Search Results (680)

Somatic variant calling is a critical step in cancer genome analysis, but the performance of available tools can vary depending on their underlying algorithms and filtering strategies. We compared three widely used variant callers—Mutect2, Strelka2, and FreeBayes—for their performance in somatic single-nucleotide variant (SNV) detection using both synthetic and real whole-exome sequencing (WES) data. Synthetic data were generated by introducing 4709 SNVs into a variant-free BAM file, while real data consisted of tumor and matched normal WES samples from five ovarian cancer (OC) patients. All callers were run using the nf-core/sarek pipeline with default settings and appropriate filtering. In the synthetic dataset, all tools showed high precision (~99.9%), with Mutect2 achieving the highest recall (63.1%), followed by Strelka2 (46.3%) and FreeBayes (45.2%). In real samples, FreeBayes detected the most variants, and only 5.1% of SNVs were shared across all three tools. We then integrated calls with SomaticSeq in consensus mode (Mutect2 + Strelka2) and kept variants with stronger allelic signals—showing higher VAFs and, typically, higher coverages relative to single-caller only. Caller-exclusive variants showed significant differences in allele frequency and sequencing depth. These results highlight substantial variability in SNV detection across tools. While all showed high specificity, differences in sensitivity and variant profiles underscore the need for context-specific caller selection or ensemble approaches in cancer genomics. Full article

Background: Genetic variability in the vitamin D receptor (VDR) may influence immune regulation and systemic inflammation, factors potentially relevant for outcomes in orthopedic surgery. This study explored the association of the VDR rs7975232 (ApaI) single nucleotide variation (SNV) with inflammatory biomarkers and health-related quality of life (HRQoL). Methods: The study included 292 orthopedic patients and 90 controls. Genotyping was performed using real-time PCR. Laboratory analyses comprised hematological parameters, C-reactive protein (CRP), and serum 25-hydroxyvitamin D₃ [25(OH)D₃]. HRQoL was assessed with the SF-36 questionnaire. Associations between genotype, inflammation, and HRQoL were examined using regression models adjusted for age and body mass index (BMI). Results: Genotype (p = 0.023) and allele (p = 0.007) distributions differed between patients and controls. In multivariable models, the CC genotype was associated with higher neutrophil counts (p = 0.029), whereas the AA genotype was associated with elevated CRP levels (p = 0.025). Genotypic variation was further associated with SF-36 scores, independently of age and BMI. Conclusions: The VDR rs7975232 SNV may be associated with baseline systemic inflammation and patient-reported quality of life in orthopedic surgery. Genotyping of this variant may complement conventional biomarkers in future research aimed at improving diagnostic precision. Moreover, it could contribute to innovation in laboratory diagnostics aimed at improving perioperative outcomes. Full article

This study provides the first integrated spatio-temporal assessment of water quality in Lakes Tonga and Oubeira, two adjacent Ramsar-designated wetlands within El Kala National Park (Algeria). The objective was to identify major pollution sources and inform targeted conservation strategies. Physico-chemical, microbiological, and heavy metal analyses were performed on water samples collected monthly over one year (September 2022–August 2023) from two sites per lake. Applying robust statistical analyses (ANOVA, Kruskal–Wallis, PCA, boxplots) and high-resolution spatial mapping, we revealed significant spatio-temporal heterogeneity and distinct pollution profiles between the two lakes. Specifically, Lake Tonga exhibited higher concentrations of organic and bacterial pollutants, likely linked to agricultural runoff and domestic discharge, while Lake Oubeira was characterized by elevated heavy metal concentrations and higher mineralization. The calculated Water Quality Index (WQI) classified the water quality of both lakes predominantly as “Moderate”, with punctual “Poor” quality episodes. Numerous parameters consistently exceeded water quality standards, indicating substantial ecological and health risks. Spatial distribution maps clearly pinpointed pollution hotspots, guiding lake-specific management measures. These findings underscore the urgent need for differentiated, targeted management interventions and an integrated, multidisciplinary approach for the effective conservation of these valuable wetland ecosystems. Full article

SARS-CoV-2 persists worldwide, driving the demand for effective antivirals that inhibit replication and limit the emergence of resistant variants. Lycorine, a non-nucleoside inhibitor of SARS-CoV-2 RNA-dependent RNA polymerase, exhibits antiviral activity without direct mutagenic effects. Here, we examine the occurrence of single-nucleotide variants (SNVs) and insertions/deletions (indels) in SARS-CoV-2 B.1.499 strain during serial passages in Vero cells, comparing lycorine-treated cultures (2.5 and 5 µg/mL) with untreated controls. Whole-genome sequencing was used to assess mutation patterns and frequencies. Lycorine-treated passages displayed greater variant diversity than controls, with fixed mutations mainly affecting non-structural proteins (Nsp3-F1375A, Nsp5-L50F, and Nsp14-G265D) and the envelope protein (E-S6L). A 15-nucleotide deletion in the spike gene (QTQTN motif) occurred in both groups but became fixed only in untreated passages, suggesting negative selection under lycorine pressure. Notably, the L50F mutation in Nsp5, previously linked to nirmatrelvir resistance, was found exclusively in lycorine-treated passages. Additionally, a 1-nucleotide deletion in the accessory gene ORF8, detected only under lycorine treatment, resulted in a frameshift mutation that added four amino acids, potentially altering the protein’s function. Overall, lycorine induces a distinct mutation profile, favoring replication-related variants while suppressing deleterious deletions. These findings suggest potential mechanisms of cross-resistance and highlight the importance of monitoring resistance during clinical use. Full article

Type 2 diabetes (T2D) is a multifactorial disease characterized by chronic hyperglycemia. The FTO variant rs9939609 has been widely associated with obesity, but emerging evidence suggests a broader role for T2D risk across diverse populations. However, Mayan ancestry individuals remain underrepresented in genetic studies. Thus, we evaluated the association of rs9939609 with T2D, fasting glucose, and HbA1c in a southeastern Mexican region with prevalent Mayan ancestry. Birthplace was used as a proxy for ancestry, although no formal ancestry assessment was conducted. The A allele was associated with increased risk for T2D in both additive (OR = 1.88 [1.08–3.40], p = 0.031) and dominant (OR = 2.09 [1.08–4.15], p = 0.032) models, even after adjusting for age, sex, BMI, waist circumference, and waist/hip ratio. The A allele was also associated with increased fasting glucose and HbA1c levels in the dominant model. Mediation analysis suggested that T2D may mediate the effect of rs9939609-A on glucose traits. rs9939609-A may be a risk factor for T2D and elevated glucose levels in this population. However, the results should be interpreted with caution due to the limited sample size, which may result in under- or overestimation of effect sizes. Full article

Acute leukemias are highly aggressive hematologic malignancies that demand intensive chemotherapy regimens. However, drug toxicity remains a major barrier to treatment success and patient survival. In this context, pharmacogenomics offers a promising strategy by identifying single-nucleotide variants (SNVs) that influence drug metabolism, efficacy, and toxicity, ultimately impacting treatment outcomes. This study analyzed data from the ClinPGx/PharmGKB database to identify clinically annotated variants related to chemotherapy response in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). A total of 24 variants were curated for AML and 57 for ALL. Among these, nonsynonymous variants were most frequent in ALL (31.6%), while synonymous variants predominated in AML (33.3%). Although traditionally considered neutral, synonymous and intronic variants may influence gene expression through regulatory or splicing mechanisms. The analysis revealed clinically significant variants associated with chemotherapy response, particularly in the ABCB1 gene, observed in 12.5% of AML and 10.5% of ALL cases. Several variants, particularly TPMT, NUDT15, ABCC1, SLC28A3, and RARG, were associated with severe adverse effects such as myelotoxicity, mucositis, cardiotoxicity, and hepatotoxicity. This study reinforces the importance of genetic variants in modulating the therapeutic response and toxicity to chemotherapy drugs in acute leukemias. Analysis of ClinPGx/PharmGKB data emphasizes ABCB1 as a potential resistance marker and supports pre-treatment genotyping of genes like TPMT and NUDT15 to prevent severe toxicities. Future advances should include the expansion of pharmacogenetic studies in underrepresented populations and the clinical validation of new markers in prospective trials, aiming to consolidate precision medicine as a routine part of the therapeutic management of acute leukemias. Full article

