Bio Ncbi Datasets CLI

Version Compatibility

Reference examples tested with: NCBI Datasets CLI 16.0+ (2024), dataformat 16.0+

Before using code patterns, verify installed versions match. If versions differ:

• CLI: datasets --version, dataformat --version
• Subcommand help: datasets <subcommand> --help

If a subcommand or flag is unrecognized, run datasets --help and adapt. The CLI is under active development; major releases (v15 -> v16) added subcommands and renamed flags.

NCBI Datasets CLI

"Pull genome / gene / ortholog data from NCBI in 2026" -> The Datasets v2 CLI (launched 2023) is the official, supported bulk endpoint for genome and gene-centric data. It replaces the prior best-practice of scraping assembly_summary.txt + parallel FTP + manual checksum verification. For genome-scale data, it is strictly better than E-utilities (EFetch).

The CLI is not the right answer for everything. PubMed, SRA reads, and custom Entrez queries still belong to E-utilities. The defection rule: if the question is about genome assemblies, gene records, or pre-computed orthologs, use Datasets; otherwise stay with E-utilities.

• CLI: datasets download genome accession GCF_...
• CLI: datasets summary gene symbol BRCA1 --taxon human
• Python: subprocess wrapper; Python client ncbi-datasets-pylib (experimental as of 2024)

Installation

# conda
conda install -c conda-forge ncbi-datasets-cli

# Or direct download (Linux, macOS, Windows binaries)
curl -O https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets

datasets --version    # 16.0+ expected
dataformat --version  # bundled companion tool

What's in scope (use Datasets) vs out of scope (use E-utilities or other tools)

Subcommand taxonomy

datasets summary always returns JSON-lines on stdout (one object per record). datasets download produces a .zip (default) or a "dehydrated" stub for cloud workflows.

Key parameters (download)

For very large pulls (1000+ genomes), --dehydrated is the right choice: download the metadata stubs first, then run datasets rehydrate later or pull URLs in parallel from the manifest.

JSON-lines output + dataformat

datasets summary returns JSON-lines (one JSON object per line) on stdout. Pipe through dataformat tsv for tabular:

datasets summary genome taxon "Escherichia coli" --reference --as-json-lines \
  | dataformat tsv genome --fields accession,organism-name,assembly-level,scaffold-n50 \
  > ecoli_refs.tsv

dataformat subcommands match summary types: genome, gene, virus-genome, etc. The --fields list is documented per type via dataformat tsv <type> --help.

When to use --dehydrated for cloud workflows

The "dehydrated" mode separates data discovery from data transfer:

1. Discover: datasets download genome taxon human --reference --dehydrated --filename human.zip (fast; ~MB).
2. Inspect: unzip -p human.zip ncbi_dataset/fetch.txt -- a TSV of all URLs to pull.
3. Pull: either datasets rehydrate --directory ./human/ or use aria2c --input-file=fetch.txt for parallel pull.

This is essential for HPC / cloud pipelines where inspection of the pending transfer is needed before committing the I/O.

Checksum verification (automatic)

datasets verifies MD5 checksums for every downloaded file automatically. Rehydrate workflows also verify. If a file fails checksum, Datasets retries up to 3 times then errors. This replaces the md5sum -c step that was required with assembly_summary.txt-based scraping.

Code patterns

Download a single reference genome

Goal: Get human reference assembly with genome + GTF + protein + CDS.

Approach: datasets download genome accession ... --include ....

Reference (NCBI Datasets CLI 16.0+):

#!/bin/bash
# Reference: NCBI Datasets CLI 16.0+ | Verify API if version differs

datasets download genome accession GCF_000001405.40 \
    --include genome,gff3,gtf,protein,cds,seq-report \
    --filename human_grch38.zip

unzip -q human_grch38.zip -d human_grch38/
ls -lh human_grch38/ncbi_dataset/data/GCF_000001405.40/

Bulk download all reference bacterial genomes

Goal: Pull every RefSeq reference bacterial assembly with annotation.

Approach: --dehydrated first for inspection; rehydrate with parallel pull.

Reference (NCBI Datasets CLI 16.0+):

#!/bin/bash
# Step 1: dehydrated discovery
datasets download genome taxon Bacteria \
    --reference --annotated --assembly-source RefSeq \
    --include genome,gff3,protein \
    --dehydrated --filename bact_refs.zip

unzip -q bact_refs.zip -d bact_refs/
wc -l bact_refs/ncbi_dataset/fetch.txt   # how many files will be pulled

# Step 2: parallel pull via aria2 (or datasets rehydrate)
aria2c --input-file=bact_refs/ncbi_dataset/fetch.txt \
       --dir=bact_refs/ncbi_dataset/data/ \
       --max-concurrent-downloads=8 \
       --retry-wait=5

Gene metadata across species

datasets summary gene symbol BRCA1 \
    --taxon Mammalia \
    --as-json-lines \
  | dataformat tsv gene --fields gene-id,symbol,taxname,description,nomenclature-authority,chromosomes \
  > brca1_mammals.tsv

head brca1_mammals.tsv

Find orthologs for a gene

datasets summary gene symbol BRCA1 --taxon human --ortholog --as-json-lines \
  | dataformat tsv gene --fields gene-id,symbol,taxname,description \
  > brca1_orthologs.tsv

--ortholog returns NCBI's ortholog set (a single representative per species; tree-aware orthology with multiple co-orthologs is in ortholog-inference / Compara / OMA).

