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<div class="res_logo"><h1 class="res_name"><a href="/dbvar/" title="dbVar home">dbVar</a></h1><h2 class="res_tagline">Database of genomic structural variation</h2></div>
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<h1 id="use-entrez-and-python-to-search-">Use Entrez and Python to search, retrieve, and  parse dbVar records.</h1>
<h4 id="objectives">Objectives:</h4>
<h5 id="1-search-dbvar-using-entrez-esea">1. Search dbVar using Entrez eSearch</h5>
<h5 id="2-retrieve-results-using-esummar">2. Retrieve results using eSummary</h5>
<h5 id="3-parse-esummary-xml-results-and">3. Parse eSummary XML results and print tab delimited output</h5>
<pre><code>#### General details on eUtils tools and options along with tutorials and examples
#### are available on NCBI bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK25499/
</code></pre>
<h4 id="first-part-of-python-script">first part of Python script:</h4>
<pre><code># You should test that your search return results first on the web
# https://www.ncbi.nlm.nih.gov/dbvar before using them
# in your python script. Available dbVar search terms are on the help page
# (https://www.ncbi.nlm.nih.gov/dbvar/content/help/#entrezsearch).
# For general Entrez help and boolean search see the online book
# (https://www.ncbi.nlm.nih.gov/books/NBK3837/#EntrezHelp.Entrez_Searching_Options)
# This example will make use of these eUtils History Server parameters
# usehistory, WebEnv, and query_key. It is highly recommended you use them in
# your pipeline and script.
# /usehistory=/
# When usehistory is set to 'y', ESearch will post the UIDs resulting from the
# search operation onto the History server so that they can be used directly in
# a subsequent E-utility call. Also, usehistory must be set to 'y' for ESearch
# to interpret query key values included in term or to accept a WebEnv as input.
# /WebEnv=/
# Web environment string returned from a previous ESearch, EPost or ELink call.
# When provided, ESearch will post the results of the search operation to this
# pre-existing WebEnv, thereby appending the results to the existing
# environment. In addition, providing WebEnv allows query keys to be used in
# term so that previous search sets can be combined or limited. As described
# above, if WebEnv is used, usehistory must be set to 'y' (ie.
# esearch.fcgi?db=dbvar&amp;term=asthma&amp;WebEnv=&lt;webenv string&gt;&amp;usehistory=y)
# /query_key=/
# Integer query key returned by a previous ESearch, EPost or ELink call. When
# provided, ESearch will find the intersection of the set specified by query_key
# and the set retrieved by the query in term (i.e. joins the two with AND). For
# query_key to function, WebEnv must be assigned an existing WebEnv string and
# usehistory must be set to 'y'.
# load python modules
# May require one time install of biopython and xml2dict.
from Bio import Entrez
import xmltodict
# initialize some default parameters
Entrez.email = 'myemail@ncbi.nlm.nih.gov' # provide your email address
db = 'dbvar' # set search to dbVar database
paramEutils = { 'usehistory':'Y' } # Use Entrez search history to cache results
# generate query to Entrez eSearch
eSearch = Entrez.esearch(db=db, term='("variant"[Object Type] AND estd214)', **paramEutils)
# get eSearch result as dict object
res = Entrez.read(eSearch)
