GEO/SRA Data Acquisition

Download raw sequencing data from NCBI GEO/SRA and prepare it for nf-core pipelines.

Use this when: Reanalyzing published datasets, validating findings, or comparing results against public cohorts.

Table of Contents

• Workflow Overview
• Step 1: Fetch Study Information
• Step 2: Review Sample Groups
• Step 3: Download FASTQ Files
• Step 4: Generate Samplesheet
• Step 5: Run nf-core Pipeline
• Supported Pipelines
• Supported Organisms
• Complete Example
• Troubleshooting

Workflow Overview

Example: "Find differentially expressed genes in GSE309891 (drug-treated vs control)"

┌─────────────────────────────────────────────────────────────────┐
│                    GEO/SRA DATA ACQUISITION                     │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
                 ┌────────────────────────┐
                 │   Fetch study info     │
                 │   • Query NCBI/SRA     │
                 │   • Get metadata       │
                 │   • Detect organism    │
                 │   • Identify data type │
                 └────────────────────────┘
                              │
                              ▼
                 ┌────────────────────────┐
                 │   Present summary      │
                 │   • Organism: Human    │
                 │   • Genome: GRCh38     │
                 │   • Type: RNA-Seq      │
                 │   • Pipeline: rnaseq   │
                 │   • Samples: 12        │
                 │     (6 treated,        │
                 │      6 control)        │
                 │   • Size: ~24 GB       │
                 └────────────────────────┘
                              │
                              ▼
                    ┌─────────────────┐
                    │  USER CONFIRMS  │◄──── Decision point
                    │  genome/pipeline│
                    └─────────────────┘
                              │
                              ▼
                 ┌────────────────────────┐
                 │   Select samples       │
                 │   • Group by condition │
                 │   • Show treated/ctrl  │
                 └────────────────────────┘
                              │
                              ▼
                    ┌─────────────────┐
                    │  USER SELECTS   │◄──── Decision point
                    │  sample subset  │
                    └─────────────────┘
                              │
                              ▼
                 ┌────────────────────────┐
                 │   Download FASTQs      │
                 │   • 24 files (R1+R2)   │
                 │   • Parallel transfers │
                 │   • Auto-resume        │
                 └────────────────────────┘
                              │
                              ▼
                 ┌────────────────────────┐
                 │   Generate samplesheet │
                 │   • Map SRR to files   │
                 │   • Pair R1/R2         │
                 │   • Assign conditions  │
                 └────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    NF-CORE PIPELINE EXECUTION                   │
│              (Continue with Step 1 of main workflow)            │
└─────────────────────────────────────────────────────────────────┘

Instructions for Claude

When assisting users with GEO/SRA data acquisition:

1. Always fetch study info first to show the user what data is available
2. Ask for confirmation before downloading - Present the sample groups and sizes, then ask which subset to download using AskUserQuestion
3. Suggest appropriate genome and pipeline based on the organism and data type
4. Return to main SKILL.md workflow after data preparation is complete

Example confirmation question:

Question: "Which sample group would you like to download?"
Options:
  - "RNA-Seq:PAIRED (42 samples, ~87 GB)"
  - "RNA-Seq:SINGLE (7 samples, ~4.5 GB)"
  - "All samples (49 samples, ~92 GB)"

Step 1: Fetch Study Information

Get metadata about a GEO study before downloading.

python scripts/sra_geo_fetch.py info <GEO_ID>

Example:

python scripts/sra_geo_fetch.py info GSE110004

Output includes:

• Study title and summary
• Organism (with auto-suggested genome)
• Number of samples and runs
• Data types (RNA-Seq, ATAC-seq, etc.)
• Estimated download size
• Suggested nf-core pipeline

Save info to JSON:

python scripts/sra_geo_fetch.py info GSE110004 -o study_info.json

Step 2: Review Sample Groups

View sample groups organized by data type and layout. This is useful for studies with mixed data types.

python scripts/sra_geo_fetch.py groups <GEO_ID>

Example output:

Sample Group          Count Layout     GSM Range                    Est. Size
--------------------------------------------------------------------------------
RNA-Seq                  42 PAIRED     GSM2879618...(42 samples)      87.4 GB
RNA-Seq                   7 SINGLE     GSM2976181-GSM2976187           4.5 GB
--------------------------------------------------------------------------------
TOTAL                    49                                           91.9 GB

Available groups for --subset option:
  1. "RNA-Seq:PAIRED" - 42 samples (~87.4 GB)
  2. "RNA-Seq:SINGLE" - 7 samples (~4.5 GB)

List individual runs:

python scripts/sra_geo_fetch.py list <GEO_ID>

# Filter by data type
python scripts/sra_geo_fetch.py list GSE110004 --filter "RNA-Seq:PAIRED"

DECISION POINT: Review the sample groups. Decide which subset to download if the study has multiple data types.

