OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow

Emerging epidemic of the Africa-type plasmid in penicillinase-producing Neisseria gonorrhoeae in Guangdong, China, 2013-2022

The prevalence of penicillinase-producing Neisseria gonorrhoeae (PPNG) is a crucial public health concern because of its resistance to penicillin and cephalosporins. From 2013 to 2022, a total of 1748 N. gonorrhoeae isolates from Guangdong, China, were examined for their antibiotic susceptibility and molecular epidemiological characteristics. PPNG prevalence increased markedly from 37.25% to 63.87%. This increase was accompanied by a shift in predominant plasmid types carried by PPNG isolates: the rate of PPNG isolates carrying the Africa-type plasmid increased from 18.42% to 91.55%, whereas the rate of isolates carrying the Asia-type plasmid decreased from 81.58% to 7.58%. The prevalence of blaTEM-135, which is linked to cephalosporin resistance, declined from 52.63% to 4.37%, whereas that of blaTEM-1 increased from 47.37% to 86.88%, and new blaTEM variants emerged (10.99% by 2022). Most blaTEM-1 (88.26%) and new blaTEM alleles (83.70%) were associated with the Africa-type plasmid, whereas 86.79% of blaTEM-135 alleles were linked to the Asia-type plasmid. Resistance to ceftriaxone was higher in the Asia-type group (11.67%) than in the Africa-type, Toronto/Rio-type and non-PPNG groups. Genotyping identified diverse sequence types (STs) among PPNGs, in which MLST ST7363, NG-STAR ST2477, NG-MAST ST17748, and NG STAR CC1124 were predominant. This study underscores the rising prevalence of PPNG in Guangdong driven by clonal expansion and changing plasmid dynamics, affecting cephalosporin resistance and highlighting the need for continued surveillance and research into effective treatment strategies.

Fine Mapping Identifies Candidate Genes Associated with Swine Inflammation and Necrosis Syndrome

Swine inflammation and necrosis syndrome (SINS) is a widespread disease in pigs, causing pain, suffering, and damage. Inflammation is documented at different levels based on clinical signs, histopathology, clinical chemistry, metabolomics and transcriptomics. The influence of sow and boar, as well as a heritability of around 0.3, suggest a genetic component to the disease. The aim of the present study was to identify functional single nucleotide polymorphisms (SNPs) in the vicinity of gene markers previously mapped using GWAS. DNA samples were available from 234 already phenotyped piglets. These animals were re-sequenced with additional prior enrichment. The nine selected chromosomal regions cover a total length of 22 Mbp. The genome-wide association study (GWAS) revealed two series with a total of 15 significant missense polymorphisms on chromosomes 11, 14, and 15. The homozygous genotypes of the most discriminating SNPs in series 1 resulted in SINS scores of 3.5 and 17.9, respectively. Despite the partial linkage of the SNPs, interesting candidate genes were defined. The results allow a significant narrowing of the possible candidate genes for understanding the pathogenesis of SINS and for future use in selection breeding to overcome the syndrome. Further studies should be carried out on larger animal populations.

Whole-Genome Resequencing Analysis of Copy Number Variations Associated with Athletic Performance in Grassland-Thoroughbred

Copy number variation (CNV) is an important source of genetic variation. However, studies utilizing whole-genome sequencing to investigate CNVs in horse populations and their effects on traits remain relatively limited. This study aims to address the lack of research on the impact of copy number variation (CNV) on racing performance in horse populations, providing new insights for locally bred racing breeds. We analyzed 60 offspring derived from the crossbreeding of Thoroughbred horses and Xilingol horses. These horses were temporarily named "Grassland-Thoroughbred" and were divided into two groups: 30 racing horses and 30 non-racing horses. A total of 89,527 CNVs were identified. After merging overlapping CNVs, 982 copy number variation regions (CNVRs) were recognized, among which the racing horse group (RH) had 29 unique CNVRs, while the non-racing horse group (NR) had 4 unique CNVRs. In addition, a total of 195 genes overlapping with CNVRs were identified. Transcriptomic analysis revealed 120 differentially expressed genes, with MTPN expressed in both CNVR-overlapping genes and mRNA. Both CNVR-overlapping genes and differentially expressed genes were enriched in the MAPK signaling pathway; CNV may affect gene expression through gene dosage effects or regulatory mechanisms. Using Vst statistical analysis, we further screened candidate CNVRs in autosomes that exceeded the 95% differentiation threshold between the RH and NR populations. Several key genes associated with energy metabolism and muscle function were identified, including AGT, IGFN1, IMMPL2, SLC41A3, AOX4, and ACAD11. These findings provide new insights into the genetic structural variation in racing performance and adaptability, fill the gap in CNV studies in the genomics of Grassland-Thoroughbred horses, and offer valuable genomic data for optimizing breeding strategies in native racing horse populations.