The efficient and non-destructive inspection of the chlorophyll content of grey jujube leaf is of great significance for its growth surveillance and nutritional diagnosis. Near-infrared spectroscopy combined with chemometric methods provides an effective approach to achieve this goal. This study took grey jujube leaves as the research object, systematically collected near-infrared spectral data in the range of 4000–10,000 cm⁻¹, and simultaneously measured their soil and plant analyzer development (SPAD) value as a reference index for chlorophyll content. Through various pretreatment and their combination methods on the original spectrum—smooth, standard normal variable transformation (SNV), first derivative (FD), second derivative (SD), smooth + first derivative (Smooth + FD), smooth + second derivative (Smooth + SD), standard normal variable transformation + first derivative (SNV + FD), standard normal variable transformation + second derivative (SNV + SD)—the effects of different methods on the quality of the spectrum and its correlation with SPAD value were compared. The competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract the characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the model performance under different training functions was compared. The results indicate that after Smooth + FD pretreatment, followed by CARS screening of the characteristic wavelength, the BP neural network model trained using the LBFGS algorithm demonstrated the best performance, with its coefficient of determination (R²) of 0.87 (training set) and 0.85 (validation set), root mean square error (RMSE) of 1.36 (training set) and 1.35 (validation set), and residual prediction deviation (RPD) of 2.81 (training set) and 2.56 (validation set) showing good prediction accuracy and robustness. Research indicates that by combining near-infrared spectroscopy with feature extraction and machine learning methods, the rapid and non-destructive inspection of the grey jujube leaf SPAD value can be achieved, providing reliable technical support for the real-time monitoring of the nutritional status of jujube trees. Full article

This study explores the application of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy combined with various multivariate calibration techniques to detect the presence of cannabis in tobacco samples and tobacco in herbal smoking products. Both MIR and NIR spectra were recorded for self-prepared samples, followed by data exploration using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and the calculation of binary classification models with Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). PCA demonstrated a clear differentiation between tobacco samples containing and not containing cannabis. On the other hand, based on PCA, only NIR was able to distinguish herbal smoking products adulterated and not adulterated with tobacco. HCA further clarified these results by revealing distinct clusters within the data. Modelling results indicated that MIR and NIR spectroscopy, particularly when paired with preprocessing techniques like Standard Normal Variate (SNV) and autoscaling, demonstrated high classification accuracy in SIMCA and PLS-DA, achieving correct classification rates of 90% to 100% for external test sets. Comparison of MIR and NIR revealed that NIR spectroscopy resulted in slightly more accurate models for the screening of tobacco samples for cannabis and herbal smoking products for tobacco. The developed approach could be useful for the initial screening of tobacco samples for cannabis, e.g., in a night life setting by law enforcement, but also for inspectors visiting shops selling tobacco and/or herbal smoking products. Full article

Ensuring the authenticity of meat products is a critical issue for consumer protection, regulatory compliance, and the integrity of local food systems. In this study, attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric and machine learning models was applied to differentiate Maltese from non-Maltese pork. Spectral datasets were subjected to a range of preprocessing techniques, including Savitzky–Golay first and second derivatives, detrending, orthogonal signal correction (OSC), and standard normal variate (SNV). Linear methods such as principal component analysis–linear discriminant analysis (PCA-LDA), the soft independent modeling of class analogy (SIMCA), and partial least squares regression (PLSR) were compared against nonlinear approaches, namely support vector machine regression (SVMR) and artificial neural networks (ANNs). The results revealed that derivative preprocessing consistently enhanced spectral resolution and model robustness, with the fingerprint region (1800–600 cm⁻¹) yielding the highest discriminative power. While PCA-LDA, SIMCA, and PLSR achieved high accuracy, SVMR and ANN models provided a superior predictive performance, with accuracies exceeding 0.99 and lower misclassification rates under external validation. These findings highlight the potential of FTIR spectroscopy combined with nonlinear chemometrics as a rapid, non-destructive, and cost-effective strategy for meat authentication, supporting both consumer safety and sustainable food supply chains. Full article

