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Incremental knowledge graph construction using LLMs with entity extraction and Neo4j visualization

BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced.

Eukaryotic Genome Annotation Pipeline-External caller scripts and documentation

ReviewAid is an open-source AI-assisted tool for full-text screening and data extraction in systematic reviews. It supports evidence synthesis workflows by using large language models to classify articles according to user-defined PICO criteria and extract structured information from full-text publications. ReviewAid is designed as a supplementary reviewer rather than a replacement for human judgement. It aims to reduce manual workload, improve consistency, and assist researchers during screening and data extraction while maintaining human oversight throughout the evidence synthesis process.

markeR is an R package that provides a modular and extensible framework for the systematic evaluation of gene sets as phenotypic markers using transcriptomic data. The package is designed to support both quantitative analyses and visual exploration of gene set behaviour across experimental and clinical phenotypes. It implements multiple methods, including score-based and enrichment approaches, and also allows the exploration of expression behaviour of individual genes. In addition, users can assess the similarity of their own gene sets against established collections (e.g., those from MSigDB), facilitating biological interpretation.

GlycoDash is an R Shiny dashboard for processing glycomics data obtained from LaCyTools, SweetSuite and Skyline.

Family of operational-quality open weather models from DeepMind and Google Research, including WeatherNext Graph (deterministic GNN medium-range forecasting, published as GraphCast), WeatherNext Gen (diffusion ensemble, published as GenCast), WeatherNext 2 (state-of-the-art global medium-range and cyclone forecasting skillful beyond 15 days, operational at 0.25° resolution), and WeatherNext Cyclones (breakthrough tropical cyclone track forecasting, Nature 2026); official code and open weights (Apache 2.0, 7.6K+ stars)

High-throughput PubChem client for batch queries with caching, validation, rate-limit-aware retries, and a simple CLI.

Diffusion MR imaging.

A comprehensive R package for identifying and ranking influential nodes in biological and other complex networks. The package implements the Integrated Value of Influence (IVI), Experimental data-based Integrative Ranking (ExIR), SIRIR, and numerous network centrality measures, enabling network topology analysis, influential node detection, feature prioritization, and candidate biomarker discovery. It also provides functions for network reconstruction, centrality assessment, visualization, and analysis of relationships between centrality measures.

Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the [Open Bioinformatics Foundation](http://open-bio.org/). Contains the very useful [Entrez](https://biopython.org/DIST/docs/api/Bio.Entrez-module.html) package for API access to the NCBI databases.

A package to 'build' collections of materials properties from the output of computational materials calculations.

First fully customizable open-source multiagent framework automating complete research lifecycle from idea conception to LaTeX papers with dynamic workflows

Pythonic access to FASTA files.

nf-core/variantbenchmarking is designed to evaluate and validate the accuracy of variant calling methods in genomic research. Initially, the pipeline is tuned well for available gold standard truth sets (for example, Genome in a Bottle and SEQC2 samples) but it can be used to compare any two variant calling results. The workflow provides benchmarking tools for small variants including SNVs and INDELs, Structural Variants (SVs) and Copy Number Variations (CNVs) for germline and somatic analysis.

Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme().

COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets.

spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.

JCVI is a versatile toolkit for comparative genomics analysis. It is a collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

Bayesian haplotype-based polymorphism discovery and genotyping.

Official Jupyter extension with `%%ai` magic commands and sidebar chat assistant, connecting multiple model providers and local inference

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

squallms is a Bioconductor R package that implements a "semi-labeled" approach to untargeted mass spectrometry data. It pulls in raw data from mass-spec files to calculate several metrics that are then used to label MS features in bulk as high or low quality. These metrics of peak quality are then passed to a simple logistic model that produces a fully-labeled dataset suitable for downstream analysis.

Tools for analyzing SingleCellExperiment objects as projects. for input into the chevreulShiny app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

First large vision-language assistant for gigapixel whole-slide pathology image understanding, released with the SlideInstruction dataset and SlideBench benchmark (uni-medical, Apache 2.0, 2025)

Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

anglemania extracts genes from multi-batch scRNA-seq experiments for downstream dataset integration. It shows improvement over the conventional usage of highly-variable genes for many integration tasks. We leverage gene-gene correlations that are stable across batches to identify biologically informative genes which are less affected by batch effects. Currently, its main use is for single-cell RNA-seq dataset integration, but it can be applied for other multi-batch downstream analyses such as NMF.