Comprehensive Molecular and Genomic Analysis of NCI-MATCH Subprotocol Y: Capivasertib in Patients With an AKT1 E17K-Mutated Tumor

PURPOSE

NCI-MATCH (EAY131) is a precision medicine trial using genomic testing to allocate patients with advanced malignancies to targeted treatments. Arm Y evaluated capivasertib, a pan AKT inhibitor, in patients with an AKT1 E17K-mutated tumor. Here, we report on the translational objectives of the study, a molecular and genomic analysis of specimens to identify potential biomarkers of response or resistance to capivasertib.

METHODS

Eligible patients had AKT1 E17K-mutated metastatic tumors that progressed with standard treatment and received capivasertib 480 mg orally twice daily for 4 days on and 3 days off weekly in 28-day cycles. The primary end point was objective response rate (ORR). We performed whole-exome sequencing, RNA sequencing, and gene set and pathway enrichment analysis on 25 pretreatment tissue samples and evaluated findings in responders (complete response [CR], n = 0, and partial response, n = 9) and nonresponders (stable disease, n = 13, and progressive disease, n = 3).

RESULTS

The ORR was 28.6% (10 of 35) in the reported primary trial and 36% (9 of 25) in this translational cohort. Mutations in the TP53 gene were more frequent in responders, whereas the PI3K/AKT/mTOR pathway genes TYRO3, SYNJ1, and CDIPT were significantly altered in nonresponders. DNA repair, p53, E2F, and Wnt-beta catenin pathways were enriched in the responder group. Unsupervised clustering of gene expression identified five genes, ANKRD30A, SUSD4, TTC6, POTEJ, and POTEI, that were significantly higher in responders and lower in nonresponders. In addition, EGFR expression was significantly increased in nonresponders.

CONCLUSION

In patients with AKT1 E17K-mutated tumors, capivasertib achieved a clinically significant ORR. TP53 mutations appear to be associated with response, whereas certain additional PI3K/AKT/mTOR pathway mutations and EGFR overexpression appear to be associated with nonresponse to capivasertib. Further investigation of predictive biomarkers is warranted.

Genome-Wide Association Study Reveals Single Nucleotide Polymorphisms Associated with Tail Length and Tail Kinks in Piglets

Tail docking is still used in pigs to reduce the prevalence of tail biting, although it is purely symptomatic and contrary to animal welfare. Genetic selection for shorter tails might, however, help to avoid tail docking and has therefore been proposed. A genetic basis for tail length is known for many species. Variability in tail length, including moderate heritability, has also been demonstrated in pigs. The aim of the present study was to identify genetic markers for tail length and to define candidate genes. To this end, 140 piglets were phenotyped and genotyped at 3 days of age and a genome-wide association study was performed. Seven SNPs were mapped on chromosomes 1, 2, 6, 11, and 15. Two linked SNPs on chromosome 2 resulted in a functional amino acid exchange. The genotypes at the SNPs were only associated with small differences in relative tail length of up to 16.5% (short genotype versus long genotype at SSC15), but at the same time with the occurrence of malformations in the form of tail kinks. The small effect size and the association between tail length and tail kinks, together with the generally pure symptomatic effect on tail biting, argue against the applicability of selection for shorter tails in pigs.

