What happened
Mass spectrometry (MS)-based proteomics now routinely profiles proteomes at scale, but extracting biological insight and integrating complementary modalities remains challenging. Here, we present AlphaPeptTools, a Python package for the analysis of MS-proteomics data built on the AnnData data structure of the scverse. AlphaPeptTools implements scalable and modular strategies for MS-data ingestion, quality control, preprocessing, and statistical analysis while seamlessly integrating with the scverse ecosystem, unlocking multi-level, spatial, and multimodal analyses. Update (2026-09-21): ProtPipe2: Multi-Platform Downstream Proteomics Analysis Tool . Proteomics studies increasingly use different technologies, including mass spectrometry, the antibody-based Olink platform, and the aptamer-based SomaScan platform. However, downstream analysis often depends on platform-specific scripts or point-and-click tools, limits flexibility and makes analyses difficult to reuse across platforms. We developed ProtPipe2, a downstream proteomics analysis framework for mass spectrometry, Olink, and SomaScan data that is available as both an R package and an interactive web application. ProtPipe2 supports quality control, preprocessing, imputation, batch correction, statistical analysis, visualization, pathway analysis, and result export. ProtPipe2 expands the original Protpipe workflow to additional proteomics platforms and provides reusable R functions for imputation, batch correction, and other downstream analyses. Its redesigned web application provides user-controlled methods and parameters through the same R-based workflow. Case studies across mass spectrometry, Olink, and SomaScan datasets demonstrated a consistent downstream analysis workflow across Update (2026-09-21): CalFluxTools: An R package for analysis of high-throughput calcium oscillation screening data . Whole plate detection of calcium flux signals using fluorescence is a powerful technology for monitoring oscillatory activity of excitable cells in a high throughput fashion that sees broad applications in neurotoxicity and cardiotoxicity research. However, analysis of calcium oscillation profiles can output more than 60 peak kinetic parameters, which makes manual data analysis challenging. We have created the CalFluxTools R package, an automated pipeline for parsing plate data for 384 well plates, generating quality control metrics and plots, and performing data analysis, including T-tests, Z-score analysis, and machine learning prediction of compound toxicity values. CalFluxTools requires a plate map and experimental manifest that includes parameter filtration specifications, as well as data in the ".statall" format. CalFluxTools runs in less than a minute on a Macbook Pro with a 2.6 GHz 6-Core Intel Core i7 processor. Update (2026-09-22): A dependency-free, streamable format and cross-language toolkit for scalable LC-MS feature detection: reading only what you need . Mass spectrometry generates data faster than it can be read, and the exchange standard, mzML, is text-based and must be parsed in full before any spectrum is accessible. Binary alternatives are smaller but depend on storage engines such as HDF5, so access is dictated by the engine, not the file. We present Ionic (.ion), an open-source, compact, streamable binary format, and Quant{middle dot}ion, a processing toolkit built on the former. Ionic stores spectra, chromatograms and metadata as independently compressed, indexed blocks, so a reader retrieves only the bytes it needs, even inside a web browser, and converts losslessly to and from mzML. Ionic was smaller than compressed mzMLb on every acquisition type tested, and extracting one compound took under 40 ms, 35 to 90 times faster than an mzML reader. Quant{middle dot}ion exposes one core to R, Python, and JavaScript with identical results, and recovered 97% of true features on a ground-truth benchmark.