Find open-source science resources

BreastSubtypeR provides an assumption-aware, multi-method framework for intrinsic molecular subtyping of breast cancer. The package harmonizes several published nearest-centroid (NC) and single-sample predictor (SSP) classifiers, supplies method-specific preprocessing and robust probe-to-gene mapping, and implements a cohort-aware AUTO mode that selectively enables classifiers compatible with the cohort composition. A local Shiny app (iBreastSubtypeR) is included for interactive analyses and to support users without programming experience.

Package to analyze transcription factor enrichment in a gene set using data from ChIP-Seq experiments.

The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats.

103B-parameter open-source medical language model with 1/32 Mixture-of-Experts architecture, achieving HealthBench-leading performance among open-source models with only 6.1B active parameters; jointly developed by Ant Group and Zhejiang Province Health Information Center (MIT License)

A Molecular Interaction-Guided Graph Learning Framework for Multi-Omics Cancer Classification

Foundation model for universal prompt-driven medical image segmentation extending SAM3 to clinical imaging, supporting 2D public benchmarks and 3D training/evaluation with text and box prompts; pretrained weights available on HuggingFace (189+ stars)

Numerical differential equation solving in JAX

Burrow-Wheeler Aligner for pairwise alignment between DNA sequences.

The scECODA R package provides a complete workflow for the analysis and visualization of compositional data, primarily focusing on cell type proportions derived from single-cell data. It implements specialized methods, such as the Centered Log-Ratio (CLR) transformation, to properly analyze proportional data while avoiding the biases introduced by the compositional constraint. The package encapsulates data management, transformation, and analysis into a single SummarizedExperiment object, offering downstream tools for dimensionality reduction via PCA, calculating critical metrics like the Adjusted Rand Index (ARI) and Modularity to quantify sample grouping quality, and generating high-quality visualizations like heatmaps and scatter plots.

DenoIST identifies and removes contamination in Image-based Spatial Transcriptomics data, using a transposed poisson mixture model with local neighbourhood offsets to infer genes that are likely to be due to neighbourhood contamination rather than endogenous expression.

Arc Institute's 40B-parameter genome foundation model trained on 9 trillion nucleotides from all domains of life, supporting 1M base pair context for generalist DNA/RNA/protein prediction and design (Nature 2026)

A single molecule sequence assembler for genomes large and small.

MeLSI (Metric Learning for Statistical Inference) is a novel machine learning method for microbiome data analysis that learns optimal distance metrics to improve statistical power in detecting group differences. Unlike traditional distance metrics (Bray-Curtis, Euclidean, Jaccard), MeLSI adapts to the specific characteristics of your dataset to maximize separation between groups. The method uses an ensemble of weak learners to identify which microbial features drive group differences, providing both improved statistical power and biological interpretability through feature importance weights.

Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, 'fasta' files, electropherograms for visual inspection, and generate reports.

A multitude of tools for comparative genomics, focused on large-scale analyses of biological data. SynExtend includes tools for working with syntenic data, clustering massive network structures, and estimating functional relationships among genes.

Fudan University's cascade machine learning forecasting system for 15-day global weather prediction, employing a 3D Earth-specific transformer with hard-constraint techniques to achieve state-of-the-art accuracy against traditional NWP and AI baselines

Diffusion-based document OCR framework replacing autoregressive decoding with block-level parallel diffusion decoding, enabling high-accuracy text recognition in scientific PDFs (613+ stars, MIT License)

Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data.

pairedGSEA makes it simple to run a paired Differential Gene Expression (DGE) and Differencital Gene Splicing (DGS) analysis. The package allows you to store intermediate results for further investiation, if desired. pairedGSEA comes with a wrapper function for running an Over-Representation Analysis (ORA) and functionalities for plotting the results.

