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A Python package for retrieving metadata from SRA/ENA/GEO

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Documentation

https://saketkc.github.io/pysradb

CLI Usage

pysradb supports command line usage. See CLI instructions or quickstart guide.

$ pysradb
usage: pysradb [-h] [--version] [--citation]
               {metadata,download,search,gse-to-gsm,gse-to-srp,gsm-to-gse,gsm-to-srp,gsm-to-srr,gsm-to-srs,gsm-to-srx,srp-to-gse,srp-to-srr,srp-to-srs,srp-to-srx,srr-to-gsm,srr-to-srp,srr-to-srs,srr-to-srx,srs-to-gsm,srs-to-srx,srx-to-srp,srx-to-srr,srx-to-srs,geo-matrix,srp-to-pmid,gse-to-pmid,pmid-to-gse,pmid-to-srp,pmc-to-identifiers,pmid-to-identifiers,doi-to-gse,doi-to-srp,doi-to-identifiers}
               ...

pysradb: Query NGS metadata and data from NCBI Sequence Read Archive.
version: 2.4.1.
Citation: 10.12688/f1000research.18676.1

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --citation            how to cite

subcommands:
  {metadata,download,search,gse-to-gsm,gse-to-srp,gsm-to-gse,gsm-to-srp,gsm-to-srr,gsm-to-srs,gsm-to-srx,srp-to-gse,srp-to-srr,srp-to-srs,srp-to-srx,srr-to-gsm,srr-to-srp,srr-to-srs,srr-to-srx,srs-to-gsm,srs-to-srx,srx-to-srp,srx-to-srr,srx-to-srs,geo-matrix,srp-to-pmid,gse-to-pmid,pmid-to-gse,pmid-to-srp,pmc-to-identifiers,pmid-to-identifiers,doi-to-gse,doi-to-srp,doi-to-identifiers}
    metadata            Fetch metadata for SRA project (SRPnnnn)
    download            Download SRA project (SRPnnnn)
    search              Search SRA/ENA for matching text
    gse-to-gsm          Get GSM for a GSE
    gse-to-srp          Get SRP for a GSE
    gsm-to-gse          Get GSE for a GSM
    gsm-to-srp          Get SRP for a GSM
    gsm-to-srr          Get SRR for a GSM
    gsm-to-srs          Get SRS for a GSM
    gsm-to-srx          Get SRX for a GSM
    srp-to-gse          Get GSE for a SRP
    srp-to-srr          Get SRR for a SRP
    srp-to-srs          Get SRS for a SRP
    srp-to-srx          Get SRX for a SRP
    srr-to-gsm          Get GSM for a SRR
    srr-to-srp          Get SRP for a SRR
    srr-to-srs          Get SRS for a SRR
    srr-to-srx          Get SRX for a SRR
    srs-to-gsm          Get GSM for a SRS
    srs-to-srx          Get SRX for a SRS
    srx-to-srp          Get SRP for a SRX
    srx-to-srr          Get SRR for a SRX
    srx-to-srs          Get SRS for a SRX
    geo-matrix          Download and parse GEO Matrix files
    srp-to-pmid         Get PMIDs for SRP accessions
    gse-to-pmid         Get PMIDs for GSE accessions
    pmid-to-gse         Get GSE accessions from PMIDs
    pmid-to-srp         Get SRP accessions from PMIDs
    pmc-to-identifiers  Extract database identifiers from PMC articles
    pmid-to-identifiers
                        Extract database identifiers from PubMed articles
    doi-to-gse          Get GSE accessions from DOIs
    doi-to-srp          Get SRP accessions from DOIs
    doi-to-identifiers  Extract database identifiers from articles via DOI

Quickstart

A Google Colaboratory version of most used commands are available in this Colab Notebook . Note that this requires only an active internet connection (no additional downloads are made).

The following notebooks document all the possible features of `pysradb`:

  1. Python API
  2. Downloading datasets from SRA - command line
  3. Parallely download multiple datasets - Python API
  4. Converting SRA-to-fastq - command line (requires conda)
  5. Downloading subsets of a project - Python API
  6. Metadata for multiple SRPs
  7. Searching SRA/GEO/ENA
  8. Extracting identifiers from PMC/DOI

Installation

To install stable version using `pip`:

pip install pysradb

Alternatively, if you use conda:

conda install -c bioconda pysradb

This step will install all the dependencies. If you have an existing environment with a lot of pre-installed packages, conda might be slow. Please consider creating a new enviroment for pysradb:

conda create -c bioconda -n pysradb PYTHON=3.13 pysradb

Dependencies

pandas
requests
tqdm
xmltodict

Installing pysradb in development mode

git clone https://github.com/saketkc/pysradb.git
cd pysradb && pip install -r requirements.txt
pip install -e .

