black Widow Optimization Algorithm (DBWO). Experimental results of 26 benchmark functions show that the exploration and exploitation abilities of DBWO are greatly improved from previous work. Secondly, DBWO is applied in designing DNA storage sets, and compared with previous work, the lower bounds of storage sets are boosted by 9%-37%. Finally, to improve the poor stabilities of sequences, the End-constraint is proposed in designing DNA storage sets. By measuring the number of hairpin structures, melting temperature, and minimum free energy, it is evaluated that with our innovative constraint, DBWO can construct not only a larger number of storage sets, but also enhance physical and thermodynamic properties of DNA storage sets.

[Autoinflammation-A clinical and genetic challenge]

In the last two decades clinical rheumatological practice has been confronted with a steadily increasing number of autoinflammatory diseases, the immunological pathomechanisms of which have been elucidated and in part can be clinically well classified. Whereas targeted genetic diagnostics previously served to confirm a clinically suspected diagnosis, genetic sequencing technology has much improved and enables a new diagnostic approach via high-throughput sequencing, e.g., panel sequencing, whole exome and whole genome sequencing. Thus, the decision to make a diagnosis clinically and/or genetically, has become a daily challenge. This article contrasts the clinical, immunological and genetic aspects of autoinflammatory diseases.

High-throughput molecular technologies for unraveling the mystery of soil microbial community: challenges and future prospects

Soil microbial communities play a crucial role in soil fertility, sustainability, and plant health. However, intensive agriculture with increasing chemical inputs and changing environments have influenced native soil microbial communities. Approaches have been developed to study the structure, diversity, and activity of soil microbes to better understand the biology and plant-microbe interactions in soils. Unfortunately, a good understanding of soil microbial community remains a challenge due to the complexity of community composition, interactions of the soil environment, and limitations of technologies, especially related to the functionality of some taxa rarely detected using conventional techniques. Culture-based methods have been shown unable and sometimes are biased for assessing soil microbial communities. To gain further knowledge, culture-independent methods relying on direct analysis of nucleic acids, proteins, and lipids are worth exploring. In recent years, metagenomics, metaproteomics, metatranscriptomics, and proteogenomics have been increasingly used in studying microbial ecology. In this review, we examined the importance of microbial community to soil quality, the mystery of rhizosphere and plant-microbe interactions, and the biodiversity and multi-trophic interactions that influence the soil structure and functionality. The impact of the cropping system and climate change on the soil microbial community was also explored. Importantly, progresses in molecular biology, especially in the development of high-throughput biotechnological tools, were extensively assessed for potential uses to decipher the diversity and dynamics of soil microbial communities, with the highlighted advantages/limitations.

Identification of colorectal cancer associated biomarkers: an integrated analysis of miRNA expression

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. This complex disease still holds severe problems concerning diagnosis due to the high invasiveness nature of colonoscopy and the low accuracy of the alternative diagnostic methods. Additionally, patient heterogeneity even within the same stage is not properly reflected in the current stratification system. This scenario highlights the need for new biomarkers to improve non-invasive screenings and clinical management of patients. MicroRNAs (miRNAs) have emerged as good candidate biomarkers in cancer as they are stable molecules, easily measurable and detected in body fluids thus allowing for non-invasive diagnosis and/or prognosis. In this study, we performed an integrated analysis first using 4 different datasets (discovery cohorts) to identify miRNAs associated with colorectal cancer development, unveil their role in this disease by identifying putative targets and regulatory networks and investigate their ability to serve as biomarkers. We have identified 26 differentially expressed miRNAs which interact with frequently deregulated genes known to participate in commonly altered pathways in colorectal cancer. Most of these miRNAs have high diagnostic power, and their prognostic potential is evidenced by panels of 5 miRNAs able to predict the outcome of stage II and III colorectal cancer patients. Notably, 8 miRNAs were validated in three additional independent cohorts (validation cohorts) including a plasma cohort thus reinforcing the value of miRNAs as non-invasive biomarkers.

The complete mitochondrial genome of the half-fin anchovy, Setipinna tenuifilis (Valenciennes, 1848)

The complete mitochondrial genome of the half-fin anchovy, Setipinna tenuifilis collected from Yellow and Bohai Seas was determined by next-generation sequencing. The mitogenome is a circular molecule 16,668 bp in length, including the typical structure of 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and a control region. The termination-associated sequence (TAS), central conserved sequence block (CSB) and CSB are detected in the control region. The gene contents of the mitogenome are identical to those observed in most bony fishes.

Do second generation sequencing techniques identify documented genetic markers for neonatal diabetes mellitus?

Neonatal diabetes mellitus (NDM) is noted as a genetic, heterogeneous, and rare disease in infants. NDM occurs due to a single-gene mutation in neonates. A common source for developing NDM in an infant is the existence of mutations/variants in the KCNJ11 and ABCC8 genes, encoding the subunits of the voltage-dependent potassium channel. Both KCNJ11 and ABCC8 genes are useful in diagnosing monogenic diabetes during infancy. Genetic analysis was previously performed using first-generation sequencing techniques, such as DNA-Sanger sequencing, which uses chain-terminating inhibitors. Sanger sequencing has certain limitations; it can screen a limited region of exons in one gene, but it cannot screen large regions of the human genome. In the last decade, first generation sequencing techniques have been replaced with second-generation sequencing techniques, such as next-generation sequencing (NGS), which sequences nucleic-acids more rapidly and economically than Sanger sequencing. NGS applications are involved in whole exome sequencing (WES), whole genome sequencing (WGS), and targeted gene panels. WES characterizes a substantial breakthrough in human genetics. Genetic testing for custom genes allows the screening of the complete gene, including introns and exons. The aim of this review was to confirm if the 22 genetic variations previously documented to cause NDM by Sanger sequencing could be detected using second generation sequencing techniques. The author has cross-checked global studies performed in NDM using NGS, ES/WES, WGS, and targeted gene panels as second-generation sequencing techniques; WES confirmed the similar variants, which have been previously documented with Sanger sequencing. WES is documented as a powerful tool and WGS as the most comprehensive test for verified the documented variants, as well as novel enhancers. This review recommends for the future studies should be performed with second generation sequencing techniques to identify the verified 22 genetic and novel variants by screening in NDM (PNDM or TNMD) children.