Genome-Wide Association Studies for Lactation Performance in Buffaloes

Background: Buffaloes are considered an indispensable genetic resource for dairy production. However, improvements in lactation performance have been relatively limited. Advances in sequencing technology, combined with genome-wide association studies, have facilitated the breeding of high-quality buffalo. Methods: We conducted an integrated analysis of genomic sequencing data from 120 water buffalo, the high-quality water buffalo genome assembly designated as UOA_WB_1, and milk production traits, including 305-day milk yield (MY), peak milk yield (PM), total protein yield (PY), protein percentage (PP), fat percentage (FP), and total milk fat yield (FY). Results: The results identified 56 significant SNPs, and based on these markers, 54 candidate genes were selected. These candidate genes were significantly enriched in lactation-related pathways, such as the cAMP signaling pathway (ABCC4), TGF-β signaling pathway (LEFTY2), Wnt signaling pathway (CAMK2D), and metabolic pathways (DGAT1). Conclusions: These candidate genes (e.g., ABCC4, LEFTY2, CAMK2D, DGAT1) provide a substantial theoretical foundation for molecular breeding to enhance milk production in buffaloes.

Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing

Cerebral cavernous malformation (CCM) is a polygenic disease with intricate genetic interactions contributing to quantitative pathogenesis across multiple factors. The principal pathogenic genes of CCM, specifically KRIT1, CCM2, and PDCD10, have been reported, accompanied by a growing wealth of genetic data related to mutations. Furthermore, numerous other molecules associated with CCM have been unearthed. However, tackling such massive volumes of unstructured data remains challenging until the advent of advanced large language models. In this study, we developed an automated analytical pipeline specialized in single nucleotide variants (SNVs) related biomedical text analysis called BRLM. To facilitate this, BioBERT was employed to vectorize the rich information of SNVs, while a deep residue network was used to discriminate the classes of the SNVs. BRLM was initially constructed on mutations from 12 different types of TCGA cancers, achieving an accuracy exceeding 99%. It was further examined for CCM mutations in familial sequencing data analysis, highlighting an upstream master regulator gene fibroblast growth factor 1 (FGF1). With multi-omics characterization and validation in biological function, FGF1 demonstrated to play a significant role in the development of CCMs, which proved the effectiveness of our model. The BRLM web server is available at http://1.117.230.196.

Bulk and single-cell RNA-seq analyses reveal canonical RNA editing associated with microglia homeostasis and its role in sepsis-associated encephalopathy

Previous studies have demonstrated the roles of both microglia homeostasis and RNA editing in sepsis-associated encephalopathy (SAE), yet their relationship remains to be elucidated. In this study, we analyzed bulk and single-cell RNA-seq (scRNA) datasets containing 107 brain tissue and microglia samples from mice with microglial depletion and repopulation to explore canonical RNA editing associated with microglia homeostasis and evaluate its role in SAE. Analysis of mouse brain RNA-Seq revealed hallmarks of microglial repopulation, including peak expressions of Apobec1 and Apobec3 at Day 5 of repopulation and dramatically altered B2m RNA editing. Significant time-dependent changes in brain RNA editing during microglial depletion and repopulation were primarily observed in synapse-related genes, such as Tbc1d24 and Slc1a2. ScRNA-Seq revealed heterogeneous RNA editing among microglia subpopulations and their distinct changes associated with microglia homeostasis. Moreover, repopulated microglia from lipopolysaccharide (LPS)-induced sepsis mice exhibited intensified up-regulation of Apobec1 and Apobec3, with distinct RNA editing responses to LPS, mainly involved in immune-related pathways. The hippocampus from sepsis mice induced by peritoneal contamination and infection showed upregulated Apobec1 and Apobec3 expression, and altered RNA editing in immune-related genes, such as B2m and Mier1, and nervous-related lncRNA Meg3 and Snhg11, both of which were repressed by microglial depletion. Furthermore, the expression of complement-related genes, such as C4b and Cd47, was substantially correlated with RNA editing activity in microglia homeostasis and SAE. Our study demonstrates canonical RNA editing associated with microglia homeostasis and provides new insights into its potential role in SAE.

