SUMMARY: Analyzing biological networks demands scalable annotation tools, yet existing methods fall short in clustering power, statistical flexibility, and broad data compatibility. We introduce Regional Inference of Significant Kinships (RISK), a next-generation tool that overcomes these challenges by integrating community detection algorithms, rigorous overrepresentation analysis, and a modular architecture that supports diverse network types. RISK identifies biologically coherent relationships within networks and generates publication-ready visualizations, as demonstrated by its ability to resolve compact functional modules in Saccharomyces cerevisiae protein-protein interaction and genetic interaction networks. Its application to a high-energy physics citation network reveals structured relationships among research subfields, highlighting its versatility beyond biological systems. As biological and interdisciplinary networks increase in size and complexity, RISK's scalability and adaptability make it a powerful solution for modern network analysis. AVAILABILITY AND IMPLEMENTATION: RISK is compatible with Python 3.8 or later, supports all major operating systems, and can be installed via pip. The software is open source under the GPLv3 license on GitHub (https://github.com/riskportal/risk) and archived on Zenodo (https://doi.org/10.5281/zenodo.17257418). Documentation and a step-by-step Jupyter notebook tutorial are available at https://github.com/riskportal/risk-docs.

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

MOTIVATION: Mass spectrometry (MS) data, used for proteomics and metabolomics analyses, have seen considerable growth in the last years. Aiming at reducing the associated storage costs, dedicated compression algorithms for MS data have been proposed, such as MassComp and MSNumpress. However, these algorithms focus on either lossless or lossy compression, respectively, and do not exploit the additional redundancy existing across scans contained in a single file. We introduce mspack, a compression algorithm for MS data that exploits this additional redundancy and that supports both lossless and lossy compression, as well as the mzML and the legacy mzXML formats. mspack applies several preprocessing lossless transforms and optional lossy transforms with a configurable error, followed by the general purpose compressors gzip or bsc to achieve a higher compression ratio. RESULTS: We tested mspack on several datasets generated by commonly used MS instruments. When used with the bsc compression backend, mspack achieves on average 76% smaller file sizes for lossless compression and 94% smaller file sizes for lossy compression, as compared with the original files. Lossless mspack achieves 10-60% lower file sizes than MassComp, and lossy mspack compresses 36-60% better than the lossy MSNumpress, for the same error, while exhibiting comparable accuracy and running time. AVAILABILITY AND IMPLEMENTATION: mspack is implemented in C++ and freely available at https://github.com/fhanau/mspack under the Apache license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). AVAILABILITY AND IMPLEMENTATION: The software is freely available at github.com/BioContainers/. CONTACT: [email protected].

MOTIVATION: Targeted mass spectrometry comprises a set of powerful methods to obtain accurate and consistent protein quantification in complex samples. To fully exploit these techniques, a cross-platform and open-source software stack based on standardized data exchange formats is required. RESULTS: We present TAPIR, a fast and efficient Python visualization software for chromatograms and peaks identified in targeted proteomics experiments. The input formats are open, community-driven standardized data formats (mzML for raw data storage and TraML encoding the hierarchical relationships between transitions, peptides and proteins). TAPIR is scalable to proteome-wide targeted proteomics studies (as enabled by SWATH-MS), allowing researchers to visualize high-throughput datasets. The framework integrates well with existing automated analysis pipelines and can be extended beyond targeted proteomics to other types of analyses. AVAILABILITY AND IMPLEMENTATION: TAPIR is available for all computing platforms under the 3-clause BSD license at https://github.com/msproteomicstools/msproteomicstools.

MOTIVATION: Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification. RESULTS: We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples. AVAILABILITY AND IMPLEMENTATION: DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Many proteomic investigations summarize the quantitative information across multiple spectral features into protein-level conclusions. Data-independent spectral acquisition (DIA) now generates a lot of interest, as it allows us to quantify many spectral features in a single run. However, the disadvantage of DIA experiments as compared, e.g., to Selected Reaction Monitoring (SRM) is that the features are subject to interferences and noise. We argue that between-run variation provides an additional insight for distinguishing good-quality and noisy DIA features. To appropriately use the quantitative between-run variation, it is important to account for the properties experimental design, and distinguish random artifacts from the biological changes. We have previously proposed a method (Chang et al., ASMS 2013) that accounts for the experimental design to eliminate features with low information content. In this project we furthermore emphasized that conducting regularization helps us avoid exploring every subset of features exhaustively, and allows us to conduct hypothesis tests later on so that we would be able to control the false discovery rate of the feature selection process. We evaluated our proposed approach by using three datasets that have some notion of ground truth: an extensive simulation study, a controlled mixture where proteins were spiked into a complex background in known concentrations, and a study of 232 plasma samples, where 18 proteins were quantified in both SWAH and SRM mode in presence of heavy labeled reference peptides. We worked on [ 1 ] protein-level estimates of fold changes between conditions, [ 2 ] sensitivity and specificity of detecting changes in protein abundance, and [ 3 ] accuracy of relative quantification of protein abundance in individual biological samples. A family of linear mixed models similar to that in MSstats http://www.msstats.org were fit to all the datasets. Then we conducted the regularization and hypothesis test to control the selection false discovery rate. The results demonstrated that our proposed feature selection approach enhanced sensitivity and specificity of the conclusions, was robust to the amount of noisy fragments, and increased the correlation of subject quantification between SRM and DIA workflows. Importantly, the performance exceeded that of the frequently used 'top 3' approach, which consists of using three spectral features with the highest average intensity between runs. Furthermore, we showed that our proposed approach outperforms using correlation to select the information features.

MOTIVATION: The determination of absolute quantities of proteins in biological samples is necessary for multiple types of scientific inquiry. While relative quantification has been commonly used in proteomics, few proteomic datasets measuring absolute protein quantities have been reported to date. Various technologies have been applied using different types of input data, e.g. ion intensities or spectral counts, as well as different absolute normalization strategies. To date, a user-friendly and transparent software supporting large-scale absolute protein quantification has been lacking. RESULTS: We present a bioinformatics tool, termed aLFQ, which supports the commonly used absolute label-free protein abundance estimation methods (TopN, iBAQ, APEX, NSAF and SCAMPI) for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation. AVAILABILITY AND IMPLEMENTATION: aLFQ is written in R and freely available under the GPLv3 from CRAN (http://www.cran.r-project.org). Instructions and example data are provided in the R-package. The raw data can be obtained from the PeptideAtlas raw data repository (PASS00321). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.