Python API

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

The simplest use case of pysradb is when you 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 SRAWeb
db = SRAWeb()
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 SRAWeb
db = SRAWeb()
df = db.sra_metadata('SRP017942')
db.download(df)

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.