pysradb 2.2.2

A Python package for interacting with SRAdb and downloading datasets from SRA/ENA/GEO

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

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}
                ...

 pysradb: Query NGS metadata and data from NCBI Sequence Read Archive.
 version: 2.0.1
 Citation: 10.12688/f1000research.18676.1

 optional arguments:
   -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}
     metadata            Fetch metadata for SRA project (SRPnnnn)
     download            Download SRA project (SRPnnnn)
     search              Search SRA 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

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. Download BAMs
7. Metadata for multiple SRPs
8. Multithreaded fastq downloads using Aspera Client
9. Searching SRA/GEO/ENA

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=3.10 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

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 parallely: 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.

Downloading only certain samples of interest

$ pysradb metadata SRP000941 --detailed | grep 'study\|RNA-Seq' | pysradb download

This will download all RNA-seq samples coming from this project.

Ultrafast fastq downloads

With aspera-client installed, [pysradb]{.title-ref} can perform ultra fast downloads:

To download all original fastqs with [aspera-client]{.title-ref} installed utilizing 8 threads:

$ pysradb download -t 8 --use_ascp -p SRP002605

Refer to the notebook for (shallow) time benchmarks.

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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