.. _apiusage: ########## 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. .. code-block:: 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` 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 `_. .. code-block:: 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. .. code-block:: 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` 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. .. code-block:: 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` column, which can be expanded: .. code-block:: python from pysradb.filter_attrs import expand_sample_attribute_columns df = db.sra_metadata('SRP017942') expand_sample_attribute_columns(df).head() .. table:: =============== ==================== ===================================================================== ========================= ======================================================================================================================================================== ============= ======== ================= ============== ================ ============== ============ ========== ========= ============ =============== ========== ========== =========== ================ =============================== 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: .. code-block:: python df = db.search_sra(search_str='"ribosome profiling"') df.head() .. table:: =============== ==================== ======================================================= ============= ======== ================= ============== ================ ============== ============ ========== ======== 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.