# take a peek of what's in the result (ie. WebEnv, Count, etc.)
for k in res:
print (k, "=", res[k])
paramEutils['WebEnv'] = res['WebEnv'] #add WebEnv and query_key to eUtils parameters to request esummary using
paramEutils['query_key'] = res['QueryKey'] #search history (cache results) instead of using IdList
paramEutils['rettype'] = 'xml' #get report as xml
paramEutils['retstart'] = 0 #get result starting at 0, top of IdList
paramEutils['retmax'] = 5 #get next five results
# generate request to Entrez eSummary
result = Entrez.esummary(db=db, **paramEutils)
# get xml result
xml = result.read()
# take a peek at xml
print(xml)
</code></pre>
<h4 id="peek-at-xml">peek at xml:</h4>
<pre><code>&lt;?xml version="1.0" encoding="UTF-8" ?&gt;
&lt;!DOCTYPE eSummaryResult PUBLIC "-//NLM//DTD esummary dbvar 20170523//EN" "https://eutils.ncbi.nlm.nih.gov/eutils/dtd/20170523/esummary_dbvar.dtd"&gt;
&lt;eSummaryResult&gt;
&lt;DocumentSummarySet status="OK"&gt;
&lt;DbBuild&gt;Build190602-0830.1&lt;/DbBuild&gt;
&lt;DocumentSummary uid="48583463"&gt;
&lt;OBJ_TYPE&gt;VARIANT&lt;/OBJ_TYPE&gt;
&lt;ST&gt;nstd102&lt;/ST&gt;
&lt;SV&gt;nsv3972446&lt;/SV&gt;
&lt;Study_type&gt;&lt;/Study_type&gt;
&lt;Variant_count&gt;0&lt;/Variant_count&gt;
&lt;dbVarPublicationList&gt;
&lt;/dbVarPublicationList&gt;
&lt;dbVarStudyOrgList&gt;
&lt;/dbVarStudyOrgList&gt;
&lt;Method_type_weight&gt;Non-BAC&lt;/Method_type_weight&gt;
&lt;Tax_ID&gt;9606&lt;/Tax_ID&gt;
&lt;Organism&gt;human&lt;/Organism&gt;
&lt;dbVarSubmittedAssemblyList&gt;
&lt;/dbVarSubmittedAssemblyList&gt;
&lt;dbVarRemappedAssemblyList&gt;
&lt;/dbVarRemappedAssemblyList&gt;
&lt;dbVarPlacementList&gt;
&lt;dbVarPlacement&gt;
&lt;Chr&gt;3&lt;/Chr&gt;
&lt;Chr_accession_version&gt;NC_000003.12&lt;/Chr_accession_version&gt;
&lt;Contig_accession_version&gt;&lt;/Contig_accession_version&gt;
&lt;Chr_start&gt;37017508&lt;/Chr_start&gt;
&lt;Chr_end&gt;37017509&lt;/Chr_end&gt;
&lt;Chr_inner_start&gt;0&lt;/Chr_inner_start&gt;
&lt;Chr_inner_end&gt;0&lt;/Chr_inner_end&gt;
&lt;Chr_outer_start&gt;0&lt;/Chr_outer_start&gt;
&lt;Chr_outer_end&gt;0&lt;/Chr_outer_end&gt;
&lt;Assembly&gt;GRCh38 (hg38)&lt;/Assembly&gt;
&lt;Assembly_accession&gt;GCF_000001405.26&lt;/Assembly_accession&gt;
&lt;Assembly_tax_ID&gt;9606&lt;/Assembly_tax_ID&gt;
&lt;Placement_type&gt;Submitted genomic&lt;/Placement_type&gt;
&lt;/dbVarPlacement&gt;
&lt;dbVarPlacement&gt;
&lt;Chr&gt;3&lt;/Chr&gt;
&lt;Chr_accession_version&gt;NC_000003.11&lt;/Chr_accession_version&gt;
&lt;Contig_accession_version&gt;&lt;/Contig_accession_version&gt;
&lt;Chr_start&gt;37058999&lt;/Chr_start&gt;
&lt;Chr_end&gt;37059000&lt;/Chr_end&gt;
&lt;Chr_inner_start&gt;0&lt;/Chr_inner_start&gt;
&lt;Chr_inner_end&gt;0&lt;/Chr_inner_end&gt;
&lt;Chr_outer_start&gt;0&lt;/Chr_outer_start&gt;
&lt;Chr_outer_end&gt;0&lt;/Chr_outer_end&gt;
&lt;Assembly&gt;GRCh37.p13&lt;/Assembly&gt;
&lt;Assembly_accession&gt;GCF_000001405.25&lt;/Assembly_accession&gt;
&lt;Assembly_tax_ID&gt;9606&lt;/Assembly_tax_ID&gt;
&lt;Placement_type&gt;Remapped&lt;/Placement_type&gt;
&lt;/dbVarPlacement&gt;
&lt;dbVarPlacement&gt;
&lt;Chr&gt;3&lt;/Chr&gt;
&lt;Chr_accession_version&gt;NC_000003.10&lt;/Chr_accession_version&gt;
&lt;Contig_accession_version&gt;&lt;/Contig_accession_version&gt;
&lt;Chr_start&gt;37034003&lt;/Chr_start&gt;
&lt;Chr_end&gt;37034004&lt;/Chr_end&gt;
