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PubMedDate not supplied

Factors that influence women's nutrition knowledge in Saudi Arabia.

We studied knowledge of nutritional needs during pregnancy and lactation in 150 pregnant Saudi women at three primary health care centers in Riyadh, Saudi Arabia. We used an interview schedule to collect data regarding the women's knowledge and to determine the effects of certain independent variables on the knowledge scores. Green et al.'s (1980) PRECEDE model provided the theoretical framework for the study. Descriptive statistics, t test, and chi-square methods were used to analyze the data. The majority of the women had poor nutrition knowledge scores, with no significant differences among the three centers. A positive relationship was found between knowledge score and educational level. Negative relationships were found between knowledge score and number of pregnancies, number of deliveries, and number of living children. The findings have several implications for efforts to improve the health status of women in Saudi Arabia.

Adult↗
PubMed1994

Construction of a high-resolution linkage map for Xp22.1-p22.2 and refinement of the genetic localization of the Coffin-Lowry syndrome gene.

The genes responsible for two X-linked diseases, the Coffin-Lowry syndrome (CLS) and juvenile retinoschisis (RS), have been previously mapped, through linkage studies, to an 8-cM region, in Xp22.1-p22.2, flanked distally by two tightly linked markers, DXS207 and DXS43, and proximally by DXS274. In the present study, five Genethon markers have been assigned to the (DXS207, DXS43)-DXS274 interval using somatic cell hybrids and a meiotic breakpoint panel and ordered together with three markers previously mapped to this region. A genetic map, which includes 13 loci and spans a distance of approximately 13 cM, was derived from linkage analysis using the CEPH families. The most likely locus order and map distances (in centimorgans) are Xpter-DXS16-(3.4)-(DXS207, DXS43, DXS1053)-(2.0)-(DXS999, DXS257)-(1.7)-AFM291 wf5-(1.4) - DXS443 - (2.0) - (DXS1229, DXS365) - (2.1) - (DXS1052, DXS274, DXS41)-Xcen. Analysis of multiply informative crossovers established AFM291 wf5 and DXS1052 as new flanking markers for CLS, which significantly reduces the candidate region for this disease gene to a 4- to 5-cM interval. Three markers, DXS443, DXS1229, and DXS365, mapping within this interval showed complete cosegregation with the disease phenotype, giving a multipoint lod score of 14.2. The present map provides the framework for constructing a YAC contig for the CLS and RS region and should be useful for refining the localization of other disease genes mapping to this region. The panel of somatic cell hybrids characterized for the present study has also allowed us to refine the localization of five genes (CALB3, GRPR, PDHA1, GLRA2, and PHKA2) and two expressed sequence tags (DXS1118E and DXS1006E) previously assigned to the Xp22 region.

Abnormalities, Multiple↗
PubMedDate not supplied

Quality of care in family planning services in Morocco.

This study was conducted to heighten awareness of quality of care as a programmatic issue in the Moroccan governmental family planning program and to test modified Situation Analysis instruments for measuring quality of care. Data were collected from 50 service-delivery points in five provinces to measure six elements of quality in accordance with the Bruce/Jain framework. A procedure for calculating quality-indicator scores is presented. Although facilities varied by province and within provinces, most had the equipment and supplies needed to deliver services; service personnel were trained and regularly supervised; the service-delivery points scored well on mechanisms to ensure continuity of use. Notable shortcomings included a dearth of materials for counseling and a widespread unavailability of the Ovrette pill. This study raises issues regarding the complexity of measuring quality, the ownership of results, and the appropriateness of a centralized study of quality in a decentralized program.

Adult↗
PubMed1993

Cognitive functioning and anhedonia in subjects at risk for schizophrenia.

This study investigated the performance of individuals with familiar loading of schizophrenia (healthy siblings of schizophrenic inpatients) on three neuropsychological tasks assumed to require frontal lobe functions: Trail Making Test (TMT), verbal fluency and Wisconsin Card Sorting Test (WCST). Healthy siblings of schizophrenics differed in performance from healthy controls not only on the WCST, but also on the Trail Making Test and the verbal fluency task. Furthermore, scores of physical anhedonia, assessed in a self-report rating scale (Chapman et al., 1976) were also significantly higher in the high risk group than in the control sample. However, healthy siblings of schizophrenics did not differ from controls with regard to experiences of perceptual aberrations, measured by the same method (Chapman et al., 1978). Neuropsychological performance and elevated anhedonia scores in the high risk group were interpreted under the conceptual framework of vulnerability markers: they were supposed to represent a trait shared by family members of schizophrenic probands. Amongst the neuropsychological tests, there were significant correlations between the physical anhedonia score and WCST and Trail Making test performance in the group of healthy siblings of schizophrenics, but not in the control group.

