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Pithos - a scalable and secure data container for FAIR-compliant research data management in life sciences.

Modern research techniques have led to exponential growth in the volume and complexity of scientific data. Consequently, managing these volumes securely and efficiently has become a major challenge. While all research domains face these challenges, life science research is particularly affected because current approaches often rely on a large set of different file formats, with metadata stored in separated databases or spreadsheets. This leads to fragmented datasets, orphaned data, and compromised research reproducibility. Traditional solutions also force researchers to choose between security and accessibility, with encrypted files preventing selective access and indexed formats lacking adequate security for sensitive data. These limitations are particularly problematic in large-scale genomic studies where researchers must decompress multi-gigabyte files to access specific regions, creating computational bottlenecks and inefficient network usage when working with cloud-stored datasets. We introduce Pithos, a next-generation file format specifically designed for scientific data management in distributed cloud environments. The format uses content-defined chunking to enable efficient deduplication across distributed storage systems, thereby reducing storage costs and bandwidth requirements. The append-only structure ensures data immutability and allows for incremental updates without compromising content. Benchmark results show that Pithos outperforms existing solutions in read and write performance, with comparable or improved storage efficiency.

Biological Science Disciplines

The Association of Allergic Rhinitis with Chronic Adenotonsillar Diseases and Chronic Rhinosinusitis: A Mendelian Randomization Study.

INTRODUCTION: Allergic rhinitis (AR) has long been considered to be associated with chronic adenotonsillar disease (CATD). However, their causal relationship remains unclear. This study aims to investigate the causal relationship between AR and CATD and to examine the mediating role of chronic rhinosinusitis (CRS) in this association. METHODS: This study employed a two-sample Mendelian randomization (MR) design using genetic instrumental variable analysis. Data for allergic rhinitis (AR) were obtained from the MRC IEU OpenGWAS data infrastructure, data for chronic adenotonsillar disease (CATD) from the FinnGen biobank, and data for chronic rhinosinusitis (CRS) from the GWAS Catalog. Several MR methods were applied. In addition, a two-step MR approach was used to investigate the mediating role of CRS in the relationship between AR and CATD. RESULTS: MR analysis identified a positive correlation between AR and CATD. IVW and weighted median analyses showed significant causal effects (beta = 0.55, 95% CI: 0.26 to 0.84); p <0.001). No causal association was found between CATD and AR. AR and CRS showed a positive correlation (beta = 1.38, 95% CI: 0.78 to 1.98; p = 6.5 &#xd7; 10-6). CRS had a beta value of 0.15 (95% CI: 0.06 to 0.24; p = 0.001) for CATD. CRS mediates 37.6% of the AR to CATD pathway (mediation effect = 0.20, 95% CI: 0.04 to 0.37; p = 0.013). DISCUSSION: These findings indicate that AR may contribute to CATD risk through CRS, highlighting the need for further research to explore underlying biological mechanisms and validate these findings. CONCLUSIONS: This study suggests a positive causal relationship between AR and CATD, with CRS acting as a mediator.

Mendelian Randomization Analysis

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score&#xa0;=&#xa0;0.67-0.90) in genus diversity and showed a high correlation (rSpearman&#xa0;=&#xa0;0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics