Skill UI

polars-bio is a high-performance Python library built on Polars for genomic interval operations and bioinformatics file I/O. It provides a familiar DataFrame-centric API for interval arithmetic (overlap, nearest, merge, complement, etc.) and supports reading/writing major bioinformatics formats like BED, VCF, BAM, and GFF. Key features include streaming, cloud-native I/O, and SQL query support, making it a fast alternative for large-scale genomic data processing.

PrimeKG is a comprehensive knowledge graph integrating over 20 primary databases to map complex biological relationships. It allows users to query multiscale data, including genes, drugs, diseases, and phenotypes. Ideal for drug discovery, identifying repurposing opportunities, and analyzing deep disease contexts in network pharmacology.

PyDESeq2 is a robust Python implementation of the DESeq2 method, designed for analyzing differential gene expression in bulk RNA-seq count data. It supports complete bioinformatics workflows, including data loading, multi-factor design specification (e.g., batch effects, covariates), normalization (size factors), dispersion shrinkage, and rigorous statistical testing (Wald tests with FDR correction). Ideal for converting R-based DESeq2 pipelines into scalable, Python-native analysis pipelines.

A powerful Python library for reading, writing, and manipulating DICOM files, the standard format for medical imaging data. Use it for extracting pixel data from various scans (CT, MRI, X-ray), managing complex metadata tags, handling data compression, performing patient data anonymization, and converting medical images to standard formats for research and clinical applications.

Pymatgen is a comprehensive Python library designed for computational materials science. It empowers users to create, manipulate, and analyze crystal structures and molecular systems. Key functionalities include calculating phase diagrams, analyzing electronic structures (Density of States, band gaps), performing symmetry analysis, generating supercells, and accessing data from the Materials Project database. It supports over 100 file formats from various computational codes.

PyMC is a powerful Python library for probabilistic programming and Bayesian statistical inference. It allows users to define complex models, including hierarchical structures, time series, and regression, and perform advanced inference using MCMC sampling (NUTS) or variational inference. This skill covers the complete workflow: model specification, prior validation, fitting, advanced diagnostics (R-hat, ESS), and posterior predictive checking, enabling rigorous uncertainty quantification.

PyOpenMS is a comprehensive platform providing Python bindings to the OpenMS library. It is designed for advanced mass spectrometry data analysis, supporting entire proteomics and metabolomics workflows. Key capabilities include handling diverse file formats (mzML, mzXML), signal processing, robust feature detection, and precise peptide/protein identification. It is ideal for quantitative biological research, allowing users to process complex LC-MS/MS pipelines.

Pysam is a powerful Python module designed for reading, manipulating, and writing core genomic file formats. It provides a Pythonic interface to htslib, enabling users to process alignment files (BAM/CRAM), variant calls (VCF/BCF), and reference sequences (FASTA/FASTQ). Use it for implementing complex bioinformatics pipelines, calculating coverage, quality control, and detailed variant annotation.

PyTDC (Therapeutics Data Commons) is an open-science platform providing standardized, AI-ready datasets and benchmarks for drug discovery and development. It supports three core types of tasks: single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (e.g., DDI, DTI), and novel molecule generation. It is essential for benchmarking and developing therapeutic machine learning models.

Qiskit is a leading open-source framework for quantum computation. It enables users to build, optimize, and execute complex quantum circuits across diverse platforms, including local simulators and real IBM Quantum hardware. Ideal for implementing advanced algorithms in quantum machine learning, chemistry simulation, and combinatorial optimization.

A comprehensive Python library for simulating quantum mechanical systems. It models both closed (unitary) and open (dissipative) quantum dynamics, covering key concepts like master equations, decoherence, and quantum optics. Users can simulate time evolution, calculate entanglement (concurrence), and visualize quantum states (Wigner functions).

RDKit is a powerful, comprehensive cheminformatics library providing Python APIs for molecular structure manipulation and analysis. It supports reading and writing multiple molecular formats (SMILES, SDF, MOL, InChI), calculating chemical descriptors (MW, LogP, TPSA), performing substructure searching, handling stereochemistry, and modeling chemical reactions. This toolkit is essential for computational chemistry, drug discovery, and advanced cheminformatics research.