Targeted Linked-Read Sequencing for Direct Haplotype Phasing of Parental GJB2/SLC26A4 Alleles: A Universal and Dependable Noninvasive Prenatal Diagnosis Method Applied to Autosomal Recessive Nonsyndromic Hearing Loss in At-Risk Families

Noninvasive prenatal diagnosis (NIPD) for autosomal recessive nonsyndromic hearing loss (ARNSHL) has been rarely reported until recent years. Additionally, the existing method can not be used for challenging genome loci (eg, copy number variations, deletions, inversions, or gene recombinants) or on families without proband genotype. This study assessed the performance of relative haplotype dosage analysis (RHDO)-based NIPD for identifying fetal genotyping in pregnancies at risk of ARNSHL. Fifty couples carrying pathogenic variants associated with ARNSHL in either GJB2 or SLC26A4 were recruited. The RHDO-based targeted linked-read sequencing combined with whole gene coverage probes was used to genotype the fetal cell-free DNA of 49 families who met the quality control standard. Fetal amniocyte samples were genotyped using invasive prenatal diagnosis (IPD) to assess the performance of NIPD. The NIPD results showed 100% (49/49) concordance with those obtained through IPD. Two families with copy number variation and recombination were also successfully identified. Sufficient specific informative single-nucleotide polymorphisms for haplotyping, as well as the fetal cell-free DNA concentration and sequencing depth, are prerequisites for RHDO-based NIPD. This method has the merits of covering the entire genes of GJB2 and SLC26A4, qualifying for copy number variation and recombination analysis with remarkable sensitivity and specificity. Therefore, it has clinical potential as an alternative to traditional IPD for ARNSHL.

Sterol 14-alpha demethylase (CYP51) activity in Leishmania donovani is likely dependent upon cytochrome P450 reductase 1

Liposomal amphotericin B is an important frontline drug for the treatment of visceral leishmaniasis, a neglected disease of poverty. The mechanism of action of amphotericin B (AmB) is thought to involve interaction with ergosterol and other ergostane sterols, resulting in disruption of the integrity and key functions of the plasma membrane. Emergence of clinically refractory isolates of Leishmania donovani and L. infantum is an ongoing issue and knowledge of potential resistance mechanisms can help to alleviate this problem. Here we report the characterisation of four independently selected L. donovani clones that are resistant to AmB. Whole genome sequencing revealed that in three of the moderately resistant clones, resistance was due solely to the deletion of a gene encoding C24-sterol methyltransferase (SMT1). The fourth, hyper-resistant resistant clone (>60-fold) was found to have a 24 bp deletion in both alleles of a gene encoding a putative cytochrome P450 reductase (P450R1). Metabolic profiling indicated these parasites were virtually devoid of ergosterol (0.2% versus 18% of total sterols in wild-type) and had a marked accumulation of 14-methylfecosterol (75% versus 0.1% of total sterols in wild-type) and other 14-alpha methylcholestanes. These are substrates for sterol 14-alpha demethylase (CYP51) suggesting that this enzyme may be a bona fide P450R specifically involved in electron transfer from NADPH to CYP51 during catalysis. Deletion of P450R1 in wild-type cells phenocopied the metabolic changes observed in our AmB hyper-resistant clone as well as in CYP51 nulls. Likewise, addition of a wild type P450R1 gene restored sterol profiles to wild type. Our studies indicate that P450R1 is essential for L. donovani amastigote viability, thus loss of this gene is unlikely to be a driver of clinical resistance. Nevertheless, investigating the mechanisms underpinning AmB resistance in these cells provided insights that refine our understanding of the L. donovani sterol biosynthetic pathway.

Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations for Targeted Therapy Through Efficient NGS Data Filtering System

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a unique algorithm that facilitates a rigorous, multi-tiered filtration process. This allows for the efficient prioritization of 420 genes and 1,193 variants from large datasets, with a particular focus on 80 variants demonstrating high clinical actionability. These variants have been aligned with FDA approvals, NCCN guidelines, and thoroughly reviewed literature, thereby equipping oncologists with a refined arsenal for targeted therapy decisions. The innovation of our system lies in its dynamic integration framework and its algorithm, tailored to emphasize clinical utility and actionability-a nuanced approach often lacking in existing methodologies. Our validation on real-world lung adenocarcinoma NGS datasets has shown not only an enhanced efficiency in identifying genetic targets but also the potential to streamline clinical workflows, thus propelling the advancement of precision oncology. Planned future enhancements include expanding the range of integrated data types and developing a user-friendly interface, aiming to facilitate easier access to data and promote collaborative efforts in tailoring cancer treatments.

