Search Results (46)

This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a formally defined local processing scheme and integrates three coordinated levels of parallelism: block-based partitioning of file-backed VCF portions read sequentially into localized fragments with data-level parallel processing; task-level decomposition of feature construction into independent transformations; and execution-level specialization via JIT compilation of numerical kernels. To prevent performance degradation caused by nested parallelism, a resource-control mechanism is introduced as an execution rule that bounds effective parallelism and mitigates oversubscription, improving throughput stability on a single multi-core CPU node. Experiments on a public chromosome-17 VCF dataset for BRCA1-region pathogenicity classification demonstrate that the proposed multi-level local CPU execution (parsing/filtering, feature construction, and JIT-specialized numeric kernels) reduces runtime from 291.25 s (sequential) to 73.82 s, yielding a 3.95× speedup. When combined with resource-coordinated parallel model training, the end-to-end runtime further decreases to 51.18 s, corresponding to a 5.69× speedup, while preserving classification quality (accuracy 0.8483, precision 0.8758, recall 0.8261, F1 0.8502). A stage-wise ablation analysis quantifies the contribution of each execution level and confirms consistent scaling under resource-bounded execution. Full article

Accessory breasts denote the formation of extra breast tissue along the milk line, and are known to be more prevalent among Black and Asian populations, affecting both genders. This first-ever study aimed to determine the genetic aetiology of accessory breasts in a multiplex family, where all female siblings present with bilateral accessory breasts. The study also ascertained secondary findings (SFs) responsible for comorbidities. Clinical data and saliva samples were obtained from all family members. Ultrasound and histopathology confirmed the diagnosis. Whole-exome sequencing was conducted on DNA samples obtained from the saliva, with variant calling conducted utilising the Sentieon workflow. Variant classification was based on American College of Medical Genetics and Genomics guidelines. After segregation analysis, 12 candidate genes emerged. Among these, PRSS50 and FANCC emerged as top candidates, being implicated in breast diseases. However, two variants in FANCC (c.360del; p.His120GlnfsTer24 and c.355_358del; p.Ser119IlefsTer24) were selected as the most probable causal variants because of the role of this gene in hereditary breast and ovarian cancer syndromes. The remaining ten genes were reported as potentially accounting for comorbidities segregating with accessory breasts. Reported SFs involve TTR and RYR1. In conclusion, pathogenic variants in FANCC cause familial accessory breasts. These novel observations impact pathophysiology, genetic counselling, and personalised medicine. Full article

Mugalzhar horses are a relatively young native breed of Kazakhstan, prized for meat and milk production and adaptation. This study was conducted to investigate genetic diversity and pinpoint genomic regions associated with selection signatures in this breed using whole-genome sequence data. Variant calling yielded a total of 21,722,393 high-quality variants, including 19,495,163 SNPs and 2,227,230 indels. Most variants were located in introns and intergenic regions, while only 1.94% were exonic. Estimates of genetic diversity were moderate, with expected and observed heterozygosity and nucleotide diversity of 0.2325, 0.2402, and 0.0021, respectively. We identified nine adaptive candidate genes (SCAPER, FHAD1, MMP15, ADGRE1, CMKLR1, MRPL15, ZNF667, CCDC66, and LOC100055310), harboring high-impact exonic variants in the homozygote state for an alternative allele. No deleterious segregating variants associated with Mendelian traits were found in this population, while seven variants linked to coat color and gaitedness were detected in a low frequency heterozygous state. Our findings suggest that there are certain genomic regions subjected to ancient selection footprints during the ancestor breed formation and adaptation. The outcome of this study serves as a foundation for future genomic-driven strategies, a broader utilization of this breed, and a reference for genomic studies on other horse breeds. Full article

Transcribed conserved non-coding elements (TCNEs), which are non-coding genomic elements that can regulate vital gene expression, play an unclear role in the development of severe diseases mainly associated with carcinogenesis. Currently, there are no mature tools for the identification of TCNEs. To compensate for the lack of a systematic interpretation of the functional characterization and regulatory mechanisms of TCNE spatiotemporal activities, we developed a flexible pipeline, called captureTCNE, to depict the landscape of TCNEs and applied it to our breast cancer cohort (SEU-BRCA). Meanwhile, we investigated the genome-wide characteristics of TCNEs and unraveled that TCNEs harbor enhancer-like chromatin signatures as well as participate in the transcriptional machinery to regulate essential genes or architect biological regulatory networks of breast cancer. Specifically, the TCNE transcripts could recruit RBPs, such as ENOX1 and PTBP1, which are involved in gene expression regulation, to participate in the formation of regulatory networks and the association with altered splicing patterns. In particular, the presence of a non-classical secondary structure, called RNA G-quadruplex, on TCNE transcripts contributed to the recruitment of RBPs associated with subtype-specific transcriptional processes related to the estrogen response in breast cancer. Ultimately, we also analyzed the mutational signatures of variant-containing TCNEs and discerned twenty-one genes as essential components of the regulatory mechanism of TCNEs in breast cancer. Our study provides an effective TCNE identification pipeline and insights into the regulatory mechanisms of TCNEs in breast cancer, contributing to further knowledge of TCNEs and the emergence of innovative therapeutic strategies for breast cancer. Full article