Adult↗
PubMed1994

The ethical appropriateness of using prognostic scoring systems in clinical management.

The four ethical principles of beneficence, nonmaleficence, autonomy, and social justice provide a framework for making medical decisions, including those that involve the administration of life-sustaining therapies. In recent years, a number of prognostic scoring systems, including the Acute Physiology Assessment and Chronic Health Evaluation (APACHE) system, have been developed to augment clinical judgment in determining which critically ill patients are likely to benefit from such therapies. Although these systems all have limitations, their use in decision making is as ethically appropriate as is the use of clinical judgment, which has its own limitations and has been used for years.

AIDS-Related Opportunistic Infections↗
PubMed2026

A framework to infer de novo exonic variants when parental genotypes are missing enhances association studies of autism.

MOTIVATION: Gene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these de novo variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer de novo status and use this inference for association studies. RESULTS: We use data from autism spectrum disorder to illustrate and evaluate methods. Separating de novo from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a de novo score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate. AVAILABILITY AND IMPLEMENTATION: Codes are available at Github (https://github.com/HaeunM/TADA-RD) and Zenodo (DOI: https://doi.org/10.5281/zenodo.18531769).

Humans↗
PubMed1989

Score tests for homogeneity of regression effect in the proportional hazards model.

A simple model, containing the proportional hazards regression model as a special case, is presented. The purpose of the model is to provide a framework in which specific alternatives to the proportional hazards assumption may be tested. Rank-invariant score tests for linear, quadratic, or exponential trends can, for instance, all be undertaken within this framework. In the case of the two-sample problem the required calculations are shown to take a particularly simple form. Special consideration is given to the two-sample case in which there is an inversion of the regression effect, i.e., where the hazard functions cross at some given point. Both of the motivating examples are concerned with this problem. Computational aspects are relatively straightforward and some discussion on this is provided.

Actuarial Analysis↗
PubMed2026

Kv11.1 (hERG) Protein Interaction Networks Connect Endocytic Trafficking to Polygenic Influences on Cardiac Repolarization.

Polygenic scores (PGS) capture the combined effect of many common genetic variants on quantitative traits and disease risk, yet their functional consequences at the protein level remain poorly defined. Here, we integrated quantitative and interaction proteomics to resolve how polygenic liability for cardiac repolarization manifests in human cells. We studied human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from donors with extreme PGS for QT interval duration, a clinically relevant electrophysiologic trait associated with arrhythmia risk. Global quantitative proteomics revealed increased abundance of mitochondrial proteins in high-PGS cardiomyocytes. To define protein network-level effects on a key repolarizing ion channel, we performed multiplexed affinity purification-mass spectrometry (AP-MS) of Kv11.1. While mitochondrial changes did not directly explain Kv11.1-associated complexes, interactome analysis revealed increased association of Kv11.1 with myosin motor proteins and endosomal recycling machinery in high-PGS cells. These findings suggest altered channel trafficking dynamics of Kv11.1, distinct from the trafficking defects observed in monogenic Kv11.1 variants. Together, these data show that integrating global and interaction proteomics can resolve how polygenic variation reshapes protein networks. Future work using these methods could connect genomic risk to subcellular remodeling and our work provides a generalizable framework to probe the proteomic basis of complex traits. SIGNIFICANCE STATEMENT: Polygenic scores (PGS) predict disease risk, but how biological pathways are influenced by these common variants remains difficult to define. We generated human induced pluripotent stem cells from individuals with extreme high- and low- PGS for QT interval, a key electrocardiographic measure linked to arrhythmia risk. By combining global proteomics and interactomics for a common ion channel involved in regulating the QT interval (Kv11.1) we found potential mechanisms that are influenced by common genetic traits in patients. Our work provides an approach to connect polygenic scores to pathway-level molecular mechanisms in human cells and a general framework for uncovering how complex genetic architecture drives disease-relevant biology.

