Sra Data

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/database-access/sra-dataThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or…

---
name: bio-sra-data
description: Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or AWS/GCP cloud mirror (STRIDES), handling --include-technical for 10x and other single-cell records, validating with MD5/vdb-validate, navigating SRR/SRX/SRS/SRP/PRJNA hierarchy, or finding accessions via pysradb. Encodes SRA cloud-egress economics, the fasterq-dump uncompressed-scratch trap, and the --max-size default that silently truncates large prefetches.
tool_type: cli
primary_tool: sra-tools
---

## Version Compatibility

Reference examples tested with: sra-tools 3.0+ (fasterq-dump, prefetch, vdb-validate, vdb-config), pysradb 2.2+, ENA portal API 2.0+

Before using code patterns, verify installed versions match. If versions differ:
- CLI: `fasterq-dump --version`, `prefetch --version`
- Python: `pip show pysradb`

If a flag is unrecognized or behavior changes, run `<tool> --help` and adapt.

# SRA Data

**"Download FASTQ from this SRA accession"** -> Two paths exist in 2026: the **SRA toolkit** (NCBI's official, with prefetch + fasterq-dump) and the **ENA mirror** (EMBL-EBI's mirror with direct FASTQ download, often faster). For >1 TB workflows, a third path: **AWS Open Data** (STRIDES program) where same-region EC2 pulls SRA data with zero egress cost.

The single most impactful decision is **where to pull from**. SRA-direct is the default but ENA is faster more often than not, and AWS Open Data is the right answer for cloud-native analysis pipelines.

- CLI: `prefetch SRR...`, `fasterq-dump SRR...`, `vdb-validate SRR...` (sra-tools)
- CLI: `curl https://ftp.sra.ebi.ac.uk/...` (ENA mirror; direct FASTQ)
- CLI: `aws s3 cp s3://sra-pub-run-odp/sra/SRR.../SRR... ./SRR....sra ...` (STRIDES; object is unsuffixed; same-region free)
- Python: `pysradb` for metadata; `subprocess` for download

## Required Setup

```bash
# sra-tools (toolkit)
conda install -c bioconda sra-tools           # 3.0+
fasterq-dump --version                        # confirm

# Configure cache location (default ~/ncbi/ -- often too small)
vdb-config --cfg                              # show current config
vdb-config --set /repository/user/main/public/root=/data/sra_cache

# Optional: pysradb for metadata
pip install pysradb
```

For STRIDES cloud:
```bash
# AWS CLI (no NCBI auth needed for public buckets)
aws s3 ls s3://sra-pub-run-odp/sra/SRR12345678/ --no-sign-request
```

## Decision matrix: where to pull from

| Source | When best | Speed | Cost |
|---|---|---|---|
| **ENA mirror** (FTP/Aspera) | Default for most workflows | Often fastest; direct FASTQ (no SRA->FASTQ conversion needed) | Free; no rate limit observed |
| **SRA toolkit + AWS STRIDES** | Same-region EC2/EKS | Fastest within AWS us-east-1 | Free egress within region; small storage cost |
| **SRA toolkit + GCP STRIDES** | Same-region GCP Compute Engine | Fastest within GCP us-central1 | Free egress within region |
| **SRA-direct (prefetch + fasterq-dump)** | On-prem; small downloads; need SRA-format access | Variable; can be slow off-peak fails | Free; NCBI throttles by IP |
| **Aspera (`ascp`)** | Institutional accounts only | Faster than HTTPS on long links | NCBI public Aspera retired 2019; ENA public Aspera retired ~2023; institutional use still possible |

**Default recommendation**: **ENA mirror** for off-cloud, **STRIDES (AWS/GCP)** for in-cloud analysis. SRA-direct only when neither is available or when SRA format itself is needed (e.g. for re-extraction of technical reads).

## SRA accession hierarchy

| Prefix | Type | Granularity |
|---|---|---|
| SRR / ERR / DRR | Run | One sequencing run (file-level) |
| SRX / ERX / DRX | Experiment | Library prep + sequencing strategy |
| SRS / ERS / DRS | Sample | Biological sample |
| SRP / ERP / DRP | Study | Project (deprecated; superseded by BioProject) |
| PRJNA / PRJEB / PRJDB | BioProject | Top-level project ID |
| SAMN / SAMEA / SAMD | BioSample | Biological sample (cross-archive) |

Conversion is via SRA metadata: `pysradb metadata <ID>` or `efetch -db sra -id <UID> -rettype runinfo`.

The actual download unit is SRR/ERR/DRR (runs). The BioProject (PRJNA...) is the convenient top-level handle for "pull all data for paper X".

## fasterq-dump vs fastq-dump

`fasterq-dump` (sra-tools 2.10+) is the multi-threaded successor. **Always prefer it**, with two exceptions noted below.

| Aspect | fasterq-dump | fastq-dump |
|---|---|---|
| Threads | Multi (`-e N`) | Single |
| Speed | ~5-10x faster | Baseline |
| Disk overhead | Writes uncompressed FASTQ to scratch (~3x final size) | In-place; lower scratch |
| Compression | NOT built-in (post-process with pigz) | `--gzip` flag built-in |
| Single-cell technical reads | `--include-technical` works | Some 10x records need fastq-dump for full extraction |
| 10x split semantics | Sometimes incomplete | Sometimes the only way to get all reads |

The **uncompressed-scratch trap**: `fasterq-dump` writes uncompressed FASTQ first, then leaves it uncompressed. A 100 GB compressed FASTQ needs ~300 GB of scratch space + 300 GB of final output. Either compress post-hoc with `pigz` or use `--mem` to control RAM/disk tradeoff.

## prefetch and the `--max-size` trap

`prefetch` downloads `.sra` files to the configured cache before extraction. Default `--max-size 20G` silently skips runs larger than 20 GB.

```bash
# Wrong: silently skips runs >20 GB
prefetch SRR12345678

# Right: set max-size explicitly to your largest expected size
prefetch SRR12345678 --max-size 100G -p
```

For unknown-size queues, set max-size to a generous upper bound (e.g. `--max-size 200G`) or query metadata first with `pysradb metadata`.

## ENA mirror: direct FASTQ URLs

ENA stores FASTQ files directly (no SRA-format intermediate). Discover URLs via the ENA portal API:

```bash
curl 'https://www.ebi.ac.uk/ena/portal/api/filereport?accession=SRR12

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pysradb
pip install pysradb                         # optional, for metadata

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Sra Data

• github.com/GPTomics/bioSkills/tree/main/database-access/sra-data — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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