What is apoptosis? The Nomenclature Committee on Cell Death and numerous other pioneering cancer/p53 biologists use the terms “apoptosis” and “cell death” interchangeably, disregard the mind-numbing complexity and heterogeneity that exists within a tumor (intratumor heterogeneity), disregard the contribution of polyploid giant cancer cells (PGCCs; the root causes of therapy resistance and relapse) to this heterogeneity, and then propose novel apoptosis-stimulating anticancer strategies. This is shocking for the following three reasons. First, clinical studies reported since the 1990s have revealed that increased apoptosis in solid tumors is associated with increased tumor diversity and poor prognosis. Second, we have known for years that dying (apoptotic) cancer cells release a panel of secretions (e.g., via phoenix rising and other pathways) that promote metastatic outgrowth. Third, over a decade ago, it was demonstrated that cancer cells can recover from late stages of apoptosis (after the formation of apoptotic bodies) via the homeostatic process of anastasis, resulting in the emergence of aggressive variants. The cell surface expression of CD24 has recently been reported to be preferentially enriched in recovered (anastatic) cancer cells that exhibit tumorigenic properties. These and related discoveries outlined herein call for a paradigm shift in oncology to focus on strategies that minimize the occurrence of treacherous apoptosis and other tumor-repopulating events (e.g., therapy-induced cancer cell dormancy and reactivation). They also raise an intriguing question: is deregulated anastasis (rather than evasion of apoptosis) a hallmark of cancer? Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tuberculosis), remains a leading cause of death from infectious diseases globally. The treatment of active TB relies on first- and second-line drugs, however, the emergence of drug resistance poses a significant challenge to global TB control efforts. Recent advances in whole-genome sequencing combined with machine learning have shown promise in predicting drug resistance. This study aimed to evaluate the performance of four machine learning models in classifying resistance to ethambutol, isoniazid, and rifampicin in M. tuberculosis isolates. Methods: Four machine learning models—Extreme Gradient Boosting Classifier (XGBC), Logistic Gradient Boosting Classifier (LGBC), Gradient Boosting Classifier (GBC), and an Artificial Neural Network (ANN)—were trained using a Variant Call Format (VCF) dataset preprocessed by the CRyPTIC consortium. Three datasets were used: the original dataset, a principal component analysis (PCA)-reduced dataset, and a dataset prioritizing significant mutations identified by the XGBC model. The models were trained and tested across these datasets, and their performance was compared using sensitivity, specificity, Precision, F1-scores and Accuracy. Results: All models were applied to the PCA-reduced dataset, while the XGBC model was also evaluated using the mutation-prioritized dataset. The XGBC model trained on the original dataset outperformed the others, achieving sensitivity values of 0.97, 0.90, and 0.94; specificity values of 0.97, 0.99, and 0.96; and F1-scores of 0.93, 0.94, and 0.92 for ethambutol, isoniazid, and rifampicin, respectively. These results demonstrate the superior accuracy of the XGBC model in classifying drug resistance. Conclusions: The study highlights the effectiveness of using a binary representation of mutations to train the XGBC model for predicting resistance and susceptibility to key TB drugs. The XGBC model trained on the original dataset demonstrated the highest performance among the evaluated models, suggesting its potential for clinical application in combating drug-resistant tuberculosis. Further research is needed to validate and expand these findings for broader implementation in TB diagnostics. Full article

Idiopathic granulomatous mastitis (IGM) is a rare condition characterised by chronic inflammation and granuloma formation in the breast. The aetiology of IGM is unclear. By focusing on the protein-coding regions of the genome, where most disease-related mutations often occur, whole-exome sequencing (WES) is a powerful approach for investigating rare and complex conditions, like IGM. We report WES results on paired blood and tissue samples from eight IGM patients. Samples were processed using standard genomic protocols. Somatic variants were called with two analytical pipelines: nf-core/sarek with Strelka2 and GATK4 with Mutect2. Our WES study of eight patients did not find evidence supporting a clear genetic component. The discrepancies between variant calling algorithms, along with the considerable genetic heterogeneity observed amongst the eight IGM cases, indicate that common genetic drivers are not readily identifiable. With only three genes, CHIT1, CEP170, and CTR9, recurrently altering in multiple cases, the genetic basis of IGM remains uncertain. The absence of validation for somatic variants by Sanger sequencing raises further questions about the role of genetic mutations in the disease. Other potential contributors to the disease should be explored. Full article