AP-MS↗
PubMed1994

Temporary remission of representational hemineglect through vestibular stimulation.

This study was aimed at finding whether vestibular stimulation could reduce neglect of representational space, in the same way it reduces other manifestations of the neglect syndrome. Representational neglect was shown in 8 patients asked to evoke mentally the map of France and to name as many towns as possible successively on each side of a vertical axis splitting the map in two equivalent halves. Performances appeared much poorer on the left than on the right side. Immediately after a cold left ear irrigation, the left side scores improved in all patients, although the right scores remained unchanged. The results are discussed within the framework of the representational theory of neglect. It is proposed that mental representation of space would result from dynamic and distributed neural mechanisms.

Attention↗
PubMed1981

There's a demon in your belly: children's understanding of illness.

Knowledge of how children come to understand the processes of causation, prevention, and treatment of illness is needed to help health professionals and educators in their work with children. Healthy children attending kindergarten through eighth grade were asked a series of standardized questions about illness, and their responses were scored on a scale corresponding to Piaget's theoretical framework of cognitive development. Children's responses varied widely at all ages, but there was a consistent systematic progression in their understanding of illness-related concepts with age. Kindergarten children typically understand illness causation as quite magical, and/or as the consequence of their transgression of rules. At fourth grade, children believe all illness to be caused by germs whose very presence is sufficient to make a child sick. The complexity of the mechanisms that must interrelate to cause illness is not understood until eighth grade at the earliest. At approximately 12 or 13 year of age children begin to understand that there are multiple causes of illness, that the body may respond variably to any or a combination of agents, and that host factors interact with the agent to cause and cure illness. This sequence in the development of understanding about illness parallels conceptual development in other content areas such as physical causality, although it seems to lag a bit. The delineation of the concepts typical of children at each developmental stage, as provided in this paper, will help to guide educational efforts and future research.

Adolescent↗
PubMed2026

EPIC: multi-objective guided diffusion for epitope design in TCR-pMHC complexes.

MOTIVATION: T cell receptor (TCR) recognition of peptide-major histocompatibility complex (pMHC) complexes is central to adaptive immunity, yet rational design of immunogenic epitopes remains elusive due to complex triplet binding constraints and data scarcity. No existing method can generate epitopes satisfying simultaneous requirements for antigenicity, MHC presentation, and TCR specificity. RESULTS: We present EPIC, a multi-objective diffusion framework that decomposes TCR-pMHC binding into three biologically grounded sub-tasks, enabling training-free gradient guidance without end-to-end retraining. By integrating ESM-based classifiers with a peptide diffusion generator, EPIC leverages heterogeneous immunological interaction datasets to generate diverse, context-aware epitopes. EPIC-designed top-three epitopes achieve lower predicted interface energies compared to ground-truth epitopes in 78.31% of test cases, while maintaining 80.1% sequence novelty and comparable structural confidence. Generated epitopes exhibit 100% uniqueness, high diversity (64.05%), and high antigenicity scores (0.4723). To our knowledge, EPIC is the first computational framework capable of de novo epitope design while explicitly integrating the triplet constraints of TCR-pMHC binding. This paradigm shift from discovery to design unlocks new potential for personalized cancer vaccines, precision adoptive T cell therapy, and rapid response to emerging infectious diseases. AVAILABILITY AND IMPLEMENTATION: The source code of EPIC is available at https://github.com/Octopus125/EPIC and archived on Zenodo (DOI: 10.5281/zenodo.18537646).

Receptors, Antigen, T-Cell↗
PubMed2026

Genome-wide association, polygenic risk scores, and machine learning for chronic post-surgical pain risk stratification: A UK biobank study.

Chronic post-surgical pain is a prevalent and debilitating complication following surgery, representing a clinical challenge. Despite the established heritability of pain phenotypes, large-scale genetic studies remain limited. This study aimed to identify genetic variants associated with chronic post-surgical pain, develop polygenic risk scores, and integrate these with clinical features for risk prediction. UK Biobank data from 47,836 participants (2490 cases and 45,346 controls) were split into training (80%; n = 38,268) and validation (20%; n = 9568) sets prior to analysis. A genome-wide association study was conducted on the training set only, across 19 million variants, and polygenic risk scores were constructed and integrated with clinical features in a logistic regression framework. Two close, rare, imputed signals crossed the genome-wide significance threshold but lacked local linkage-disequilibrium support, while 220 variants crossed the suggestive threshold. In the held-out validation set, cases had higher mean polygenic risk scores than controls (0.138 vs. -0.021; Cohen's d = 0.16, p < 0.001). A logistic regression model integrating clinical features and polygenic risk scores achieved an area under the curve of 0.639 (95% CI: 0.583-0.693), higher than models using either feature set alone. The polygenic risk score for chronic post-surgical pain was among the most important predictors. Risk stratification revealed the top quartile had 3.84-fold higher odds of chronic post-surgical pain than the bottom quartile (95% CI: 2.00-7.37). These findings suggest a possible modest genetic contribution to chronic post-surgical pain. Polygenic risk scores may complement clinical factors in surgical risk stratification. PERSPECTIVE: Chronic post-surgical pain may have a modest genetic contribution. This UK Biobank study identified over 220 variants at suggestive significance and constructed a polygenic risk score that was significantly elevated in cases. A combined clinical-genomic model achieved a 3.84-fold difference in odds across predicted-risk quartiles.

Chronic post-surgical pain↗
PubMed1988

Asymmetric stratification. An outline for an efficient method for controlling confounding in cohort studies.

Confounding is usually controlled by either cross-stratification or multivariate modeling. The first approach is simple and intuitive, but it is not practical for controlling many factors. The second approach, although less intuitive, may provide a more efficient means for controlling many confounders, but its ability to control confounding depends on the appropriateness of the chosen model. Hybrid methods based on a multivariate confounder score or a propensity score combine the favorable characteristics of both methods and may be better suited for controlling many confounders. However, the resulting strata are defined by subranges of a multivariate model, and, therefore, may possess little intrinsic meaning. The authors propose the principle of asymmetric stratification to control efficiently a number of confounders in cohort studies while retaining the intuitive appeal and general framework of cross-stratification. The proposed method resembles a propensity score analysis but does not use a multivariate model to define the strata. Instead, strata are defined by the categories of only a subset of the original potential confounders. The authors also demonstrate how our proposed method can be implemented by an application of classification and regression trees (CART) (recursive partitioning), as outlined by Breiman et al. (Classification and Regression Trees. Belmont, CA: Wadsworth, 1984). Computer simulations and an actual example suggest that the proposed method is a potentially simpler alternative to the standard propensity score analysis. Specific recommendations on how the proposed method can be improved are also presented.

Aged↗
PubMed2026

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

Sequence Alignment↗
PubMed1996

[Analysis of outcome quality control in intensive care medicine using the Simplified Acute Physiology Score II].

AIM: the main aim of the study was to assess the applicability of the Simplified Acute Physiology Score II (SAPS II) to the evaluation of outcome quality within the framework of quality assurance in patients in a medical intensive care unit. The outcome parameter employed was hospital mortality, measured as mortality index (hospital mortality actually observed/predicted mortality), the predicted mortality being derived from the individual mortality risk calculated for each patient in accordance with SAPS II. METHOD: For the period of one year, the SAPS II score, the individual mortality risk, the mean scores, mortality risk, intensive care and hospital mortality, and the mortality index (99% confidence interval) were calculated with the aid of a specially developed program for all 1,114 patients kept under observation or treated for longer than 4 hours in the intensive care unit. The entries (data) were monitored by random checks for the correctness of the individual entries and overall completeness of patient inclusion. The applicability of the SAPS II for our own patient material was checked with the aid of Receiver Operating Characteristic curves. In compliance with the original SAPS II to include patients of a coronary care unit but not to evaluate them, only the 604 patients with the diseases of medical intensive care were taken into account for quality control. High-risk groups (patients older than 76, critically ill patients with a mortality risk of more than 0,5, patients receiving respiratory support) and individual diagnostic categories were considered separately as subgroups. RESULTS: In the entire group, the mean mortality risk was 21,1% the observed intensive care mortality 11,2%, the hospital mortality 18,0%, and the mortality index 0,86 (0,75 to 1,00). The mortality actually observed, therefore, corresponded to that predicted on the basis of the SAPS prognostic system. Also in the subgroups of elderly patients, and individual diagnostic categories (cerebral, bronchopulmonary cardiovascular, gastrointestinal diseases), the mortality index did not differ significantly from 1,0. A mortality index significantly less than 1,0 (observed mortality significantly lower than predicted mortality) was found in the sub-groups of the seriously ill, of patients receiving respiratory support, and in the diagnostic category of intoxications. The monthly analysis showed fluctuating mortality indices which, however, never differed significantly from 1,0. The surface under the ROC curve for the entire group was 0,89, and 0.81-0.99 for the various diagnostic categories. CONCLUSIONS: The prognostic system SAPS II can be employed to evaluate the quality of outcome measured by hospital mortality in patients of a medical intensive care unit, provided that the applicability of the score is demonstrated for the patient material involved, the outcome of the overall group and of the high-risk groups is referred to the accuracy and completeness of the entered data is checked, and the scoring systems accepted as quality standard.