Neuronal Ceroid Lipofuscinosis in a Mixed-Breed Dog with a Splice Site Variant in CLN6

A 23-month-old neutered male dog of unknown ancestry presented with a history of progressive neurological signs that included anxiety, cognitive impairment, tremors, seizure activity, ataxia, and pronounced visual impairment. The clinical signs were accompanied by global brain atrophy. Due to progression in the severity of disease signs, the dog was euthanized at 26 months of age. An examination of the tissues collected at necropsy revealed dramatic intracellular accumulations of autofluorescent inclusions in the brain, retina, and cardiac muscle. The inclusions were immunopositive for subunit c of mitochondrial ATP synthase, and their ultrastructural appearances were similar to those of lysosomal storage bodies that accumulate in some neuronal ceroid lipofuscinosis (NCL) diseases. The dog also exhibited widespread neuroinflammation. Based on these findings, the dog was deemed likely to have suffered from a form of NCL. A whole genome sequence analysis of the proband's DNA revealed a homozygous C to T substitution that altered the intron 3-exon 4 splice site of CLN6. Other mutations in CLN6 cause NCL diseases in humans and animals, including dogs. The CLN6 protein was undetectable with immunolabeling in the tissues of the proband. Based on the clinical history, fluorescence and electron-microscopy, immunohistochemistry, and molecular genetic findings, the disorder in this dog was classified as an NCL resulting from the absence of the CLN6 protein. Screening the dog's genome for a panel of breed-specific polymorphisms indicated that its ancestry included numerous breeds, with no single breed predominating. This suggests that the CLN6 disease variant is likely to be present in other mixed-breed dogs and at least some ancestral breeds, although it is likely to be rare since other cases have not been reported to date.

Genomic and Transcriptome Analysis Reveals the Biosynthesis Network of Cordycepin in Cordyceps militaris

Cordycepin is the primary active compound of Cordyceps militaris. However, the definitive genetic mechanism governing cordycepin synthesis in fruiting body growth and development remains elusive, necessitating further investigation. This study consists of 64 C. militaris strains collected from northeast China. The high-yielding cordycepin strain CMS19 was selected for the analysis of cordycepin production and the genetic basis of cordycepin anabolism. First, the whole-genome sequencing of CMS19 yielded a final size of 30.96 Mb with 8 contigs and 9781 protein-coding genes. The genome component revealed the presence of four additional secondary metabolite gene clusters compared with other published genomes, suggesting the potential for the production of new natural products. The analyses of evolutionary and genetic differentiation revealed a close relationship between C. militaris and Beauveria bassiana. The population of strains distributed in northeast China exhibited the significant genetic variation. Finally, functional genes associated with cordycepin synthesis were identified using a combination of genomic and transcriptomic analyses. A large number of functional genes associated with energy and purine metabolism were significantly enriched, facilitating the reconstruction of a hypothetical cordycepin metabolic pathway. Therefore, our speculation of the cordycepin metabolism pathway involved 24 genes initiating from the glycolysis and pentose phosphate pathways, progressing through purine metabolism, and culminating in the core region of cordycepin synthesis. These findings could offer fundamental support for scientific utilizations of C. militaris germplasm resources and standardized cultivation for cordycepin production.

Drought responses and population differentiation of Calohypnum plumiforme inferred from comparative transcriptome analysis

Bryophytes, known as poikilohydric plants, possess vegetative desiccation-tolerant (DT) ability to withstand water deficit stress. Consequently, they offer valuable genetic resources for enhancing resistance to water scarcity stress. In this research, we examined the physiological, phytohormonal, and transcriptomic changes in DT mosses Calohypnum plumiforme from two populations, with and without desiccation treatment. Comparative analysis revealed population differentiation at physiological, gene sequence, and expression levels. Under desiccation stress, the activities of superoxide dismutase (SOD) and peroxidase (POD) showed significant increases, along with elevation of soluble sugars and proteins, consistent with the transcriptome changes. Notable activation of the bypass pathway of JA biosynthesis suggested their roles in compensating for JA accumulation. Furthermore, our analysis revealed significant correlations among phytohormones and DEGs in their respective signaling pathway, indicating potential complex interplays of hormones in C plumiforme. Protein phosphatase 2C (PP2C) in the abscisic acid signaling pathway emerged as the pivotal hub in the phytohormone crosstalk regulation network. Overall, this study was one of the first comprehensive transcriptome analyses of moss C. plumiforme under slow desiccation rates, expanding our knowledge of bryophyte transcriptomes and shedding light on the gene regulatory network involved in response to desiccation, as well as the evolutionary processes of local adaptation across moss populations.

