Python package for interacting with SRAdb and downloading datasets from SRA
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README.rst

pysradb

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Python package for interacting with SRAdb and downloading datasets from SRA.

Demo Notebooks

These notebooks document all the possible features of pysradb:

  1. SRAmetadb operations
  2. GEOmetadb operations
  3. Command line usage

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 except aspera-client (which is not required, but highly recommended). Both Python 2 and Python 3 are supported.

Dependecies

pandas>=0.23.4
tqdm>=4.28
click>=7.0
aspera-client
SRAmetadb.sqlite

Downloading SRAmetadb

We need a SQLite database file that has preprocessed metadata made available by the SRAdb project.

SRAmetadb can be downloaded using:

wget -c https://starbuck1.s3.amazonaws.com/sradb/SRAmetadb.sqlite.gz && gunzip SRAmetadb.sqlite.gz

Alternatively, you can also download it using pysradb:

from pysradb import download_sradb_file
download_sradb_file()

SRAmetadb.sqlite.gz: 2.44GB [01:10, 36.9MB/s]

aspera-client

We strongly recommend using aspera-client (which uses UDP) since it warrants faster downloads as compared to ftp/http based downloads.

PDF intructions are available on IBM's website.

Direct download links:

Once you download the tar relevant to your OS, say linux, follow these steps to install aspera:

tar -zxvf ibm-aspera-connect-3.8.1.161274-linux-g2.12-64.tar.gz
bash ibm-aspera-connect-3.8.1.161274-linux-g2.12-64.sh
Installing IBM Aspera Connect
Deploying IBM Aspera Connect (/home/saket/.aspera/connect) for the current user only.
Install complete.

Installing pysradb in development mode

pip install -U pandas tqdm
git clone https://github.com/saketkc/pysradb.git
cd pysradb
pip install -e .

Interacting with SRA

Use Case 1: Fetch the metadata table (SRA-runtable)

The simplest use case of pysradb is when you apriopri know the SRA project ID (SRP) and would simply want to fetch the metadata associated with it. This is generally reflected in the SraRunTable.txt that you get from NCBI's website. See an example of a SraRunTable.

from pysradb import SRAdb
db = SRAdb('SRAmetadb.sqlite')
df = db.sra_metadata('SRP098789')
df.head()
study_accession experiment_accession experiment_title run_accession taxon_id library_selection library_layout library_strategy library_source library_name bases spots adapter_spec avg_read_length
SRP098789 SRX2536403 GSM2475997: 1.5 µM PF-067446846, 10 min, rep 1; Homo sapiens; OTHER SRR5227288 9606 other SINGLE - OTHER TRANSCRIPTOMIC   2104142750 42082855   50
SRP098789 SRX2536404 GSM2475998: 1.5 µM PF-067446846, 10 min, rep 2; Homo sapiens; OTHER SRR5227289 9606 other SINGLE - OTHER TRANSCRIPTOMIC   2082873050 41657461   50
SRP098789 SRX2536405 GSM2475999: 1.5 µM PF-067446846, 10 min, rep 3; Homo sapiens; OTHER SRR5227290 9606 other SINGLE - OTHER TRANSCRIPTOMIC   2023148650 40462973   50
SRP098789 SRX2536406 GSM2476000: 0.3 µM PF-067446846, 10 min, rep 1; Homo sapiens; OTHER SRR5227291 9606 other SINGLE - OTHER TRANSCRIPTOMIC   2057165950 41143319   50
SRP098789 SRX2536407 GSM2476001: 0.3 µM PF-067446846, 10 min, rep 2; Homo sapiens; OTHER SRR5227292 9606 other SINGLE - OTHER TRANSCRIPTOMIC   3027621850 60552437   50

The metadata is returned as a pandas dataframe and hence allows you to perform all regular select/query operations available through pandas.

Use Case 2: Downloading an entire project arranged experiment wise

Once you have fetched the metadata and made sure, this is the project you were looking for, you would want to download everything at once. NCBI follows this hiererachy: SRP => SRX => SRR. Each SRP (project) has multiple SRX (experiments) and each SRX in turn has multiple SRR (runs) inside it. We want to mimick this hiereachy in our downloads. The reason to do that is simple: in most cases you care about SRX the most, and would want to "merge" your SRRs in one way or the other. Having this hierearchy ensures your downstream code can handle such cases easily, without worrying about which runs (SRR) need to be merged.

