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CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions.

The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.

This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data.

This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor.

Recurrent breakpoint gene detection on copy number aberration profiles.

This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software.

rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks.

This package provides a web interface to compute transcriptional regulatory modules with rTRM.

D3 JavaScript based genome viewer. Constructs SVGs.

Automate common SAM & BAM conversions.

HyPPI classifies a protein-protein complex based on its interaction type into permanent, transient, or crystal artifact. Permanent protein-protein complexes are only stable in their complexed state. Their subunits would denature upon dissociation of the protein-protein complex. Transient protein-protein complexes are stable in the complexed as well as in the monomeric form, depending on the necessary function of the complex. Crystal artifacts have no biological function and are artificially formed during the crystallization process. The discrimination is performed using two characteristics of the protein-protein complex, the hydrophobicity of the interface (ΔGhydrophobic) and the quotient of interface area ratios (IF-quotient). The IF-quotient considers whether the protein-protein interface is symmetric.

JAMDA enables the preparation of individual protein structures and the docking of small molecules in preprocessed binding sites of choice. JAMDA simplifies the process of protein-ligand docking by automatic preprocessing protocols for the protein and binding sites of interest. The JAMDAscore scoring function retrieved 75% of the native poses in the three highest-ranked solutions for high-quality protein-ligand complexes with default settings. Individual configurations for protein preparation are available, e.g., considering protein ensembles, relevant binding site water molecules, or cofactors. A user-defined number of input conformations for the ligands of interest can be generated fully automated using Conformator. Alternatively, users can also provide externally prepared ligand conformers.

DoGSite3 was developed for predicting robust and reliable small molecule binding sites and computing their geometrical and chemical descriptors. It is based on the grid-based DoGSite algorithm for predicting pockets and their sub-pockets. The new tool is largely rotation- and translation-invariant due to a normalization procedure before binding site prediction. Known ligands in the structure can be used to bias the grid by sufficiently buried ligand fragments. The output encompasses novel chemical binding site descriptors considering solvent accessibility. Compared to its predecessor, it shows increased robustness through comprehensive parameter optimization. DoGSite3 runs finish within seconds.

DoGSiteScorer is a grid-based automated pocket detection and analysis tool. It applies a Difference of Gaussian filter to detect potential binding pockets and splits them into sub-pockets. The method solely uses the 3D structure of the protein. Global properties, describing the size, shape, and chemical features of the predicted (sub-)pockets, are calculated. Per default, a simple druggability score based on a linear combination of the three descriptors describing volume, hydrophobicity, and enclosure is provided for each (sub-)pocket. Furthermore, a subset of meaningful descriptors is incorporated in a support vector machine (libsvm) to predict the (sub-)pocket druggability score (values are between zero and one). The higher the score, the more druggable the pocket is estimated to be.

This desktop application enables users to upload DICOM data along with associated clinical information to QP-Insights—the data management platform of the UPV Reference Node within EUCAIM.

This module provides a command line tool to validate DICOM SEG files against predefined requirements specified in an Excel file. It contains components for finding relevant DICOM files, loading and parsing validation requests and applying validation rules. The main validation process checks each DICOM file for compliance with the Type 1, 1C, 2, 2C and 3 attributes specified in the requirements file. A detailed report is generated highlighting issues such as missing, invalid or conditionally required attributes, including file paths and affected DICOM tags. The tool is designed to ensure data integrity and compliance with DICOM standards.

PoseEdit automatically generates 2D diagrams of protein-ligand complexes, focusing on the interactions between protein and ligand. Interactions between molecules are estimated by an underlying interaction model that relies on atom types and simple geometric criteria. The structure mining tool GeoMine also uses this model to describe binding sites. In addition, users can manipulate the diagrams by translating, rotating, mirroring parts of the structure, adding additional interactions, or removing them. Furthermore, users can add individual labels or adjust available labels. Users can download the final 2D diagrams for a binding site of interest in JSON or SVG format.

METALizer predicts the coordination geometry of metal ions in metalloproteins. Users can compare potential coordination geometries to those found in the examined structure. The predicted coordination geometries and the observed metal interaction distances can be interactively compared to statistics calculated based on the PDB.

A tool that checks the clinical metadata quality (validity, completeness), the integrity between images and clinical metadata provided as well as their accuracy, the de-identification protocol applied, and existence of annotation together with the consistency between the images and the annotation files and informs the user on corrective actions prior to data upload.

Automatically detects duplicate and near-duplicate DICOM image series in large medical imaging datasets. Uses a tiered pipeline combining DICOM metadata analysis, SHA-based pixel hashing, and image similarity metrics (SSIM, cosine, MAD) to identify exact copies, re-exported series, and near-identical acquisitions. All findings are reported for human expert review — no files are modified or deleted automatically. For scenarios requiring strict, image-level deduplication based on pixel content, fully agnostic to metadata changes, consider using [https://bio.tools/image_duplicate_check_tool]

NanoporeDB is an open-access structural database dedicated to the exploration, analysis, and design of protein nanopores, which serve as essential molecular gateways in biological membranes and form the basis of many advanced biosensing and sequencing technologies. This platform integrates large-scale structure-guided mining and deep learning-based modeling using AlphaFold-Multimer and AlphaFold3 to provide about 7,000 high-confidence multimeric nanopore structures. Each entry includes detailed information on membrane embedding, pore geometry annotation, and constriction profiling to support functional and biophysical interpretation. Through an interactive 3D visualization interface and quantitative parameters such as tilt angle, insertion depth, and pore geometry, NanoporeDB enables researchers to explore nanopore diversity, discover novel scaffolds, and accelerate innovation in molecular sensing, precision diagnostics, and synthetic biology.

CompuCell3D is a multiscale multicellular virtual tissue modeling and simulation environment. CompuCell3D is written in C++ and provides Python bindings for model and simulation development in Python.

Miniconda is a minimal Python distribution that includes the Conda package and environment manager plus only essential dependencies. It provides a lightweight way to create isolated environments and install Python packages as needed, without the large preinstalled package set of Anaconda.

FigCanvas is an AI scientific figure generator for life-science researchers. It produces publication-ready biological diagrams (mechanism diagrams, pathway figures, cell biology visuals), CONSORT and methodology flowcharts, and data visualizations such as volcano plots from text prompts or uploaded datasets. The tool turns methods-section text or structured data into editable vector figures suitable for manuscripts, posters, and slides, helping researchers iterate on figures without rebuilding them in Illustrator.

AlphaFind v2 is a tool for fast, structure‑based search for protein structures against the AlphaFold DB (https://alphafold.ebi.ac.uk/) and TED DB (https://ted.cathdb.info/). The tool uses protein‑level embeddings to provide a rapid pre‑filter, with top candidates undergoing TM-Score, RMSD and residue‑level alignments computations. Four complementary search modes are available: (i) whole‑chain search, (ii) pLDDT‑aware search that restricts similarity to high‑confidence regions, (iii) domain search against the TED database, and (iv) multidomain search that combines several chain‑level matches into a single score. Users can restrict queries to a given organism, CATH superfamily or to proteins with experimental structures, and submit queries by UniProt/AlphaFold identifier. Results comprise a ranked list with similarity metrics, rich metadata and an interactive 3‑D superposition view. The service is freely accessible at https://alphafind.ics.muni.cz/.

PoseView automatically generates 2D diagrams of protein-ligand complexes, focusing on the interactions between protein and ligand. Interactions between molecules are estimated by an underlying interaction mode that relies on atom types and simple geometric criteria. It adheres to the conventions of chemical structure diagram generation. The quality of the resulting diagrams is comparable to manually drawn examples from books and scientific publications.

MicroMiner assists in identifying single-residue substitutions in protein structure databases. It searches protein residue environments with local sequence and structural similarity based on the SIENA methodology. Users can search for structural mutation in the entire PDB, their in-house structure collection, or (subsets of) the AlphaFold Database. They can use the method to explore the mutation landscape of proteins with experimental or predicted structures. MicroMiner can be applied to single domains or even protein-protein or protein-ligand interfaces. Several filter options to simplify downstream analysis are available.

SIENA is a software pipeline enabling the fully automated construction of protein structure ensembles from the PDB. Starting with a single query structure, all binding sites with high sequence similarity are extracted from the PDB, aligned, and superimposed. SIENA also handles complicated cases, such as comparing binding sites at protein domain interfaces or within multimeric proteins.

GeoMine enables the automated mining of protein-ligand binding sites. Based on individually designed queries, users can search for spatial interaction patterns in huge collections of protein-ligand complexes and binding pockets. The regularly updated GeoMine database relies on the free database systems SQLite and PostgreSQL. It supports radius-based pockets (based on ligands and predicted pockets (based on DoGSite3) for query generation. The query management is based on XML (for the REST service) or JSON in the GUI mode. Its output consists of the query-based superpositions of the matched binding sites and statistics on matching points, distances, and angles.