&lt;Chr_inner_start&gt;0&lt;/Chr_inner_start&gt;
&lt;Chr_inner_end&gt;0&lt;/Chr_inner_end&gt;
&lt;Chr_outer_start&gt;0&lt;/Chr_outer_start&gt;
&lt;Chr_outer_end&gt;0&lt;/Chr_outer_end&gt;
&lt;Assembly&gt;NCBI36 (hg18)&lt;/Assembly&gt;
&lt;Assembly_accession&gt;GCF_000001405.12&lt;/Assembly_accession&gt;
&lt;Assembly_tax_ID&gt;9606&lt;/Assembly_tax_ID&gt;
&lt;Placement_type&gt;Remapped&lt;/Placement_type&gt;
&lt;/dbVarPlacement&gt;
&lt;/dbVarPlacementList&gt;
&lt;dbVarGeneList&gt;
&lt;dbVarGene&gt;
&lt;id&gt;4292&lt;/id&gt;
&lt;name&gt;MLH1&lt;/name&gt;
&lt;/dbVarGene&gt;
&lt;dbVarGene&gt;
&lt;id&gt;100131713&lt;/id&gt;
&lt;name&gt;RPL29P11&lt;/name&gt;
&lt;/dbVarGene&gt;
&lt;/dbVarGeneList&gt;
&lt;dbVarMethodList&gt;
&lt;string&gt;Multiple&lt;/string&gt;
&lt;/dbVarMethodList&gt;
&lt;dbVarClinicalSignificanceList&gt;
&lt;string&gt;Pathogenic&lt;/string&gt;
&lt;/dbVarClinicalSignificanceList&gt;
&lt;dbVarVariantTypeList&gt;
&lt;string&gt;indel&lt;/string&gt;
&lt;/dbVarVariantTypeList&gt;
&lt;Validation_status_weight&gt;0&lt;/Validation_status_weight&gt;
&lt;Variant_call_count&gt;1&lt;/Variant_call_count&gt;
&lt;Validation_status&gt;&lt;/Validation_status&gt;
&lt;/DocumentSummary&gt;
...
</code></pre>
<h4 id="second-part-of-python-script">second part of Python script:</h4>
<pre><code>#convert xml to python dict object for convenient parsing
dsdocs = xmltodict.parse(xml)
#get set of dbVar DocumentSummary (dsdocs) and print report for each (ds)
for ds in dsdocs ['eSummaryResult']['DocumentSummarySet']['DocumentSummary']:
for p in ds['dbVarPlacementList']['dbVarPlacement']:
print (ds['@uid'], ds['ST'], ds['SV'],p['Chr'], p['Chr_start'], p['Chr_end'], p['Chr_inner_start'], p['Chr_inner_end'])
</code></pre>
<h3 id="output">output:</h3>
<pre><code>Count = 55722
RetMax = 20
RetStart = 0
QueryKey = 1
WebEnv = NCID_1_77655541_130.14.22.76_9001_1560183999_561741772_0MetA0_S_MegaStore
IdList = ['48583463', '48583462', '48583461', '48583460', '48583459', '48583458', '48583457', '48583456', '48583455', '48583454', '48583453', '48583452', '48583451', '48583450', '48583449', '48583448', '48583447', '48583446', '48583445', '48583444']
TranslationSet = []
TranslationStack = [DictElement({'Term': '"variant"[Object Type]', 'Field': 'Object Type', 'Count': '5535034', 'Explode': 'N'}, attributes={}), DictElement({'Term': 'nstd102[All Fields]', 'Field': 'All Fields', 'Count': '55723', 'Explode': 'N'}, attributes={}), 'AND', 'GROUP']
QueryTranslation = "variant"[Object Type] AND nstd102[All Fields]
48583463 nstd102 nsv3972446 3 37017508 37017509 0 0
48583463 nstd102 nsv3972446 3 37058999 37059000 0 0
48583463 nstd102 nsv3972446 3 37034003 37034004 0 0
48583462 nstd102 nsv3972445 12 6022792 6022793 0 0
48583462 nstd102 nsv3972445 12 6131958 6131959 0 0
48583462 nstd102 nsv3972445 12 6002219 6002220 0 0
48583461 nstd102 nsv3972444 2 219570775 219570776 0 0
48583461 nstd102 nsv3972444 2 220435497 220435498 0 0
48583461 nstd102 nsv3972444 2 220143741 220143742 0 0
48583460 nstd102 nsv3972443 1 109610052 109610058 0 0
48583460 nstd102 nsv3972443 1 110152674 110152680 0 0
48583460 nstd102 nsv3972443 1 109954197 109954203 0 0
48583459 nstd102 nsv3972442 9 35092494 35092495 0 0
48583459 nstd102 nsv3972442 9 35092491 35092492 0 0
48583459 nstd102 nsv3972442 9 35082491 35082492 0 0
</code></pre>
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