Adolescent↗
PubMed1996

FASTA-SWAP and FASTA-PAT: pattern database searches using combinations of aligned amino acids, and a novel scoring theory.

We introduce two new pattern database search tools that utilize statistical significance and information theory to improve protein function identification. Both the general pattern scoring theory with the specific matrices introduced here and the low redundancy of pattern databases increase search sensitivity and selectivity. Pattern scoring preferentially rewards matches at conserved positions in a pattern with higher scores than matches at variable positions, and assigns more negative scores to mismatches at conserved positions than to mismatches at variable positions. The theory of pattern scoring can be used to create log-odds pattern scores for patterns derived from any set of multiple alignments. This theoretical framework can be used to adapt existing sequence database search tools to pattern analysis. Our FASTA-SWAP and FASTA-PAT tools are extensions of the FASTA program that search a sequence query against a pattern database. In the first step, FASTA-SWAP searches the diagonals of the query sequence and the library pattern for high-scoring segments, while FASTA-PAT performs an extended version of hashing. In the second step, both methods refine the alignments and the scores using dynamic programming. The tools utilize an extremely compact binary representation of all possible combinations of amino acid residues in aligned positions. Our FASTA-SWAP and FASTA-PAT tools are well suited for functional identification of distant relatives that may be missed by sequence database search methods. FASTA-SWAP and FASTA-PAT searches can be performed using our World-Wide Web Server (http://dot.imgen.bcm.tmc.edu:9331/seq-search/Op tions/fastapat.html).

Algorithms↗
PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗
PubMed2025

OctopuSV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and analysis.

MOTIVATION: Structural variants (SVs) influence gene regulation, disease progression, and diagnostics, yet integrating SV calls across platforms remains difficult due to inconsistent annotations, limited merging flexibility, and fragmented workflows. Ambiguous breakend (BND) annotations, which comprise many variant calls, are often discarded or misclassified, hindering variant characterization. Existing tools lack advanced merging operations essential for precise identification of disease-specific or somatic variants across samples or patient groups. Additionally, current SV analysis pipelines require extensive manual intervention and complex parameter tuning, compromising reproducibility and scalability. Addressing these gaps is crucial for improving the accuracy, interpretability, and clinical utility of SV analyses. RESULTS: We developed OctopuSV and TentacleSV to address these long-standing challenges in SV analysis. OctopuSV features a specialized BND correction module that converts ambiguous BND annotations into canonical SV types, recovering important variants that are often overlooked by existing tools. Additionally, it provides advanced set operations (difference, complement, custom-defined) that enable sophisticated variant filtering without programming expertise, critical for identifying tumor-specific SVs or variants unique to specific sample groups. TentacleSV completes our solution by automating the entire SV analysis process from raw sequencing data to high-confidence callsets, ensuring consistency and reproducibility across projects. Benchmarking across short-read and long-read platforms showed superior F1 score, complete SV type consistency compared to existing tools. Our framework enables experimental biologists and clinical researchers to perform sophisticated analyses ranging from cancer subtype-specific SV identification to multi-sample comparative studies without requiring specialized programming skills. AVAILABILITY AND IMPLEMENTATION: All codes are available at https://github.com/ylab-hi/OctopuSV; https://github.com/ylab-hi/TentacleSV.

Software↗