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PubMed2025

polars-bio-fast, scalable, and out-of-core operations on large genomic interval datasets.

MOTIVATION: Genomic studies very often rely on computationally intensive analyses of relationships between features, which are typically represented as intervals along a 1D coordinate system (such as positions on a chromosome). In this context, the Python programming language is extensively used for manipulating and analyzing data stored in a tabular form of rows and columns, called a DataFrame. Pandas is the most widely used Python DataFrame package and has been criticized for inefficiencies and scalability issues, which its modern alternative-Polars-aims to address with a native backend written in the Rust programming language. RESULTS: polars-bio is a Python library that enables fast, parallel and out-of-core operations on large genomic interval datasets. Its main components are implemented in Rust, using the Apache DataFusion query engine and Apache Arrow for efficient data representation. It is compatible with Polars and Pandas DataFrame formats. In a real-world comparison (107 versus 1.2×106 intervals), our library runs overlap queries 6.5×, nearest queries 15.5×, count_overlaps queries 38×, and coverage queries 15× faster than Bioframe. On equally sized synthetic sets (107 versus 107), the corresponding speedups are 1.6×, 5.5×, 6×, and 6×. In streaming mode, on real and synthetic interval pairs, our implementation uses 90× and 15× less memory for overlap, 4.5× and 6.5× less for nearest, 60× and 12× less for count_overlaps, and 34× and 7× less for coverage than Bioframe. Multi-threaded benchmarks show good scalability characteristics. To the best of our knowledge, polars-bio is the most efficient single-node library for genomic interval DataFrames in Python. AVAILABILITY AND IMPLEMENTATION: polars-bio is an open-source Python package distributed under the Apache License available for major platforms, including Linux, macOS, and Windows in the PyPI registry. The online documentation is https://biodatageeks.org/polars-bio/ and the source code is available on GitHub: https://github.com/biodatageeks/polars-bio and Zenodo: https://doi.org/10.5281/zenodo.16374290. are available at Bioinformatics online.

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PubMed2026

Comparison of Adult Tracheostomy Outcomes Before and After Implementation of a Dedicated Tracheostomy Team.

OBJECTIVE: (A) Determine patient and institutional factors associated with increased length of stay (LOS) and complications of tracheostomy, (B) Develop data-driven quality improvement in tracheostomy care. DESIGN: Mixed methods protocol used cross-sectional survey assessing institutional trends in tracheostomy-specific care. Retrospective chart review compared tracheostomy patient outcomes prior to and during implementation of a multidisciplinary tracheostomy care team and standardized guidelines. PARTICIPANTS: Patients undergoing tracheostomy from January 2019 to December 2021 at a tertiary hospital. METHODS: Patient factors, procedure type and indication, tracheostomy tube size, management timeline, insurance, and disease severity using Acute Physiology and Chronic Health Evaluation II (APACHE II) score were analyzed for associations with LOS and adverse events. Multivariate analyses controlled for APACHE II score and payer source. LOS and complications were compared between patients before and during implementation of multidisciplinary tracheostomy care team and standardized guidelines. RESULTS: Three-hundred and eighteen patients met criteria with a 21.7% complication rate, average LOS of 24 days (SD = 28.523), and median LOS post-tracheostomy of 14 days (IQR 7, 29). Departments without standardized protocols had greater LOS (F[1,316] = 28.706, P < .001]) and complication odds (OR = 2.92, P = .015, 95% CI[1.231, 6.930]). Larger tracheostomy tube size was linked to increased LOS (β = .253, t(314) = 4.741, P < .001, 95% CI[4.137, 10.081]). Delays from ventilation discontinuation to cuffless tube exchange and decannulation correlated with increased LOS (β = .406, t(184) = 6.321, P < .001, 95% CI[.928, 1.771]); (β = .554, t(129) = 7.625, P < .001, 95% CI[1.008, 1.715]). When focusing on a single department, comparing 2019 patients to 2021 patients (pre- and post-guideline and care team), overall, LOS decreased from 33.08 to 30.83 days (P = .586). When excluding patients discharged on a ventilator, the 2019 group had average LOS of 37.938 days versus 33.41 days in 2021 (P = .344). CONCLUSIONS: Standardized tracheostomy care guidelines and multidisciplinary care teams are critical to improving institutional outcomes. Data-driven approaches to quality improvement ensure efficient and targeted methods to improving patient care. LEVEL OF EVIDENCE: 4.

Humans↗

PubMed2026

Chronic neurological diseases with acute respiratory failure in a real-life cohort: insights into ICU and long-term survival-A retrospective study.