Pervasive G × E interactions shape adaptive trajectories and the exploration of the phenotypic space in artificial selection experiments

Quantitative genetics models have shown that long-term selection responses depend on initial variance and mutational influx. Understanding limits of selection requires quantifying the role of mutational variance. However, correlative responses to selection on nonfocal traits can perturb the selection response on the focal trait; and generations are often confounded with selection environments so that genotype by environment (G×E) interactions are ignored. The Saclay divergent selection experiments (DSEs) on maize flowering time were used to track the fate of individual mutations combining genotyping data and phenotyping data from yearly measurements (DSEYM) and common garden experiments (DSECG) with four objectives: (1) to quantify the relative contribution of standing and mutational variance to the selection response, (2) to estimate genotypic mutation effects, (3) to study the impact of G×E interactions in the selection response, and (4) to analyze how trait correlations modulate the exploration of the phenotypic space. We validated experimentally the expected enrichment of fixed beneficial mutations with an average effect of +0.278 and +0.299 days to flowering, depending on the genetic background. Fixation of unfavorable mutations reached up to 25% of incoming mutations, a genetic load possibly due to antagonistic pleiotropy, whereby mutations fixed in the selection environment (DSEYM) turned to be unfavorable in the evaluation environment (DSECG). Global patterns of trait correlations were conserved across genetic backgrounds but exhibited temporal patterns. Traits weakly or uncorrelated with flowering time triggered stochastic exploration of the phenotypic space, owing to microenvironment-specific fixation of standing variants and pleiotropic mutational input.

GWAS reveals genomic associations with swine inflammation and necrosis syndrome

The recently identified swine inflammation and necrosis syndrome (SINS) occurs in high prevalence from newborn piglets to fattening pigs and resembles an important concern for animal welfare. The primary endogenous syndrome affects the tail, ears, teats, coronary bands, claws and heels. The basis of clinical inflammation and necrosis has been substantiated by histopathology, metabolomic and liver transcriptomic. Considerable variation in SINS scores is evident in offspring of different boars under the same husbandry conditions. The high complexity of metabolic alterations and the influence of the boar led to the hypothesis of a polygenic architecture of SINS. This should be investigated by a genome-wide association study. For this purpose, 27 sows were simultaneously inseminated with mixed semen from two extreme boars. The mixed semen always contained ejaculate from a Pietrain boar classified as extremely SINS susceptible and additionally either the ejaculate from a Pietrain boar classified as SINS stable or from a Duroc boar classified as SINS stable. The 234 piglets were phenotyped on day 3 of life, sampled and genetically assigned to the respective boar. The piglets showed the expected genetic differentiation with respect to SINS susceptibility. The suspected genetic complexity was confirmed both in the number and genome-wide distribution of 221 significantly associated SNPs, and led to 49 candidate genes. As the SNPs were almost exclusively located in noncoding regions, functional nucleotides have not yet been identified. The results suggest that the susceptibility of piglets to SINS depends not only on environmental conditions but also on genomic variation.

ILIAD: a suite of automated Snakemake workflows for processing genomic data for downstream applications

BACKGROUND

Processing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability for biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data.

RESULTS

The Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster.

CONCLUSION

Iliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists that are less familiar with programming can manage their own big data with this open-source suite of workflows.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Transcriptomic profiling of near-isogenic lines reveals candidate genes for a significant locus conferring metribuzin resistance in wheat

BACKGROUND

Weeds reduce wheat yields in dryland farming systems. Herbicides such as metribuzin are commonly used to control weeds. However, wheat has a narrow safety margin against metribuzin. Standing crops such as wheat with weeds in the same field can also be killed by the same dose of metribuzin. Therefore, it is important to identify metribuzin resistance genes and understand the resistance mechanism in wheat for sustainable crop production. A previous study identified a significant metribuzin resistance wheat QTL, Qsns.uwa.4 A.2, explaining 69% of the phenotypic variance for metribuzin resistance.

