Assessment tools: oncology patient assessment tool.
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Humans↗
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Detection of short tandem repeats in the cattle genome: a comparison of bioinformatic tools.
BACKGROUND: Short tandem repeats (STRs) are repetitive DNA sequences with 1–6 nucleotide repeat units, exhibiting high polymorphism due to varying repeat counts. STRs are more variable than SNPs and can cause genetic disorders. With population-scale cattle whole-genome sequencing data available, whole-genome STR identification has attracted new interest, but challenges remain due to the lack of standardized methods, sequencing data limitations, and the diversity of STR-calling tools. This study compared six STR-calling tools: HipSTR, GangSTR, and ExpansionHunter for short-read data, and Straglr, RepeatHMM, and LongTR for Oxford Nanopore (ONT) long-read data—using sequences from five Holstein cattle (two parent–offspring trios with a shared sire). This is the first cattle study to evaluate short- and long-read STR callers using both data types from the same animals. RESULTS: In short-read data, ExpansionHunter identified the highest number of polymorphic STRs (pSTRs) (327,690), followed by HipSTR (205,900) and GangSTR (110,680), with 93,023 loci detected by all three tools. In long-read data, LongTR detected 470,250 pSTRs, RepeatHMM 224,185, and Straglr 90,275, with only 33,253 loci shared among them. Mendelian consistency of STR genotypes in the trio offspring was high (> 0.8) for all short-read tools, with HipSTR and GangSTR highest at 0.98. LongTR was the only long-read tool with high consistency (0.88). Short-read tools also showed higher concordance in STR genotypes among themselves than was observed among long-read tools. However, long-read tools had a clear advantage in detecting large STRs. Relative to computational efficiency, HipSTR and GangSTR (short-reads), and LongTR (long-reads) required less memory and shorter runtimes than the other tools. CONCLUSIONS: Tool selection is critical for accurate whole-genome STR identification in cattle. For short-read data, HipSTR showed relatively high Mendelian consistency and concordance compared to the other tools, while ExpansionHunter was able to detect longer STRs but with lower Mendelian consistency. For long-read data, LongTR demonstrated higher consistency and computational efficiency relative to the other tools. Based on these results, HipSTR and LongTR are suggested as preferred options for short-read and ONT long-read datasets, respectively, in cattle STR analysis. These recommendations are based on the metrics observed in this study, and confirmatory analyses across additional breeds, larger sample sizes, and validated truth sets are encouraged.
Animals↗
A Digital Tool for Clinical Evidence-Driven Guideline Development by Studying Properties of Trial Eligible and Ineligible Populations: Development and Usability Study.
BACKGROUND: Clinical guideline development preferentially relies on evidence from randomized controlled trials (RCTs). RCTs are gold-standard methods to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to or generalizable to clinical populations or population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease or condition definitions, and reduplicated effort in analysis code. OBJECTIVE: This study aims to develop a digital tool that characterizes the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease or condition regarding demography (eg, age, gender, ethnicity), comorbidity, coprescription, hospitalization, and mortality. Currently, the process is complex, onerous, and time-consuming, whereas a real-time tool may be used to rapidly inform a guideline developer's judgment about the applicability of evidence. METHODS: The National Institute for Health and Care Excellence-particularly the gout guideline development group-and the Scottish Intercollegiate Guidelines Network guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R Shiny (R Foundation for Statistical Computing) tool was designed and developed using electronic primary health care data linked with hospitalization and mortality data built upon an optimized data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality. The tool was deployed within a Trusted Research Environment, allowing only trusted preapproved researchers to conduct analysis. RESULTS: The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables 2 types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce an overview of statistical tables (eg, age, gender) of the index condition population and, within the overview groupings, produce details on, for example, electronic frailty index, comorbidities, and coprescriptions. The disclosure control mechanism is integral to the tool, limiting tabular counts to meet local governance needs. An exemplary result for gout as an index condition is presented to demonstrate the tool's functionality. Guideline developers from the National Institute for Health and Care Excellence and the Scottish Intercollegiate Guidelines Network provided positive feedback on the tool. CONCLUSIONS: The tool is a proof-of-concept, and the user feedback has demonstrated that this is a step toward computer-interpretable guideline development. Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time.
