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PubMed2005

The Amiel-Tison Neurological Assessment at Term: conceptual and methodological continuity in the course of follow-up.

The Amiel-Tison Neurological Assessment at Term (ATNAT) is part of a set of three different instruments based on a neuro-maturative framework. By sharing a same methodology and a similar scoring system, the use of these three assessments prevents any rupture in the course of high risk children follow-up from 32 weeks post-conception to 6 years of age. The ATNAT which takes 5 minutes to administer may be used in clinical setting as well as in research. Clustering of severe to mild neuro-cranial signs in the neonatal period permits identification of children who could benefit from early intervention.

Child↗

PubMed2001

Meta-analysis for model-free methods.

The intricate nature of complex genetic traits dictates that novel methodologies be developed and utilized to achieve better power, better accuracy, and more favorable balance between type I and type II errors than could be achieved by the traditional methods as they are used in mapping Mendelian traits. Meta-analysis provides one such method for synthesizing information from multiple studies. This has the advantage of being able to pool relatively weak signals from individual studies into a collectively stronger evidence of genetic effects, while at the same time providing a quantitative framework for modeling variability among studies. The traditional lod score measures significance level of a linkage effect in an individual study, and its additive property make it a natural candidate for combining results across independent studies. To incorporate the within-study variation of the linkage effect into the pooled overall measure of genetic effect, the effect sizes (such as the proportion of genes shared identical-by-descent, IBD) should be pooled directly across studies. Traditional regression models and mixed effects models can be used to estimate the overall genetic effect size and its variance, and to test heterogeneity among studies. Our simulation studies show that designing studies with moderate power and pooling their results via meta-analysis may be more cost-effective than large dedicated studies. We believe that, as a newly emerging methodology, the meta-analysis approach has the potential to become an integral part of our toolbox that will expedite the search for complex human disease genes.

Alleles↗

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↗

PubMed2005

Social cognitive theory and physical activity during breast cancer treatment.

PURPOSE/OBJECTIVES: To measure the association between physical activity and social cognitive theory constructs during breast cancer treatment. DESIGN: Cross-sectional survey. SETTING: Midwestern, academic oncology clinic. SAMPLE: 21 primarily Caucasian (90%) female patients with breast cancer undergoing treatment. 76% were > 50 years old; 76% had stage I or II disease. 17 completed the study. METHODS: Survey (structured interview or self-administration), chart audit, pedometer, and seven-day physical activity recall. MAIN RESEARCH VARIABLES: Steps per day, energy expenditure, self-efficacy, barriers, partners and role models, prior physical activity counseling, physical activity knowledge, pretreatment physical activity, outcome expectations and values, goals, reinforcement management, and emotional well-being. FINDINGS: A higher average of steps per day was significantly associated with having an exercise role model and higher annual income. A higher daily energy expenditure (kilocalories per kilogram body weight per day) was significantly associated with higher barrier self-efficacy, higher task self-efficacy, having an exercise partner, having an exercise role model, higher physical activity enjoyment, and lower negative value score. CONCLUSIONS: Social cognitive theory may provide a useful framework for understanding physical activity among patients with breast cancer during treatment, but correlation strength varies with physical activity measurement type. IMPLICATIONS FOR NURSING: Social cognitive theory and physical activity during breast cancer treatment warrant additional study with larger sample sizes and multivariate analyses. Interventions to increase physical activity among patients with breast cancer may use social cognitive theory and assess theory constructs as potential mediators or moderators in intervention evaluation.

Breast Neoplasms↗

PubMedDate not supplied

Critical care competencies.

In the absence of nationally accepted critical care competencies, each educational institution providing critical care programmes is forced to define the essential competencies necessary for practice, leading to variations in expected practice and the emergence of 'postcode' competencies. This research report aims to build upon competency activity for all areas of nursing practice within critical care levels 1, 2 and 3. A functional analysis to elicit core critical care competency statements was conducted and a modified Delphi technique was used to generate consensus opinion from a pan-London purposive sample of nurses working in critical care. The functional analysis group identified four competency statements and elements of competencies. Consensus agreement of 80% was achieved with mean agreement scores that exceed 97%. A core critical care competency framework was refined and developed by expert nurses drawing on their own experience and knowledge of critical care nursing. The framework could be useful to: educationalists designing competency-based curricula; critical care managers as a tool for recruitment and retention and for education and training of staff; individual critical care nurses to facilitate continuous professional development.

Clinical Competence↗

PubMedDate not supplied

Youth safety education and injury prevention program.

Unintentional injury remains the leading cause of death in U.S. children. This article provides a case evaluation of a school based youth safety education program based on the Think First National Injury Prevention Program curriculum. Partners for the program included an urban elementary school, a school of nursing, a Safe Kids Coalition, a regional trauma center, and pediatric, community, and critical care nurses. A convenience sample of 140 second grade children in a mid-Atlantic elementary school participated in a 1 hour-a-week injury prevention class over the course of 6 weeks. The Think First National Injury Prevention Program served as the core curriculum and evaluation framework. At the end of the program, knowledge test scores increased an average of 35% over pre-test measures. Ninety-eight percent of the faculty, staff nurses, student nurses, children, and parents indicated a positive overall value of the program and the need for it to continue on an ongoing basis. Safety resources are provided for pediatric nurses to use in their diverse practice settings.

