Skill UI

Datamol provides a simplified, Pythonic abstraction layer over RDKit, making complex molecular operations accessible for chemical data science. It supports core functionalities including SMILES parsing, structure standardization, calculation of molecular descriptors (e.g., LogP, MW), 3D conformer generation, and efficient parallel batch processing. It is ideal for streamlining molecular data handling in drug discovery and chemical research pipelines.

A lightweight Python library designed for reading, parsing, and writing Flow Cytometry Standard (FCS) files. It extracts event data as NumPy arrays, handles complex metadata, and supports multiple FCS versions (2.0-3.1). Ideal for data preprocessing, building cytometry pipelines, and inspecting experimental results.

Gtars is a high-performance toolkit written in Rust with Python bindings, designed for comprehensive analysis of genomic interval data. It provides specialized modules for overlap detection, generating coverage tracks (WIG/BigWig), tokenization for Machine Learning models, and detailed fragment analysis in single-cell genomics. Use it for robust bioinformatics pipelines and computational genomics research.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.

MarkItDown is a powerful tool designed to convert a wide array of file types—including PDF, DOCX, images, audio, and structured data (JSON/XML)—into clean, token-efficient Markdown format. It supports advanced features like OCR for scanned documents, transcription for audio, and AI-enhanced descriptions, making diverse content readily consumable and structured for modern Language Models and AI pipelines.

Matchms is an open-source Python library designed for comprehensive mass spectrometry data processing. It handles spectrum comparison, calculates various similarity scores (like cosine and modified cosine), and aids in identifying unknown compounds. It is highly valuable for metabolomics and spectral library searching pipelines.

Medchem is a powerful Python library designed for molecular filtering and prioritization in drug discovery. It allows users to apply rigorous, literature-derived drug-likeness rules (e.g., Lipinski, Veber, CNS), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and combine multiple criteria using a custom query language. It is essential for compound library triage and hit-to-lead optimization.

Molfeat is a comprehensive Python library designed for molecular featurization. It converts chemical structures (SMILES/RDKit) into numerical feature vectors, supporting over 100 featurizers like ECFP, MACCS, and deep embeddings (ChemBERTa). It unifies featurization pipelines, making it ideal for QSAR modeling, virtual screening, and integrating into deep learning ML workflows.

This comprehensive skill provides a standardized pipeline for reconstructing the evolutionary history of biological sequences (genes, proteins, genomes). It guides users through essential steps, including multiple sequence alignment using MAFFT, maximum likelihood tree inference with IQ-TREE 2, and visualization with ETE3. Ideal for research in viral phylodynamics, molecular clock studies, and large-scale comparative genomics.

Polars is a lightning-fast DataFrame library built on Apache Arrow for Python. It provides an expression-based API, lazy query optimization, and parallel computation capabilities for efficient data manipulation, analytics, and ETL pipelines. It is designed to handle large, memory-intensive datasets, making it an ideal alternative for migrating from pandas or optimizing complex data workflows.

PyDESeq2 is a robust Python implementation for conducting differential gene expression analysis on bulk RNA-seq count data. It supports complete statistical workflows, including data loading from various formats, complex multi-factor design specifications (e.g., accounting for batch effects or covariates), normalization, and performing advanced Wald tests. It is ideal for researchers transitioning R-based DESeq2 analyses into scalable Python pipelines.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.