Humans↗
Self-Report Health Screening Tools in Female Athletes: A Systematic Review of Domain Coverage, Validation, and Use Across Participation Levels.
BACKGROUND: Female athlete health encompasses multiple interconnected domains; however, the self-report screening tools used to assess these domains have not been comprehensively synthesised. OBJECTIVE: To systematically identify self-report health screening tools used to assess female athlete health, map domain coverage, determine validation reporting, and describe application across participation levels. METHODS: This systematic review was pre-registered with PROSPERO ( CRD420251056910 ) and conducted in accordance with PRISMA guidelines. Four databases (PubMed, MEDLINE, SPORTDiscus and Web of Science) were searched from inception to January 2026 using female health and screening-related terms. Methodological quality was appraised using Joanna Briggs Institute and National Institutes of Health tools, and findings were synthesised descriptively. Eligible, peer-reviewed studies reported the use, development or validation of self-report health screening tools assessing one or more domains relevant to female health applied in female athlete populations, spanning recreational through elite participation levels. All sports and activities were included. The search was restricted to English language with no date limits. RESULTS: In total, 360 studies (1990-2026) representing 134,506 female participants spanning recreational to elite sport and 273 screening tools were included. Mental health (n = 77, 34.1%), disordered eating (n = 33, 14.6%) and body image (n = 30, 13.3%) predominated. Domains related to female health, including menstrual health, pelvic floor health, pregnancy/postpartum and breast health were comparatively underrepresented. Most studies reported tools were used for risk identification (n = 323, 80.3%). Validation reporting was inconsistent, with half (n = 180, 50%) reporting use of at least one validated tool. Tool use was concentrated in professional and elite sport, with limited inclusion of recreational, masters and disability athlete cohorts. Health literacy constructs were explicitly assessed in 12.5% of studies (n = 45). CONCLUSIONS: Health screening in female athlete populations remains fragmented and uneven in domain coverage, with inconsistent validation reporting. Development of integrated, multi-domain and contextually inclusive screening frameworks is warranted.
Journal Article↗
PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.
MOTIVATION: Genome-wide association study (GWAS) meta-analysis tools are essential for integrating summary statistics across multiple cohorts, thereby increasing statistical power and validating genetic associations. Widely cited tools, such as METAL, PLINK, and GWAMA, have facilitated numerous significant discoveries in the field of GWAS. Nevertheless, these tools offer a limited set of meta-analysis methods and typically require users to have prior experience with command-line tools to be executed. RESULTS: We present here PYRAMA, an open-source tool which is designed for meta-analysis of genome wide association studies. This work introduces an easy-to-use software package that includes several meta-analysis methods that are absent in similar software packages. PYRAMA is faster compared to other tools, supports robust methods for analysis and meta-analysis, fixed-effects, random-effects and Bayesian meta-analysis and it is currently the only tool that supports meta-analysis with imputation of summary statistics. It is available both as a standalone tool and as a freely available web server. AVAILABILITY AND IMPLEMENTATION: https://github.com/pbagos/PYRAMA, https://doi.org/10.5281/zenodo.17830449.
Genome-Wide Association Study↗
Assessing the influence of different alignment tools on the accuracy of a forensic epigenetic clock.