Child↗

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↗

PubMed2001

Psychometric properties of child- and teacher-reported curl-up scores in children ages 10-12 years.

The purpose of this study was to examine the psychometric properties of child- and teacher-reported curl-up (CU) scores in children ages 10-12 years in both a norm-referenced (NR) and criterion-referenced (CR) framework. Eighty-four children, 36 boys and 48 girls, performed the FITNESSGRAM (Cooper Institute for Aerobics Research, 1992) CU test on 2 days separated by 48-72 hr. Two video cameras were used to record students' CU performances. Two students performed the CU at the same time, with each child's performance recorded by one camera. The test was terminated when the child stopped due to fatigue or after two form errors occurred. Teacher-reported scores were the average of two independent ratings of each video performance, while child-reported scores came from data collected and recorded by the children. Single trial norm-referenced reliability was R = .75 for girls and R = .80 for boys for teacher-reported CU and R = .69 and R = .70 for child-reported CU for girls and boys, respectively. CR reliability was examined using P, proportion of students who consistently passed or failed the test across 2 days, and km, defined as reliability with chance removed. For teacher-reported scores, P = .89 and km = .78 for boys and P = .81 and km = .62 for girls. For child-reported scores, P = .86 and km = .72 for boys, while P = .79 and km = .58 for girls. For teacher-reported data, 39% of boys passed and 50% failed the test on both days, while for girls the percentages were 27% pass and 54% fail. For child-reported data, 64% of boys passed and 22% failed on both days, while 54% of girls passed and 25% failed. NR validity was examined by correlating teacher and child-reported scores. The resultant coefficient was r = .42 (95% CI = .11-.66) for boys and r = .67 (95% CI = .58-.74) for girls. Additionally, child-reported scores were significantly higher than teacher-reported scores. CR validity was examined with a contingency coefficient, and results indicated C = .55 with 44% false master errors for boys and C = .65 with 29% false master errors for girls. The findings of this study suggest that while NR reliability estimates were moderate for teacher-reported scores, single trial estimates suggest that child-reported CU should be viewed with caution. In regard to CR reliability, both teacher-reported and child-reported reliability were moderate. However, there were marked differences between teacher- and child-reported scores, with children reporting higher percentages of students passing and lower percentage of student failing the test when compared with scores reported by teachers. Validity was rather moderate when viewed in either a NR and CR framework. It is suggested that problems with child-reported scores may be due to the need for additional practice or simplification of the testing protocol.

Analysis of Variance↗

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↗

PubMed2006

Body mass index and body weight perception as risk factors for internalizing and externalizing problem behavior among adolescents.

PURPOSE: To examine the relationship between body mass index (BMI), body weight perception (BWP), and indicators of internalizing and externalizing distress and social, attention and thought problems in a large representative sample of Dutch youth. METHODS: A total of 1826 pupils in the eighth grade of primary education and 5730 students in the first four years of secondary education gave their height and weight to obtain an estimate of their BMI. They reported their evaluation of their body weight and completed Achenbach's Youth Self-Report (YSR) (1991), which assesses eight types of problem behavior. Data were analyzed in a multivariate framework with BMI and BWP as predictors and the YSR scores on different kinds of problem behavior as dependent variables, controlling for background characteristics. RESULTS: Both BMI and BWP are associated with internalizing and externalizing problem behavior, and social, attention and thought problems. Multivariate tests show that BWP is more closely linked to problem behavior than BMI. Adolescents who were either underweight or overweight but considered themselves in good shape had no more problems than the group with normal BMI and BWP 'good'. The perception of being 'too thin' and particularly the perception of being 'too heavy' best predict problem behavior in both male and female adolescents. Overweight youngsters with an adequate perception of their weight have less somatic complaints than their normal-weight peers who perceive themselves as too heavy, but they show higher withdrawnness, social problems, and anxiety/depression. CONCLUSIONS: Adolescent girls are more dissatisfied with their weight than boys; however, the relationship between weight perception and problem behavior is the same for both genders.

Adolescent↗

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↗

PubMed2006

Alignment of optical maps.

We introduce a new scoring method for calculation of alignments of optical maps. Missing cuts, false cuts, and sizing errors present in optical maps are addressed by our alignment score through calculation of corresponding likelihoods. The size error model is derived through the application of Central Limit Theorem and validated by residual plots collected from real data. Missing cuts and false cuts are modeled as Bernoulli and Poisson events, respectively, as suggested by previous studies. Likelihoods are used to derive an alignment score through calculation of likelihood ratios for a certain hypothesis test. This allows us to achieve maximal descriminative power for the alignment score. Our scoring method is naturally embedded within a well known DP framework for finding optimal alignments.

Algorithms↗

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↗