数据与 AI技能

Plan DNA variant detection workflows by identifying the assay and sample model, then routing to germline, cancer somatic, or molecular-barcode panel skills. It also supports focused local BAM/CRAM checks with variant files, depth summaries, and optional filters.

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• Choosing a workflow for WGS, WES, or DNA panels
• Routing inherited, cancer, and molecular-barcode analyses
• Checking reference genomes and required input files

Plan or run targeted DNA panel analysis using UMIs, barcodes that identify DNA molecules. Organize consensus reads, coverage checks, and low-frequency variant results with molecular support, filtering reasons, and explicit quality limitations.

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• Plan a targeted DNA panel workflow using UMIs
• Review molecular depth and target coverage
• Check evidence supporting low-frequency variants

Analyze marker-gene sequencing data to study microbial communities. Supports FASTQ quality checks and, with primers, metadata, and taxonomy resources, workflows that produce sequence variant tables, taxonomic assignments, diversity measurements, and plots.

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• Check amplicon FASTQ quality and analysis readiness
• Generate sequence variant and taxonomy tables
• Visualize microbial diversity and taxon composition

Plan or run DNA analysis to find small genetic changes in tumors, using tumor samples alone or paired with normal tissue. Check sample pairing, contamination, data quality, filtering, and annotations that add information to detected variants.

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• Planning analysis of paired tumor and normal samples
• Reviewing filtering and limitations for tumor-only analysis
• Checking quality in whole-genome, exome, or cancer-panel data

Plan or run bulk RNA-seq analysis to find genes whose expression differs between conditions. Check sample replication, study design, batch effects, and data quality, then produce comparison tables, diagnostic plots, and ranked gene lists.

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• Comparing gene expression between experimental conditions
• Planning analyses with donor pairing or batch variables
• Checking sample quality with PCA and distance plots

Guide the use of NVIDIA cuOpt for route planning and mathematical optimization, choosing values to minimize cost or maximize profit under constraints. Covers requirements, environment checks, installation guidance, and verification of solver results.

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• Clarify vehicle routing requirements and constraints
• Plan how to use cuOpt through Python, REST, C, or CLI
• Review installation and server setup options

Plan or run ATAC-seq analysis to identify accessible DNA regions. Check sequencing quality, align reads, detect peaks, combine evidence across replicate samples, and prepare comparisons between conditions with quality summaries and visualization tracks.

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• Check the quality of ATAC-seq samples
• Detect accessible DNA regions from FASTQ or BAM files
• Build consensus peaks across replicate samples

Start shotgun metagenomics workflows for mixed-community DNA reads in FASTQ files. Check read quality, remove host sequences, and profile organisms and biological functions using supported tools and databases, producing tables and visual summaries when results are available.

中文简介正在准备中。当前显示英语简介。

• Checking FASTQ quality and analysis readiness
• Removing host-derived sequences before profiling
• Summarizing microbial composition across samples

Plan or run inherited DNA variant detection from whole-genome, exome, or targeted-panel sequencing. Check reference compatibility, sample quality, and combined analysis across samples, with documented commands, resource versions, and paths to variant and annotation outputs.

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• Planning inherited DNA variant detection from sequencing data
• Preparing joint variant analysis for cohorts or families
• Checking reference resources and sequencing quality

Prepare a Python environment for Mixpanel analysis by installing required libraries and checking credentials and connectivity. Use it for first-time setup, missing dependencies, or guidance on service account and OAuth sign-in options.

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• Set up a new environment for Mixpanel analysis
• Install missing Python analysis libraries
• Configure service account or OAuth credentials

Validate Illumina BCL run folders and sample sheets, plan demultiplexing—the separation of reads by sample—and convert data to FASTQ using installed tools. Review conversion metrics, index settings, and reads that could not be assigned.

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• Checking run folders and sample sheets before conversion
• Reviewing index, UMI, and lane settings
• Converting Illumina BCL data to FASTQ

Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.