BACKGROUND: Patients with chronic neurological diseases (CND) are at increased risk of pulmonary complications that often require ICU admission. This study aimed to identify clinical factors associated with ICU mortality and long-term survival in patients with CND who developed acute respiratory failure (ARF). METHODS: This retrospective cohort study was conducted in a level III respiratory ICU. Patients with pre-existing CND admitted to the ICU with ARF were included. ICU mortality was analyzed using multivariable logistic regression. Long-term survival after ICU discharge was evaluated using Kaplan-Meier survival analysis and Cox proportional hazards models. Mortality timing was further characterized using hazard function analysis. RESULTS: A total of 220 patients were included; the most common neurological diagnoses were dementia (37.3%), stroke (22.7%), and amyotrophic lateral sclerosis (14.1%). ICU mortality was 33.6%. Higher APACHE II scores were independently associated with increased ICU mortality (OR 1.076 per point increase; 95% CI 1.029-1.126; p < 0.001). Long-term survival differed significantly by post-discharge respiratory support strategy, with Kaplan-Meier analysis demonstrating more favorable survival patterns among patients receiving home non-invasive mechanical ventilation (NIMV) (p = 0.003). In Cox regression analysis, age, home NIMV, and feeding modality at discharge were independently associated with long-term outcomes. Survival analyses revealed an early clustering of deaths within the first months after ICU discharge, particularly among patients with dementia. CONCLUSIONS: In patients with CND, acute physiological severity was the main determinant of ICU mortality, whereas long-term survival after ICU discharge was poor, with deaths clustering within the first months thereafter. Post-discharge respiratory support and nutritional management should be individualized according to the expected clinical trajectory and patient values.

Humans↗

PubMed2025

mettannotator: a comprehensive and scalable Nextflow annotation pipeline for prokaryotic assemblies.

SUMMARY: In recent years, there has been a surge in prokaryotic genome assemblies, coming from both isolated organisms and environmental samples. These assemblies often include novel species that are poorly represented in reference databases creating a need for a tool that can annotate both well-described and novel taxa, and can run at scale. Here, we present mettannotator-a comprehensive, scalable Nextflow pipeline for prokaryotic genome annotation that identifies coding and noncoding regions, predicts protein functions, including antimicrobial resistance, and delineates gene clusters. The pipeline summarizes these results in a GFF (General Feature Format) file that can be easily utilized in downstream analysis or visualized using common genome browsers. Here, we show how it works on 200 genomes from 29 prokaryotic phyla, including isolate genomes and known and novel metagenome-assembled genomes, and present metrics on its performance in comparison to other tools. AVAILABILITY AND IMPLEMENTATION: The pipeline is written in Nextflow and Python and published under an open source Apache 2.0 licence. Instructions and source code can be accessed at https://github.com/EBI-Metagenomics/mettannotator. The pipeline is also available on WorkflowHub: https://workflowhub.eu/workflows/1069.

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PubMed2025

Associations on the Fly, a new feature aiming to facilitate exploration of the Open Targets Platform evidence.

MOTIVATION: The Open Targets Platform (https://platform.opentargets.org) is a unique, comprehensive, open-source resource supporting systematic identification and prioritisation of targets for drug discovery. The Platform combines, harmonizes and integrates data from >20 diverse sources to provide target-disease associations, covering evidence derived from genetic associations, somatic mutations, known drugs, differential expression, animal models, pathways and systems biology. An in-house target identification scoring framework weighs the evidence from each data source and type, contributing to an overall score for each of the 7.8M target-disease associations. However, the old infrastructure did not allow user-led dynamic adjustments in the contribution of different evidence types for target prioritisation, a limitation frequently raised by our user community. Furthermore, the previous Platform user interface did not support navigation and exploration of the underlying target-disease evidence on the same page, occasionally making the user journey counterintuitive. RESULTS: Here, we describe 'Associations on the Fly' (AOTF), a new Platform feature-developed with a user-centred vision-that enables the user to formulate more flexible therapeutic hypotheses through dynamic adjustment of the weight of contributing evidence from each source, altering the prioritisation of targets. AVAILABILITY AND IMPLEMENTATION: The codebases that power the Platform-including our pipelines, GraphQL API, and React UI-are all open source and licensed under the APACHE LICENSE, VERSION 2.0. You can find all of our code repositories on GitHub at https://github.com/opentargets and on Zenodo at https://zenodo.org/records/14392214. This tool was implemented using React v18 and its code is accessible here: (https://github.com/opentargets/ot-ui-apps). The tools are accessible through the Open Targets Platform web interface (https://platform.opentargets.org/) and GraphQL API (https://platform-docs.opentargets.org/data-access/graphql-api). Data is available for download here: (https://platform.opentargets.org/downloads) and from the EMBL-EBI FTP: (https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/).

