adaptiseq 0.1.3

Fast, importable downloader for public sequencing data (GSA/SRA/ENA/DDBJ/GEO) with a segmented, resumable, self-tuning batch engine.

An adaptive tool to fetch public sequencing data

📖 Documentation  ·  Installation  ·  Usage  ·  Comparison

Description

adaptiSeq is a fast, importable Python tool that downloads sequencing data and metadata from GSA, SRA, ENA, DDBJ, and GEO. It is built for the workload real pipelines actually have: lists of accessions, downloaded in parallel, with a segmented, resumable, self-tuning engine and a real Python API.

Important To use adaptiSeq your system must be connected to the network and able to reach the databases over HTTP, HTTPS, and (optionally) FTP / Aspera.

Update Notes

0.1.3

• Fix: a segmented download whose .part file is missing at finalize (e.g. every segment's connection was refused) now fails cleanly and retries, instead of raising a confusing FileNotFoundError from an unguarded .part rename.

0.1.2

• Fix: the notebook event-loop handling from 0.1.1 now also works when nest_asyncio is active (it previously hung/errored on batch downloads). The API detects a re-entrant loop and drives the coroutine on it directly; plain Jupyter still uses a worker thread. Works with or without nest_asyncio.

0.1.1

• Fix: the Python API (fetch/resolve) now works inside a running asyncio event loop (Jupyter / Google Colab / IPython), where it previously raised asyncio.run() cannot be called from a running event loop. The CLI was unaffected.

0.1.0 (initial release)

• First public release: segmented engine + adaptive batch pool + parallel metadata resolution + adaptive Aspera + Python API.
• Validated against the real ENA Aspera endpoint with a genuine IBM ascp.
• Offline test suite (fixtures, engine, batch, adaptive) green; CI on Python 3.10–3.12.

Design highlights

• Segmented, resumable HTTP(S)/FTP engine (the default) with .part/.part.meta resume — interrupt and re-run to continue, not restart.
• Batch-parallel download of accession lists through an asyncio worker pool.
• Adaptive concurrency: an adaptive controller (top-down knee search by default) tunes how many downloads run at once from measured throughput.
• Parallel metadata resolution under polite per-endpoint rate limits.
• Adaptive parallel Aspera via an efficiency-hysteresis controller.
• Python API (fetch, resolve, get_metadata) that returns values and raises typed exceptions.
• Correctly downloads 3-file runs (orphan/barcode + _1 + _2) that iSeq mishandles.

Features

• Multiple database support: GSA / SRA / ENA / DDBJ / GEO.
• Multiple input formats: Project, Study, BioSample, Sample, Experiment, or Run accession — single, or a file of many (mixed databases allowed).
• Metadata download: per-accession sample metadata (-m).

More features

• File-format selection: direct gzip FASTQ (-g) or SRA→FASTQ conversion (-q).
• Multi-threaded conversion/compression (-t).
• File merging per Experiment/Sample/Study (-e).
• Batch-parallel download of accession lists (-j), with adaptive concurrency (--adaptive) and parallel metadata resolution (--meta-jobs).
• Segmented, resumable transfers with HTTPS-first transport selection.
• Aspera high-speed download for GSA/ENA (-a), adaptive for ENA.
• Automatic retry (up to three rounds) and md5 / vdb-validate verification.
• Importable Python API with typed exceptions and py.typed.
• Error handling: clear, actionable messages with suggested solutions.

Installation

1. From PyPI (once published)

pip install adaptiseq            # not yet on PyPI — see "From source" below

2. From source

git clone https://github.com/swargo98/adaptiSeq.git
cd adaptiSeq
pip install -e .
adaptiseq --version

3. Conda environment

conda env create -f environment.yml && conda activate adaptiseq

External tools: wget (always), sra-tools, pigz, md5sum, axel (classic -p), and IBM ascp (-a). adaptiSeq requires each only when a run actually needs it. Full detail: docs/installation.md.

Example

1. Download all Runs and metadata for an accession.

adaptiseq -i PRJNA211801

2. Batch download a list, directly as gzip FASTQ, through the adaptive pool.

adaptiseq -i SRR_Acc_List.txt -g

See docs/examples.md for more.