MOTIVATION: DNA methylation (DNAm) has long been a commonly investigated biomarker in biomedical research. The current gold standard for DNAm detection is bisulfite sequencing which requires dedicated alignment tools that can handle reduced sequence complexity. One commonly used application of DNAm are epigenetic clock measurements. These clocks have been adapted by many fields for their specific needs, including forensic genetics. Here, epigenetic clocks were designed to help estimate the chronological age of a biological stain donor for investigative purposes. RESULTS: In this study, data generated with a well-established forensic epigenetic clock is aligned with four different bisulfite-specific alignment tools: "Bwa-meth," "Abismal," "Bismark," and "BS-Seeker2." For each tool, we tested up to six different settings, altering parameters such as the maximum number of mismatches or the score function setting. The goal was to investigate whether the final predicted ages differed considerably between the tested alignment tools and settings. Quality controls such as read depth, precision, recall, F1 score, and alignment run time were also assessed. To allow other researchers to easily perform such methylation comparison analyses on their own data, a Shiny app called "MethylAge Explorer" was developed within this study. None of the tested settings for the three alignment tools "Abismal," "Bismark," and "BS-Seeker2" outperformed the originally used alignment tool "Bwa-meth" in terms of age prediction accuracy. However, differences in final age predictions were observed between the different alignment tools. Therefore, it is necessary to be aware of which alignment tool to use for particular epigenetic clocks. AVAILABILITY AND IMPLEMENTATION: The data underlying this article and the code for the shiny app are available on GitHub (https://github.com/charlsut/methylage_explorer).
DNA Methylation↗
AQuA Tools: clear and reliable BEDPE operations for 3D genomics.
MOTIVATION: The genome interacts with itself within the volume of the cell nucleus to process information. These interactions mediate signal integration, gene regulation, and cell identity. The identification of new therapeutic targets from non-coding disease-associated variants relies critically on correctly assigning variants to genes through 3D interactions. Experimental techniques in 3D genomics, such as HiC and HiChIP, allow the mapping of interactions through sequencing. Bioinformatics for 3D genomics contends primarily with contact matrices that contain interaction frequencies for all possible element pairs, and BEDPE files that store element pairs that interact. Whereas the tools available for processing linear genomic data are mature, operating on contact matrices and BEDPE files remains cumbersome, opaque, and error-prone, as researchers have had to shoehorn tools originally designed for linear data. A genome arithmetic designed from the ground up for 3D genomics does not yet exist. RESULTS: We present AQuA Tools, a suite of shell- and R-based command-line tools that provide a set of core operations on contact matrices and BEDPE files motivated by key questions in population genetics, cancer research, and precision medicine. We have designed our core operations to be clear, reliable, intuitive and versatile. Core operations can be chained together along with standard UNIX commands. Our goal is to make AQuA Tools easy for the novice to learn and the go-to choice for power users. We hope our tools will motivate more researchers to use 3D genomic data in their projects. AVAILABILITY AND IMPLEMENTATION: We provide and maintain AQuA Tools at https://github.com/axiotl/aqua-tools.
Genomics↗
Comprehensive evaluation of ACMG/AMP-based variant classification tools.
MOTIVATION: The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines represent the gold standard for clinical variant interpretation. Despite the widespread adoption of ACMG/AMP guidelines, a comprehensive comparison of the software tools designed to implement them has been lacking. This represents a significant gap, as clinicians require evidence-based guidance on which tools to use in their practice. RESULTS: We benchmarked four ACMG/AMP-based tools (Franklin, InterVar, TAPES, Genebe) selected from 22 tools, and compared their performance with LIRICAL, a top-performing phenotype-driven tool, using 151 expert-curated datasets from Mendelian disorders. Selection criteria included free availability, VCF compatibility, operational reliability, and not being disease-specific. Our evaluation framework assessed top-N accuracy (N = 1, 5, 10, 20, 50), retention rates, precision, recall, F1 scores, and area under the curve (AUC). Statistical validation employed bootstrap confidence intervals (n = 1000) and Friedman tests. LIRICAL (68.21%) and Franklin (61.59%) demonstrated superior top-10 variant prioritization accuracy in Mendelian disorders, significantly outperforming other tools (P = .0000). Results demonstrate that tools with advanced phenotypic integration significantly outperform those relying primarily on genomic features. AVAILABILITY AND IMPLEMENTATION: All data and source code required to reproduce the findings of this study are openly available in the Code Ocean repository at https://doi.org/10.24433/CO.6562438.v1.
Software↗
Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.
PURPOSE: We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches has since been released, and these methods must be independently calibrated for clinical application. METHODS: Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by 3 new tools (AlphaMissense, ESM1b, and VARITY) and calibrated scores meeting each evidence strength. RESULTS: All 3 tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, although sometimes for few variants. Compared with previously recommended tools, these yielded at best only modest improvements in the trade-offs between evidence strength and false-positive predictions. CONCLUSION: At calibrated thresholds, 3 new computational predictors provided evidence for variant pathogenicity at similar strength to the 4 previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.