Understanding the correlation between genotype and phenotype remains challenging for modern genetics. Digenic network analysis may provide useful models for understanding complex phenotypes that traditional Mendelian monogenic models cannot explain. Clinical data, whole exome sequencing data, in silico, and machine learning analysis were combined to construct a digenic network that may help unveil the complex genotype–phenotype correlations in a child presenting with inherited seizures and thrombocytopenia. The proband inherited a maternal heterozygous missense variant in SCN1A (NM_001165963.4:c.2722G>A) and a paternal heterozygous missense variant in MYH9 (NM_002473.6:c.3323A>C). In silico analysis showed that these two variants may be pathogenic for inherited seizures and thrombocytopenia in the proband. Moreover, focusing on 230 epilepsy-associated genes and 35 thrombopoiesis genes, variant call format data of the proband were analyzed using machine learning tools (VarCoPP 2.0) and Digenic Effect predictor. A digenic network was constructed, and SCN1A and MYH9 were found to be core genes in the network. Further analysis showed that MYH9 might be a modifier of SCN1A, and the variant in MYH9 might not only influence the severity of SCN1A-related seizure but also lead to thrombocytopenia in the bone marrow. In addition, another eight variants might also be co-factors that account for the proband’s complex phenotypes. Our data show that as a supplement to the traditional Mendelian monogenic model, digenic network analysis may provide reasonable models for the explanation of complex genotype–phenotype correlations. Full article

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects the motoneurons. More than 40 genes are related with ALS, and amyloidogenic proteins like SOD1 and/or TDP-43 mutants are directly involved in the onset of ALS through the formation of polymorphic fibrillogenic aggregates. However, efficacious therapeutic approaches are still lacking. Notably, heterozygous missense mutations affecting the gene coding for RNase 5, an enzyme also called angiogenin (ANG), were found to favor ALS onset. This is also true for the less-studied but angiogenic RNase 4. This review reports the substrate targets and illustrates the neuroprotective role of native ANG in the neo-vascularization of motoneurons. Then, it discusses the molecular determinants of many pathogenic ANG mutants, which almost always cause loss of function related to ALS, resulting in failures in angiogenesis and motoneuron protection. In addition, ANG mutations are sometimes combined with variants of other factors, thereby potentiating ALS effects. However, the activity of the native ANG enzyme should be finely balanced, and not excessive, to avoid possible harmful effects. Considering the interplay of these angiogenic RNases in many cellular processes, this review aims to stimulate further investigations to better elucidate the consequences of mutations in ANG and/or RNase 4 genes, in order to achieve early diagnosis and, possibly, successful therapies against ALS. Full article

Background and objective: The symptoms of most neurodegenerative diseases, including Parkinson’s disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Materials and Methods: Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Results: Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Conclusions: Genetic biomarkers are crucial for the early detection of Parkinson’s disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits. Full article

One of the most important areas of modern genome research is the search for meaningful relationships between genetic variants and phenotypes. In the livestock field, there has been research demonstrating the influence of copy number variants (CNVs) on phenotypic variation. Despite the wide range in the number and size of detected CNVs, a significant proportion differ between breeds and their functional effects are underestimated in the pig industry. In this work, we focused on the problem of leg defects in pigs (lumps/growths in the area of the hock joint on the hind legs) and focused on searching for molecular genetic predictors associated with this trait for the selection of breeding stock. The study was conducted on Large White pigs using three CNV calling tools (PennCNV, QuantiSNP and R-GADA) and the CNVRanger association analysis tool (CNV-GWAS). As a result, the analysis identified three candidate CNVRs associated with the formation of limb defects. Subsequent functional analysis suggested that all identified CNVs may act as potential predictors of the hock joint phenotype of pigs. It should be noted that the results obtained indicate that all significant regions are localized in genes (CTH, SRSF11, MAN1A1 and LPIN1) responsible for the metabolism of amino acids, fatty acids, glycerolipids and glycerophospholipids, thereby related to the immune response, liver functions, content intramuscular fat and animal fatness. These results are consistent with previously published studies, according to which a predisposition to the formation of leg defects can be realized through genetic variants associated with the functions of the liver, kidneys and hematological characteristics. Full article

