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Datamol Cheminformatics Toolkit

Pythonic wrapper around RDKit with simplified interface and sensible defaults for drug discovery cheminformatics. Use for SMILES parsing, molecular standardization, descriptor computation, fingerprints, similarity search, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformer generation, and molecular visualization. Returns native rdkit.Chem.Mol objects. Prefer datamol over raw RDKit for standard workflows; use RDKit directly for advanced control or custom parameters.

jaechang-hits119

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MDAnalysis — Molecular Dynamics Trajectory Analysis

Python library for analyzing molecular dynamics (MD) trajectories from GROMACS, AMBER, NAMD, CHARMM, and LAMMPS. Reads topology and trajectory files into Universe objects; supports RMSD, RMSF, radius of gyration, contact maps, hydrogen bond analysis, PCA, and custom distance/angle calculations across millions of frames. Use for structural analysis after MD simulations; use OpenMM or GROMACS directly for running simulations.

jaechang-hits119

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Neuropixels Analysis

Pipeline for analyzing Neuropixels extracellular electrophysiology recordings. Covers probe geometry loading (ProbeInterface), spike sorting with Kilosort via SpikeInterface, quality metrics computation, unit curation (ISI violations, firing rate, signal-to-noise), and post-sort analysis (PSTH, tuning curves, population decoding) using pandas and matplotlib. Designed for acute and chronic Neuropixels 1.0/2.0/Ultra recordings from rodent and primate experiments.

jaechang-hits119

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Nansen Web Searcher

Search the web for one or more queries in parallel. Use when you need current information, news, prices, or any web content to complement on-chain Nansen data.

nansen-ai118

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Organism Classification

Organism Classification & Database - Classify organism: NCBI taxonomy, Ensembl taxonomy, ChEMBL organisms, and genome info. Use this skill for taxonomy tasks involving get taxonomy get taxonomy id get organism by id get genome dataset report by taxon. Combines 4 tools from 3 SCP server(s).

InternScience132

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Peptide Properties Calculation

Calculate peptide sequence properties including molecular weight, isoelectric point, extinction coefficient, and chemical formula.

InternScience132

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Mutation Impact Analysis

Mutation Impact Analysis - Analyze mutation impact: predict structure, predict mutations from sequence and structure, and check variant effects with Ensembl VEP. Use this skill for molecular biology tasks involving pred protein structure esmfold zero shot sequence prediction predict zero shot structure get vep hgvs. Combines 4 tools from 3 SCP server(s).

InternScience132

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Multiomics Integration

Multi-Omics Integration - Integrate multi-omics: gene expression, protein data, pathway enrichment, and metabolic pathways. Use this skill for multi-omics tasks involving get gene expression across cancers get uniprotkb entry by accession get functional enrichment kegg get. Combines 4 tools from 4 SCP server(s).

InternScience132

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Protein Similarity Search

Protein Similarity Search - Search for similar proteins: extract sequence from PDB, search structures with FoldSeek, find homologs with STRING, and check UniProt. Use this skill for bioinformatics tasks involving extract pdb sequence foldseek search get best similarity hits between species search uniprotkb entries. Combines 4 tools from 3 SCP server(s).

InternScience132

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Drugsda Data Valid

Check if the input protein sequence or molecule SMILES string is valid.

InternScience132

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Metabolomics Xcms Preprocessing

XCMS3 workflow for LC-MS/GC-MS metabolomics preprocessing. Peak detection (CentWave/MatchedFilter), RT alignment (Obiwarp), correspondence, gap filling, and CAMERA adduct/isotope annotation.

TianGzlab120

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Spatial De

Differential expression and marker discovery for spatial transcriptomics using Scanpy Wilcoxon / t-test or sample-aware pseudobulk PyDESeq2.

TianGzlab120

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Orchestrator

Multi-omics query routing and pipeline orchestration across all OmicsClaw domains. Routes natural language queries to the correct analysis skill across spatial transcriptomics, single-cell omics, genomics, proteomics, and metabolomics.

TianGzlab120

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Sc Filter

Filter cells and genes from single-cell RNA-seq AnnData objects using QC-derived thresholds or tissue presets. This wrapper removes low-quality cells/genes but does not normalize, cluster, or annotate the dataset.

TianGzlab120

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Metabolomics Normalization

Metabolomics data normalization, scaling and transformation.

TianGzlab120

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Metabolomics Statistics

Statistical analysis for metabolomics — PCA, PLS-DA, clustering, and univariate tests.

TianGzlab120

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Proteomics Ptm

Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Site localization, motif analysis, and quantitative PTM analysis with MSstatsPTM.

TianGzlab120

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Metabolomics De

Metabolomics differential analysis using univariate tests (t-test, FDR), multivariate methods (PCA, PLS-DA, OPLS-DA, sPLS-DA), Random Forest, and ROC analysis for biomarker discovery.

TianGzlab120

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Grad Sem

Apply Structural Equation Modeling (SEM) to test hypothesized causal structures by combining measurement models (CFA) and structural models (path analysis). Use this skill when the user needs to validate latent constructs, test mediation or moderation paths, assess model fit with CFI/TLI/RMSEA/SRMR, or when they ask 'do these variables form a causal chain', 'how do I test my theoretical model', or 'is my measurement model valid'.

asgard-ai-platform120

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Spatial Condition

Compare experimental conditions in spatial transcriptomics data using pseudobulk differential expression with method-aware PyDESeq2 or Wilcoxon testing and explicit replicate handling.

TianGzlab120

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Brenda Database

Query BRENDA Enzyme Database for kinetic parameters (Km, Vmax, kcat, Ki), enzyme classifications, substrate specificity, inhibitors, cofactors, and organism-specific enzyme data via SOAP/REST API. 80,000+ enzyme entries, 7M+ kinetic values. Requires free academic registration. For metabolic pathway modeling use cobrapy-metabolic-modeling; for metabolite structures use hmdb-database.

jaechang-hits119

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Biopython: Computational Molecular Biology Toolkit

Computational molecular biology toolkit for sequence manipulation, file I/O (FASTA/GenBank/PDB), NCBI database access (Entrez), BLAST automation, pairwise/multiple sequence alignment, protein structure analysis (Bio.PDB), and phylogenetic tree construction. Use for batch sequence processing, custom bioinformatics pipelines, format conversion, and programmatic PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.

jaechang-hits119

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NeuroKit2

NeuroKit2 is a Python toolkit for neurophysiological signal processing. Process ECG (heart rate, HRV, R-peak detection), EEG (complexity, power spectral density), EMG (muscle activation onset), EDA/GSR (skin conductance, SCR decomposition), PPG (photoplethysmography), and RSP (respiration) signals. Simulate synthetic signals for testing. Alternatives: BioSPPy (older, less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP without biosignal abstraction).

jaechang-hits119

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Pathml

PathML is an open-source toolkit for computational pathology. Use it to process whole-slide images (WSIs): load slides, extract tiles, apply stain normalization and nuclear segmentation preprocessing, extract features, and train machine learning models. Supports H&E and multiplex imaging. Ideal for building end-to-end digital pathology pipelines from raw WSI files to quantitative outputs.

jaechang-hits119

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MaxQuant + Perseus — Proteomics Analysis Pipeline

MaxQuant + Perseus proteomics pipeline: configure and run MaxQuant for label-free quantification (LFQ) and SILAC; parse proteinGroups.txt in Python; filter contaminants/reverse decoys; log2-transform and median-normalize LFQ intensities; impute MNAR missing values; t-test with FDR correction; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo instrument-native processing; FragPipe/MSFragger for GPU-accelerated database search.

jaechang-hits119

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Cellpose — Deep Learning Cell Segmentation

Deep learning cell and nucleus segmentation from fluorescence and brightfield microscopy images. Uses pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm that segments cells without requiring retraining on new image types. Outputs label masks for downstream morphology measurement and tracking. Use scikit-image watershed for rule-based segmentation; use Cellpose when deep learning generalization across staining conditions is needed.

jaechang-hits119

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Cosmic Database

Query COSMIC (Catalogue Of Somatic Mutations In Cancer) for cancer somatic mutations, gene census data, mutational signatures, drug resistance variants, and cancer gene annotations. REST API v3.1 supports gene/sample/variant queries. Free registration required. For germline clinical variants use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity.

jaechang-hits119

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PyOpenMS — Mass Spectrometry Analysis

Mass spectrometry data processing with PyOpenMS. Use for LC-MS/MS proteomics and metabolomics workflows — mzML/mzXML file I/O, signal processing (smoothing, peak picking, centroiding), feature detection and linking across samples, peptide/protein identification with FDR control, untargeted metabolomics pipelines. For simple spectral matching and metabolite ID, use matchms instead.

jaechang-hits119

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mouse-phenome-database

Retrieve quantitative phenotype measurements across inbred mouse strains from the Mouse Phenome Database (MPD) for metabolic, behavioral, and physiological traits. Query strain means and raw individual measurements for body weight, glucose, blood pressure, behavioral assays, and 40+ additional procedures. Use for QTL analysis support, cross-strain phenotype comparison, and identifying mouse models for metabolic or behavioral traits. For mouse gene-disease-phenotype associations use monarch-database; for mouse genome annotations use ensembl-database.

jaechang-hits119

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pymatgen

pymatgen (Python Materials Genomics) is a materials science Python library for structure analysis, thermodynamics, and electronic property calculation. Parse and create crystal structures (CIF, POSCAR, CIF), query the Materials Project database for DFT-computed properties, analyze phase diagrams and pourbaix diagrams, compute X-ray diffraction patterns, and generate DFT input files for VASP, Quantum ESPRESSO, and CP2K. Alternatives: ASE (Atomic Simulation Environment) for MD/geometry; AFLOW for high-throughput; OVITO for visualization.

jaechang-hits119