Gene expression prediction

DANTE is a software tool for genotyping and characterizing tandem repeats (TRs) from both second- and third-generation sequencing data. It supports the analysis of short-read massively parallel sequencing (sr-MPS) and long-read massively parallel sequencing (lr-MPS), enabling accurate repeat characterization across a wide range of loci. A key feature of DANTE is its ability to determine genotypes at nucleotide resolution, including the characterization and phasing of complex repeat motifs. For sr-MPS data, the tool determines allele size and sequence composition of alleles for which spanning reads are generated. In addition, it identifies alleles that exceed the sequencing read length by estimating their presence from partial read evidence and supports the visualisation of the sequence composition of partial reads. For lr-MPS data, where complete repeat regions are typically sequenced, DANTE determines the allele size and sequence composition of identified alleles.

GraphExperiment provides users and developers with an S4 class that extends `SingleCellExperiment` by offering infrastructure to store and retrieve networks (`igraph` objects) representing how assay features and/or observations are associated with each other. The class was designed to store networks inferred from high-dimensional quantitative data, with feature-feature networks including gene coexpression networks (GCNs), gene regulatory networks (GRNs), and co-abundance networks (from proteomics and metabolomics), and observation-observation network including cell-cell distances, species-species relationships, and sample-sample similarities.

Annotate a VCF with other VCFs/BEDs/tabixed files.

Provides C and C++ hdf5 libraries.

SPAdes (St. Petersburg genome assembler) is an assembly toolkit containing various assembly pipelines and the de-facto standard for prokaryotic genome assemblies.

Cross-platform system optimizations for accelerating AlphaFold3 training with 1.73x speedup and 1.23x memory reduction

Minimalist, batteries-included repository for training video world models with diffusion-forcing, supporting long-horizon rollouts, 3D point-cloud generation, and model-predictive control with pretrained checkpoints (Simchowitz Lab, 700+ stars, MIT License, 2026)

Learning the language of protein-protein interactions

Genomic foundation model for metagenomic and genome annotation, featuring an 8k base-pair context and 500M parameters trained on 386B base pairs of eukaryotic DNA; provides expert models and a unified CLI for prokaryotic/eukaryotic coding-sequence annotation with strong performance on Genomic Benchmarks, Nucleotide Transformer tasks, and custom Gener tasks (GenerTeam, 314+ stars, MIT License)

Automatic atomic model building program for cryo-EM maps using deep learning, enabling rapid de novo protein structure determination from electron density with high accuracy (3DEM/EMBL, 169+ stars)

Performs laboratory unit conversions across molarity, OD600 cell density, C₁V₁ dilution, and related dimensional pairs from mass, volume, molecular weight, and organism-specific OD factors. A browser calculator combines four modes in one tabbed workspace with compound MW lookup, species-aware OD uncertainty ranges, cross-tab chaining, and shareable links; a Python library and command-line tool submit the same parameters to the Pepkio Tools API for scripted use. Calculator arithmetic for the API client is hosted remotely; the client transmits conversion inputs and returns structured results and shareable run identifiers.

Functions helpful for LIBD deconvolution project. Includes tools for marker finding with mean ratio, expression plotting, and plotting deconvolution results. Working to include DLPFC datasets.

Translates spectrophotometer and NanoDrop readings into mass and molar concentrations for dsDNA, ssDNA, ssRNA, and protein from a single anchor input, with optional sequence-specific nearest-neighbor extinction coefficients. A browser calculator supports bidirectional unit conversion, batch processing of up to ninety-six NanoDrop export rows, and A260/A280 purity interpretation with plain-language warnings; a REST API exposes converter, batch, and purity modes for scripted use. Calculator arithmetic is hosted remotely; API clients transmit parameters and return structured result fields and shareable run identifiers.

Calculates sequence-derived molecular properties and related laboratory planning outputs from FASTA and assay setup inputs. The tool supports sequence analysis for DNA, RNA, and protein entries, plus dilution and ligation calculation modes through one API-backed workflow. Programmatic use is available through a Python library and command-line interface that submit run payloads and return structured result objects.

