Skill UI

FlowIO is a specialized, low-level library designed for reading, inspecting, and writing Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect detailed metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset structures. It is crucial for data preparation, format conversion, and foundational metadata management in bioinformatics pipelines, but does not perform complex analysis like gating or compensation.

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.

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.

MarkItDown is a lightweight utility designed to convert heterogeneous documents (PDF, Office files, HTML, etc.) into structured, plain Markdown. It is specifically optimized for text analysis, search indexing, and preparing data for Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) systems, prioritizing content structure over visual fidelity. Supports local, stream, and advanced multimodal extraction scenarios.

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.

A comprehensive guide for designing, reviewing, and migrating numerical workflows in MATLAB or GNU Octave. It emphasizes building reproducible projects, adhering to strict safety boundaries, and ensuring code semantics are explicit for robust data processing and analysis.

Molfeat is a comprehensive Python library designed for molecular featurization, providing over 100 pre-trained and hand-crafted featurizers. It unifies the process of converting chemical structures (SMILES strings or RDKit molecules) into robust numerical feature vectors. This capability is essential for advanced cheminformatics tasks, including Quantitative Structure-Activity Relationship (QSAR) modeling, virtual screening, similarity searching, and training deep learning models on molecular data.

A comprehensive research toolkit for building and auditing reproducible workflows for physiological time-series data. It specializes in preprocessing, event/interval analysis, multimodal alignment, and extracting variability and complexity features from signals like ECG and EDA. Designed strictly for academic and research use, emphasizing rigorous data contracts and method-aware validation, while strictly disclaiming any diagnostic or medical device validation role.

PathML is a robust computational platform engineered for local, research-only workflows in computational pathology. It facilitates comprehensive analysis pipelines, including whole slide image (WSI) loading and tiling, preprocessing, quality control (QC), quantification of multiplex images, and the construction of spatial graphs. Designed with strict adherence to data privacy and research reproducibility, it supports advanced biological data analysis.

PyDESeq2 is a robust Python implementation of DESeq2, designed for comprehensive differential expression analysis using bulk RNA-seq count data. It supports complex formulaic designs, performs Wald tests with multiple testing correction (FDR), and includes advanced features like LFC shrinkage, enabling researchers to analyze gene expression changes between various experimental conditions within Python pipelines.

PyMC is a powerful Python library designed for probabilistic programming and sophisticated Bayesian modeling. It enables users to build complex models, such as hierarchical structures and time series, and perform rigorous inference using methods like MCMC (NUTS) sampling and Variational Inference. Key features include model comparison (LOO, WAIC) and comprehensive diagnostic tools for uncertainty quantification.