We strongly recommend installing aspera-client which uses UDP and is designed to be faster.

from pysradb import SRAdb
db = SRAdb('SRAmetadb.sqlite')
df = db.sra_metadata('SRP017942')
db.download(df)

The default download location is pysradb_downloads/ created inside your current working directory. You can specify a location by:

db.download(df=df, out_dir='/pysradb_downloads')

Use Case 3: Downloading a subset of experiments

Often, you need to process only a smaller set of samples from a project (SRP). Consider this project which has data spanning four assays.

df = db.sra_metadata('SRP000941')
print(df.library_strategy.unique())
['ChIP-Seq' 'Bisulfite-Seq' 'RNA-Seq' 'WGS' 'OTHER']

But, you might be only interested in analyzing the RNA-seq samples and would just want to download that subset. This is simple using pysradb since the metadata can be subset just as you would subset a dataframe in pandas.

df_rna = df[df.library_strategy == 'RNA-Seq']
db.download(df=df_rna, out_dir='/pysradb_downloads')

Use Case 4: Getting cell-type/treatment information from sample_attributes

Cell type/tissue informations is usually hidden in the sample_attributes column, which can be expanded:

from pysradb.filter_attrs import expand_sample_attribute_columns
df = db.sra_metadata('SRP017942')
expand_sample_attribute_columns(df).head()
study_accession experiment_accession experiment_title experiment_attribute sample_attribute run_accession taxon_id library_selection library_layout library_strategy library_source library_name bases spots adapter_spec avg_read_length assay_type cell_line source_name transfected_with treatment
SRP017942 SRX217028 GSM1063575: 293T_GFP; Homo sapiens; RNA-Seq GEO Accession: GSM1063575 source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-GFP || assay type: Riboseq SRR648667 9606 other SINGLE - RNA-Seq TRANSCRIPTOMIC   1806641316 50184481   36 riboseq 293t cells 293t cells 3xflag-gfp NaN
SRP017942 SRX217029 GSM1063576: 293T_GFP_2hrs_severe_Heat_Shock; Homo sapiens; RNA-Seq GEO Accession: GSM1063576 source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-GFP || treatment: severe heat shock (44C 2 hours) || assay type: Riboseq SRR648668 9606 other SINGLE - RNA-Seq TRANSCRIPTOMIC   3436984836 95471801   36 riboseq 293t cells 293t cells 3xflag-gfp severe heat shock (44c 2 hours)
SRP017942 SRX217030 GSM1063577: 293T_Hspa1a; Homo sapiens; RNA-Seq GEO Accession: GSM1063577 source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-Hspa1a || assay type: Riboseq SRR648669 9606 other SINGLE - RNA-Seq TRANSCRIPTOMIC   3330909216 92525256   36 riboseq 293t cells 293t cells 3xflag-hspa1a NaN
SRP017942 SRX217031 GSM1063578: 293T_Hspa1a_2hrs_severe_Heat_Shock; Homo sapiens; RNA-Seq GEO Accession: GSM1063578 source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-Hspa1a || treatment: severe heat shock (44C 2 hours) || assay type: Riboseq SRR648670 9606 other SINGLE - RNA-Seq TRANSCRIPTOMIC   3622123512 100614542   36 riboseq 293t cells 293t cells 3xflag-hspa1a severe heat shock (44c 2 hours)
SRP017942 SRX217956 GSM794854: 3T3-Control-Riboseq; Mus musculus; RNA-Seq GEO Accession: GSM794854 source_name: 3T3 cells || treatment: control || cell line: 3T3 cells || assay type: Riboseq SRR649752 10090 cDNA SINGLE - RNA-Seq TRANSCRIPTOMIC   594945396 16526261   36 riboseq 3t3 cells 3t3 cells NaN control

Use Case 5: Searching for datasets

Another common operation that we do on SRA is seach, plain text search.

If you want to look up for all projects where ribosome profiling appears somewhere in the description:

df = db.search_sra(search_str='"ribosome profiling"')
df.head()
study_accession experiment_accession experiment_title run_accession taxon_id library_selection library_layout library_strategy library_source library_name bases spots
DRP003075 DRX019536 Illumina Genome Analyzer IIx sequencing of SAMD00018584 DRR021383 83333 other SINGLE - OTHER TRANSCRIPTOMIC GAII05_3 978776480 12234706
DRP003075 DRX019537 Illumina Genome Analyzer IIx sequencing of SAMD00018585 DRR021384 83333 other SINGLE - OTHER TRANSCRIPTOMIC GAII05_4 894201680 11177521
DRP003075 DRX019538 Illumina Genome Analyzer IIx sequencing of SAMD00018586 DRR021385 83333 other SINGLE - OTHER TRANSCRIPTOMIC GAII05_5 931536720 11644209
DRP003075 DRX019540 Illumina Genome Analyzer IIx sequencing of SAMD00018588 DRR021387 83333 other SINGLE - OTHER TRANSCRIPTOMIC GAII07_4 2759398700 27593987
DRP003075 DRX019541 Illumina Genome Analyzer IIx sequencing of SAMD00018589 DRR021388 83333 other SINGLE - OTHER TRANSCRIPTOMIC GAII07_5 2386196500 23861965

Again, the results are available as a pandas dataframe and hence you can perform all subset operations post your query. Your query doesn't need to be exact.

Citation

Pending.

A lot of functionality in pysradb is based on ideas from the original SRAdb package. Please cite the original SRAdb publication:

Zhu, Yuelin, Robert M. Stephens, Paul S. Meltzer, and Sean R. Davis. "SRAdb: query and use public next-generation sequencing data from within R." BMC bioinformatics 14, no. 1 (2013): 19.