Humans↗
Machine learning-based clinical tool for identifying factors associated with symptomatic knee osteoarthritis: the Nagahama study.
BACKGROUND: A clinical tool that evaluates factors associated with symptomatic knee osteoarthritis (OA) based on modifiable factors is lacking. This study aimed to develop a machine learning-based clinical assessment tool using modifiable factors to identify factors associated with symptomatic knee OA and to determine its accuracy. METHODS: This study included 429 participants (81.8% women; age, 69.0 ± 5.3 years) from the Nagahama Study who were ≥60 years old and had radiographically confirmed knee OA. A Knee Society Knee Scoring System 2011 symptom score of <23 points defined symptomatic knee OA. Participants were randomly assigned to training (70%) and test (30%) datasets. A machine learning model was developed using Extreme Gradient Boosting with 27 variables, and the SHapley Additive exPlanation (SHAP) values were used to assess feature importance. The top 8 features were translated into a 100-point clinical scoring tool weighted by their SHAP contributions. The cutoff value indicating symptomatic knee OA in the clinical assessment tool was determined using receiver operating characteristic analysis, and model performance was evaluated in both datasets. RESULTS: The clinical assessment tool consisted of low back pain, OA severity, depressive tendencies, knee flexion/extension range of motion, knee extension and hip abduction strength, and lower limb muscle quality. The model showed moderate discriminative performance (AUC 0.771 and 0.773 in the training and test datasets, respectively), with a cutoff point of 47. CONCLUSION: The proposed clinical assessment tool may provide a structured framework for assessing modifiable factors associated with symptomatic knee OA, reflecting their contribution to current symptom status.
Humans↗
MaizeGDB Phylostrata Tool: exploring evolutionary origins of maize proteins.
MOTIVATION: Phylostratigraphic analysis identifies the evolutionary origins and level of conservation of proteins, facilitating research in evolutionary biology and comparative genomics. RESULTS: We developed the MaizeGDB Phylostrata Tool, a custom web application that enables users to explore the evolutionary origins of proteins in maize (Zea mays), a globally important crop and model organism. This tool features interactive visualizations and detailed gene pages incorporating subcellular localization, Gene Ontology (GO) terms, and links to resources for homologs, facilitating comparison of gene functions across evolutionary time. The tool also provides downloadable links for full-proteome phylostratigraphic results for 26 maize inbreds (B73 and the NAM founders). From these, we identified genome- and subgenome-wide trends, finding that more conserved proteins tended to be longer and more highly expressed. Finally, we provide code including updates to the "phylostratr" R package to make it more robust against taxonomic updates, as well as example scripts for phylostratigraphic analysis and web tool development for researchers and curators of other species. AVAILABILITY AND IMPLEMENTATION: The MaizeGDB Phylostrata Tool is freely available at https://phylostrata.maizegdb.org. Scripts used for the analysis and web tool are available at https://github.com/LTibbs/PhylostrataWebtool.
Journal Article↗
ChromBERT-tools: a versatile toolkit for context-specific regulatory representations of transcription regulators across different cell types.