Software↗

PubMed2025

HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries.

SUMMARY: The Sequencing Read Archive is one of the largest and fastest-growing repositories of sequencing data, containing tens of petabytes of sequenced reads. Its data is used by a wide scientific community, often beyond the primary study that generated them. Such analyses rely on accurate metadata concerning the type of experiment and library, as well as the organism from which the sequenced reads were derived. These metadata are typically entered manually by contributors in an error-prone process, and are frequently incomplete. In addition, easy-to-use computational tools that verify the consistency and completeness of metadata describing the libraries to facilitate data reuse, are largely unavailable. Here, we introduce HTSinfer, a Python-based tool to infer metadata directly and solely from bulk RNA-sequencing data generated on Illumina platforms. HTSinfer leverages genome sequence information and diagnostic genes to rapidly and accurately infer the library source and library type, as well as the relative read orientation, 3' adapter sequence and read length statistics. HTSinfer is written in a modular manner, published under a permissible free and open-source license and encourages contributions by the community, enabling easy addition of new functionalities, e.g. for the inference of additional metrics, or the support of different experiment types or sequencing platforms. AVAILABILITY AND IMPLEMENTATION: HTSinfer is released under the Apache License 2.0. Latest code is available via GitHub at https://github.com/zavolanlab/htsinfer, while releases are published on Bioconda. A snapshot of the HTSinfer version described in this article was deposited at Zenodo at 10.5281/zenodo.13985958.

Metadata↗

PubMed2025

OLS4: a new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem.

SUMMARY: The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently, there has been a significant increase in the size and complexity of ontologies due to new scales of biological knowledge, such as spatial transcriptomics, new ontology development methodologies, and curation on an increased scale. Existing Web-based tools for ontology browsing such as BioPortal and OntoBee do not support the full range of definitions used by today's ontologies. In order to support the community going forward, we have developed OLS4, implementing the complete OWL2 specification, internationalization support for multiple languages, and a new user interface with UX enhancements such as links out to external databases. OLS4 has replaced OLS3 in production at EMBL-EBI and has a backward compatible API supporting users of OLS3 to transition. AVAILABILITY AND IMPLEMENTATION: The source code of OLS is available at https://github.com/EBISPOT/ols4 and DOI 10.5281/zenodo.14960290 with Apache 2.0 License. A freely available implementation is accessible at https://www.ebi.ac.uk/ols4.

Biological Ontologies↗

PubMed2025

SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences.

SUMMARY: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use interface. AVAILABILITY AND IMPLEMENTATION: SBMLtoOdin is published under the open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under the MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.

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PubMed2026

Incidence of invasive group A streptococcal infections and comparison of emm types from invasive infections, pharyngitis, and throat carriage in American Indian communities in the Southwest United States.

BACKGROUND: American Indian/Alaska Native (AI/AN) communities in the US have high rates of group A streptococcal (GAS) infections. We determined the incidence of invasive infections in AI communities in the Southwest and compared emm types from invasive infections, pharyngitis, and throat carriage. METHODS: Activities conducted in the White Mountain Apache Tribal lands (WMA) and Navajo Nation (NN) included active, laboratory-based surveillance for invasive GAS infections (WMA: 2019─2024; NN: 2023─2024; all ages); surveillance for GAS pharyngitis (2023-2024; children 0─17 years); and culture for GAS from oropharyngeal carriage samples (2019 and 2022─2023; children 0─14 years). Emm types were determined by whole-genome sequencing. Annual incidence rates were calculated using Poisson regression. RESULTS: In WMA, age-standardized rates of invasive infections ranged from 80-270/100,000 persons between 2019-2024. Predominant emm types varied (n=74 isolates): 91 (59%) and 49 (32%) in 2019-2020, and 43 (40%) and 53 (30%) in 2023. In NN, rates were 40-60/100,000 persons in 2023-2024; common emm types (n=51) were 53 (28%), 101 (18%), and 12 (16%). In WMA and NN, emm types 1, 12, and 53 predominated in pharyngitis (n=190), and 1, 12, and 91 in throat carriage (n=119). CONCLUSIONS: Rates of invasive GAS infections in these communities were 3-35 times higher than the national US average (12.2/100,000 in 2024). Emm types varied over time with limited overlap in strains from throat carriage or pharyngitis isolates and those from invasive infections. Findings support continuing GAS surveillance and engaging AI/AN communities throughout vaccine development and evaluation.

Indigenous health↗