Step 3: Download FASTQ Files

Download FASTQ files from ENA (faster than SRA).

python scripts/sra_geo_fetch.py download <GEO_ID> -o <OUTPUT_DIR>

Options:

• -o, --output: Output directory (required)
• -i, --interactive: Interactively select sample group to download
• -s, --subset: Filter by data type (e.g., "RNA-Seq:PAIRED")
• -p, --parallel: Parallel downloads (default: 4)
• -t, --timeout: Download timeout in seconds (default: 600)

Interactive Mode (Recommended)

Use -i flag for interactive sample selection when the study has multiple data types:

python scripts/sra_geo_fetch.py download GSE110004 -o ./fastq -i

Interactive output:

============================================================
  SELECT SAMPLE GROUP TO DOWNLOAD
============================================================

  [1] RNA-Seq (paired)
      Samples: 42
      GSM: GSM2879618...(42 samples)
      Size: ~87.4 GB

  [2] RNA-Seq (single)
      Samples: 7
      GSM: GSM2976181-GSM2976187
      Size: ~4.5 GB

  [0] Download ALL (49 samples)
------------------------------------------------------------

Enter selection (0-2):

Direct Subset Selection

Alternatively, specify the subset directly:

# Download only RNA-Seq paired-end data
python scripts/sra_geo_fetch.py download GSE110004 -o ./fastq \
    --subset "RNA-Seq:PAIRED" --parallel 6

Note: Downloads automatically skip existing files. Resume interrupted downloads by re-running the command.

Step 4: Generate Samplesheet

Create a samplesheet compatible with nf-core pipelines.

python scripts/sra_geo_fetch.py samplesheet <GEO_ID> \
    --fastq-dir <FASTQ_DIR> \
    -o samplesheet.csv

Options:

• -f, --fastq-dir: Directory containing downloaded FASTQ files (required)
• -o, --output: Output samplesheet path (default: samplesheet.csv)
• -p, --pipeline: Target pipeline (auto-detected if not specified)

Example:

python scripts/sra_geo_fetch.py samplesheet GSE110004 \
    --fastq-dir ./fastq \
    -o samplesheet.csv

Output: The script will:

1. Create samplesheet in the format required by the target pipeline
2. Display suggested genome reference
3. Show suggested nf-core command

Step 5: Run nf-core Pipeline

After generating the samplesheet, the script provides a suggested command.

Example output:

Suggested command:
   nextflow run nf-core/rnaseq \
       --input samplesheet.csv \
       --outdir results \
       --genome R64-1-1 \
       -profile docker

DECISION POINT: Review and confirm:

1. Is the suggested pipeline correct?
2. Is the genome reference correct for your organism?
3. Do you need additional pipeline options?

Then return to the main SKILL.md workflow (Step 1: Environment Check) to proceed with pipeline execution.

Supported Pipelines

The skill auto-detects appropriate pipelines based on library strategy. Pipelines marked with ★ are fully supported with configs, samplesheet generation, and documentation. Others are suggested but require manual setup following nf-core documentation.

Supported Organisms

Common organisms with auto-suggested genomes:

See scripts/config/genomes.yaml for the full list.

Complete Example

Reanalyze GSE110004 (yeast RNA-seq):

# 1. Get study info and sample groups
python scripts/sra_geo_fetch.py info GSE110004

# 2. Download with interactive selection
python scripts/sra_geo_fetch.py download GSE110004 -o ./fastq -i
# Select option [1] for RNA-Seq paired-end samples

# 3. Generate samplesheet
python scripts/sra_geo_fetch.py samplesheet GSE110004 \
    --fastq-dir ./fastq \
    -o samplesheet.csv

# 4. Run nf-core/rnaseq (continue with main SKILL.md workflow)
nextflow run nf-core/rnaseq \
    --input samplesheet.csv \
    --outdir results \
    --genome R64-1-1 \
    -profile docker

Alternative: Non-interactive Download

# Review sample groups first
python scripts/sra_geo_fetch.py groups GSE110004

# Download specific subset directly
python scripts/sra_geo_fetch.py download GSE110004 \
    --subset "RNA-Seq:PAIRED" \
    -o ./fastq \
    --parallel 4

Troubleshooting

ENA Download Fails

If ENA downloads fail, the data may need to be fetched directly from SRA:

# Create SRA tools environment
conda create -n sra_tools -c bioconda sra-tools

# Download with prefetch + fasterq-dump
conda run -n sra_tools prefetch SRR6357070
conda run -n sra_tools fasterq-dump SRR6357070 -O ./fastq

No SRA Runs Found

Some GEO datasets only have processed data, not raw sequencing reads. Check:

python scripts/sra_geo_fetch.py info <GEO_ID>

If "Runs: 0", the dataset may not have raw data in SRA.

SuperSeries Support

GEO SuperSeries (which contain multiple SubSeries) are automatically handled. The tool will:

1. Detect that a GEO ID is a SuperSeries
2. Find the linked BioProject accession
3. Fetch all SRA runs from the BioProject

Example: GSE110004 is a SuperSeries that links to BioProject PRJNA432544.

Genome Not Recognized

If the organism is not in the genome mapping, manually specify the genome:

# Check available iGenomes
python scripts/manage_genomes.py list

# Or provide custom reference files to nf-core
nextflow run nf-core/rnaseq --fasta /path/to/genome.fa --gtf /path/to/genes.gtf

Requirements

• Python 3.8+
• requests library (optional but recommended)
• pyyaml library (optional, for genome config)
• Network access to NCBI and ENA

Install optional dependencies:

pip install requests pyyaml