WarPP predicts the position and orientation of water molecules in small-molecule binding sites. It places and scores water molecules in binding sites of crystallographic structures based on EDIAscorer results and interaction geometries as known from experimentally solved protein structures. WarPP was validated on a high-quality set of 1,500 protein-ligand complexes, containing 20,000 crystallographically observed water molecules. It is sufficiently fast for high-throughput analyses. It correctly places water molecules in approx. 80% of the cases. Users can export the predictions as PDB files for, e.g., molecular docking with JAMDA.

Protoss is a fully automated hydrogen atom placement tool for protein-ligand complexes. It adds missing hydrogen atoms to protein structures and detects reasonable protonation states, tautomeric states, and hydrogen coordinates of both protein and ligand molecules by optimizing the hydrogen bond network.

Primerpickr is an open-source tool for rt-PCr primer picking powered by the aggregation of public usage of rt-pcr primers from open source papers. The database is validated with 154 genes and contains over 31,000 genes across 10 species.

The electron density score for individual atoms (EDIA) quantifies the electron density fit of each atom in a crystallographically resolved structure. Multiple EDIA values can be combined using the power mean to compute the EDIAm, i.e., the electron density score for a group of several atoms. It enables users to score a set of atoms, such as a ligand, a residue, or an active site.

Three-dimensional protein structures play a vital role in drug design. Structure-based design necessitates an in-depth examination of the available quality data before using the structure in computational experiments and for method evaluation. StructureProfiler assists in automatically profiling sets of protein-ligand complex structures based on multiple quality indicators, ranging from model characteristics, e.g., the R factor, and active site features, e.g., bond length deviations, to ligand properties such as electron density support and the validity of torsion angles.

LifeSoaks was designed to find solvent channels in macromolecular structures solved by X-ray crystallography. It predicts their accessibility by molecules through an automated annotation of so-called bottleneck radii. It simplifies the process of manually checking a crystal structure for solvent channels. Bottleneck radii can be calculated for solvent channels and small molecule binding sites. The tool is ideally suited for channel analyses before the actual soaking experiments to select the most promising experimental conditions and crystal forms. LifeSoaks runs fully automated and will finish within seconds to minutes for moderately sized crystals.

PlantiSMASH is a specialized extension of antiSMASH for the identification and analysis of biosynthetic gene clusters (BGCs) in plant genomes. It supports advanced plant-specific detection rules and features for comparative genomics, visualization, and more.

DigestedProteinDB provides a scalable computational infrastructure for indexing and querying peptide cleavage data. Designed for seamless integration into high-throughput mass spectrometry pipelines, it enables low-latency searches and advanced filtering of digested protein datasets to accelerate experimental spectra cross-referencing.

The Open Neuroscience Graph (openneuroscience.org) is an open-access, curated knowledge graph that maps the open science ecosystem in neuroscience as a browsable digital garden. Built from an Obsidian vault and published as a static website using Quartz, the project replaces traditional linear presentation with a networked structure of interlinked Markdown notes. Bidirectional links, full-text search, and an integrated graph visualization allow users to navigate thematic relationships dynamically rather than sequentially. The complete source material is openly available to sustain, replicate and extend the resource, includding all Markdown content, media attachments, Quartz configuration files, and site customizations. Researchers, educators, and open-science practitioners may explore the site directly, download the vault for offline use in Obsidian, or fork the material to build new, derivative knowledge bases. PID=https://doi.org/10.5281/zenodo.20181900

Molecular property prediction with unified API for diverse models and respresentations,

Deep learning system for de novo design of high-affinity protein binders, achieving strong binding across diverse target classes including challenging intracellular proteins with significantly higher success rates than traditional wet-lab screening methods (Google DeepMind, Nature 2024)

Multimodal generative AI assistant for computational pathology enabling interactive visual-language conversations over histopathology images for diagnostic reasoning, case discussion, and education, built on a Mistral-7B backbone with domain-specific fine-tuning (Mahmood Lab, Harvard Medical School, 1.2K+ stars)

Google DeepMind and Google Quantum AI's transformer-based neural-network decoder for quantum error correction, trained on real Sycamore quantum processor data to outperform tensor-network and correlated matching decoders at code distances 3 and 5, demonstrating ML's role in enabling fault-tolerant quantum computing (Nature 2024)

Ecological modeling and conservation AI

Mathematical reasoning

A Python program to compute quasi-harmonic thermochemical data from Gaussian frequency calculations.

Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package.

Base utility functions are available for the Automated Affymetrix Array Analysis set of packages.

Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages.

Utility functions for the Automated Affymetrix Array Analysis set of packages.

Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages.