Skill UI

Biopython is a comprehensive Python toolkit for computational molecular biology. It supports essential bioinformatics tasks, including sequence manipulation, file parsing (FASTA, GenBank, PDB), phylogenetic analysis, sequence alignment (BLAST/Multiple), and programmatic access to major databases like NCBI using Bio.Entrez. Ideal for building custom, robust bioinformatics pipelines and conducting research in genomics and structural biology.

Provides programmatic and standardized access to massive, versioned datasets of single-cell and spatial transcriptomics from CZ CELLxGENE. This skill allows users to query millions of cells, analyze gene expression, and access pre-calculated embeddings across various tissues, diseases, and species without downloading entire datasets. Essential for large-scale bioinformatics analysis, machine learning model training, and comprehensive cross-dataset comparison.

COBRApy is a comprehensive Python library for Constraint-Based Reconstruction and Analysis (COBRA) of metabolic models. It enables users to simulate complex cellular metabolism, performing crucial analyses such as Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), and virtual gene/reaction knockouts. It is an essential tool for systems biology research and metabolic engineering design.

deepTools is a comprehensive command-line toolkit designed for processing and analyzing high-throughput sequencing data. It handles core genomics tasks including BAM to bigWig conversion, quality control assessment (PCA, correlation), normalization, and generating publication-quality visualizations (heatmaps, profiles) for ChIP-seq, RNA-seq, and ATAC-seq data. Essential for reproducible genomic research workflows.

Geniml provides a robust framework for running machine learning and statistical workflows over genomic interval sets. It treats coordinates, assemblies, and model artifacts as explicit contracts, enabling users to validate data structures (like BED files) and plan complex runs (such as Region2Vec or scEmbed) locally. The tool emphasizes reproducibility, adherence to biological data standards, and running without unnecessary network calls.

This skill provides comprehensive tools for analyzing and engineering protein glycosylation patterns. It scans sequences for canonical N-glycosylation sequons (N-X-S/T), predicts O-glycosylation hotspots, and supports rational modification. It is essential for optimizing therapeutic antibodies, improving protein stability, or designing complex vaccine antigens.

Gtars is a robust toolkit providing native Rust implementations and Python bindings for advanced genomic interval modeling. It supports core bioinformatic tasks including set algebra, coverage calculation, tokenization, and fragment processing. The library emphasizes data integrity by enforcing strict genomic data contracts, ensuring correct handling of coordinates, assemblies, and reference sequences across complex computational pipelines.

Histolab is a specialized Python library for digital pathology designed to process Whole Slide Images (WSI). It automates critical steps including sophisticated tissue detection, customizable tile extraction (random, grid, score-based), and stain normalization. It is essential for preparing high-quality, structured datasets for deep learning model training and comprehensive quantitative whole slide analysis.

LaminDB is an open-source, lineage-native lakehouse designed for managing complex biological datasets and computational workflows. It makes diverse scientific data (like scRNA-seq, AnnData) queryable, traceable, and compliant with FAIR principles. Core capabilities include robust data lineage tracking, schema validation, ontology-backed annotation (Bionty), and unified querying across multiple cloud/local storage systems. It is essential for bioinformatics research and building reproducible data infrastructure.

This skill guides the creation of highly auditable, evidence-traceable market research reports. It provides a structured workflow for defining market scope, mapping every claim to primary sources (like official filings and statistics), and conducting rigorous, assumption-driven market sizing (TAM/SAM/SOM) and forecasting scenarios. Essential for professional market analysis and strategic planning.

Matchms is a powerful Python package designed for importing, cleaning, processing, and comparing tandem mass spectra (MS/MS). It supports various file formats (mzML, MGF) and provides core functionalities including library searching, metadata harmonization, peak filtering, and calculating multiple similarity scores (e.g., Cosine, Neutral-loss). It is essential for metabolomics and mass spectrometry workflows, enabling the construction of structured score matrices and spectral similarity networks.

This powerful Python library provides comprehensive medicinal chemistry filters for compound triage in drug discovery. Users can apply established drug-likeness rules (like Lipinski's Rule of Five, Veber, and CNS rules), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and filter molecular libraries using a custom query language. It is essential for prioritizing hit compounds and optimizing lead structures.