Filter assemblies by quality and date

datasets summary genome taxon "Salmonella enterica" \
    --assembly-level chromosome,complete \
    --released-after 2024-01-01 \
    --as-json-lines \
  | dataformat tsv genome --fields accession,organism-name,assembly-level,scaffold-n50,submission-date \
  > sal_2024.tsv

Python wrapper with checksum + retry awareness

Reference (NCBI Datasets CLI 16.0+):

import subprocess
import json
from pathlib import Path


def datasets_summary(subcommand, *args):
    '''Run `datasets summary` and parse JSON-lines stdout.'''
    cmd = ['datasets', 'summary', subcommand, *args, '--as-json-lines']
    out = subprocess.run(cmd, capture_output=True, text=True, check=True)
    return [json.loads(line) for line in out.stdout.strip().split('\n') if line]


def datasets_download(subcommand, *args, out='dataset.zip', include=None):
    cmd = ['datasets', 'download', subcommand, *args, '--filename', out]
    if include:
        cmd += ['--include', ','.join(include)]
    subprocess.run(cmd, check=True)
    return Path(out)


genomes = datasets_summary('genome', 'taxon', 'Escherichia coli', '--reference')
print(f'{len(genomes)} reference E. coli assemblies')
for g in genomes[:3]:
    acc = g.get('accession')
    n50 = g.get('assemblyStats', {}).get('contigN50')
    print(f'  {acc}  N50={n50}')

datasets_download('genome', 'accession', 'GCF_000005845.2',
                  out='ecoli_k12.zip',
                  include=['genome', 'gff3', 'protein'])

Comparison vs E-utilities

# E-utilities path: ESearch in assembly db -> ESummary -> manual FTP pull
#   ~30 API calls + manual md5 + serial download
# Datasets path:
#   datasets download genome accession GCF_...  # one command, automatic md5, parallel inside

For genome workflows, Datasets is 5-50x faster than the equivalent E-utilities pipeline and far more reliable.

Failure modes

Choosing Datasets for the wrong question

• Trigger: Trying to pull raw SRA reads via Datasets.
• Mechanism: Datasets covers genome/gene/ortholog, not raw reads.
• Symptom: Subcommand not found or empty result.
• Fix: Use sra-data skill (prefetch/fasterq-dump) for raw reads.

--reference filter loses too much

• Trigger: Bulk pull of "all assemblies for a species"; --reference returns one per species.
• Mechanism: Reference subset is the canonical single representative.
• Symptom: Far fewer assemblies than expected for a species with hundreds of submissions.
• Fix: Drop --reference for full set; add --assembly-level chromosome,complete for quality filter instead.

Dehydrated workflow forgotten

• Trigger: 1000-genome pull without --dehydrated.
• Mechanism: Datasets downloads serially within one ZIP; can take hours.
• Symptom: Slow; no parallelism; one giant ZIP.
• Fix: Use --dehydrated + aria2c with --max-concurrent-downloads.

dataformat field name guessing

• Trigger: dataformat tsv genome --fields foo,bar with invented field names.
• Mechanism: Field names are constrained per summary type.
• Symptom: "Unknown field" error.
• Fix: dataformat tsv genome --help lists valid field names; pull JSON-lines and inspect with jq to discover fields.

Old assembly_summary.txt-based scripts still in use

• Trigger: Legacy pipeline scraping https://ftp.ncbi.nlm.nih.gov/genomes/all/refseq/....
• Mechanism: Pre-2023 best practice; FTP listing parsing is fragile.
• Symptom: Slow; brittle; no checksums; broken when NCBI restructures FTP.
• Fix: Switch to Datasets CLI; the FTP path still works but Datasets is the supported modern path.

API key not used for high-volume

• Trigger: 1000+ summary calls in a loop without --api-key.
• Mechanism: NCBI rate-limits unauthenticated bulk traffic.
• Symptom: Throttling; slow downloads.
• Fix: Pass --api-key YOUR_KEY to bulk commands; obtain from https://www.ncbi.nlm.nih.gov/account/settings/.

CLI version drift

• Trigger: Using Datasets v14 with v16 docs.
• Mechanism: Subcommands and flags renamed between major versions.
• Symptom: "Unknown flag" or different output structure.
• Fix: Pin to v16+; conda update ncbi-datasets-cli.

Common errors

References

• O'Leary NA, Cox E, Holmes JB, et al. (2024) Exploring and retrieving sequence and metadata for species across the tree of life with NCBI Datasets. Sci Data 11:732.
• NCBI Datasets documentation: https://www.ncbi.nlm.nih.gov/datasets/docs/v2/
• NCBI. Datasets CLI usage. https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/command-line/datasets/

Related Skills

• entrez-search - For PubMed, custom queries, and non-genome data
• entrez-fetch - For single-record fetches outside genome/gene scope
• batch-downloads - Bulk E-utilities (when not genome-scale)
• sra-data - Raw sequencing reads (NOT covered by Datasets)
• ensembl-rest - Ensembl REST as alternative for Ensembl-native species
• ortholog-inference - Compara/OMA/OrthoDB for tree-aware orthology