This is a set of functions used for pulling SNP information and parsing it into an array from the Entrez Database in Python. Requires biopython (pip install biopython)

get_snp.py

from pprint import pprint as pp
from Bio import Entrez

Entrez.email = "YOUR@EMAIL.HERE"

def pull_line(var_set,line):
 	"""
	This function parses data from lines in one of three ways:

	1.) Pulls variables out of a particular line when defined as "variablename=[value]" - uses a string to find the variable.
	2.) Pulls variables based on a set position within a line [splits the line by '|']
	3.) Defines variables that can be identified based on a limited possible set of values - [categorical variable, specified using an array]

	"""
	line_set = {}
	for k,v in var_set.items():
		if type(v) == str:
			try:
				line_set[k] = [x for x in line if x.startswith(v)][0].replace(v,'')
			except:
				pass
		elif type(v) == int:
			try:
				line_set[k] = line[v]
			except:
				pass
		else:
			try:
				line_set[k] = [x for x in line if x in v][0]
			except:
				pass
	return line_set

def pull_vars(var_set,line_start,line,multi=False):
	"""
	Delegates and compiles data from entrez flat files dependent on whether
	the type of data trying to be pulled is contained in unique vs. non-unique lines.
	For example - the first line of the flat file is always something like this:

	rs12009 | Homo Sapiens | 9606 | etc.

	This line is unique (refers to RefSnp identifier)- and only occurs once in each flat file. On the other hand, lines
	beginning with "ss[number]" refer to 'submitted snp' numbers and can appear multiple times.

	"""
	lineset = [x.split(' | ') for x in line if x.startswith(line_start)]
	if len(lineset) == 0:
		return 
	# If the same line exists multiple times - place results into an array
	if multi == True:
		pulled_vars = []
		for line in lineset:
			# Pull date in from line and append
			pulled_vars.append(pull_line(var_set,line))
		return pulled_vars	
	else:
	# Else if the line is always unique, output single dictionary
		line = lineset[0]
		pulled_vars = {}
		return pull_line(var_set,line)

def get_snp(q):
	""" 
	Takes as input an array of snp identifiers and returns 
	a parsed dictionary of their data from Entrez.
	"""
	response = Entrez.efetch(db='SNP', id=','.join(q), rettype='flt', retmode='flt').read()
	r = {} # Return dictionary variable
	# Parse flat file response
	for snp_info in filter(None,response.split('\n\n')):
		print snp_info
		# Parse the First Line. Details of rs flat files available here:
		# ftp://ftp.ncbi.nlm.nih.gov/snp/specs/00readme.txt
		snp = snp_info.split('\n')
		# Parse the 'rs' line:
		rsId = snp[0].split(" | ")[0]
		r[rsId] = {}

		# rs vars
		rs_vars = {"organism":1,
				   "taxId":2,
				   "snpClass":3,
				   "genotype":"genotype=",
				   "rsLinkout":"submitterlink=",
				   "date":"updated "}

		# rs vars
		ss_vars = {"ssId":0,
				   "handle":1,
				   "locSnpId":2,
				   "orient":"orient=",
				   "exemplar":"ss_pick=",
				   }

		# SNP line variables:
		SNP_vars = {"observed":"alleles=",
					"value":"het=",
					"stdError":"se(het)=",
					"validated":"validated=",
					"validProbMin":"min_prob=",
					"validProbMax":"max_prob=",
					"validation":"suspect=",
					"AlleleOrigin":['unknown',
									'germline',
									'somatic',
									'inherited',
									'paternal',
									'maternal',
									'de-novo',
									'bipaternal',
									'unipaternal',
									'not-tested',
									'tested-inconclusive'],
					"snpType":['notwithdrawn',
							   'artifact',
							   'gene-duplication',
							   'duplicate-submission',
							   'notspecified',
							   'ambiguous-location;',
							   'low-map-quality']}
		
		# CLINSIG line variables:
		CLINSIG_vars = {"ClinicalSignificance":['probable-pathogenic','pathogenic','other']}

		# GMAF line variables
		GMAF_vars = {"allele":"allele=",
					 "sampleSize":"count=",
					 "freq":"MAF="}

		# CTG line variables
		CTG_vars = {"groupLabel":"assembly=",
					"chromosome":"chr=",
					"physmapInt":"chr-pos=",
					"asnFrom":"ctg-start=",
					"asnTo":"ctg-end=",
					"loctype":"loctype=",
					"orient":"orient="}

		# LOC line variables
		LOC_vars = {"symbol":1,
					"geneId":"locus_id=",
					"fxnClass":"fxn-class=",
					"allele":"allele=",
					"readingFrame":"frame=",
					"residue":"residue=",
					"aaPosition":"aa_position="}

		# LOC line variables
		SEQ_vars = {"gi":1,
					"source":"source-db=",
					"asnFrom":"seq-pos=",
					"orient":"orient="}

		# Pull out variable information:
		r[rsId]['rs']       = pull_vars(rs_vars,"rs",snp)
		r[rsId]['ss']       = pull_vars(ss_vars,"ss",snp,True)
		r[rsId]['SNP']      = pull_vars(SNP_vars,"SNP",snp)
		r[rsId]['CLINSIG']  = pull_vars(CLINSIG_vars,"CLINSIG",snp)
		r[rsId]['GMAF']     = pull_vars(GMAF_vars,"GMAF",snp)
		r[rsId]['CTG']      = pull_vars(CTG_vars,"CTG",snp,True)
		r[rsId]['LOC']      = pull_vars(LOC_vars,"LOC",snp,True)
		r[rsId]['SEQ']      = pull_vars(SEQ_vars,"SEQ",snp,True)
	return r
		

snp = get_snp(["12009"])
print pp(snp)