# Python API ## Use Case 1: Fetch the metadata table (SRA-runtable) The simplest use case of [pysradb]{.title-ref} 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]{.title-ref} that you get from NCBI\'s website. See an [example](https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP098789) of a SraRunTable. ``` python 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]{.title-ref} dataframe and hence allows you to perform all regular select/query operations available through [pandas]{.title-ref}. ## 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]{.title-ref}. Each [SRP]{.title-ref} (project) has multiple [SRX]{.title-ref} (experiments) and each [SRX]{.title-ref} in turn has multiple [SRR]{.title-ref} (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]{.title-ref} 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]{.title-ref} which uses UDP and is [designed to be faster](http://www.skullbox.net/tcpudp.php). ``` python 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. ``` python 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]{.title-ref} samples and would just want to download that subset. This is simple using [pysradb]{.title-ref} since the metadata can be subset just as you would subset a dataframe in pandas. ``` python 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]{.title-ref} column, which can be expanded: ``` python 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 SRP017942 SRP017942 SRP017942 SRP017942

SRX217028 SRX217029 SRX217030 SRX217031 SRX217956

GSM1063575: 293T_GFP; Homo sapiens; RNA-Seq GSM1063576: 293T_GFP_2hrs_severe_Heat_Shock; Homo sapiens; RNA-Seq GSM1063577: 293T_Hspa1a; Homo sapiens; RNA-Seq GSM1063578: 293T_Hspa1a_2hrs_severe_Heat_Shock; Homo sapiens; RNA-Seq GSM794854: 3T3-Control-Riboseq; Mus musculus; RNA-Seq

GEO Accession: GSM1063575 GEO Accession: GSM1063576 GEO Accession: GSM1063577 GEO Accession: GSM1063578 GEO Accession: GSM794854

source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-GFP || assay type: Riboseq source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-GFP || treatment: severe heat shock (44C 2 hours) || assay type: Riboseq source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-Hspa1a || assay type: Riboseq source_name: 293T cells || cell line: 293T cells || transfected with: 3XFLAG-Hspa1a || treatment: severe heat shock (44C 2 hours) || assay type: Riboseq source_name: 3T3 cells || treatment: control || cell line: 3T3 cells || assay type: Riboseq

SRR648667 SRR648668 SRR648669 SRR648670 SRR649752

9606 9606 9606 9606 10090

other other other other cDNA

SINGLE -SINGLE -SINGLE -SINGLE -SINGLE -

RNA-Seq RNA-Seq RNA-Seq RNA-Seq RNA-Seq

TRANSCRIPTOMIC TRANSCRIPTOMIC TRANSCRIPTOMIC TRANSCRIPTOMIC TRANSCRIPTOMIC

1806641316 3436984836 3330909216 3622123512 594945396

50184481 95471801 92525256

100614542

16526261

36 36 36 36 36

riboseq riboseq riboseq riboseq riboseq

293t cells 293t cells 293t cells 293t cells 3t3 cells

293t cells 293t cells 293t cells 293t cells 3t3 cells

3xflag-gfp 3xflag-gfp 3xflag-hspa1a 3xflag-hspa1a NaN

NaN severe heat shock (44c 2 hours) NaN severe heat shock (44c 2 hours) 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]{.title-ref} appears somewhere in the description: ``` python 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]{.title-ref} dataframe and hence you can perform all subset operations post your query. Your query doesn\'t need to be exact. ## Use Case 8: Finding publications (PMIDs) associated with SRA data Sometimes you have SRA accessions and want to find the publications that describe the data generation. ``` python from pysradb import SRAweb db = SRAweb() # Get PMIDs for a study accession (SRP) pmids_df = db.srp_to_pmid('SRP002605') pmids_df.head() ``` sra_accession bioproject pmid SRP002605 PRJNA129385 20703300 You can also get PMIDs for other SRA accession types: ``` python # Get PMIDs for run accessions (SRR) srr_pmids = db.srr_to_pmid('SRR057511') # Get PMIDs for experiment accessions (SRX) srx_pmids = db.srx_to_pmid('SRX021967') # Get PMIDs for sample accessions (SRS) srs_pmids = db.srs_to_pmid('SRS079386') # Get PMIDs for multiple accessions at once multi_pmids = db.sra_to_pmid(['SRP002605', 'SRP016501']) ``` You can also directly query BioProject accessions for their associated publications: ``` python # Get PMIDs directly from BioProject accessions bioproject_pmids = db.fetch_bioproject_pmids(['PRJNA257197', 'PRJNA129385']) print(bioproject_pmids) # Output: {'PRJNA257197': ['25214632'], 'PRJNA129385': ['20703300']} ``` **Note**: This functionality relies on the cross-references maintained between BioProjects and PubMed. Not all SRA datasets have associated publications, and some publications may not be properly cross-referenced in the NCBI databases. The success rate depends on: - Whether the authors included SRA/BioProject accessions in their manuscript - Whether NCBI has established the cross-references - The publication date relative to data submission