Find open-source science resources

Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

RankMap is a fast and scalable tool for reference-based cell type annotation of single-cell and spatial transcriptomics data. It uses ranked gene expression and multinomial regression to achieve robust predictions, even with partial gene coverage. Compatible with Seurat, SingleCellExperiment, and SpatialExperiment objects, RankMap offers flexible preprocessing and significantly faster runtime than tools like SingleR, Azimuth, and RCTD.

Biological vision foundation model trained on TreeOfLife-200M, yielding extraordinary accuracy on diverse biological visual tasks including habitat classification and trait prediction despite a narrow training objective (Ohio State University Imageomics Institute)

Rust implementations of algorithms and data structures useful for bioinformatics.

Fully open-source (Apache 2.0) biomolecular structure prediction reproducing AlphaFold3, free for academic and commercial use (Columbia AlQuraishi Lab & OpenFold Consortium, 2025)

The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.

Ensemble of automated machine learning protocols that can be run sequentially through a single command line. The program works for regression and classification problems.

University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks

Predicts transcription factor binding sites in up to 316 vertebrate species by scoring JASPAR matrices against Ensembl promoter sequences and combining the match with seven contextual experimental datapoints, including evolutionary conservation, CAGE-defined transcription start sites, eQTLs, ChIP-seq peaks, ATAC-seq accessibility, DNase footprints and gene expression correlation, into a single score per site.

GSABenchmark is a package designed for benchmarking scRNA-seq gene set analysis (scGSA) methods. It provides both traditional and novel benchmark metrics, as well as visualization tools. Currently, GSABenchmark supports 17 scGSA methods.

This is the Provenance Information for Materials Science (PRIMA) Ontology, version 3.0, aligned with PMDco v3 and based on BFO (Basic Formal Ontology). This complete module imports all PRIMA modules (core, data-analysis-lifecycle, dataset, experiment, and computational) in their v3.0 versions. [from https://purls.helmholtz-metadaten.de/prima/complete]

Cell Set Overlap Analysis (CSOA) is a tool for calculating per-cell gene signature scores in an scRNA-seq dataset. CSOA constructs a set for each gene in the signature, consisting of the cells that highly express the gene. Next, all overlaps of pairs of cell sets are computed, ranked, filtered and scored. The CSOA per-cell score is calculated by summing up all products of the overlap scores and the min-max-normalized expression of the two involved genes. CSOA can run on a Seurat object, a SingleCellExperiment object, a matrix and a dgCMatrix.

A local command-line tool for ancestral sequence reconstruction with gap-state inference using IQ-TREE. It supports nucleotide, amino acid, and codon sequence alignments and reports site-wise posterior probabilities of ancestral states.

Tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization.

Phylogeny-aware genomic language model trained on whole-genome alignments across multiple evolutionary timescales, predicting functional constraints and variant effects for human, mouse, chicken, fly, worm, and Arabidopsis genomes (344+ stars, MIT License)

AlphaFold/ESMFold accessible implementation with AF3 JSON export, database updates

`amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems.

Ultra-fast, sensitive search and clustering suite for protein and nucleotide sequence sets.

Quantum chemisttry web platform that brings all the necessary tools to perform quantum chemistry in a user-friendly web interface.

RiSPICE (Rice SNP Prioritization Integrating Chromatin Effects) is a computational framework for prioritizing non-coding rice variants by integrating predicted chromatin effects from a fine-tuned DNA language model.

AI-driven desktop workbench for computational materials science with an interactive 3D structure editor, natural-language CatBot assistant, visual DAG workflow engine, remote-cluster access, and HPC job submission for VASP, ORCA, CP2K, Quantum ESPRESSO, GPAW, DFTB+, SIESTA, and LAMMPS (172+ stars, AGPL-3.0, 2026)

Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.

