Skill UI

Hypogenic is a powerful framework designed to accelerate scientific discovery by automating the process of hypothesis generation and testing on complex tabular datasets. It supports three advanced modes: HypoGeniC for purely data-driven pattern discovery; HypoRefine for synergistically integrating literature insights with empirical data; and Union methods for comprehensive hypothesis coverage. Ideal for domains like deception detection, content analysis, and advanced predictive modeling where systematic exploration of multiple theories is required.

Matchms is a powerful open-source Python library designed for comprehensive mass spectrometry data processing and analytical workflows. It enables users to import spectra from various formats (mzML, MGF, etc.), apply advanced filtering (metadata harmonization, peak selection), and calculate various spectral similarities (e.g., Cosine, Modified Cosine). It is invaluable for metabolite identification, library searching, and building reproducible metabolomics pipelines.

Matplotlib is a foundational Python library used for creating static, animated, and highly customizable plots. It supports diverse visualization types, including line plots, heatmaps, scatter charts, and contour plots. This skill guides users on both the convenient pyplot and the powerful object-oriented API, enabling the creation of publication-quality, multi-panel scientific figures for data analysis and reporting.

A comprehensive toolkit for running and analyzing molecular dynamics (MD) simulations. This skill covers the entire workflow: system setup, force field definition, energy minimization, and equilibration using industry-standard tools like OpenMM and MDAnalysis. Essential for structural biology, drug binding studies, and biophysics research.

NetworkX is a powerful Python library designed for creating, manipulating, and analyzing complex graph and network structures. It supports core graph algorithms such as finding shortest paths, calculating centrality measures (e.g., PageRank, Betweenness), detecting communities, and generating various synthetic network models. It is indispensable for fields like social network analysis, bioinformatics, and transportation system modeling.

This toolkit provides comprehensive processing and analysis capabilities for various physiological biosignals, including ECG, EEG, EDA, PPG, and EMG. It is designed for advanced psychophysiology research, clinical diagnosis, and human-computer interaction studies, supporting complex analyses like HRV, microstate detection, and autonomic assessment.

An end-to-end toolkit for analyzing Neuropixels high-density neural recordings, covering the entire electrophysiology workflow. Features include loading diverse formats (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4, etc.), quality metric calculation, and unit curation (AI-assisted and model-based). Ideal for researchers working with multi-shank, high-channel count neural data.

Comprehensive Python integration for OMERO, an open-source platform designed for managing, visualizing, and analyzing complex microscopy datasets. This skill allows users to programmatically access images, retrieve structured datasets, manage annotations (ROIs, tags), perform deep pixel analysis, and execute batch processing for high-content screening and advanced bioimaging workflows.

PathML is a comprehensive Python toolkit designed for advanced computational pathology workflows. It facilitates the end-to-end analysis of whole-slide pathology images (WSI) from over 160 proprietary formats. Key functionalities include image preprocessing (stain normalization, tissue detection), constructing spatial cell/tissue graphs, and training deep learning models (e.g., HoVer-Net). It is highly specialized for analyzing multiparametric data from technologies like CODEX and Vectra.

This comprehensive toolkit facilitates the entire phylogenetic analysis workflow, from sequence alignment to tree inference. It integrates industry-standard tools like MAFFT for multiple sequence alignment, TrimAl for alignment trimming, and powerful maximum likelihood methods using IQ-TREE 2 or FastTree. Ideal for molecular genomics, tracing viral phylodynamics, studying protein evolution, or conducting molecular clock analysis. Built using Python for seamless integration.

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.

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.