Skill UI

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.

The ETE Toolkit is a powerful Python library designed for analyzing, manipulating, comparing, and visualizing phylogenetic trees stored in Newick or Nexus formats. It allows users to perform topology edits, calculate phylogenetic distances (e.g., Robinson-Foulds), detect evolutionary events, and query external taxonomic databases like NCBI/GTDB. Note that this tool analyzes existing trees; it is not used for inferring trees from raw sequences.

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.

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.

A comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq data. It supports the complete workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker identification, and cell type annotation. Users benefit from a suite of ready-to-run CLI scripts that guide the entire process from raw data loading to publication-quality visualization.

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust tools for sequence manipulation (DNA, RNA, Protein), performing sophisticated sequence alignments (global, local, multiple), constructing and analyzing phylogenetic trees, and conducting complex ecological statistics. Key features include calculating diversity metrics (alpha/beta), running ordination analyses (PCoA), and handling diverse biological file formats (FASTA, Newick, BIOM). Ideal for researchers in microbiology, genomics, and ecology.

scVelo is a leading Python package for analyzing RNA velocity in single-cell RNA-seq data. It infers cell state transitions and developmental trajectories by modeling the kinetics of mRNA splicing (unspliced/spliced ratios). This allows researchers to determine cell fate decisions, predict differentiation paths, and identify driver genes without requiring time-course experiments. It complements tools like Scanpy for comprehensive analysis.

scvi-tools is a comprehensive Python framework utilizing deep generative models and variational inference for analyzing diverse single-cell data modalities. It excels in complex analyses such as probabilistic batch correction (scVI), multi-modal integration (MultiVI), and differential expression analysis with uncertainty. It supports single-cell RNA-seq, ATAC-seq, and various multi-omics datasets, providing advanced tools for modern genomic research.

This skill provides the core mechanism for integrating Hex into external data pipelines. It enables triggering, monitoring, and managing Hex projects (e.g., via Airflow or Dagster) programmatically. Users can implement parameterized runs, execute multi-step ETL workflows sequentially, and handle status polling and error management, making it ideal for production data orchestration.

This internal agent skill automates the complex process of determining the most likely owner or team for a bug cluster. It follows a strict 6-level precedence model, starting with cached overrides, and sequentially querying sources like Service Owners, Oncall status, CODEOWNERS, recent assignees, and committers. The process includes staleness detection and confidence scoring, providing highly accurate and auditable routing recommendations for internal development triage.

This technique guides users through a rigorous two-phase research strategy. First, conduct a broad search to identify potential papers or authors. Crucially, the system instructs the user to use the extracted "author full name" as a highly specific anchor in subsequent queries. Combining the author's name with details like journal, institution, year, or biographical information ensures the most precise and reliable results, significantly narrowing down the search scope and achieving deep academic insights.

This skill orchestrates a complete, reproducible pipeline for bulk RNA-seq differential expression analysis. It guides users from raw FASTQ reads through QC, trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and concluding with differential expression analysis (DESeq2) and pathway enrichment. It ensures statistical soundness and reproducibility by connecting dedicated tools and enforcing proper experimental design.