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Conversion module for Geode-solutions OpenGeode modules

tacular is a Python library for mass spectrometry and proteomics reference data. It packages versioned snapshots of UniMod, PSI-MOD, RESID, XLMOD, GNOme, and UniProt PTM records behind a common lookup…

tacular is a Python library for mass spectrometry and proteomics reference data. It packages versioned snapshots of UniMod, PSI-MOD, RESID, XLMOD, GNOme, and UniProt PTM records behind a common lookup interface. Additional tables describe amino acids, elements and their isotopes, fragment-ion types, neutral deltas, proteases, and mzPAF reference molecules. Applications can query these records by identifiers and names while working with typed Python objects that expose masses, formulas, cross-references, and other available ontology fields. Bundled snapshots allow reproducible and offline use. A command-line interface can download supported upstream sources, regenerate the tables, and place refreshed data in a user cache without reinstalling the package. The same generation tools can export the packaged records as JSON for use outside Python. tacular serves as the shared reference-data layer for peptacular and paftacular, while remaining usable as a standalone lookup library. It requires Python 3.12 or later and has no runtime dependencies.

psimodpy is a typed Python package for parsing and querying the PSI-MOD protein modification ontology. It ships with the bundled PSI-MOD OBO data so it works offline, parses OBO source files into stru…

psimodpy is a typed Python package for parsing and querying the PSI-MOD protein modification ontology. It ships with the bundled PSI-MOD OBO data so it works offline, parses OBO source files into structured PsiModEntry objects covering terms, synonyms, cross-references, relationships, and formula/mass annotations, and exposes a queryable in-memory database with TSV/CSV export and a round-trip OBO writer. An optional FastAPI and Model Context Protocol server exposes the same database over HTTP for interactive lookups and integration with proteomics tooling. psimodpy is intended for proteomics software, annotation pipelines, and research scripts that need programmatic access to PSI-MOD terms. It requires Python 3.12 or later.

fastatacular is a pure-Python library for reading and writing FASTA sequence files. It parses standard FASTA records into structured SequenceEntry objects and supports optional parsing of UniProt-styl…

fastatacular is a pure-Python library for reading and writing FASTA sequence files. It parses standard FASTA records into structured SequenceEntry objects and supports optional parsing of UniProt-style description keys (OS=, OX=, GN=, PE=, SV=) and pipe-delimited identifiers such as sp|P12345|EX_HUMAN and gi|12345|ref|NP_000001.1|. It is the plain-FASTA companion to pefftacular, sharing the same read/write API shape, and is intended for proteomics and genomics pipelines that need lightweight, dependency-free FASTA parsing. It requires Python 3.12 or later.

unimodpy is a typed Python package for parsing and querying the UNIMOD protein modification database used in mass spectrometry-based proteomics. It ships with a bundled UNIMOD OBO file so it works off…

unimodpy is a typed Python package for parsing and querying the UNIMOD protein modification database used in mass spectrometry-based proteomics. It ships with a bundled UNIMOD OBO file so it works offline, parses OBO source files into typed, frozen dataclasses (UnimodEntry, Specificity, NeutralLoss), and exposes a queryable UnimodDatabase with lookup by integer ID, UNIMOD accession string, case-insensitive name, and full-text search. It computes elemental composition and Hill-notation ProForma formulas, including monosaccharide abbreviation expansion and isotope label support, and offers TSV/CSV export and a round-trip OBO writer. An optional FastAPI and Model Context Protocol server exposes the same database over HTTP. unimodpy is intended for proteomics software, annotation pipelines, and research scripts that need programmatic access to UNIMOD modification data. It requires Python 3.12 or later.

mzmlpy is a typed Python library for reading mzML mass spectrometry files. It parses run and spectrum metadata into explicit Python models while delaying binary-array decoding until peak data is reque…

mzmlpy is a typed Python library for reading mzML mass spectrometry files. It parses run and spectrum metadata into explicit Python models while delaying binary-array decoding until peak data is requested. This separates metadata inspection from the cost of decoding mass-to-charge, intensity, ion mobility, and other arrays. The reader supports mzML and gzipped mzML files, indexed spectrum access, and metadata for ion mobility and data-independent acquisition. Binary decoding covers uncompressed arrays, zlib compression, MS-Numpress encodings, and Zstandard-compressed data when the corresponding optional support is installed. Gzipped input can be extracted to a reusable temporary cache, accessed through an indexed gzip backend, or streamed sequentially. Spectrum collections provide iteration and random access without requiring all binary arrays to reside in memory at once. mzmlpy is intended for proteomics pipelines, format-conversion tools, metadata inspection, and applications that need direct control over when spectral arrays are decoded. It requires Python 3.12 or later.

<p><strong>PMR</strong> (phase-manifold registration) is a memory-scalable implementation of 4D-CT deformable image registration: the ten breathing phases are represented as a single…

<p><strong>PMR</strong> (phase-manifold registration) is a memory-scalable implementation of 4D-CT deformable image registration: the ten breathing phases are represented as a single anchored, 2&#960;-periodic curve, so loop closure is an algebraic identity rather than a penalty whose weight must be traded against the data term. Deformation parameters live on a coarse control grid composed with the phase weights before upsampling, which reduces the coefficient tensor by 519&#215; (tensor-size arithmetic, not a measured allocation) and lets a full-resolution 1 mm ten-phase series complete with a measured peak of 3.25 GiB on an 8 GB consumer card.</p> <p>The package ships an executable verification suite that runs alongside training; it has caught <strong>fourteen documented defects in this codebase, eleven of them completely silent</strong>, with the full ledger maintained in the repository. It supports DIR-Lab and CREATIS, whose differing intensity-offset, image-origin and landmark-coverage conventions are handled explicitly and locked down by a regression test.</p> <p><strong>Scope.</strong> This is about scalability, verification and reproducibility, not state-of-the-art accuracy; see LIMITATIONS.md. Ten-case mean TRE on DIR-Lab is 1.597 mm.</p> <p>Version 1.0.5 ships the five data-dependent checks the entry point previously listed but did not include, so a fresh clone now exits zero with six checks executed and eight explicitly skipped; it also labels derived tensor-size figures as arithmetic, and records a larger-volume memory measurement that makes the fine-grid extrapolations lower bounds.</p> <p>Source archived independently in Software Heritage: snapshot swh:1:snp:2871fbfe6e030ec739c82f1c5bdb3c855155fdf1. Neither dataset is redistributed here.</p>

This technical verification report tests the OpenScholar publication workflow, including editor approval, mandatory archival on Zenodo, first-publication email batches, and metadata-only weekly digest…

This technical verification report tests the OpenScholar publication workflow, including editor approval, mandatory archival on Zenodo, first-publication email batches, and metadata-only weekly digests. It is a software release verification record, not a research finding. The attached report documents the verification scope and limitations. Available from https://caitlin-development-lib.opendev.space/lib/YPLK86VH

Common module for proprietary Geode-solutions OpenGeode modules