Find open-source science resources
A directory of tools, AI models, datasets, and research resources for biotech, bioinformatics, and other scientific fields. Aggregated from curated GitHub awesome-lists, HuggingFace, bio.tools, Bioconductor, and more.
Filters
Health
Domain
Language(1)
License(1)
Source(1)
Type
24 of 6,590 resources
Automated pipeline for proteome-scale protein-protein interaction screening with AlphaFold-Multimer and AlphaFold 3, supporting flexible inputs (UniProt IDs, FASTA, residue regions, multimers, AF3 JSON features) and integrated downstream analysis for hit prioritization (Kosinski Lab, EMBL, Nature Protocols 2024, 317+ stars, GPL-3.0)
Unified Python framework for bulk, single-cell, and spatial RNA-seq multi-omics analysis with deep learning deconvolution (VAE) and graph neural networks, bridging Bindea, Bindea, scanpy and squidpy ecosystems (Nature Communications 2024)
Toolbox for comparative genomics of MAGs
AI coding assistant for JupyterLab with agent mode, supporting arbitrary LLM providers (2025+)
A Simulation Tool for Fractured and Deformable Porous Media.
Rapid & standardized annotation of bacterial genomes, MAGs & plasmids
Neural network-based cryo-EM heterogeneous reconstruction, modeling continuous 3D structure distributions from single-particle images, with CryoDRGN-ET extending to in-cell cryo-electron tomography (MIT CSAIL, Nature Methods 2021/2024)
Self-hostable scientific claim-verification and literature-review tool combining Semantic Scholar retrieval, bibliometric scoring, and LLM-based evidence synthesis for large-batch validation workflows
Convert AMBER forcefields from ANTECHAMBER to GROMACS format.
Behavioral and neuroimaging experiments.
Fast spike sorting with drift correction for extracellular electrophysiology, enabling universal neural spike sorting via deep learning on high-density neural probe recordings (MouseLand, 609+ stars)
The application of 2nd and 3rd generation High Throughput Sequencing (HTS) technologies has deeply reshaped experimental method to investigate microbial communities and obtain a taxonomic and functional profile of the invetigated community. Shotgun Metagenomics allow to quickly obtain a representation of microorganisms genomes characterizing a particular environment. In order to obtain a fast e reliable taxonomic classification of microorganisms genomes we present kMetaShot, an alignment-free taxonomic classifier based on k-mer/minimizer counting.
an automated workflow for the generation and storage of DFT calculations for organic molecules.
General purpose tools for high-throughput catalysis.
Convert PDF files into editable slides with three lines of code
Partial-Order Alignment for fast alignment and consensus of multiple homologous sequences.
Pangolin is a deep-learning based method for predicting splice site strengths (for details, see Zeng and Li, Genome Biology 2022). It is available as a command-line tool that can be run on a VCF or CSV file containing variants of interest; Pangolin will predict changes in splice site strength due to each variant, and return a file of the same format. Pangolin's models can also be used with custom sequences.
file format conversion in Biopython in a convenient way.
AlphaPickle is a Python tool that converts AlphaFold and ColabFold output files into user-friendly CSV files and plots, enabling easy analysis and visualization of protein prediction data without requiring programming expertise. It processes .pkl, .json, and PDB files to extract and visualize metrics like pLDDT and PAE.
Single-cell BERT for gene expression
NuclearPhaser is a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs. This is an overview of the phasing pipeline for dikaryons.
Filtering and trimming of long read sequencing data.
qam is a Python library and command-line tool to compute 3D surface-distances for evaluating liver ablation/tumor completeness based on segmentation images.
Prediction of mRNA subcellular localization using deep recurrent neural networks | RNATracker is a deep learning approach to learn mRNA subcellular localization patterns and to infer its outcome. It operates on the cDNA of the longest isoformic protein-coding transcript of a gene with or without its corresponding secondary structure annnotations. The learning targets are fractions/percentage of the transcripts being localized to a fixed set of subcellular compartments of interest