REFUTE is an open benchmark for scientific critique honesty and epistemic calibration on recent life-science and biomedical literature. It tests whether models keep claims inside what the evidence allows (overclaim / planted-flaw / falsifier selection) and whether stated confidence is calibrated, with judge-free MCQ axes plus open-ended critique scoring.

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements.

Microsoft's AI-powered geospatial Earth science application for natural-language exploration, visualization, and analysis of 130+ satellite collections, with STAC integration, multi-agent backend, MCP server, and deployable React/FastAPI stack (MIT, 2025)

The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools.

netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.

Some IDs may represent experiment sets, e.g. https://www.mavedb.org/#/experiment-sets/urn:mavedb:00000011 Others represent genomic regions (specifically deep mutational scans thereof) e.g. https://www.mavedb.org/#/experiment-sets/urn:mavedb:00000011-a

iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user).

Nallo is a bioinformatics analysis pipeline for long-reads from both PacBio and (targeted) ONT-data, focused on rare-disease. The pipeline detects a wide range of genetic variants, performs genome assembly, and reports CpG methylation. It also enables annotation and ranking of variants based on their predicted functional consequences.

Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package.

The Europeana Data Model (EDM) is aimed at being an integration medium for collecting, connecting and enriching the descriptions provided by Europeana data providers. The RDF vocabulary for http://www.europeana.eu/schemas/edm/ defines the elements introduced by EDM (as opposed to the ones EDM re-uses from other namespaces).

197 bioinformatics and life science skills for Claude Code and AI agents, achieving 92.0% accuracy on BixBench. Covers RNA-seq, single-cell analysis, drug discovery, proteomics, and more. Powers OmicsHorizon (195+ stars, 2026)

Deep learning package for many-body potential energy representation and molecular dynamics, achieving quantum-mechanical accuracy with classical MD efficiency (DeepModeling, Gordon Bell Prize 2020, 1.9k+ stars)

An issue on the MONDO GitHub issue tracker

Machine learning model predicting cellular perturbation response across diverse contexts with State Transition (ST) and State Embedding (SE) variants, featuring CLI tooling, PyPI distribution, and Virtual Cell Challenge integration (575+ stars)

Chemical reaction network and systems biology interface for scientific machine learning (SciML), enabling high-performance, GPU-parallelized simulation and analysis of complex biochemical systems with O(1) solvers (SciML, 518+ stars, Julia)

Developer toolkit for accelerating training and inference for AI in chemistry and material science, providing optimized GPU-accelerated workflows for molecular and materials machine learning (NVIDIA, 2026)

Use this database to browse the CMECS classification and to get definitions for individual CMECS Units. This database contains the units that were published in the Coastal and Marine Ecological Classification Standard.

Python astronomy tools

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)

15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)

First open-source agentic AI physicist turning research questions into structured workflows with rigorous verification and multi-step analytical work for long-horizon physics projects; integrates with Claude Code, Codex, Gemini CLI, and OpenCode (804+ stars, Apache 2.0, 2026)

nnU-Net is a self-configuring method for deep learning-based biomedical image segmentation, developed by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ). It is designed to automatically adapt to a given dataset, analyzing the provided training cases to configure a matching U-Net-based segmentation pipeline without requiring expertise from the user. The tool provides pretrained models for Pancreas and Pancreas tumor segmentation, Colon cancer primaries segmentation, Abdominal organ segmentation, Liver and liver tumor segmentation, Kidney and kidney tumor segmentation, Brain Tumor segmentation and Hippocampus (MR data) segmentation

This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way.

Tools for adding mutations to existing `.bam` files, used for testing mutation callers.

Comprehensive R package for differential composition and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data. Provides robust Bayesian modeling with outlier detection, random effects, and advanced statistical methods for cell type proportion analysis. Features include probabilistic outlier identification, mixed-effect modeling, differential variability testing, and comprehensive visualization tools. Perfect for cancer research, immunology, developmental biology, and single-cell genomics applications.