Usage

$ adaptiseq --help

Usage:
  adaptiseq -i accession [options]

Required option:
  -i, --input     [text|file]   Single accession or a file containing multiple accessions.
                                Note: only one accession per line in the file.

Optional options:
  -m, --metadata                Skip the sequencing data downloads and only fetch metadata.
  -g, --gzip                    Download FASTQ files in gzip format directly (*.fastq.gz).
  -q, --fastq                   Convert SRA files to FASTQ format.
  -t, --threads   int           Threads for SRA->FASTQ / compression (default: 8).
  -e, --merge     [ex|sa|st]    Merge fastq files per Experiment, Sample, or Study.
  -d, --database  [ena|sra]     Force database for SRA data (default: auto-detect).
  -p, --parallel  int           axel connection count (classic engine only), e.g. -p 10.
  -a, --aspera                  Use Aspera (ascp); GSA/ENA only.
  -s, --speed     int           Download speed limit (MB/s) (default: 1000).
  -k, --skip-md5                Skip the md5 check for downloaded files.
  -r, --protocol  [ftp|https]   ENA transport (default: auto, HTTPS-first).
  -Q, --quiet                   Suppress download progress bars.
  -o, --output    text          Output directory (created if missing; default: cwd).
  --engine        [segmented|classic]   Download engine (default: segmented).
  --segment-size  int           Target segment size in MB (default: 512).
  --max-segments  int           Max connections per file (default: 8).
  --max-conns-per-host int      Global cap on connections to one host (default: 8).
  -j, --jobs      int           Max worker-pool size for batch download (default: 20).
  --adaptive / --no-adaptive    Gradient adaptive-concurrency controller (default: on).
  --probe-window  int           Adaptive probe window in seconds (default: 5).
  --cc-penalty    float         Worker-cost penalty K (default: 1.01).
  --meta-jobs     int           Parallelism for metadata/URL resolution (default: 3).
  --aspera-efficiency float     Keep an extra ascp worker only above this efficiency (0.70).
  -h, --help                    Show the help information.
  -v, --version                 Show the program version.

1. -i, --input

Input the accession to download, or a file with one accession per line (databases may be mixed). adaptiSeq retrieves the accession's metadata, then downloads each Run it contains.

adaptiseq -i PRJNA211801
adaptiseq -i accessions.txt        # batch

Currently supports 6 accession formats across 5 databases:

Databases	BioProject	Study	BioSample	Sample	Experiment	Run
GSA	PRJC	CRA	SAMC	\	CRX	CRR
SRA	PRJNA	SRP	SAMN	SRS	SRX	SRR
ENA	PRJEB	ERP	SAME	ERS	ERX	ERR
DDBJ	PRJDB	DRP	SAMD	DRS	DRX	DRR
GEO	GSE	\	GSM	\	\	\

GEO GSE/GSM accessions are resolved to their associated PRJNA/SAMN and then downloaded from SRA. Every contained Run is md5-checked; on mismatch adaptiSeq retries up to three rounds, recording results in success.log / fail.log.

2. -m, --metadata

Download only the sample metadata and skip the sequence data.

adaptiseq -i PRJNA211801 -m
adaptiseq -i CRR343031 -m

ENA → TSV (${accession}.metadata.tsv); GSA → CSV + project XLSX (${accession}.metadata.csv, CRA*.metadata.xlsx). Details: docs/usage/metadata.md.

3. -g, --gzip

Download FASTQ directly in gzip format; if unavailable on ENA, download the .sra and convert with fasterq-dump + pigz.

adaptiseq -i SRR1178105 -g

4. -q, --fastq

Convert the downloaded .sra into FASTQ (single-cell friendly: I1/R1/R2/R3).

adaptiseq -i SRR1178105 -q -t 10

5. -e, --merge

Merge a group of Runs into one FASTQ per Experiment (ex), Sample (sa), or Study (st).

adaptiseq -i SRX003906 -g -e ex

6. -d / -r / -p / -s / -k / -Q

Force database (-d ena|sra), ENA transport (-r https|ftp, default auto HTTPS-first), classic-engine axel connections (-p, with --engine classic), speed cap (-s MB/s), skip md5 (-k), and quiet (-Q). See docs/usage/download.md.