Recent technological breakthroughs have enabled multidimensional phenotyping, with unprecedented single-cell resolution and genome-wide coverage, across multiple osteoarthritis (OA)-relevant tissues, such as articular cartilage, synovium, infrapatellar fat pad, and subchondral bone. The majority of the single nucleotide variations (SNVs) that have been associated with OA are located in non-protein coding regions and confer risk for disease by altering the expression level, instead of the amino acid sequence of the gene product. These data have shaped the concept of OA as a polygenic disease, where genetic factors disrupt the chromatin landscape in disease-relevant cells, leading to aberrant expression of effector genes. Pharmacologic manipulation of the OA-driving epigenetic landscape has recently emerged as an attractive path for the development of disease-modifying drugs. Novel clustered regulatory interspaced short palindromic repeats (CRISPR)-based technologies provide opportunities for precise epigenetic editing at the desired genomic regions and may allow a targeted transcriptional regulation of disease-relevant genes in disease-relevant cells. The aim of the present narrative review is to summarize the emerging data on the role of epigenetic factors and chromatin structure as calibrators of the risk for developing OA and to discuss the opportunities and challenges arising from the use of chromatin landscape to guide drug discovery. Full article

Background/Objectives: Short inverted repeats (SIRs) are abundant DNA motifs capable of forming secondary structures, such as hairpins and cruciforms, that can induce genome instability. However, their mutational consequences in cancer, particularly in osteosarcoma (OS), remain largely unexplored. Methods: In this study, we systematically identified over 5.2 million SIRs in the human genome and analyzed their mutational patterns across six common cancer types. Results: We found that increased small insertion and deletion (INDEL) density within SIR spacer regions represents a consistent feature across cancers, whereas elevated single nucleotide variant (SNV) and structural breakpoint density is cancer-type specific. Integrating whole-genome sequencing data from 13 OS patients, we found that both SNVs and INDELs are significantly enriched within SIR spacer regions in OS. Notably, genomic regions with higher SIR density tend to accumulate more somatic mutations, suggesting a link between SIR abundance and local genome instability. SIR-associated mutations frequently occur in oncogenes and tumor suppressor genes, including TP53, NFATC2, MECOM, LRP1B, RB1, CNTNAP2, and PTPRD, as well as in long non-coding RNAs. Mutational signature analysis further suggests that defective DNA mismatch repair and homologous recombination may act in concert with SIR-induced DNA structural instability to drive OS development. Conclusions: Our findings highlight SIRs as mutational hotspots and potential drivers of osteosarcoma pathogenesis. Full article

Background: Rubinstein–Taybi syndrome (RSTS) is characterized by intellectual disability, short stature, distinctive facial dysmorphism, broad thumbs/halluces, hearing loss, congenital heart or renal defects, and cryptorchidism in males. Pathogenic variants in CREBBP (~90% of cases) or EP300 (~10%) underlie the disorder, with ~88% single nucleotide variants (SNVs) and ~12% copy number variants (CNVs) in CREBBP. Materials and Methods: We investigated 17 patients clinically diagnosed with RSTS at a tertiary hospital in Poland. Genetic confirmation was achieved by next-generation sequencing, multiplex ligation-dependent probe amplification (MLPA), array comparative genomic hybridization (aCGH), or Sanger sequencing. Results: Pathogenic variants were identified in CREBBP (13/17, 76%) and EP300 (4/17, 24%). Variant types included frameshift indels (6/17, 35%), missense (4/17, 24%), nonsense (3/17, 18%), splice-site (2/17, 12%), and gross deletions (2/17, 12%). Notably, 13/17 (76%) were novel: ten in CREBBP (c.−49_12del, c.289C>T, c.1093_1096del, c.1094A>G, c.3178A>T, c.3401A>T, c.(3836+1_3837−1)_(4394+1_4395−1)del, c.4133+2T>G, c.4963dup, c.5028_5029dup) and three in EP300 (c.1942C>T, c.3044_3045del, c.4713_4722del). Among the novel CREBBP variants, eight occurred de novo and two had unknown inheritance. Two novel EP300 variants occurred de novo and one was of unknown origin. Conclusions: This first Polish RSTS cohort demonstrates a considerable proportion of gross deletions (12% overall; 15% in CREBBP) and an unexpectedly high rate of novel variants (76%), suggesting possible population-specific differences. These findings underscore the genetic heterogeneity of RSTS and highlight the importance of comprehensive molecular diagnostics and studies in underrepresented populations. Full article