SUMMARY: Representations that encode the genome-wide regulatory behavior of transcription regulators provide a foundation for flexible transcription modeling and in silico regulatory analysis. Existing regulator representations are commonly derived from gene co-expression, motif annotations, or static protein features, which capture useful but limited aspects of regulator identity but do not directly model how regulators participate in region-specific regulatory programs across the genome. ChromBERT addresses this gap by learning context-aware regulatory representations from large-scale ChIP-seq data. However, routine bioinformatics applications require lightweight, accessible, and modular tools for generating, adapting, and interpreting these representations in user-defined biological contexts. Here, we present ChromBERT-tools, a user-oriented toolkit built upon ChromBERT that converts its regulatory representation framework into practical workflows for customizable analysis across cellular contexts. ChromBERT-tools provides command-line interfaces and Python APIs organized into three functional layers: representation generation, predictive modeling, and regulatory interpretation. The representation generation layer produces representations of genomic regions and transcription regulators. The predictive modeling layer fine-tunes ChromBERT for genome-wide regulatory activity prediction through classification or regression tasks, with optimized implementation to reduce running time and computational resource requirements. The regulatory interpretation layer supports inference of the context-specific roles of cis-regulatory elements and transcription regulators. These modules can be used independently or integrated into end-to-end workflows, enabling flexible analyses across diverse datasets. ChromBERT-tools lowers the barrier to applying context-specific regulatory representations in routine genomic analyses. AVAILABILITY AND IMPLEMENTATION: ChromBERT-tools is freely available at https://github.com/TongjiZhanglab/ChromBERT-tools, with documentation at https://chrombert-tools.readthedocs.io/en/latest/. A frozen archival snapshot is available on Zenodo under DOI: 10.5281/zenodo.20094206.
Software↗
Inference and visualization of complex genotype-phenotype maps with gpmap-tools.
Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.
Gaussian process↗
Benchmarking of Reference-Based Tools for Strain-Level Resolution of Plant Microbiome.
Strain-level identification of each microbe is crucial for understanding its role in the host. Most of the existing tools have primarily been evaluated on human metagenomic datasets, whereas the plant microbiome exhibits greater diversity and complexity and thus poses a challenge in the strain-level resolution of individual microbes. In this study, we conducted a comprehensive benchmarking of available reference-based tools for strain-level resolution of the plant microbiome. We evaluated seven tools on various performance parameters, like computational requirements, F1-score and relative abundances using synthetic datasets comprising microbes known to have strong associations with plants as well as real plant microbiome datasets. Our results demonstrated a better performance of StrainScan on the synthetic data, achieving higher F1-score and more accurate relative abundance estimates as compared to other tools, but its performance declined gradually with increasing strain diversity. However, StrainGE and StrainScan exhibited competitive performance on real plant metagenome data. Overall, though StrainGE exhibited better performance, it was more computationally expensive. However, StrainScan performed better in detecting low-abundance strains. Our findings suggest the comparative suitability of the available tools for the strain-level analysis of plant metagenome data and highlight the need for the development of more efficient and accurate taxonomic classifiers capable of handling the complex plant metagenome data while maintaining computational efficiency.
Microbiota↗
Digital Structured Education With Behavioral Nudge Tools for Adults With Type 2 Diabetes: Multicenter Randomized Controlled Trial.
BACKGROUND: Digital interventions offer scalable alternatives to traditional face-to-face diabetes education, but often face challenges related to inconsistent clinical effectiveness, and declining user engagement. However, whether a digital structured education program integrated with behavioral nudge tools can improve metabolic, behavioral, and psychological outcomes in adults with type 2 diabetes remains unclear. OBJECTIVE: This study aimed to evaluate the effectiveness of a digital structured education program integrated with behavioral nudge tools in improving metabolic, behavioral, and psychological outcomes among adults with type 2 diabetes. METHODS: This multicenter randomized controlled trial was conducted in the endocrinology departments of 4 hospitals in China. Adults with type 2 diabetes were randomly assigned to an intervention group receiving a digital structured education program integrated with behavioral nudge tools (n=146) or a control group receiving standard digital diabetes education (n=147). Assessments were conducted at baseline and 12-week follow-up. The primary outcome was hemoglobin A1c (HbA1c) at 12 weeks, adjusted for baseline HbA1c, and study center. Secondary outcomes included fasting blood glucose (FBG), weight, BMI, waist circumference, blood pressure, lipid profiles, self-management behaviors, self-efficacy, and habit strength. RESULTS: Among 293 participants (mean age 49.19, SD 10.02 y), 287 (97.9%) completed follow-up. At 12 weeks, the intervention group demonstrated significantly greater improvements than the control group in HbA1c (adjusted mean difference -0.38%, 95% CI -0.68% to -0.09%; P=.01), FBG (adjusted mean difference -0.75, 95% CI -1.27 to -0.44 mmol/L; P<.001), weight (adjusted mean difference -0.84, 95% CI -1.61 to -0.07 kg; P=.03), BMI (adjusted mean difference -0.38, 95% CI -0.65 to -0.11 kg/m²; P=.01), systolic blood pressure (adjusted mean difference -2.71, 95% CI -4.62 to -0.79 mm Hg; P=.01), diastolic blood pressure (adjusted mean difference -2.92, 95% CI -4.47 to -1.37 mm Hg; P<.001), and total cholesterol (adjusted mean difference -0.27, 95% CI -0.48 to -0.05 mmol/L; P=.02). The intervention was also associated with significantly greater improvements in self-management behaviors, self-efficacy, and habit strength (all P<.05). CONCLUSIONS: Digital structured education integrated with behavioral nudge tools improved metabolic outcomes and strengthened psychological and behavioral determinants of self-management among adults with type 2 diabetes over a 12-week period. These findings suggest that a digital structured education program integrated with behavioral nudge tools may enhance diabetes self-management beyond standard digital diabetes education. Further studies with longer follow-up and real-world implementation are warranted to evaluate the sustainability, generalizability, and long-term clinical impact of this integrated intervention.