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• Generate count matrices from FASTQ files
• Check reference files and sample metadata
• Review mapping and quantification quality

Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.

中文简介正在准备中。当前显示英语简介。

• Generate expression counts from FASTQ files
• Compare gene expression between sample groups
• Plan quality checks and statistical analysis together

Validate FASTQ sequencing files, run local quality checks, and interpret reports to decide whether trimming is needed. Optionally remove adapters or low-quality read ends while preserving original reads and their quality reports.

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• Check sequencing quality before downstream analysis
• Summarize quality reports across multiple samples
• Assess whether adapter or quality trimming is needed

Plan or run quality checks and peak calling, which identifies enriched genomic regions, for ChIP-seq, CUT&RUN, and CUT&Tag. The workflow accounts for controls and replicates and prepares signal tracks, quality reports, and differential binding designs.

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• Check data quality and enrichment signals
• Choose narrow or broad peaks for the biological target
• Create bigWig tracks and quality dashboards

Prepare single-cell or single-nucleus RNA-seq analysis by checking inputs and selecting workflows that turn FASTQ sequencing files into count matrices. Route quality checks, cell annotation, clustering, and UMAP visualization to the embedded downstream skill.

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• Start single-cell RNA-seq analysis from FASTQ files
• Choose a workflow for generating count matrices
• Route existing matrices to quality checks and cell annotation

Find the right workflow for next-generation sequencing data by inspecting files and clarifying only missing setup details. Receive a recommended analysis skill, checks of available tools, and plans for software installation and reference data.

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• Choosing an analysis workflow from sequencing files
• Clarifying requirements for RNA-seq or variant analysis
• Checking tools and preparing an installation plan

Analyze Mixpanel data with Python to explore usage trends, conversion steps, returning users, and differences between user groups. Also manage business context documents that explain an organization's structure and goals to AI assistants.

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• Find where users drop out before purchasing
• Compare retention across user groups
• Explore feature usage and revenue trends

Prioritizes candidate genes from GWAS, studies linking genetic variants to traits. Combines locus predictions, gene expression evidence, and rare variant support into ranked results with source attribution, confidence labels, reproducible files, and optional figures.

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• Find candidate genes for a disease or trait
• Rank genes near specified lead variants
• Compare evidence and tissue support across genes

Retrieve concise FinnGen association results for one genetic variant. Accepts rsID identifiers or GRCh37/GRCh38 genome coordinates, resolves the query format, and reports association counts, truncation, and optional raw output files.

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• Look up FinnGen associations for a single rsID
• Query a variant using GRCh37 or GRCh38 coordinates
• Review association counts and whether results were truncated

Query gnomAD for variant frequencies, gene constraint measures, and effects on gene transcripts. Get concise Markdown summaries from focused requests, with options to return machine-readable JSON or save the full response to a file.

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• Checking the frequency of a specific genetic variant
• Retrieving gene constraint measures
• Looking up variant effects on gene transcripts

Retrieve GTEx associations between a genetic variant and gene expression in individual tissues. Supply one rsID or genome coordinate, and receive JSON with the resolved variant, association rows, counts, and warnings.

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• Retrieve tissue-specific eQTL associations for an rsID
• Query GTEx using GRCh37 coordinates
• Limit association rows returned as JSON

Create, edit, or validate Jupyter notebooks for SQL and Python analysis. Organize data sources, assumptions, calculations, and findings into a readable document that others can review and rerun, with execution gaps clearly identified.

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• Preparing an analysis notebook for handoff
• Organizing experiment logs and data-quality checks
• Making existing notebooks easier to read and rerun

Choose, build, and review charts that answer a clear analytical question. The skill checks chart types, labels, units, colors, and data sufficiency, then refines the visual in its intended report, dashboard, notebook, or other output.

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• Choose a chart for a specific analytical question
• Improve charts that are hard to interpret
• Review charts for reports or dashboards