This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant importance for the rational and efficient utilization of cottonseed resources, advancing research on the genetic improvement of cottonseed nutritional quality, and promoting the development of equipment for raw cottonseed protein detection. Fuzzy cottonseed samples from three varieties were collected, and their NIR fiber-optic spectra were acquired. Reference protein contents were measured using the Kjeldahl method. Spectra were denoised through preprocessing, after which informative wavelengths were selected by combining Uninformative Variable Elimination (UVE) with Competitive Adaptive Reweighted Sampling (CARS) and the Random Frog (RF) algorithm. Partial least squares regression (PLSR), least-squares support vector machine (LSSVM), and support vector regression (SVR) models were then constructed to predict protein content. Model performance was assessed using the coefficient of determination (R²), root-mean-square error (RMSE), residual predictive deviation (RPD), and range error ratio (RER). The results indicate that the standard normal variate (SNV) is the most effective preprocessing step. The best performance was achieved by the LSSVM model coupled with UVE + CARS, yielding R² = 0.8571, RMSE = 0.0033, RPD = 2.7078, and RER = 10.72, outperforming the PLSR and SVR counterparts. These findings provide technical support for the rapid detection of fuzzy cottonseed protein and lay the groundwork for the development of related detection equipment. Full article

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article

The methylation of CpG sites with 5mC mark is a dynamic epigenetic modification. However, the relationship between the methylation and the surrounding genomic sequence context remains poorly explored. Investigation of the allele methylation provides an opportunity to decipher the interplay between differences in the primary DNA sequence and epigenetic variation. Here, we performed high-coverage long-read whole-genome direct DNA sequencing of one individual using Oxford Nanopore technology. We also used Illumina whole-genome sequencing of the parental genomes in order to identify allele-specific methylation sites with a trio-binning approach. We have compared the results of the haplotype-specific methylation detection and revealed that trio binning outperformed other approaches that do not take into account parental information. Also, we analysed the cis-regulatory effects of the genomic variations for influence on CpG methylation. To this end, we have used available Deep Learning models trained on the primary DNA sequence to score the cis-regulatory potential of the genomic loci. We evaluated the functional role of the allele-specific epigenetic changes with respect to gene expression using long-read Nanopore RNA sequencing. Our analysis revealed that the frequency of SNVs near allele-specific methylation positions is approximately four times higher compared to the biallelic methylation positions. In addition, we identified that allele-specific methylation sites are more conserved and enriched at the chromatin states corresponding to bivalent promoters and enhancers. Together, these findings suggest that significant impact on methylation can be encoded in the DNA sequence context. In order to elucidate the effect of the SNVs around sites of allele-specific methylation, we applied the Deep Learning model for detection of the cis-regulatory modules and estimated the impact that a genomic variant brings with respect to changes to the regulatory activity of a DNA loci. We revealed higher cis-regulatory impact variants near differentially methylated sites that we further coupled with transcriptomic long-read sequencing results. Our investigation also highlights technical aspects of allele methylation analysis and the impact of sequencing coverage on the accuracy of genomic phasing. In particular, increasing coverage above 30X does not lead to a significant improvement in allele-specific methylation discovery, and only the addition of trio binning information significantly improves phasing. We investigated genomic variation in a single human individual and coupled computational discovery of cis-regulatory modules with allele-specific methylation (ASM) profiling. In this proof-of-concept analysis, we observed that SNPs located near methylated CpG sites on the same haplotype were enriched for sequence features suggestive of high-impact regulatory potential. This finding—derived from one deeply sequenced genome—illustrates how phased genetic and epigenetic data analyses can jointly put forward a hypotheses about the involvement of regulatory protein machinery in shaping allele-specific epigenetic states. Our investigation provides a methodological framework and candidate loci for future studies of genomic imprinting and cis-mediated epigenetic regulation in humans. Full article