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

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.

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.

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. However, the previous method could not be performed on challenging genome loci (eg, copy number variations, deletions, inversions, or gene recombinants) or on families without proband genotype. Here, this study assesses 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.

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.

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.

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.

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.