Translates between centrifuge RPM and relative centrifugal force using rotor geometry, reporting g-force or speed at rmin, ravg, and rmax. Convert mode handles rpm_to_rcf and rcf_to_rpm with rotor presets or manual radii in mm; transfer mode maps a source RPM on one rotor to an equivalent target RPM at matched rmax RCF; batch mode processes multiple spin steps from CSV or row arrays. A browser calculator and a Python library with command-line interface submit the same parameters to the Pepkio Tools API and return structured results with optional methods text and safety warnings.

Derives cells per well and suspension pipette volumes for standard 6-, 12-, 24-, 48-, 96-, and 384-well plates from a hemocytometer stock count, trypan blue viability, and target seeding confluency, with QC flags for low viability and impractical transfers. A browser calculator supports interactive planning with cell-line presets; a Python library and command-line tool submit the same parameters to the Pepkio Tools API for scripted and pipeline use. Calculator arithmetic is hosted remotely; the client transmits parameters and returns structured plate tables and shareable run identifiers.

Constructs Punnett squares and offspring genotype and phenotype ratios for complete, incomplete, codominant, ABO multiple-allele, and sex-linked Mendelian crosses from parent genotypes, with step-by-step walkthroughs and reduced ratio output. A browser calculator provides live grids, textbook presets, PNG and SVG export, and shareable links; a Python library and command-line tool submit the same parameters to the Pepkio Tools API for scripted use. Calculator arithmetic for the API client is hosted remotely; the client transmits cross inputs and returns structured grids, ratios, walkthroughs, and shareable run identifiers.

Evaluates Hardy-Weinberg equilibrium for diploid loci with two to six alleles using chi-square and Guo-Thompson exact tests, inbreeding coefficient F, and plain-language verdicts from observed genotype counts, allele frequencies, or biallelic disease incidence. A browser calculator provides De Finetti plots, export, and Wright-Fisher simulation under selection, drift, mutation, and migration; a Python library and command-line tool submit the same parameters to the Pepkio Tools API for scripted use. Calculator arithmetic for the API client is hosted remotely; the client transmits genotype or simulation inputs and returns structured results and shareable run identifiers.

Performs batch four-parameter and five-parameter logistic regression on multi-compound concentration–response screens to estimate IC50, EC50, pIC50, Hill slope, and related potency metrics with per-compound QC grades. A browser calculator supports CSV upload, curve review, and figure export; a Python library and command-line tool submit the same parameters to the Pepkio Tools API for scripted and pipeline use. Calculator arithmetic is hosted remotely; the client transmits concentration–response data and returns structured fit results and shareable run identifiers.

Processes 96-well plate absorbance data through blank subtraction, regression fitting, and dilution correction to report sample concentrations with QC flags for BCA, Bradford, and ELISA workflows. A browser calculator supports interactive grid entry with CSV and PDF export; a Python library and command-line tool submit the same parameters to the Pepkio Tools API for scripted and pipeline use. Calculator arithmetic is hosted remotely; the client transmits plate layout and absorbance values and returns model comparison, per-sample concentrations, and shareable run identifiers.

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Terminal AI coding assistant with a built-in math formalization engine that converts plain-language math problems into Lean 4 theorems and attempts formal proofs; bundles a local Lean toolchain and WebUI for interactive mathematical reasoning (math-ai-org, 582+ stars, 2026)

Generalist deep learning algorithm for cell and nucleus segmentation across diverse image types, with human-in-the-loop training (2.0) and one-click image restoration (3.0), 70K+ training objects (Nature Methods 2021/2022/2025)

The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.

Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided.

The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring.

Python library to train, interpret, and apply deep learning models to DNA sequences, providing a unified framework for regulatory genomics with support for CNN and transformer architectures, variant effect prediction, and attribution analysis (325+ stars)

General-purpose deep learning backbone for molecular modeling

The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint.

The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.