by K-Dense-AI
The hugging-science skill helps you find and use scientific AI resources from the Hugging Science catalog and the `hugging-science` Hugging Face org. It fits biology, chemistry, climate, genomics, materials, astronomy, and similar work when you need a dataset, model, Space, or blog post you can actually run or cite. Use it for hugging-science usage and hugging-science guide workflows instead of generic search.
by K-Dense-AI
dnanexus-integration is a practical skill for DNAnexus cloud genomics work. Use it to build apps and applets, manage uploads and downloads, run workflows, and automate pipelines with dxpy. The dnanexus-integration guide helps Backend Development tasks involving FASTQ, BAM, and VCF files, plus platform-specific configuration and job execution.
by K-Dense-AI
scvi-tools is a Python framework for probabilistic single-cell analysis. Use this scvi-tools skill for batch correction, latent embeddings, differential expression with uncertainty, transfer learning, and multimodal integration. It is a strong fit for single-cell RNA-seq, ATAC, CITE-seq, multiome, and spatial workflows, especially for advanced Machine Learning use cases.
by K-Dense-AI
scvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. Use it to estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and identify driver genes. It is especially useful for scvelo for Data Analysis when you need directionality beyond standard clustering or pseudotime.
by K-Dense-AI
scanpy skill for single-cell RNA-seq data analysis in Python. Use it for QC, normalization, PCA, UMAP/t-SNE, clustering, marker gene discovery, trajectory analysis, and publication-quality plots. Best for exploratory scRNA-seq workflows built around AnnData, with clear scanpy usage and install guidance.
by K-Dense-AI
pydeseq2 is a Python DESeq2 skill for bulk RNA-seq differential gene expression analysis. Use it to compare conditions, fit single- or multi-factor designs, apply Wald tests and FDR correction, and generate volcano or MA plots in pandas and AnnData workflows.
by K-Dense-AI
gget is a bioinformatics skill for fast, unified access to 20+ genomic databases and analysis tools from CLI or Python. Use it for gene info, BLAST-related lookups, AlphaFold structures, expression data, disease associations, and enrichment-style analysis. It suits quick exploration and gget for Data Analysis workflows.
by K-Dense-AI
geniml is a skill for genomic interval machine learning on BED files, scATAC-seq outputs, and chromatin accessibility data. Use it for Region2Vec, BEDspace, scEmbed, consensus peaks, and other region-level ML workflows. It is a good fit when you need embeddings, clustering, or preprocessing guidance for genomic regions.
by K-Dense-AI
etetoolkit is a phylogenetic tree toolkit for ETE workflows. Use the etetoolkit skill to parse, edit, compare, root, prune, and visualize trees in Newick, NHX, PhyloXML, or NeXML. It supports phylogenomics, orthology/paralogy analysis, NCBI taxonomy, and publication-style PDF or SVG output.
by K-Dense-AI
depmap helps analyze the Cancer Dependency Map for cancer cell line gene dependency scores, drug sensitivity, and gene effect profiles. Use it to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets with a reproducible depmap guide for Data Analysis.
by K-Dense-AI
The deeptools skill helps with NGS analysis workflows in deepTools: BAM to bigWig conversion, QC, sample comparison, and heatmaps or profile plots for ChIP-seq, RNA-seq, ATAC-seq, and related assays. Use it as a practical deeptools guide when you need reproducible command-line analysis and visualization.
