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Found 33 Skills
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
Guides users through bioinformatics analysis on AWS HealthOmics. Covers analysis type selection (alignment, variant calling, QC, interpretation), workflow generation in Nextflow or WDL, container sourcing from public registries, infrastructure setup (S3, ECR, IAM), workflow registration, parameter preparation, execution, and monitoring. Use when a user wants to run any bioinformatics pipeline on HealthOmics, set up genomics infrastructure on AWS, create a new analysis workflow, or run existing tools like BWA, GATK, DeepVariant, FastQC, or similar on HealthOmics. Triggers include "HealthOmics", "bioinformatics", "genomics pipeline", "Nextflow", "WDL", "BWA", "GATK", "DeepVariant", "FastQC", "variant calling", "alignment", "RNA-seq", "whole genome sequencing", "ECR pull-through", "workflow registration", "run cache"
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
Route NVIDIA Parabricks pbrun tools, assess GPU/runtime readiness, and provide version-aware command guidance for FASTQ/BAM processing, RNA-seq, variant calling, BAM QC, and GVCF workflows. Do NOT use for inspecting or accelerating whole pipelines — use genomics-workflow-acceleration.
Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Reason about ML experiment design for healthcare and life sciences data. Use when the user asks to design an ML study, choose a model for clinical/biomedical data, set up cross-validation, pick evaluation metrics, audit fairness, plan a regulatory submission, or critique an ML pipeline on EHR, medical imaging, genomics, molecules, or clinical text. Triggers include "design an ML experiment", "which model for this clinical data", "how should I split", "nested CV", "class imbalance", "AUROC vs AUPRC", "calibration", "decision curve", "net benefit", "TRIPOD+AI", "PROBAST", "CLAIM", "FDA SaMD", "PCCP", "GMLP", "site generalization", "temporal leakage", "scaffold split", "foundation model evaluation", "subgroup fairness", "is this model ready for deployment".
Reasoning skill for designing AWS GenAI and ML architectures for healthcare and life sciences workloads. Use when the user asks to choose between SageMaker and Bedrock, design a RAG system over medical literature, architect clinical NLP or medical imaging inference, plan genomics or drug discovery pipelines on AWS, address HIPAA/PHI compliance in ML systems, design MLOps for regulated clinical models, or optimize cost for HCLS ML workloads. Triggers include "AWS architecture", "SageMaker vs Bedrock", "HIPAA ML", "clinical RAG", "medical imaging inference", "genomics on AWS", "PHI training", "MLOps healthcare", "Bedrock guardrails", "HealthLake", "HCLS cloud architecture", "BAA compliance", "SageMaker endpoint", "Bedrock knowledge base", "clinical NLP on AWS", "FDA SaMD on AWS".
Research plant genes, pathways, and species using PlantReactome, Ensembl Plants, POWO, UniProt, KEGG, and literature tools. Covers plant pathway analysis, gene function annotation, species identification, crop genomics, and comparative plant biology. Use when asked about plant genes, Arabidopsis, crop improvement, plant pathways, plant metabolism, photosynthesis, plant development, or plant species identification.
Reasoning skill for multi-omics data integration strategy selection. Use when the user asks to integrate transcriptomics with proteomics, combine multi-omic layers, choose between early intermediate or late integration, apply batch correction across omics, handle partial sample overlap, run MOFA+ or iCluster, interpret multi-omic factors, select enrichment methods for multi-omic signatures, or decide how to merge genomics epigenomics transcriptomics proteomics and metabolomics data. Triggers include "multi-omics integration", "combine omics layers", "early vs late fusion", "MOFA+", "iCluster", "batch correction across omics", "partial overlap", "multi-omic enrichment", "kernel integration", "concatenation vs stacking", "SNF", "similarity network fusion", "intermediate integration", "multi-omic factor analysis".
Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.