Humans↗
Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.
BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. OBJECTIVE: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG-derived inputs. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. RESULTS: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92-0.96; 95% PI 0.71-0.99), 0.87 (95% CI 0.84-0.89; 95% PI 0.66-0.96), and 0.83 (95% CI 0.79-0.87; 95% PI 0.61-0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69-0.84; 95% PI 0.30-0.96), 0.81 (95% CI 0.75-0.85; 95% PI 0.39-0.96), and 0.91 (95% CI 0.87-0.94; 95% PI 0.55-0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG-derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. CONCLUSIONS: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG-derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG-derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.
Humans↗
MAJIQ-CLIN: A novel tool to help identify Mendelian disease-causing variants from RNA-seq data.
PURPOSE: The current diagnostic rate for patients with suspected Mendelian genetic disorders is low, despite exome/genome sequencing being the standard of care. One reason for this low diagnostic rate is that traditional exome/genome sequencing analysis methods struggle to detect RNA splicing aberrations. Causative variants often involve splicing changes, with numerous splice-altering variants being responsible for known Mendelian disorders. Therefore, it is crucial to develop reliable tools to detect, quantify, prioritize, and visualize RNA splicing aberrations from patient RNA sequencing data. METHODS: We developed Modeling Alternative Junction Inclusion Quantification for Clinical Applications (MAJIQ-CLIN), a method to identify RNA splicing aberrations in patients' RNA sequencing data compared with a cohort of control samples. MAJIQ-CLIN can efficiently process large datasets, avoiding reprocessing when new data are added, while effectively detecting local splicing variations with deviations in a given patient, termed outlier local splicing variation, or unique to the patient, termed private local splicing variation. RESULTS: We performed a systematic evaluation of the accuracy of tools for detecting patients' RNA splicing aberrations from RNA sequence using synthetic data across several aberration types and transcript inclusion levels. Then, we used several real datasets to assess MAJIQ-CLINs ability to identify solved test cases and control for the effect of confounders such as batches. We showed that MAJIQ-CLIN compares favorably to existing tools in both accuracy and efficiency. We also used MAJIQ-CLIN to investigate several unsolved patient cases from the Undiagnosed Diseases Network. CONCLUSION: MAJIQ-CLIN offers an efficient, accurate, and user-friendly tool to aid in diagnosing Mendelian disease-causing variants from RNA sequence data.
Bioinformatics↗
Phage bioinformatics tools: a review of computational approaches for bacteriophage research.
Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection, and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 in identification tasks and, through structure-informed prediction, raise functional annotation to over half of phage genes. Furthermore, we map the upstream components, namely, gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalizable strain-level host predictor exists for phage therapy; varying true-positive rates (0%-97%) underscore the absence of standardized community benchmarks analogous to Critical Assessment of Structure Prediction or Critical Assessment of Metagenome Interpretation. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.
Computational Biology↗