7. Batch & adaptive (-j, --adaptive, --meta-jobs)

For accession lists, adaptiSeq resolves in parallel (--meta-jobs) then downloads through an asyncio worker pool (-j) whose active size the adaptive controller tunes (--adaptive; topdown by default, ASEQ_ADAPTIVE_MODE=bottomup for the gradient controller — see docs/ADAPTIVE_CONTROLLER.md).

adaptiseq -i accessions.txt -g -j 8
adaptiseq -i accessions.txt -g --no-adaptive

Full detail: docs/usage/batch.md.

8. -a, --aspera

Use IBM Aspera (ascp); GSA/ENA only. ENA uses an adaptive parallel pool (efficiency hysteresis); GSA is sequential, best-effort (Huawei-Cloud preferred).

adaptiseq -i PRJNA211801 -a -g

Detail: docs/usage/aspera.md.

9. Python API

from adaptiseq import fetch, resolve, get_metadata

rows   = get_metadata("SRR7706354")               # parsed metadata rows
urls   = resolve("SRR7706354", database="ena")    # resolved URLs (no download)
result = fetch("accessions.txt", outdir="data/", gzip=True, jobs=20, adaptive=True)
print(result.success_ids, result.fail_ids, result.failed)

The API never calls sys.exit and never prints colour codes; it raises the typed exceptions in adaptiseq.errors. Full reference: docs/usage/python-api.md.

Output

• SRA / ENA / DDBJ / GEO accessions:

Output	Description
SRA files	Convert to FASTQ with -q
.metadata.tsv	Metadata for the query accession
success.log	Runs downloaded successfully
fail.log	Runs that failed

• GSA accessions:

Output	Description
GSA files	Mostly *.gz (a few bam/tar/bz2)
.metadata.csv	Metadata for the query accession
.metadata.xlsx	Project metadata (xlsx) for the accession
success.log	Runs downloaded successfully
fail.log	Runs that failed

Documentation

Full documentation lives in docs/: Installation · Usage overview · Downloading · Metadata · Merging · Batch & adaptive · Aspera · Python API · Method details · Examples · FAQ.

Related tools

adaptiSeq sits among other downloaders for public sequencing data — iSeq, fastq-dl, fetchngs, pysradb, and Kingfisher. A comparison:

Software	Languages	Databases	Accessions	Formats	Methods	Metadata	MD5	Resumable	Parallel	Merge	Skip done	Conda
adaptiSeq	Python	GSA, SRA, ENA, DDBJ, GEO	All	fq, fq.gz, sra, bam	segmented http(s)/ftp, aspera, wget/axel	✔	✔	✔	✔	✔	✔	TBD (pip ✔)
iSeq	Shell	GSA, SRA, ENA, DDBJ, GEO	All	fq, fq.gz, sra, bam	wget, axel, aspera	✔	✔	✔	✔	✔	✔	✔
SRA Toolkit	C	SRA, ENA, DDBJ	All except Run	fq, fq.gz, sra	prefetch	❌	✔	✔	❌	❌	✔	✔
enaBrowserTools	Python	SRA, ENA, DDBJ	All except GSA/GEO	fq, fq.gz, sra	urllib, aspera	✔	✔	✔	❌	❌	✔	✔
fastq-dl	Python	SRA, ENA, DDBJ	All except GSA/GEO	fq, fq.gz, sra	wget	✔	✔	❌	❌	✔	✔	✔
fetchngs	Python	SRA, ENA, DDBJ, GEO	All except GSA	fq, fq.gz	wget, aspera, prefetch	✔	✔	✔	❌	❌	✔	❌
pysradb	Python	SRA, ENA, DDBJ, GEO	All except GSA	fq, fq.gz, sra, bam	requests, aspera	✔	✔	✔	❌	❌	✔	✔
Kingfisher	Python	SRA, ENA, DDBJ	All except GSA/GEO	fq, fq.gz, sra	curl, aria2c, aspera	✔	✔	❌	✔	❌	✔	✔

Beyond this matrix, adaptiSeq adds what the single-shot downloaders do not: an adaptive, batch-parallel download path, parallel metadata resolution, a segmented resumable engine, and an importable Python API.

Contributing

Contributions are welcome — please open an issue for bugs/suggestions, or fork, branch, and submit a pull request. The offline test suite runs with python -m pytest.

Cite us

TBD (manuscript in preparation).

License

This project is licensed under the MIT License.