Retrieving data from SRA is a common task. NCBI has provided a nice tool collection named E-utilities to query and retrieve data from it. The example Python snippet below shows how to query NCBI SRA database using sample identifiers and get a table of linked NCBI BioProject, BioSample, Run, Download location and Size.
import sys, os
import subprocess
import shlex
import pandas as pd
.....
def get_SRR_from_biosamples(csv: str, batch_size=10, debug=True):
"""Gete SRA run ID from BioSample ID.
"""
epost_cmd = 'epost -db biosample -format acc'
elink_cmd = 'elink -target sra'
efetch_cmd = 'efetch -db sra -format runinfo -mode xml'
xtract_cmd = """xtract -pattern Row -def "NA" -element BioProject\n
BioSample Run download_path size_MB"""
sample_ids = []
results = []
with open(csv, 'r') as fh:
total_samples = fh.readlines()
print(f'Total samples: {total_samples}')
for idx, l in enumerate(total_samples):
l = l.rstrip()
sample_ids.append(l)
batch_num = int(idx/batch_size) + 1
run_flag = True
if debug:
if batch_num > 1:
print('Debug mode. Stop execution after 1 batch.')
run_flag = None
break
if run_flag:
if ((idx+1)%batch_size == 0) | (idx == len(total_samples) - 1):
print(f'Processing batch {batch_num}: {sample_ids}')
batch_results = []
sample_ids = ','.join(sample_ids)
epost_cmd += f' -id "{sample_ids}"'
epost = subprocess.Popen(shlex.split(epost_cmd),
stdout=subprocess.PIPE,
encoding='utf8')
elink = subprocess.Popen(shlex.split(elink_cmd),
stdin=epost.stdout,
stdout=subprocess.PIPE,
encoding='utf8')
efetch = subprocess.Popen(shlex.split(efetch_cmd),
stdin=elink.stdout,
stdout=subprocess.PIPE,
encoding='utf8')
xtract = subprocess.Popen(shlex.split(xtract_cmd),
stdin=efetch.stdout,
stdout=subprocess.PIPE,
encoding='utf8')
while epost.returncode is None:
epost.poll()
for l in xtract.stdout.readlines():
if not l.startswith('PRJ'): # "502 Bad Gateway" when server is busy
sys.stderr.write(f'Error processing {sample_ids}: {l}')
else:
if debug:
print(l.rstrip())
batch_results.append(l.split())
print(f'\nTotal SRA Runs in batch {batch_num}: {len(batch_results)}.\n')
results.extend(batch_results)
sample_ids = []
print(f'Total runs in collection: {len(results)} with {idx+1} samples.')
data = pd.DataFrame(results, columns=['BioProject', 'BioSample', 'Run', 'Download', 'size_MB'])
return data
These E-utilities tools are used and need to be accessible from the environment: epost, elink, efetch, xtract. The subprocess module in Python is used to chain together these steps similar to Linux pipes. The samples are queried in batches to prevent too frequent queries to NCBI, which could lead to blocking of your future queries. After receiving the sample run identifiers, one can use the prefetch tool from E-utilities to download the files. And, of course, prefetch can be wrapped and chained together as well.