Using pysradb

Obtaining SRA metadata

$ pysradb metadata SRP000941 | head

study_accession experiment_accession experiment_title                                                                                                                 experiment_desc                                                                                                                  organism_taxid  organism_name library_strategy library_source  library_selection sample_accession sample_title instrument                    total_spots total_size    run_accession run_total_spots run_total_bases
SRP000941       SRX056722                                                                         Reference Epigenome: ChIP-Seq Analysis of H3K27ac in hESC H1 Cells                                                               Reference Epigenome: ChIP-Seq Analysis of H3K27ac in hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC    ChIP            SRS184466                              Illumina HiSeq 2000    26900401     531654480   SRR179707     26900401         807012030
SRP000941       SRX027889                                                                            Reference Epigenome: ChIP-Seq Analysis of H2AK5ac in hESC Cells                                                                  Reference Epigenome: ChIP-Seq Analysis of H2AK5ac in hESC Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC    ChIP            SRS116481                      Illumina Genome Analyzer II    37528590     779578968   SRR067978     37528590        1351029240
SRP000941       SRX027888                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS116483                      Illumina Genome Analyzer II    13603127    3232309537   SRR067977     13603127         489712572
SRP000941       SRX027887                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS116562                      Illumina Genome Analyzer II    22430523     506327844   SRR067976     22430523         807498828
SRP000941       SRX027886                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS116560                      Illumina Genome Analyzer II    15342951     301720436   SRR067975     15342951         552346236
SRP000941       SRX027885                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS116482                      Illumina Genome Analyzer II    39725232     851429082   SRR067974     39725232        1430108352
SRP000941       SRX027884                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS116481                      Illumina Genome Analyzer II    32633277     544478483   SRR067973     32633277        1174797972
SRP000941       SRX027883                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS004118                      Illumina Genome Analyzer II    22150965    3262293717   SRR067972      9357767         336879612
SRP000941       SRX027883                                                                                     Reference Epigenome: ChIP-Seq Input from hESC H1 Cells                                                                           Reference Epigenome: ChIP-Seq Input from hESC H1 Cells  9606            Homo sapiens       ChIP-Seq           GENOMIC  RANDOM            SRS004118                      Illumina Genome Analyzer II    22150965    3262293717   SRR067971     12793198         460555128

Obtaining detailed SRA metadata

$ pysradb metadata SRP075720 --detailed | head

study_accession experiment_accession experiment_title                                  experiment_desc                                   organism_taxid  organism_name library_strategy library_source  library_selection sample_accession sample_title instrument           total_spots total_size run_accession run_total_spots run_total_bases
SRP075720       SRX1800476            GSM2177569: Kcng4_2la_H9; Mus musculus; RNA-Seq   GSM2177569: Kcng4_2la_H9; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467643                    Illumina HiSeq 2500  2547148      97658407  SRR3587912    2547148         127357400
SRP075720       SRX1800475            GSM2177568: Kcng4_2la_H8; Mus musculus; RNA-Seq   GSM2177568: Kcng4_2la_H8; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467642                    Illumina HiSeq 2500  2676053     101904264  SRR3587911    2676053         133802650
SRP075720       SRX1800474            GSM2177567: Kcng4_2la_H7; Mus musculus; RNA-Seq   GSM2177567: Kcng4_2la_H7; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467641                    Illumina HiSeq 2500  1603567      61729014  SRR3587910    1603567          80178350
SRP075720       SRX1800473            GSM2177566: Kcng4_2la_H6; Mus musculus; RNA-Seq   GSM2177566: Kcng4_2la_H6; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467640                    Illumina HiSeq 2500  2498920      94977329  SRR3587909    2498920         124946000
SRP075720       SRX1800472            GSM2177565: Kcng4_2la_H5; Mus musculus; RNA-Seq   GSM2177565: Kcng4_2la_H5; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467639                    Illumina HiSeq 2500  2226670      83473957  SRR3587908    2226670         111333500
SRP075720       SRX1800471            GSM2177564: Kcng4_2la_H4; Mus musculus; RNA-Seq   GSM2177564: Kcng4_2la_H4; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467638                    Illumina HiSeq 2500  2269546      87486278  SRR3587907    2269546         113477300
SRP075720       SRX1800470            GSM2177563: Kcng4_2la_H3; Mus musculus; RNA-Seq   GSM2177563: Kcng4_2la_H3; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467636                    Illumina HiSeq 2500  2333284      88669838  SRR3587906    2333284         116664200
SRP075720       SRX1800469            GSM2177562: Kcng4_2la_H2; Mus musculus; RNA-Seq   GSM2177562: Kcng4_2la_H2; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467637                    Illumina HiSeq 2500  2071159      79689296  SRR3587905    2071159         103557950
SRP075720       SRX1800468            GSM2177561: Kcng4_2la_H1; Mus musculus; RNA-Seq   GSM2177561: Kcng4_2la_H1; Mus musculus; RNA-Seq  10090           Mus musculus  RNA-Seq          TRANSCRIPTOMIC  cDNA              SRS1467635                    Illumina HiSeq 2500  2321657      89307894  SRR3587904    2321657         116082850