RESULTS

Two NIL pairs with the most contrasting performance in the metribuzin treatment and different in genetic backgrounds were compared using RNA sequence analysis, identifying nine candidate genes underlying Qsns.uwa.4 A.2 responsible for metribuzin resistance. Quantitative RT-qPCR further validated the candidate genes, with TraesCS4A03G1099000 (nitrate excretion transporter), TraesCS4A03G1181300 (aspartyl protease), and TraesCS4A03G0741300 (glycine-rich proteins) identified as key factors for metribuzin resistance.

CONCLUSION

Identified markers and key candidate genes can be used for selecting metribuzin resistance in wheat.

The challenges of the genome-based identification of antifungal resistance in the clinical routine

The increasing number of chronic and life-threatening infections caused by antimicrobial resistant fungal isolates is of critical concern. Low DNA sequencing cost may facilitate the identification of the genomic profile leading to resistance, the resistome, to rationally optimize the design of antifungal therapies. However, compared to bacteria, initiatives for resistome detection in eukaryotic pathogens are underdeveloped. Firstly, reported mutations in antifungal targets leading to reduced susceptibility must be extensively collected from the literature to generate comprehensive databases. This information should be complemented with specific laboratory screenings to detect the highest number possible of relevant genetic changes in primary targets and associations between resistance and other genomic markers. Strikingly, some drug resistant strains experience high-level genetic changes such as ploidy variation as much as duplications and reorganizations of specific chromosomes. Such variations involve allelic dominance, gene dosage increments and target expression regime effects that should be explicitly parameterized in antifungal resistome prediction algorithms. Clinical data indicate that predictors need to consider the precise pathogen species and drug levels of detail, instead of just genus and drug class. The concomitant needs for mutation accuracy and assembly quality assurance suggest hybrid sequencing approaches involving third-generation methods will be utilized. Moreover, fatal fast infections, like fungemia and meningitis, will further require both sequencing and analysis facilities are available in-house. Altogether, the complex nature of antifungal resistance demands extensive sequencing, data acquisition and processing, bioinformatic analysis pipelines, and standard protocols to be accomplished prior to genome-based protocols are applied in the clinical setting.

Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice (Oryza sativa), plus the genomes of two wild relatives (O. rufipogon and O. punctata), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance.

Hypersaline Lake Urmia: a potential hotspot for microbial genomic variation

Lake Urmia located in Iran is a hypersaline environment with a salinity of about 27% (w/v). Metagenomic analyses of water samples collected from six locations in the lake exhibited a microbial community dominated by representatives of the family Haloferacaceae (69.8%), mainly those affiliated to only two genera, Haloquadratum (59.3%) and Halonotius (9.1%). Similar to other hypersaline lakes, the bacterial community was dominated by Salinibacter ruber (23.3%). Genomic variation analysis by inspecting single nucleotide variations (SNVs) and insertions/deletions (INDELs) exhibited a high level of SNVs and insertions, most likely through transformation for abundant taxa in the Lake Urmia community. We suggest that the extreme conditions of Lake Urmia and specifically its high ionic concentrations could potentially increase the SNVs and insertions, which can consequently hamper the assembly and genome reconstruction from metagenomic reads of Lake Urmia.

Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.

Detection of Potential Mutated Genes Associated with Common Immunotherapy Biomarkers in Non-Small-Cell Lung Cancer Patients

Microsatellite instability (MSI), high tumor mutation burden (TMB-H) and programmed cell death 1 ligand 1 (PD-L1) expression are hot biomarkers related to the improvement of immunotherapy response. Two cohorts of non-small-cell lung cancer (NSCLC) were collected and sequenced via targeted next-generation sequencing. Drug analysis was then performed on the shared genes using three different databases: Drugbank, DEPO and DRUGSURV. A total of 27 common genes were mutated in at least two groups of TMB-H-, MSI- and PD-L1-positive groups. AKT1, SMAD4, SCRIB and AXIN2 were severally involved in PI3K-activated, transforming growth factor beta (TGF-β)-activated, Hippo-repressed and Wnt-repressed pathways. This study provides an understanding of the mutated genes related to the immunotherapy biomarkers of NSCLC.