Although Genome Reference Consortium Human Build 38 (GRCh38) was released with improvement over GRCh37, it has not been widely adopted. Several liftover tools have been developed as a convenient approach for GRCh38 implementation. This study aimed to investigate the accuracy of liftover tools for genome conversion. Two Variant Call Format (VCF) files aligned to GRCh37 and GRCh38 were downloaded from ClinVar (clinvar_20221217.vcf.gz). Liftover tools such as CrossMap, NCBI Remap, and UCSC liftOver were used to convert genome coordinates from GRCh37 to GRCh38. The accuracy of CrossMap, NCBI Remap, and UCSC liftOver were 99.81% (1,567,838/1,570,748), 99.69% (1,565,953/1,570,748), and 99.99% (1,570,550/1,570,748), respectively. Variants that failed conversion via all three liftover tools were all indels/duplications: a pathogenic/likely pathogenic variant (n = 1) and benign/likely benign variants (n = 7). The eight variants that failed conversion were identified in the ALMS, TTN, CFTR, SLCO, LDLR, PCNT, MID1, and GRIA3 genes, and all the variants were not in the VCF files aligned to GRCh37. This study demonstrated that three liftover tools could successfully convert reference genomes from GRCh37 to GRCh38 in more than 99% of ClinVar variants. This study takes the first step to clinically implement GRCh38 using liftover tools. Further clinical studies are warranted to compare the performance of liftover tools and to validate re-alignment approaches in routine clinical settings. Full article

The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with Machine Reliability (GCFP-MR). This problem is known to be NP-Hard, and finding efficient solutions is of utmost importance. Metaheuristics have been recognized as effective optimization techniques due to their adaptability and ability to generate high-quality solutions in a short time. Since BWO was originally designed for continuous optimization problems, its adaptation involves binarization. Accordingly, our proposal focuses on adapting the Black Widow Optimization (BWO) metaheuristic to tackle GCFP-MR, leading to a new approach named Binary Black Widow Optimization (B-BWO). We compare our proposal in two ways. Firstly, it is benchmarked against a previous Clonal Selection Algorithm approach. Secondly, we evaluate B-BWO with various parameter configurations. The experimental results indicate that the best configuration of parameters includes a population size $\left(Pop\right)$ set to 100, and the number of iterations $\left(Ma{x}_{i}ter\right)$ defined as 75. Procreating Rate $\left(PR\right)$ is set at 0.8, Cannibalism Rate $\left(CR\right)$ is set at 0.4, and the Mutation Rate $\left(PM\right)$ is also set at 0.4. Significantly, the proposed B-BWO outperforms the state-of-the-art literature’s best result, achieving a noteworthy improvement of 1.40%. This finding reveals the efficacy of B-BWO in solving GCFP-MR and its potential to produce superior solutions compared to alternative methods. Full article

In recent decades, the use of genetic polymorphisms related to specific phenotypes, such as eye color, has greatly contributed to the development of the research field called forensic DNA phenotyping (FDP), enabling the investigators of crime cases to reduce the number of suspects, making their work faster and more precise. Eye color is a polygenic phenotype, and many genetic variants have been highlighted, with the major contributor being the HERC2-OCA2 locus, where many single nucleotide variations (SNPs) were identified. Interestingly, the HERC2-OCA2 locus, containing the intronic SNP rs12913832, the major eye color determinant, shows a high level of evolutionary conservation across many species of vertebrates. Currently, there are some genetic panels to predict eye color by genomic DNA analysis, even if the exact role of the SNP variants in the formation of eye color is still poorly understood, with a low level of predictivity in the so-called intermediate eye color. Many variants in OCA2, HERC2, and other genes lie in introns or correspond to synonymous variants, highlighting greater complexity in the mechanism of action of such genes than a simple missense variation. Here, we show the main genes involved in oculocutaneous pigmentation and their structural and functional features, as well as which genetic variants show the highest level of eye color predictivity in currently used FDP assays. Despite the great recent advances and impact of FDP in criminal cases, it is necessary to enhance scientific research to better understand the mechanism of action behind each genetic variant involved in eye color, with the goal of obtaining higher levels of prediction. Full article

There have been outbreaks of SARS-CoV-2 around the world for over three years, and its variants continue to evolve. This has become a major global health threat. The main protease (M^pro, also called 3CL^pro) plays a key role in viral replication and proliferation, making it an attractive drug target. Here, we have identified a novel potential inhibitor of M^pro, by applying the virtual screening of hundreds of nilotinib-structure-like compounds that we designed and synthesized. The screened compounds were assessed using SP docking, XP docking, MM-GBSA analysis, IFD docking, MD simulation, ADME/T prediction, and then an enzymatic assay in vitro. We finally identified the compound V291 as a potential SARS-CoV-2 M^pro inhibitor, with a high docking affinity and enzyme inhibitory activity. Moreover, the docking results indicate that His41 is a favorable amino acid for pi-pi interactions, while Glu166 can participate in salt-bridge formation with the protonated primary or secondary amines in the screened molecules. Thus, the compounds reported here are capable of engaging the key amino acids His41 and Glu166 in ligand-receptor interactions. A pharmacophore analysis further validates this assertion. Full article