Converting SRP to GSE

$ pysradb srp-to-gse SRP075720

study_accession study_alias
SRP075720       GSE81903

Converting GSM to SRP

$ pysradb gsm-to-srp GSM2177186

experiment_alias study_accession
GSM2177186       SRP075720

Converting GSM to GSE

$ pysradb gsm-to-gse GSM2177186

experiment_alias study_alias
GSM2177186       GSE81903

Converting GSM to SRX

$ pysradb gsm-to-srx GSM2177186

experiment_alias experiment_accession
GSM2177186       SRX1800089

Converting GSM to SRR

$ pysradb gsm-to-srr GSM2177186

experiment_alias run_accession
GSM2177186       SRR3587529

Converting SRP to PMID

$ pysradb srp-to-pmid SRP045778

srp_accession bioproject pmid
SRP045778     PRJNA257197 27373336

Converting GSE to PMID

$ pysradb gse-to-pmid GSE253406

gse_accession pmid
GSE253406     39528918

Extracting identifiers from PMC/DOI

Extract database identifiers (GSE, PRJNA, SRP, etc.) from PubMed Central articles or DOIs. This feature automatically converts between GSE and SRP identifiers even when papers only mention one type!

Get all identifiers from a PMID

$ pysradb pmid-to-identifiers 39528918

pmid      pmc_id       gse_ids     prjna_ids    srp_ids
39528918  PMC10802650  GSE253406   PRJNA1058002 SRP484103

Get only GSE or SRP from PMID

$ pysradb pmid-to-gse 39528918

pmid      pmc_id       gse_ids
39528918  PMC10802650  GSE253406

$ pysradb pmid-to-srp 39528918

pmid      pmc_id       srp_ids
39528918  PMC10802650  SRP484103

Extract from DOI

$ pysradb doi-to-identifiers 10.12688/f1000research.18676.1

doi                                 pmid      pmc_id      gse_ids  srp_ids
10.12688/f1000research.18676.1      30873266  PMC6411813  GSE...   SRP...

Extract from PMC ID

$ pysradb pmc-to-identifiers PMC10802650

pmc_id       gse_ids     prjna_ids    srp_ids
PMC10802650  GSE253406   PRJNA1058002 SRP484103

Downloading supplementary files from GEO

$ pysradb download -g GSE161707

Downloading an entire SRA/ENA project (multithreaded)

pysradb makes it super easy to download datasets from SRA in parallel: Using 8 threads to download:

$ pysradb download -y -t 8 --out-dir ./pysradb_downloads -p SRP063852

Downloads are organized by SRP/SRX/SRR mimicking the hierarchy of SRA projects.

Publication

pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive

Presentation slides from BOSC (ISMB-ECCB) 2019: https://f1000research.com/slides/8-1183

Citation

Choudhary, Saket. "pysradb: A Python Package to Query next-Generation Sequencing Metadata and Data from NCBI Sequence Read Archive." F1000Research, vol. 8, F1000 (Faculty of 1000 Ltd), Apr. 2019, p. 532 (https://f1000research.com/articles/8-532/v1)

@article{Choudhary2019,
doi = {10.12688/f1000research.18676.1},
url = {https://doi.org/10.12688/f1000research.18676.1},
year = {2019},
month = apr,
publisher = {F1000 (Faculty of 1000 Ltd)},
volume = {8},
pages = {532},
author = {Saket Choudhary},
title = {pysradb: A {P}ython package to query next-generation sequencing metadata and data from {NCBI} {S}equence {R}ead {A}rchive},
journal = {F1000Research}
}

Zenodo archive: https://zenodo.org/badge/latestdoi/159590788

Zenodo DOI: 10.5281/zenodo.2306881

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About

Package for fetching metadata and downloading data from SRA/ENA/GEO

saketkc.github.io/pysradb

Topics

bioinformatics bioinformatics-pipeline ncbi-sra-archive sra ena sratoolkit ncbi-sra

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