The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.

BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.

Library for fast calculations of **mo**lecula**r** **fe**at**u**re**s** from 3D structures for machine learning with a focus on steric descriptors.

Lightweight Expression displaYer (plotter / viewer) of SummarizedExperiment object in R. This package provides a quick and easy Shiny-based GUI to empower a user to use a SummarizedExperiment object to view

Comprehensive collection of 125+ ready-to-use scientific skill modules for Claude AI across bioinformatics, cheminformatics, clinical research, ML, and materials science

CalibraCurve is a computational tool designed to generate calibration curves for targeted mass spectrometry-based quantitative data. It is applicable to various omics disciplines, including proteomics, lipidomics, and metabolomics. The package also offers functionalities for data and calibration curve visualization and concentration prediction from new datasets based on the established curves.

jaxQTL is a flexible and efficient sc-eQTL mapping framework using count-based models, designed to analyze sparse counts in scRNA-seq data and large datasets. It has been shown to identify more eGenes compared with existing approaches, improving our ability to identify distal eQTLs.

Provides a graphical user interface to integrate, visualize and explore results from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid species. The app is meant for interactive use and allows users to optionally upload different sources of information, including gene annotation and alignment files, enabling the exploitation and search for candidate genes in a genome browser. In its current version, 'VIEWpoly' supports inputs from 'MAPpoly', 'polymapR', 'diaQTL', 'QTLpoly', 'polyqtlR', 'GWASpoly', and 'HIDECAN' packages.

Converts Protein Data Bank structures into 3D-printable models. Each polymer chain is meshed separately and written as a named object in a single 3MF file, so a multi-material printer can assign one filament per chain. Protein chains can be rendered as a solvent-excluded surface, a cartoon, or a backbone tube; nucleic acids as a tube-and-rung form with the strands of a duplex welded at every base pair. Press-fit magnet pockets are optionally placed at chain interfaces, so a complex comes apart where its subunits actually meet. All meshes are checked for watertightness before export.

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)

Open-source hybrid scientific research agent and workbench replicating Claude Science, combining JSON tool orchestration with persistent Python/R Code-as-Action kernels, 604 bundled science skills, MCP connectors, sandboxed local execution, and multi-provider LLM support for end-to-end scientific workflows (PKU–YuanKong Intelligence, 377+ stars, MIT License, 2026)

bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.

Directed message passing neural networks for property prediction of molecules and reactions with uncertainty and interpretation.

Comprehensive Claude Code skill suite covering the full academic pipeline from deep research and paper writing to multi-perspective peer review, revision, and finalization; features multi-agent teams, PRISMA systematic review, style calibration, claim-level citation audits, integrity gates, and human-in-the-loop safeguards (38K+ stars, CC BY-NC 4.0, 2026)

Python library from KIT for training deep learning models on large-sample hydrology, introducing LSTM-based rainfall-runoff modeling that learns universal, regional, and local hydrological behaviors from hundreds of catchments; widely used for streamflow forecasting, flood prediction, and drought research, supporting the CAMELS and Caravan benchmark datasets (581+ stars, BSD-3-Clause, actively maintained)

An interactive structure/property explorer for materials and molecules.

This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell.

Galaxy workflow for BlockClust pipeline.

A molecule manipulation library.

pretext-to-asm takes the AGP output from PretextView and assembles it into FASTA format to produce a new assembly.

The `TrIdent` R package automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events. Transductomics is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction.

Tools for detecting drug-protein interactions and estimating IC50 values from chemoproteomics data. Implements semi-parametric isotonic regression, bootstrapping, and curve fitting to evaluate compound effects on protein abundance.

Noodle is a public, read-only web application for semantic discovery and exploration of biomedical literature sourced from PubMed, Europe PMC, and OpenAlex. It accepts natural-language research questions and presents ranked publications together with gene, genetic variant, and phenotype views. Publication pages provide provenance, identifiers, source links, indexed concepts, and navigable semantic-neighbourhood graphs. The companion Noodle Biomedical Literature Discovery MCP exposes the same public discovery domain through a read-only, source-linked Model Context Protocol interface with bounded citation and semantic graph traversal. Semantic proximity, citation distance, co-mention, and search rank are discovery signals and do not establish scientific evidence, causality, diagnosis, or treatment.