Source of the materials: Biopython cookbook (adapted) Status: Draft
Accessing NCBI’s Entrez databases¶
EInfo: Obtaining information about the Entrez databases
ESearch: Searching the Entrez databases
EPost: Uploading a list of identifiers
EFetch: Downloading full records from Entrez
Entrez (http://www.ncbi.nlm.nih.gov/Entrez) is a data retrieval system
that provides users access to NCBI’s databases such as PubMed, GenBank,
GEO, and many others. You can access Entrez from a web browser to
manually enter queries, or you can use Biopython’s Bio.Entrez module
for programmatic access to Entrez. The latter allows you for example to
search PubMed or download GenBank records from within a Python script.
The Bio.Entrez module makes use of the Entrez Programming Utilities
(also known as EUtils), consisting of eight tools that are described in
detail on NCBI’s page at http://www.ncbi.nlm.nih.gov/entrez/utils/. Each
of these tools corresponds to one Python function in the Bio.Entrez
module, as described in the sections below. This module makes sure that
the correct URL is used for the queries, and that not more than one
request is made every three seconds, as required by NCBI.
The output returned by the Entrez Programming Utilities is typically in XML format. To parse such output, you have several options:
- Use
Bio.Entrez’s parser to parse the XML output into a Python object; - Use the DOM (Document Object Model) parser in Python’s standard library;
- Use the SAX (Simple API for XML) parser in Python’s standard library;
- Read the XML output as raw text, and parse it by string searching and manipulation.
For the DOM and SAX parsers, see the Python documentation. The parser in
Bio.Entrez is discussed below.
NCBI uses DTD (Document Type Definition) files to describe the structure
of the information contained in XML files. Most of the DTD files used by
NCBI are included in the Biopython distribution. The Bio.Entrez
parser makes use of the DTD files when parsing an XML file returned by
NCBI Entrez.
Occasionally, you may find that the DTD file associated with a specific
XML file is missing in the Biopython distribution. In particular, this
may happen when NCBI updates its DTD files. If this happens,
Entrez.read will show a warning message with the name and URL of the
missing DTD file. The parser will proceed to access the missing DTD file
through the internet, allowing the parsing of the XML file to continue.
However, the parser is much faster if the DTD file is available locally.
For this purpose, please download the DTD file from the URL in the
warning message and place it in the directory
...site-packages/Bio/Entrez/DTDs, containing the other DTD files. If
you don’t have write access to this directory, you can also place the
DTD file in ~/.biopython/Bio/Entrez/DTDs, where ~ represents
your home directory. Since this directory is read before the directory
...site-packages/Bio/Entrez/DTDs, you can also put newer versions of
DTD files there if the ones in ...site-packages/Bio/Entrez/DTDs
become outdated. Alternatively, if you installed Biopython from source,
you can add the DTD file to the source code’s Bio/Entrez/DTDs
directory, and reinstall Biopython. This will install the new DTD file
in the correct location together with the other DTD files.
The Entrez Programming Utilities can also generate output in other formats, such as the Fasta or GenBank file formats for sequence databases, or the MedLine format for the literature database, discussed in Section Specialized parsers.
Entrez Guidelines¶
Before using Biopython to access the NCBI’s online resources (via
Bio.Entrez or some of the other modules), please read the NCBI’s
Entrez User
Requirements.
If the NCBI finds you are abusing their systems, they can and will ban
your access!
To paraphrase:
- For any series of more than 100 requests, do this at weekends or outside USA peak times. This is up to you to obey.
- Use the http://eutils.ncbi.nlm.nih.gov address, not the standard NCBI Web address. Biopython uses this web address.
- Make no more than three requests every seconds (relaxed from at most one request every three seconds in early 2009). This is automatically enforced by Biopython.
- Use the optional email parameter so the NCBI can contact you if there is a problem. You can either explicitly set this as a parameter with each call to Entrez (e.g. include email=“A.N.Other@example.com” in the argument list), or you can set a global email address:
In [1]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com"
Bio.Entrez will then use this email address with each call to Entrez. The example.com address is a reserved domain name specifically for documentation (RFC 2606). Please DO NOT use a random email – it’s better not to give an email at all. The email parameter will be mandatory from June 1, 2010. In case of excessive usage, NCBI will attempt to contact a user at the e-mail address provided prior to blocking access to the E-utilities.
If you are using Biopython within some larger software suite, use the tool parameter to specify this. You can either explicitly set the tool name as a parameter with each call to Entrez (e.g. include tool=“MyLocalScript” in the argument list), or you can set a global tool name:
In [2]:
from Bio import Entrez
Entrez.tool = "MyLocalScript"
The tool parameter will default to Biopython.
- For large queries, the NCBI also recommend using their session history feature (the WebEnv session cookie string, see Section History and WebEnv). This is only slightly more complicated.
In conclusion, be sensible with your usage levels. If you plan to download lots of data, consider other options. For example, if you want easy access to all the human genes, consider fetching each chromosome by FTP as a GenBank file, and importing these into your own BioSQL database (see Section [sec:BioSQL]).
EInfo: Obtaining information about the Entrez databases¶
EInfo provides field index term counts, last update, and available links
for each of NCBI’s databases. In addition, you can use EInfo to obtain a
list of all database names accessible through the Entrez utilities. The
variable result now contains a list of databases in XML format:
In [3]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.einfo()
result = handle.read()
print(result)
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD einfo 20130322//EN" "http://eutils.ncbi.nlm.nih.gov/eutils/dtd/20130322/einfo.dtd">
<eInfoResult>
<DbList>
<DbName>pubmed</DbName>
<DbName>protein</DbName>
<DbName>nuccore</DbName>
<DbName>nucleotide</DbName>
<DbName>nucgss</DbName>
<DbName>nucest</DbName>
<DbName>structure</DbName>
<DbName>genome</DbName>
<DbName>annotinfo</DbName>
<DbName>assembly</DbName>
<DbName>bioproject</DbName>
<DbName>biosample</DbName>
<DbName>blastdbinfo</DbName>
<DbName>books</DbName>
<DbName>cdd</DbName>
<DbName>clinvar</DbName>
<DbName>clone</DbName>
<DbName>gap</DbName>
<DbName>gapplus</DbName>
<DbName>grasp</DbName>
<DbName>dbvar</DbName>
<DbName>epigenomics</DbName>
<DbName>gene</DbName>
<DbName>gds</DbName>
<DbName>geoprofiles</DbName>
<DbName>homologene</DbName>
<DbName>medgen</DbName>
<DbName>mesh</DbName>
<DbName>ncbisearch</DbName>
<DbName>nlmcatalog</DbName>
<DbName>omim</DbName>
<DbName>orgtrack</DbName>
<DbName>pmc</DbName>
<DbName>popset</DbName>
<DbName>probe</DbName>
<DbName>proteinclusters</DbName>
<DbName>pcassay</DbName>
<DbName>biosystems</DbName>
<DbName>pccompound</DbName>
<DbName>pcsubstance</DbName>
<DbName>pubmedhealth</DbName>
<DbName>seqannot</DbName>
<DbName>snp</DbName>
<DbName>sra</DbName>
<DbName>taxonomy</DbName>
<DbName>unigene</DbName>
<DbName>gencoll</DbName>
<DbName>gtr</DbName>
</DbList>
</eInfoResult>
Since this is a fairly simple XML file, we could extract the information
it contains simply by string searching. Using Bio.Entrez’s parser
instead, we can directly parse this XML file into a Python object:
In [4]:
from Bio import Entrez
handle = Entrez.einfo()
record = Entrez.read(handle)
Now record is a dictionary with exactly one key:
In [5]:
record.keys()
Out[5]:
dict_keys(['DbList'])
The values stored in this key is the list of database names shown in the XML above:
In [6]:
record["DbList"]
Out[6]:
['pubmed', 'protein', 'nuccore', 'nucleotide', 'nucgss', 'nucest', 'structure', 'genome', 'annotinfo', 'assembly', 'bioproject', 'biosample', 'blastdbinfo', 'books', 'cdd', 'clinvar', 'clone', 'gap', 'gapplus', 'grasp', 'dbvar', 'epigenomics', 'gene', 'gds', 'geoprofiles', 'homologene', 'medgen', 'mesh', 'ncbisearch', 'nlmcatalog', 'omim', 'orgtrack', 'pmc', 'popset', 'probe', 'proteinclusters', 'pcassay', 'biosystems', 'pccompound', 'pcsubstance', 'pubmedhealth', 'seqannot', 'snp', 'sra', 'taxonomy', 'unigene', 'gencoll', 'gtr']
For each of these databases, we can use EInfo again to obtain more information:
In [7]:
from Bio import Entrez
handle = Entrez.einfo(db="pubmed")
record = Entrez.read(handle)
record["DbInfo"]["Description"]
Out[7]:
'PubMed bibliographic record'
In [8]:
record['DbInfo'].keys()
Out[8]:
dict_keys(['LastUpdate', 'Count', 'DbName', 'Description', 'MenuName', 'FieldList', 'DbBuild', 'LinkList'])
In [9]:
handle = Entrez.einfo(db="pubmed")
record = Entrez.read(handle)
record["DbInfo"]["Description"]
Out[9]:
'PubMed bibliographic record'
In [10]:
record["DbInfo"]["Count"]
Out[10]:
'25641704'
In [11]:
record["DbInfo"]["LastUpdate"]
Out[11]:
'2016/01/12 18:56'
Try record["DbInfo"].keys() for other information stored in this
record. One of the most useful is a list of possible search fields for
use with ESearch:
In [12]:
for field in record["DbInfo"]["FieldList"]:
print("%(Name)s, %(FullName)s, %(Description)s" % field)
ALL, All Fields, All terms from all searchable fields
UID, UID, Unique number assigned to publication
FILT, Filter, Limits the records
TITL, Title, Words in title of publication
WORD, Text Word, Free text associated with publication
MESH, MeSH Terms, Medical Subject Headings assigned to publication
MAJR, MeSH Major Topic, MeSH terms of major importance to publication
AUTH, Author, Author(s) of publication
JOUR, Journal, Journal abbreviation of publication
AFFL, Affiliation, Author's institutional affiliation and address
ECNO, EC/RN Number, EC number for enzyme or CAS registry number
SUBS, Supplementary Concept, CAS chemical name or MEDLINE Substance Name
PDAT, Date - Publication, Date of publication
EDAT, Date - Entrez, Date publication first accessible through Entrez
VOL, Volume, Volume number of publication
PAGE, Pagination, Page number(s) of publication
PTYP, Publication Type, Type of publication (e.g., review)
LANG, Language, Language of publication
ISS, Issue, Issue number of publication
SUBH, MeSH Subheading, Additional specificity for MeSH term
SI, Secondary Source ID, Cross-reference from publication to other databases
MHDA, Date - MeSH, Date publication was indexed with MeSH terms
TIAB, Title/Abstract, Free text associated with Abstract/Title
OTRM, Other Term, Other terms associated with publication
INVR, Investigator, Investigator
COLN, Author - Corporate, Corporate Author of publication
CNTY, Place of Publication, Country of publication
PAPX, Pharmacological Action, MeSH pharmacological action pre-explosions
GRNT, Grant Number, NIH Grant Numbers
MDAT, Date - Modification, Date of last modification
CDAT, Date - Completion, Date of completion
PID, Publisher ID, Publisher ID
FAUT, Author - First, First Author of publication
FULL, Author - Full, Full Author Name(s) of publication
FINV, Investigator - Full, Full name of investigator
TT, Transliterated Title, Words in transliterated title of publication
LAUT, Author - Last, Last Author of publication
PPDT, Print Publication Date, Date of print publication
EPDT, Electronic Publication Date, Date of Electronic publication
LID, Location ID, ELocation ID
CRDT, Date - Create, Date publication first accessible through Entrez
BOOK, Book, ID of the book that contains the document
ED, Editor, Section's Editor
ISBN, ISBN, ISBN
PUBN, Publisher, Publisher's name
AUCL, Author Cluster ID, Author Cluster ID
EID, Extended PMID, Extended PMID
DSO, DSO, Additional text from the summary
AUID, Author - Identifier, Author Identifier
PS, Subject - Personal Name, Personal Name as Subject
That’s a long list, but indirectly this tells you that for the PubMed
database, you can do things like Jones[AUTH] to search the author
field, or Sanger[AFFL] to restrict to authors at the Sanger Centre.
This can be very handy - especially if you are not so familiar with a
particular database.
ESearch: Searching the Entrez databases¶
To search any of these databases, we use Bio.Entrez.esearch(). For
example, let’s search in PubMed for publications related to Biopython:
In [13]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.esearch(db="pubmed", term="biopython")
record = Entrez.read(handle)
record["IdList"]
Out[13]:
['24929426', '24497503', '24267035', '24194598', '23842806', '23157543', '22909249', '22399473', '21666252', '21210977', '20015970', '19811691', '19773334', '19304878', '18606172', '21585724', '16403221', '16377612', '14871861', '14630660']
In [14]:
record
Out[14]:
{'TranslationStack': [{'Explode': 'N', 'Term': 'biopython[All Fields]', 'Field': 'All Fields', 'Count': '21'}, 'GROUP'], 'IdList': ['24929426', '24497503', '24267035', '24194598', '23842806', '23157543', '22909249', '22399473', '21666252', '21210977', '20015970', '19811691', '19773334', '19304878', '18606172', '21585724', '16403221', '16377612', '14871861', '14630660'], 'TranslationSet': [], 'QueryTranslation': 'biopython[All Fields]', 'Count': '21', 'RetStart': '0', 'RetMax': '20'}
In this output, you see seven PubMed IDs (including 19304878 which is the PMID for the Biopython application), which can be retrieved by EFetch (see section EFetch: Downloading full records from Entrez).
You can also use ESearch to search GenBank. Here we’ll do a quick search for the matK gene in Cypripedioideae orchids (see Section [sec:entrez-einfo] about EInfo for one way to find out which fields you can search in each Entrez database):
In [15]:
handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae[Orgn] AND matK[Gene]")
record = Entrez.read(handle)
record["Count"]
Out[15]:
'334'
In [16]:
record["IdList"]
Out[16]:
['844174433', '937957673', '694174838', '944541375', '575524123', '575524121', '575524119', '575524117', '575524115', '575524113', '575524111', '575524109', '575524107', '575524105', '575524103', '575524101', '575524099', '575524097', '575524095', '575524093']
Each of the IDs (126789333, 37222967, 37222966, …) is a GenBank identifier. See section EFetch: Downloading full records from Entrez for information on how to actually download these GenBank records.
Note that instead of a species name like Cypripedioideae[Orgn], you
can restrict the search using an NCBI taxon identifier, here this would
be txid158330[Orgn]. This isn’t currently documented on the ESearch
help page - the NCBI explained this in reply to an email query. You can
often deduce the search term formatting by playing with the Entrez web
interface. For example, including complete[prop] in a genome search
restricts to just completed genomes.
As a final example, let’s get a list of computational journal titles:
In [17]:
# nlmcatalog
# handle = Entrez.esearch(db="nlmcatalog", term="computational")
# record = Entrez.read(handle)
# record["Count"]
handle = Entrez.esearch(db="nlmcatalog", term="biopython[Journal]", RetMax='20')
record = Entrez.read(handle)
print("{} computational Journals found".format(record["Count"]))
print("The first 20 are\n{}".format(record['IdList']))
0 computational Journals found
The first 20 are
[]
Again, we could use EFetch to obtain more information for each of these journal IDs.
ESearch has many useful options — see the ESearch help page for more information.
EPost: Uploading a list of identifiers¶
EPost uploads a list of UIs for use in subsequent search strategies; see
the EPost help
page
for more information. It is available from Biopython through the
Bio.Entrez.epost() function.
To give an example of when this is useful, suppose you have a long list of IDs you want to download using EFetch (maybe sequences, maybe citations – anything). When you make a request with EFetch your list of IDs, the database etc, are all turned into a long URL sent to the server. If your list of IDs is long, this URL gets long, and long URLs can break (e.g. some proxies don’t cope well).
Instead, you can break this up into two steps, first uploading the list of IDs using EPost (this uses an “HTML post” internally, rather than an “HTML get”, getting round the long URL problem). With the history support, you can then refer to this long list of IDs, and download the associated data with EFetch.
Let’s look at a simple example to see how EPost works – uploading some PubMed identifiers:
In [18]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
print(Entrez.epost("pubmed", id=",".join(id_list)).read())
<?xml version="1.0"?>
<!DOCTYPE ePostResult PUBLIC "-//NLM//DTD ePostResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/ePost_020511.dtd">
<ePostResult>
<QueryKey>1</QueryKey>
<WebEnv>NCID_1_242547791_130.14.22.215_9001_1452651583_567658845_0MetA0_S_MegaStore_F_1</WebEnv>
</ePostResult>
The returned XML includes two important strings, QueryKey and
WebEnv which together define your history session. You would extract
these values for use with another Entrez call such as EFetch:
In [19]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
search_results = Entrez.read(Entrez.epost("pubmed", id=",".join(id_list)))
webenv = search_results["WebEnv"]
query_key = search_results["QueryKey"]
Section History and WebEnv shows how to use the history feature.
ESummary: Retrieving summaries from primary IDs¶
ESummary retrieves document summaries from a list of primary IDs (see
the ESummary help
page
for more information). In Biopython, ESummary is available as
Bio.Entrez.esummary(). Using the search result above, we can for
example find out more about the journal with ID 30367:
In [20]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.esummary(db="nlmcatalog", term="[journal]", id="101660833")
record = Entrez.read(handle)
info = record[0]['TitleMainList'][0]
print("Journal info\nid: {}\nTitle: {}".format(record[0]["Id"], info["Title"]))
Journal info
id: 101660833
Title: IEEE transactions on computational imaging.
EFetch: Downloading full records from Entrez¶
EFetch is what you use when you want to retrieve a full record from Entrez. This covers several possible databases, as described on the main EFetch Help page.
For most of their databases, the NCBI support several different file
formats. Requesting a specific file format from Entrez using
Bio.Entrez.efetch() requires specifying the rettype and/or
retmode optional arguments. The different combinations are described
for each database type on the pages linked to on NCBI efetch
webpage
(e.g.
literature,
sequences
and
taxonomy).
One common usage is downloading sequences in the FASTA or
GenBank/GenPept plain text formats (which can then be parsed with
Bio.SeqIO, see Sections [sec:SeqIO_GenBank_Online] and EFetch:
Downloading full records from
Entrez). From the
Cypripedioideae example above, we can download GenBank record
186972394 using Bio.Entrez.efetch:
In [21]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
print(handle.read())
LOCUS EU490707 1302 bp DNA linear PLN 15-JAN-2009
DEFINITION Selenipedium aequinoctiale maturase K (matK) gene, partial cds;
chloroplast.
ACCESSION EU490707
VERSION EU490707.1 GI:186972394
KEYWORDS .
SOURCE chloroplast Selenipedium aequinoctiale
ORGANISM Selenipedium aequinoctiale
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; Liliopsida; Asparagales; Orchidaceae;
Cypripedioideae; Selenipedium.
REFERENCE 1 (bases 1 to 1302)
AUTHORS Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A., Endara,L.,
Williams,N.H. and Moore,M.
TITLE Phylogenetic utility of ycf1 in orchids: a plastid gene more
variable than matK
JOURNAL Plant Syst. Evol. 277 (1-2), 75-84 (2009)
REFERENCE 2 (bases 1 to 1302)
AUTHORS Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
Endara,C.L., Williams,N.H. and Moore,M.J.
TITLE Direct Submission
JOURNAL Submitted (14-FEB-2008) Department of Botany, University of
Florida, 220 Bartram Hall, Gainesville, FL 32611-8526, USA
FEATURES Location/Qualifiers
source 1..1302
/organism="Selenipedium aequinoctiale"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/specimen_voucher="FLAS:Blanco 2475"
/db_xref="taxon:256374"
gene <1..>1302
/gene="matK"
CDS <1..>1302
/gene="matK"
/codon_start=1
/transl_table=11
/product="maturase K"
/protein_id="ACC99456.1"
/db_xref="GI:186972395"
/translation="IFYEPVEIFGYDNKSSLVLVKRLITRMYQQNFLISSVNDSNQKG
FWGHKHFFSSHFSSQMVSEGFGVILEIPFSSQLVSSLEEKKIPKYQNLRSIHSIFPFL
EDKFLHLNYVSDLLIPHPIHLEILVQILQCRIKDVPSLHLLRLLFHEYHNLNSLITSK
KFIYAFSKRKKRFLWLLYNSYVYECEYLFQFLRKQSSYLRSTSSGVFLERTHLYVKIE
HLLVVCCNSFQRILCFLKDPFMHYVRYQGKAILASKGTLILMKKWKFHLVNFWQSYFH
FWSQPYRIHIKQLSNYSFSFLGYFSSVLENHLVVRNQMLENSFIINLLTKKFDTIAPV
ISLIGSLSKAQFCTVLGHPISKPIWTDFSDSDILDRFCRICRNLCRYHSGSSKKQVLY
RIKYILRLSCARTLARKHKSTVRTFMRRLGSGLLEEFFMEEE"
ORIGIN
1 attttttacg aacctgtgga aatttttggt tatgacaata aatctagttt agtacttgtg
61 aaacgtttaa ttactcgaat gtatcaacag aattttttga tttcttcggt taatgattct
121 aaccaaaaag gattttgggg gcacaagcat tttttttctt ctcatttttc ttctcaaatg
181 gtatcagaag gttttggagt cattctggaa attccattct cgtcgcaatt agtatcttct
241 cttgaagaaa aaaaaatacc aaaatatcag aatttacgat ctattcattc aatatttccc
301 tttttagaag acaaattttt acatttgaat tatgtgtcag atctactaat accccatccc
361 atccatctgg aaatcttggt tcaaatcctt caatgccgga tcaaggatgt tccttctttg
421 catttattgc gattgctttt ccacgaatat cataatttga atagtctcat tacttcaaag
481 aaattcattt acgccttttc aaaaagaaag aaaagattcc tttggttact atataattct
541 tatgtatatg aatgcgaata tctattccag tttcttcgta aacagtcttc ttatttacga
601 tcaacatctt ctggagtctt tcttgagcga acacatttat atgtaaaaat agaacatctt
661 ctagtagtgt gttgtaattc ttttcagagg atcctatgct ttctcaagga tcctttcatg
721 cattatgttc gatatcaagg aaaagcaatt ctggcttcaa agggaactct tattctgatg
781 aagaaatgga aatttcatct tgtgaatttt tggcaatctt attttcactt ttggtctcaa
841 ccgtatagga ttcatataaa gcaattatcc aactattcct tctcttttct ggggtatttt
901 tcaagtgtac tagaaaatca tttggtagta agaaatcaaa tgctagagaa ttcatttata
961 ataaatcttc tgactaagaa attcgatacc atagccccag ttatttctct tattggatca
1021 ttgtcgaaag ctcaattttg tactgtattg ggtcatccta ttagtaaacc gatctggacc
1081 gatttctcgg attctgatat tcttgatcga ttttgccgga tatgtagaaa tctttgtcgt
1141 tatcacagcg gatcctcaaa aaaacaggtt ttgtatcgta taaaatatat acttcgactt
1201 tcgtgtgcta gaactttggc acggaaacat aaaagtacag tacgcacttt tatgcgaaga
1261 ttaggttcgg gattattaga agaattcttt atggaagaag aa
//
The arguments rettype="gb" and retmode="text" let us download
this record in the GenBank format.
Note that until Easter 2009, the Entrez EFetch API let you use “genbank” as the return type, however the NCBI now insist on using the official return types of “gb” or “gbwithparts” (or “gp” for proteins) as described on online. Also not that until Feb 2012, the Entrez EFetch API would default to returning plain text files, but now defaults to XML.
Alternatively, you could for example use rettype="fasta" to get the
Fasta-format; see the EFetch Sequences Help
page
for other options. Remember – the available formats depend on which
database you are downloading from - see the main EFetch Help
page.
If you fetch the record in one of the formats accepted by Bio.SeqIO
(see Chapter [chapter:Bio.SeqIO]), you could directly parse it into a
SeqRecord:
In [22]:
from Bio import Entrez, SeqIO
handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
record = SeqIO.read(handle, "genbank")
handle.close()
print(record)
ID: EU490707.1
Name: EU490707
Description: Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast.
Number of features: 3
/gi=186972394
/taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta', 'Tracheophyta', 'Spermatophyta', 'Magnoliophyta', 'Liliopsida', 'Asparagales', 'Orchidaceae', 'Cypripedioideae', 'Selenipedium']
/date=15-JAN-2009
/references=[Reference(title='Phylogenetic utility of ycf1 in orchids: a plastid gene more variable than matK', ...), Reference(title='Direct Submission', ...)]
/organism=Selenipedium aequinoctiale
/sequence_version=1
/accessions=['EU490707']
/data_file_division=PLN
/source=chloroplast Selenipedium aequinoctiale
/keywords=['']
Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA', IUPACAmbiguousDNA())
Note that a more typical use would be to save the sequence data to a
local file, and then parse it with Bio.SeqIO. This can save you
having to re-download the same file repeatedly while working on your
script, and places less load on the NCBI’s servers. For example:
In [23]:
import os
from Bio import SeqIO
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
filename = "gi_186972394.gbk"
if not os.path.isfile(filename):
# Downloading...
with Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text") as net_handle:
with open(filename, "w") as out_handle:
out_handle.write(net_handle.read())
print("Saved")
print("Parsing...")
record = SeqIO.read(filename, "genbank")
print(record)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-23-d6d046a39755> in <module>()
6 if not os.path.isfile(filename):
7 # Downloading...
----> 8 with Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text") as net_handle:
9 with open(filename, "w") as out_handle:
10 out_handle.write(net_handle.read())
AttributeError: __exit__
To get the output in XML format, which you can parse using the
Bio.Entrez.read() function, use retmode="xml":
In [24]:
from Bio import Entrez
handle = Entrez.efetch(db="nucleotide", id="186972394", retmode="xml")
record = Entrez.read(handle)
handle.close()
record[0]["GBSeq_definition"]
Out[24]:
'Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast'
In [25]:
record[0]["GBSeq_source"]
Out[25]:
'chloroplast Selenipedium aequinoctiale'
So, that dealt with sequences. For examples of parsing file formats
specific to the other databases (e.g. the MEDLINE format used in
PubMed), see Section Specialized parsers.
If you want to perform a search with Bio.Entrez.esearch(), and then
download the records with Bio.Entrez.efetch(), you should use the
WebEnv history feature – see Section History and
WebEnv.
EGQuery: Global Query - counts for search terms¶
EGQuery provides counts for a search term in each of the Entrez databases (i.e. a global query). This is particularly useful to find out how many items your search terms would find in each database without actually performing lots of separate searches with ESearch (see the example in [subsec:entrez_example_genbank] below).
In this example, we use Bio.Entrez.egquery() to obtain the counts
for “Biopython”:
In [31]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.egquery(term="biopython")
record = Entrez.read(handle)
for row in record["eGQueryResult"]:
print(row["DbName"], row["Count"])
pubmed 21
pmc 560
mesh 0
books 2
pubmedhealth 2
omim 0
ncbisearch 0
nuccore 0
nucgss 0
nucest 0
protein 0
genome 0
structure 0
taxonomy 0
snp 0
dbvar 0
epigenomics 0
gene 0
sra 0
biosystems 0
unigene 0
cdd 0
clone 0
popset 0
geoprofiles 0
gds 16
homologene 0
pccompound 0
pcsubstance 0
pcassay 0
nlmcatalog 0
probe 0
gap 0
proteinclusters 0
bioproject 0
biosample 0
See the EGQuery help page for more information.
ESpell: Obtaining spelling suggestions¶
ESpell retrieves spelling suggestions. In this example, we use
Bio.Entrez.espell() to obtain the correct spelling of Biopython:
In [32]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.espell(term="biopythooon")
record = Entrez.read(handle)
record["Query"]
Out[32]:
'biopythooon'
In [33]:
record["CorrectedQuery"]
Out[33]:
'biopython'
See the ESpell help page for more information. The main use of this is for GUI tools to provide automatic suggestions for search terms.
Parsing huge Entrez XML files¶
The Entrez.read function reads the entire XML file returned by
Entrez into a single Python object, which is kept in memory. To parse
Entrez XML files too large to fit in memory, you can use the function
Entrez.parse. This is a generator function that reads records in the
XML file one by one. This function is only useful if the XML file
reflects a Python list object (in other words, if Entrez.read on a
computer with infinite memory resources would return a Python list).
For example, you can download the entire Entrez Gene database for a
given organism as a file from NCBI’s ftp site. These files can be very
large. As an example, on September 4, 2009, the file
Homo_sapiens.ags.gz, containing the Entrez Gene database for human,
had a size of 116576 kB. This file, which is in the ASN format, can
be converted into an XML file using NCBI’s gene2xml program (see
NCBI’s ftp site for more information):
gene2xml -b T -i Homo_sapiens.ags -o Homo_sapiens.xml
The resulting XML file has a size of 6.1 GB. Attempting Entrez.read
on this file will result in a MemoryError on many computers.
The XML file Homo_sapiens.xml consists of a list of Entrez gene
records, each corresponding to one Entrez gene in human.
Entrez.parse retrieves these gene records one by one. You can then
print out or store the relevant information in each record by iterating
over the records. For example, this script iterates over the Entrez gene
records and prints out the gene numbers and names for all current genes:
TODO: need alternate example, download option or ...
from Bio import Entrez
handle = open("Homo_sapiens.xml")
records = Entrez.parse(handle)
for record in records:
status = record['Entrezgene_track-info']['Gene-track']['Gene-track_status']
if status.attributes['value']=='discontinued':
continue
geneid = record['Entrezgene_track-info']['Gene-track']['Gene-track_geneid']
genename = record['Entrezgene_gene']['Gene-ref']['Gene-ref_locus']
print(geneid, genename)
This will print:
1 A1BG
2 A2M
3 A2MP
8 AA
9 NAT1
10 NAT2
11 AACP
12 SERPINA3
13 AADAC
14 AAMP
15 AANAT
16 AARS
17 AAVS1
...
Handling errors¶
Three things can go wrong when parsing an XML file:
- The file may not be an XML file to begin with;
- The file may end prematurely or otherwise be corrupted;
- The file may be correct XML, but contain items that are not represented in the associated DTD.
The first case occurs if, for example, you try to parse a Fasta file as if it were an XML file:
In [34]:
from Bio import Entrez
from Bio.Entrez.Parser import NotXMLError
handle = open("data/NC_005816.fna", 'rb') # a Fasta file
try:
record = Entrez.read(handle)
except NotXMLError as e:
print('We are expecting to get NotXMLError')
print(e)
We are expecting to get NotXMLError
Failed to parse the XML data (syntax error: line 1, column 0). Please make sure that the input data are in XML format.
Here, the parser didn’t find the <?xml ... tag with which an XML
file is supposed to start, and therefore decides (correctly) that the
file is not an XML file.
When your file is in the XML format but is corrupted (for example, by ending prematurely), the parser will raise a CorruptedXMLError. Here is an example of an XML file that ends prematurely:
<?xml version="1.0"?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
<eInfoResult>
<DbList>
<DbName>pubmed</DbName>
<DbName>protein</DbName>
<DbName>nucleotide</DbName>
<DbName>nuccore</DbName>
<DbName>nucgss</DbName>
<DbName>nucest</DbName>
<DbName>structure</DbName>
<DbName>genome</DbName>
<DbName>books</DbName>
<DbName>cancerchromosomes</DbName>
<DbName>cdd</DbName>
which will generate the following traceback:
---------------------------------------------------------------------------
ExpatError Traceback (most recent call last)
/Users/vincentdavis/anaconda/envs/py35/lib/python3.5/site-packages/Bio/Entrez/Parser.py in read(self, handle)
214 try:
--> 215 self.parser.ParseFile(handle)
216 except expat.ExpatError as e:
ExpatError: syntax error: line 1, column 0
During handling of the above exception, another exception occurred:
NotXMLError Traceback (most recent call last)
<ipython-input-63-ac0523d72453> in <module>()
----> 1 Entrez.read(handle)
/Users/vincentdavis/anaconda/envs/py35/lib/python3.5/site-packages/Bio/Entrez/__init__.py in read(handle, validate)
419 from .Parser import DataHandler
420 handler = DataHandler(validate)
--> 421 record = handler.read(handle)
422 return record
423
/Users/vincentdavis/anaconda/envs/py35/lib/python3.5/site-packages/Bio/Entrez/Parser.py in read(self, handle)
223 # We have not seen the initial <!xml declaration, so probably
224 # the input data is not in XML format.
--> 225 raise NotXMLError(e)
226 try:
227 return self.object
NotXMLError: Failed to parse the XML data (syntax error: line 1, column 0). Please make sure that the input data are in XML format.
Note that the error message tells you at what point in the XML file the error was detected.
The third type of error occurs if the XML file contains tags that do not have a description in the corresponding DTD file. This is an example of such an XML file:
<?xml version="1.0"?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
<eInfoResult>
<DbInfo>
<DbName>pubmed</DbName>
<MenuName>PubMed</MenuName>
<Description>PubMed bibliographic record</Description>
<Count>20161961</Count>
<LastUpdate>2010/09/10 04:52</LastUpdate>
<FieldList>
<Field>
...
</Field>
</FieldList>
<DocsumList>
<Docsum>
<DsName>PubDate</DsName>
<DsType>4</DsType>
<DsTypeName>string</DsTypeName>
</Docsum>
<Docsum>
<DsName>EPubDate</DsName>
...
</DbInfo>
</eInfoResult>
In this file, for some reason the tag <DocsumList> (and several
others) are not listed in the DTD file eInfo_020511.dtd, which is
specified on the second line as the DTD for this XML file. By default,
the parser will stop and raise a ValidationError if it cannot find some
tag in the DTD:
from Bio import Entrez
handle = open("data/einfo3.xml", 'rb')
record = Entrez.read(handle)
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
<ipython-input-65-cfb96ec3d2ca> in <module>()
1 from Bio import Entrez
2 handle = open("data/einfo3.xml", 'rb')
----> 3 record = Entrez.read(handle)
/Users/vincentdavis/anaconda/envs/py35/lib/python3.5/site-packages/Bio/Entrez/__init__.py in read(handle, validate)
419 from .Parser import DataHandler
420 handler = DataHandler(validate)
--> 421 record = handler.read(handle)
422 return record
423
/Users/vincentdavis/anaconda/envs/py35/lib/python3.5/site-packages/Bio/Entrez/Parser.py in read(self, handle)
213 raise IOError("Can't parse a closed handle")
214 try:
--> 215 self.parser.ParseFile(handle)
216 except expat.ExpatError as e:
217 if self.parser.StartElementHandler:
-------src-dir--------/Python-3.5.1/Modules/pyexpat.c in StartElement()
/Users/vincentdavis/anaconda/envs/py35/lib/python3.5/site-packages/Bio/Entrez/Parser.py in startElementHandler(self, name, attrs)
348 # Element not found in DTD
349 if self.validating:
--> 350 raise ValidationError(name)
351 else:
352 # this will not be stored in the record
ValidationError: Failed to find tag 'DocsumList' in the DTD. To skip all tags that are not represented in the DTD, please call Bio.Entrez.read or Bio.Entrez.parse with validate=False.
Optionally, you can instruct the parser to skip such tags instead of
raising a ValidationError. This is done by calling Entrez.read or
Entrez.parse with the argument validate equal to False:
In [35]:
from Bio import Entrez
handle = open("data/einfo3.xml", 'rb')
record = Entrez.read(handle, validate=False)
Of course, the information contained in the XML tags that are not in the
DTD are not present in the record returned by Entrez.read.
Specialized parsers¶
The Bio.Entrez.read() function can parse most (if not all) XML
output returned by Entrez. Entrez typically allows you to retrieve
records in other formats, which may have some advantages compared to the
XML format in terms of readability (or download size).
To request a specific file format from Entrez using
Bio.Entrez.efetch() requires specifying the rettype and/or
retmode optional arguments. The different combinations are described
for each database type on the NCBI efetch
webpage.
One obvious case is you may prefer to download sequences in the FASTA or
GenBank/GenPept plain text formats (which can then be parsed with
Bio.SeqIO, see Sections [sec:SeqIO_GenBank_Online] and EFetch:
Downloading full records from
Entrez). For the
literature databases, Biopython contains a parser for the MEDLINE
format used in PubMed.
Parsing Medline records¶
You can find the Medline parser in Bio.Medline. Suppose we want to
parse the file pubmed_result1.txt, containing one Medline record.
You can find this file in Biopython’s Tests\Medline directory. The
file looks like this:
PMID- 12230038
OWN - NLM
STAT- MEDLINE
DA - 20020916
DCOM- 20030606
LR - 20041117
PUBM- Print
IS - 1467-5463 (Print)
VI - 3
IP - 3
DP - 2002 Sep
TI - The Bio* toolkits--a brief overview.
PG - 296-302
AB - Bioinformatics research is often difficult to do with commercial software. The
Open Source BioPerl, BioPython and Biojava projects provide toolkits with
...
We first open the file and then parse it:
In [36]:
from Bio import Medline
with open("data/pubmed_result1.txt") as handle:
record = Medline.read(handle)
The record now contains the Medline record as a Python dictionary:
In [37]:
record["PMID"]
Out[37]:
'12230038'
In [38]:
record["AB"]
Out[38]:
'Bioinformatics research is often difficult to do with commercial software. The Open Source BioPerl, BioPython and Biojava projects provide toolkits with multiple functionality that make it easier to create customised pipelines or analysis. This review briefly compares the quirks of the underlying languages and the functionality, documentation, utility and relative advantages of the Bio counterparts, particularly from the point of view of the beginning biologist programmer.'
The key names used in a Medline record can be rather obscure; use
In [39]:
help(record)
Help on Record in module Bio.Medline object:
class Record(builtins.dict)
| A dictionary holding information from a Medline record.
|
| All data are stored under the mnemonic appearing in the Medline
| file. These mnemonics have the following interpretations:
|
| ========= ==============================
| Mnemonic Description
| --------- ------------------------------
| AB Abstract
| CI Copyright Information
| AD Affiliation
| IRAD Investigator Affiliation
| AID Article Identifier
| AU Author
| FAU Full Author
| CN Corporate Author
| DCOM Date Completed
| DA Date Created
| LR Date Last Revised
| DEP Date of Electronic Publication
| DP Date of Publication
| EDAT Entrez Date
| GS Gene Symbol
| GN General Note
| GR Grant Number
| IR Investigator Name
| FIR Full Investigator Name
| IS ISSN
| IP Issue
| TA Journal Title Abbreviation
| JT Journal Title
| LA Language
| LID Location Identifier
| MID Manuscript Identifier
| MHDA MeSH Date
| MH MeSH Terms
| JID NLM Unique ID
| RF Number of References
| OAB Other Abstract
| OCI Other Copyright Information
| OID Other ID
| OT Other Term
| OTO Other Term Owner
| OWN Owner
| PG Pagination
| PS Personal Name as Subject
| FPS Full Personal Name as Subject
| PL Place of Publication
| PHST Publication History Status
| PST Publication Status
| PT Publication Type
| PUBM Publishing Model
| PMC PubMed Central Identifier
| PMID PubMed Unique Identifier
| RN Registry Number/EC Number
| NM Substance Name
| SI Secondary Source ID
| SO Source
| SFM Space Flight Mission
| STAT Status
| SB Subset
| TI Title
| TT Transliterated Title
| VI Volume
| CON Comment on
| CIN Comment in
| EIN Erratum in
| EFR Erratum for
| CRI Corrected and Republished in
| CRF Corrected and Republished from
| PRIN Partial retraction in
| PROF Partial retraction of
| RPI Republished in
| RPF Republished from
| RIN Retraction in
| ROF Retraction of
| UIN Update in
| UOF Update of
| SPIN Summary for patients in
| ORI Original report in
| ========= ==============================
|
| Method resolution order:
| Record
| builtins.dict
| builtins.object
|
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Methods inherited from builtins.dict:
|
| __contains__(self, key, /)
| True if D has a key k, else False.
|
| __delitem__(self, key, /)
| Delete self[key].
|
| __eq__(self, value, /)
| Return self==value.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __getitem__(...)
| x.__getitem__(y) <==> x[y]
|
| __gt__(self, value, /)
| Return self>value.
|
| __init__(self, /, *args, **kwargs)
| Initialize self. See help(type(self)) for accurate signature.
|
| __iter__(self, /)
| Implement iter(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __len__(self, /)
| Return len(self).
|
| __lt__(self, value, /)
| Return self<value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| __repr__(self, /)
| Return repr(self).
|
| __setitem__(self, key, value, /)
| Set self[key] to value.
|
| __sizeof__(...)
| D.__sizeof__() -> size of D in memory, in bytes
|
| clear(...)
| D.clear() -> None. Remove all items from D.
|
| copy(...)
| D.copy() -> a shallow copy of D
|
| fromkeys(iterable, value=None, /) from builtins.type
| Returns a new dict with keys from iterable and values equal to value.
|
| get(...)
| D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.
|
| items(...)
| D.items() -> a set-like object providing a view on D's items
|
| keys(...)
| D.keys() -> a set-like object providing a view on D's keys
|
| pop(...)
| D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
| If key is not found, d is returned if given, otherwise KeyError is raised
|
| popitem(...)
| D.popitem() -> (k, v), remove and return some (key, value) pair as a
| 2-tuple; but raise KeyError if D is empty.
|
| setdefault(...)
| D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D
|
| update(...)
| D.update([E, ]**F) -> None. Update D from dict/iterable E and F.
| If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
| If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
| In either case, this is followed by: for k in F: D[k] = F[k]
|
| values(...)
| D.values() -> an object providing a view on D's values
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from builtins.dict:
|
| __hash__ = None
for a brief summary.
To parse a file containing multiple Medline records, you can use the
parse function instead:
In [40]:
from Bio import Medline
with open("data/pubmed_result2.txt") as handle:
for record in Medline.parse(handle):
print(record["TI"])
A high level interface to SCOP and ASTRAL implemented in python.
GenomeDiagram: a python package for the visualization of large-scale genomic data.
Open source clustering software.
PDB file parser and structure class implemented in Python.
Instead of parsing Medline records stored in files, you can also parse
Medline records downloaded by Bio.Entrez.efetch. For example, let’s
look at all Medline records in PubMed related to Biopython:
In [41]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.esearch(db="pubmed", term="biopython")
record = Entrez.read(handle)
record["IdList"]
Out[41]:
['24929426', '24497503', '24267035', '24194598', '23842806', '23157543', '22909249', '22399473', '21666252', '21210977', '20015970', '19811691', '19773334', '19304878', '18606172', '21585724', '16403221', '16377612', '14871861', '14630660']
We now use Bio.Entrez.efetch to download these Medline records:
In [42]:
idlist = record["IdList"]
handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="text")
Here, we specify rettype="medline", retmode="text" to obtain the
Medline records in plain-text Medline format. Now we use Bio.Medline
to parse these records:
In [43]:
from Bio import Medline
records = Medline.parse(handle)
for record in records:
print(record["AU"])
['Waldmann J', 'Gerken J', 'Hankeln W', 'Schweer T', 'Glockner FO']
['Mielke CJ', 'Mandarino LJ', 'Dinu V']
['Gajda MJ']
['Mathelier A', 'Zhao X', 'Zhang AW', 'Parcy F', 'Worsley-Hunt R', 'Arenillas DJ', 'Buchman S', 'Chen CY', 'Chou A', 'Ienasescu H', 'Lim J', 'Shyr C', 'Tan G', 'Zhou M', 'Lenhard B', 'Sandelin A', 'Wasserman WW']
['Morales HF', 'Giovambattista G']
['Baldwin S', 'Revanna R', 'Thomson S', 'Pither-Joyce M', 'Wright K', 'Crowhurst R', 'Fiers M', 'Chen L', 'Macknight R', 'McCallum JA']
['Talevich E', 'Invergo BM', 'Cock PJ', 'Chapman BA']
['Prins P', 'Goto N', 'Yates A', 'Gautier L', 'Willis S', 'Fields C', 'Katayama T']
['Schmitt T', 'Messina DN', 'Schreiber F', 'Sonnhammer EL']
['Antao T']
['Cock PJ', 'Fields CJ', 'Goto N', 'Heuer ML', 'Rice PM']
['Jankun-Kelly TJ', 'Lindeman AD', 'Bridges SM']
['Korhonen J', 'Martinmaki P', 'Pizzi C', 'Rastas P', 'Ukkonen E']
['Cock PJ', 'Antao T', 'Chang JT', 'Chapman BA', 'Cox CJ', 'Dalke A', 'Friedberg I', 'Hamelryck T', 'Kauff F', 'Wilczynski B', 'de Hoon MJ']
['Munteanu CR', 'Gonzalez-Diaz H', 'Magalhaes AL']
['Faircloth BC']
['Casbon JA', 'Crooks GE', 'Saqi MA']
['Pritchard L', 'White JA', 'Birch PR', 'Toth IK']
['de Hoon MJ', 'Imoto S', 'Nolan J', 'Miyano S']
['Hamelryck T', 'Manderick B']
For comparison, here we show an example using the XML format:
In [44]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.esearch(db="pubmed", term="biopython")
record = Entrez.read(handle)
idlist = record["IdList"]
handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="xml")
records = Entrez.read(handle)
for record in records:
print(record["MedlineCitation"]["Article"]["ArticleTitle"])
FastaValidator: an open-source Java library to parse and validate FASTA formatted sequences.
AMASS: a database for investigating protein structures.
HPDB-Haskell library for processing atomic biomolecular structures in Protein Data Bank format.
JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles.
BioSmalltalk: a pure object system and library for bioinformatics.
A toolkit for bulk PCR-based marker design from next-generation sequence data: application for development of a framework linkage map in bulb onion (Allium cepa L.).
Bio.Phylo: a unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython.
Sharing programming resources between Bio* projects through remote procedure call and native call stack strategies.
Letter to the editor: SeqXML and OrthoXML: standards for sequence and orthology information.
interPopula: a Python API to access the HapMap Project dataset.
The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants.
Exploratory visual analysis of conserved domains on multiple sequence alignments.
MOODS: fast search for position weight matrix matches in DNA sequences.
Biopython: freely available Python tools for computational molecular biology and bioinformatics.
Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices.
msatcommander: detection of microsatellite repeat arrays and automated, locus-specific primer design.
A high level interface to SCOP and ASTRAL implemented in python.
GenomeDiagram: a python package for the visualization of large-scale genomic data.
Open source clustering software.
PDB file parser and structure class implemented in Python.
Note that in both of these examples, for simplicity we have naively combined ESearch and EFetch. In this situation, the NCBI would expect you to use their history feature, as illustrated in Section History and WebEnv.
Parsing GEO records¶
GEO (Gene Expression Omnibus) is
a data repository of high-throughput gene expression and hybridization
array data. The Bio.Geo module can be used to parse GEO-formatted
data.
The following code fragment shows how to parse the example GEO file
GSE16.txt into a record and print the record:
In [45]:
from Bio import Geo
handle = open("data/GSE16.txt")
records = Geo.parse(handle)
for record in records:
print(record)
GEO Type: SAMPLE
GEO Id: GSM804
Sample_author: Antoine,M,Snijders
Sample_author: Norma,,Nowak
Sample_author: Richard,,Segraves
Sample_author: Stephanie,,Blackwood
Sample_author: Nils,,Brown
Sample_author: Jeffery,,Conroy
Sample_author: Greg,,Hamilton
Sample_author: Anna,K,Hindle
Sample_author: Bing,,Huey
Sample_author: Karen,,Kimura
Sample_author: Sindy,,Law
Sample_author: Ken,,Myambo
Sample_author: Joel,,Palmer
Sample_author: Bauke,,Ylstra
Sample_author: Jingzhu,P,Yue
Sample_author: Joe,W,Gray
Sample_author: Ajay,N,Jain
Sample_author: Daniel,,Pinkel
Sample_author: Donna,G,Albertson
Sample_description: Coriell Cell Repositories cell line <a h
ref="http://locus.umdnj.edu/nigms/nigms_cgi/display.cgi?GM05296">GM05296</a>.
Sample_description: Fibroblast cell line derived from a 1 mo
nth old female with multiple congenital malformations, dysmorphic features, intr
auterine growth retardation, heart murmur, cleft palate, equinovarus deformity,
microcephaly, coloboma of right iris, clinodactyly, reduced RBC catalase activit
y, and 1 copy of catalase gene.
Sample_description: Chromosome abnormalities are present.
Sample_description: Karyotype is 46,XX,-11,+der(11)inv ins(1
1;10)(11pter> 11p13::10q21>10q24::11p13>11qter)mat
Sample_organism: Homo sapiens
Sample_platform_id: GPL28
Sample_pubmed_id: 11687795
Sample_series_id: GSE16
Sample_status: Public on Feb 12 2002
Sample_submission_date: Jan 17 2002
Sample_submitter_city: San Francisco,CA,94143,USA
Sample_submitter_department: Comprehensive Cancer Center
Sample_submitter_email: albertson@cc.ucsf.edu
Sample_submitter_institute: University of California San Francisco
Sample_submitter_name: Donna,G,Albertson
Sample_submitter_phone: 415 502-8463
Sample_target_source1: Cell line GM05296
Sample_target_source2: normal male reference genomic DNA
Sample_title: CGH_Albertson_GM05296-001218
Sample_type: dual channel genomic
Column Header Definitions
ID_REF: Unique row identifier, genome position o
rder
LINEAR_RATIO: Mean of replicate Cy3/Cy5 ratios
LOG2STDDEV: Standard deviation of VALUE
NO_REPLICATES: Number of replicate spot measurements
VALUE: aka LOG2RATIO, mean of log base 2 of LIN
EAR_RATIO
0: ID_REF VALUE LINEAR_RATIO LOG2STDDEV NO_REPLICATES
1: 1 1.047765 0.011853 3
2: 2 0
3: 3 0.008824 1.006135 0.00143 3
4: 4 -0.000894 0.99938 0.001454 3
5: 5 0.075875 1.054 0.003077 3
6: 6 0.017303 1.012066 0.005876 2
7: 7 -0.006766 0.995321 0.013881 3
8: 8 0.020755 1.014491 0.005506 3
9: 9 -0.094938 0.936313 0.012662 3
10: 10 -0.054527 0.96291 0.01073 3
11: 11 -0.025057 0.982782 0.003855 3
12: 12 0
13: 13 0.108454 1.078072 0.005196 3
14: 14 0.078633 1.056017 0.009165 3
15: 15 0.098571 1.070712 0.007834 3
16: 16 0.044048 1.031003 0.013651 3
17: 17 0.018039 1.012582 0.005471 3
18: 18 -0.088807 0.9403 0.010571 3
19: 19 0.016349 1.011397 0.007113 3
20: 20 0.030977 1.021704 0.016798 3
You can search the “gds” database (GEO datasets) with ESearch:
In [46]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.esearch(db="gds", term="GSE16")
record = Entrez.read(handle)
record["Count"]
Out[46]:
'27'
In [47]:
record["IdList"]
Out[47]:
['200000016', '100000028', '300000818', '300000817', '300000816', '300000815', '300000814', '300000813', '300000812', '300000811', '300000810', '300000809', '300000808', '300000807', '300000806', '300000805', '300000804', '300000803', '300000802', '300000801']
From the Entrez website, UID “200000016” is GDS16 while the other hit “100000028” is for the associated platform, GPL28. Unfortunately, at the time of writing the NCBI don’t seem to support downloading GEO files using Entrez (not as XML, nor in the Simple Omnibus Format in Text (SOFT) format).
However, it is actually pretty straight forward to download the GEO files by FTP or HTTP from http://ftp.ncbi.nih.gov/pub/geo/ instead. In this case you might want http://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz (a compressed file, see the Python module gzip).
Parsing UniGene records¶
UniGene is an NCBI database of the transcriptome, with each UniGene record showing the set of transcripts that are associated with a particular gene in a specific organism. A typical UniGene record looks like this:
ID Hs.2
TITLE N-acetyltransferase 2 (arylamine N-acetyltransferase)
GENE NAT2
CYTOBAND 8p22
GENE_ID 10
LOCUSLINK 10
HOMOL YES
EXPRESS bone| connective tissue| intestine| liver| liver tumor| normal| soft tissue/muscle tissue tumor| adult
RESTR_EXPR adult
CHROMOSOME 8
STS ACC=PMC310725P3 UNISTS=272646
STS ACC=WIAF-2120 UNISTS=44576
STS ACC=G59899 UNISTS=137181
...
STS ACC=GDB:187676 UNISTS=155563
PROTSIM ORG=10090; PROTGI=6754794; PROTID=NP_035004.1; PCT=76.55; ALN=288
PROTSIM ORG=9796; PROTGI=149742490; PROTID=XP_001487907.1; PCT=79.66; ALN=288
PROTSIM ORG=9986; PROTGI=126722851; PROTID=NP_001075655.1; PCT=76.90; ALN=288
...
PROTSIM ORG=9598; PROTGI=114619004; PROTID=XP_519631.2; PCT=98.28; ALN=288
SCOUNT 38
SEQUENCE ACC=BC067218.1; NID=g45501306; PID=g45501307; SEQTYPE=mRNA
SEQUENCE ACC=NM_000015.2; NID=g116295259; PID=g116295260; SEQTYPE=mRNA
SEQUENCE ACC=D90042.1; NID=g219415; PID=g219416; SEQTYPE=mRNA
SEQUENCE ACC=D90040.1; NID=g219411; PID=g219412; SEQTYPE=mRNA
SEQUENCE ACC=BC015878.1; NID=g16198419; PID=g16198420; SEQTYPE=mRNA
SEQUENCE ACC=CR407631.1; NID=g47115198; PID=g47115199; SEQTYPE=mRNA
SEQUENCE ACC=BG569293.1; NID=g13576946; CLONE=IMAGE:4722596; END=5'; LID=6989; SEQTYPE=EST; TRACE=44157214
...
SEQUENCE ACC=AU099534.1; NID=g13550663; CLONE=HSI08034; END=5'; LID=8800; SEQTYPE=EST
//
This particular record shows the set of transcripts (shown in the
SEQUENCE lines) that originate from the human gene NAT2, encoding en
N-acetyltransferase. The PROTSIM lines show proteins with
significant similarity to NAT2, whereas the STS lines show the
corresponding sequence-tagged sites in the genome.
To parse UniGene files, use the Bio.UniGene module:
TODO: Need a working example
In [48]:
# from Bio import UniGene
# input = open("data/myunigenefile.data")
# record = UniGene.read(input)
The record returned by UniGene.read is a Python object with
attributes corresponding to the fields in the UniGene record. For
example,
In [49]:
# record.ID
In [50]:
# record.title
The EXPRESS and RESTR_EXPR lines are stored as Python lists of
strings:
['bone', 'connective tissue', 'intestine', 'liver', 'liver tumor', 'normal', 'soft tissue/muscle tissue tumor', 'adult']
Specialized objects are returned for the STS, PROTSIM, and
SEQUENCE lines, storing the keys shown in each line as attributes:
In [51]:
# record.sts[0].acc
In [52]:
# record.sts[0].unists
and similarly for the PROTSIM and SEQUENCE lines.
To parse a file containing more than one UniGene record, use the
parse function in Bio.UniGene:
TODO: Need a working example
In [53]:
# from Bio import UniGene
# input = open("unigenerecords.data")
# records = UniGene.parse(input)
# for record in records:
# print(record.ID)
Using a proxy¶
Normally you won’t have to worry about using a proxy, but if this is an
issue on your network here is how to deal with it. Internally,
Bio.Entrez uses the standard Python library urllib for accessing
the NCBI servers. This will check an environment variable called
http_proxy to configure any simple proxy automatically.
Unfortunately this module does not support the use of proxies which
require authentication.
You may choose to set the http_proxy environment variable once (how
you do this will depend on your operating system). Alternatively you can
set this within Python at the start of your script, for example:
import os
os.environ["http_proxy"] = "http://proxyhost.example.com:8080"
See the urllib documentation for more details.
Examples¶
PubMed and Medline¶
If you are in the medical field or interested in human issues (and many times even if you are not!), PubMed (http://www.ncbi.nlm.nih.gov/PubMed/) is an excellent source of all kinds of goodies. So like other things, we’d like to be able to grab information from it and use it in Python scripts.
In this example, we will query PubMed for all articles having to do with orchids (see section [sec:orchids] for our motivation). We first check how many of such articles there are:
In [54]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.egquery(term="orchid")
record = Entrez.read(handle)
for row in record["eGQueryResult"]:
if row["DbName"]=="pubmed":
print(row["Count"])
1376
Now we use the Bio.Entrez.efetch function to download the PubMed IDs
of these 463 articles:
In [55]:
handle = Entrez.esearch(db="pubmed", term="orchid", retmax=463)
record = Entrez.read(handle)
idlist = record["IdList"]
print("The first 10 Id's containing all of the PubMed IDs of articles related to orchids:\n {}".format(idlist[:10]))
The first 10 Id's containing all of the PubMed IDs of articles related to orchids:
['26752741', '26743923', '26738548', '26732875', '26732614', '26724929', '26715121', '26713612', '26708054', '26694378']
Now that we’ve got them, we obviously want to get the corresponding
Medline records and extract the information from them. Here, we’ll
download the Medline records in the Medline flat-file format, and use
the Bio.Medline module to parse them:
In [56]:
from Bio import Medline
handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline")
In [57]:
records = Medline.parse(handle)
NOTE - We’ve just done a separate search and fetch here, the NCBI much prefer you to take advantage of their history support in this situation. See Section History and WebEnv.
Keep in mind that records is an iterator, so you can iterate through
the records only once. If you want to save the records, you can convert
them to a list:
In [58]:
records = list(records)
Let’s now iterate over the records to print out some information about each record:
In [59]:
for record in records:
print("title:", record.get("TI", "?"))
print("authors:", record.get("AU", "?"))
print("source:", record.get("SO", "?"))
print("")
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title: Mycorrhizal preference promotes habitat invasion by a native Australian orchid: Microtis media.
authors: ['De Long JR', 'Swarts ND', 'Dixon KW', 'Egerton-Warburton LM']
source: Ann Bot. 2013 Mar;111(3):409-18. doi: 10.1093/aob/mcs294. Epub 2012 Dec 28.
title: [Plant rhabdoviruses with bipartite genomes].
authors: ['Kondo H']
source: Uirusu. 2013;63(2):143-54.
title: Functional characterization of Candida albicans Hos2 histone deacetylase.
authors: ['Karthikeyan G', 'Paul-Satyaseela M', 'Dhatchana Moorthy N', 'Gopalaswamy R', 'Narayanan S']
source: F1000Res. 2013 Nov 11;2:238. doi: 10.12688/f1000research.2-238.v3. eCollection 2013.
title: Eufriesea zhangi sp. n. (Hymenoptera: Apidae: Euglossina), a new orchid bee from Brazil revealed by molecular and morphological characters.
authors: ['Nemesio A', 'Junior JE', 'Santos FR']
source: Zootaxa. 2013 Feb 4;3609:568-82. doi: 10.11646/zootaxa.3609.6.2.
title: Specificity and preference of mycorrhizal associations in two species of the genus Dendrobium (Orchidaceae).
authors: ['Xing X', 'Ma X', 'Deng Z', 'Chen J', 'Wu F', 'Guo S']
source: Mycorrhiza. 2013 May;23(4):317-24. doi: 10.1007/s00572-012-0473-8. Epub 2012 Dec 28.
title: Transcriptomic analysis of floral organs from Phalaenopsis orchid by using oligonucleotide microarray.
authors: ['Hsiao YY', 'Huang TH', 'Fu CH', 'Huang SC', 'Chen YJ', 'Huang YM', 'Chen WH', 'Tsai WC', 'Chen HH']
source: Gene. 2013 Apr 10;518(1):91-100. doi: 10.1016/j.gene.2012.11.069. Epub 2012 Dec 20.
title: mRNA profiling using a minimum of five mRNA markers per body fluid and a novel scoring method for body fluid identification.
authors: ['Roeder AD', 'Haas C']
source: Int J Legal Med. 2013 Jul;127(4):707-21. doi: 10.1007/s00414-012-0794-3. Epub 2012 Dec 20.
title: Monophyly or paraphyly--the taxonomy of Holcoglossum (Aeridinae: Orchidaceae).
authors: ['Xiang X', 'Li D', 'Jin X', 'Hu H', 'Zhou H', 'Jin W', 'Lai Y']
source: PLoS One. 2012;7(12):e52050. doi: 10.1371/journal.pone.0052050. Epub 2012 Dec 14.
title: A comparative study of vitrification and encapsulation-vitrification for cryopreservation of protocorms of Cymbidium eburneum L., a threatened and vulnerable orchid of India.
authors: ['Gogoi K', 'Kumaria S', 'Tandon P']
source: Cryo Letters. 2012 Nov-Dec;33(6):443-52.
title: Understanding the association between injecting and sexual risk behaviors of injecting drug users in Manipur and Nagaland, India.
authors: ['Suohu K', 'Humtsoe C', 'Saggurti N', 'Sabarwal S', 'Mahapatra B', 'Kermode M']
source: Harm Reduct J. 2012 Dec 18;9:40. doi: 10.1186/1477-7517-9-40.
title: A new antibacterial phenanthrenequinone from Dendrobium sinense.
authors: ['Chen XJ', 'Mei WL', 'Zuo WJ', 'Zeng YB', 'Guo ZK', 'Song XQ', 'Dai HF']
source: J Asian Nat Prod Res. 2013;15(1):67-70. doi: 10.1080/10286020.2012.740473. Epub 2012 Dec 11.
title: The Doctrine of Signatures, Materia Medica of Orchids, and the Contributions of Doctor - Orchidologists.
authors: ['Pearn J']
source: Vesalius. 2012 Dec;18(2):99-106.
title: Reference-free comparative genomics of 174 chloroplasts.
authors: ['Kua CS', 'Ruan J', 'Harting J', 'Ye CX', 'Helmus MR', 'Yu J', 'Cannon CH']
source: PLoS One. 2012;7(11):e48995. doi: 10.1371/journal.pone.0048995. Epub 2012 Nov 20.
title: Multiple shoot induction from axillary bud cultures of the medicinal orchid, Dendrobium longicornu.
authors: ['Dohling S', 'Kumaria S', 'Tandon P']
source: AoB Plants. 2012;2012:pls032. doi: 10.1093/aobpla/pls032. Epub 2012 Nov 5.
title: Germination failure is not a critical stage of reproductive isolation between three congeneric orchid species.
authors: ['De Hert K', 'Honnay O', 'Jacquemyn H']
source: Am J Bot. 2012 Nov;99(11):1884-90. doi: 10.3732/ajb.1200381. Epub 2012 Nov 6.
title: Three new cryptic species of Euglossa from Brazil (Hymenoptera, Apidae).
authors: ['Nemesio A', 'Engel MS']
source: Zookeys. 2012;(222):47-68. doi: 10.3897/zookeys.222.3382. Epub 2012 Sep 21.
title: Two new species of Euglossa from South America, with notes on their taxonomic affinities (Hymenoptera, Apidae).
authors: ['Hinojosa-Diaz IA', 'Nemesio A', 'Engel MS']
source: Zookeys. 2012;(221):63-79. doi: 10.3897/zookeys.221.3659. Epub 2012 Sep 13.
title: Genetic variation and structure within 3 endangered Calanthe species (Orchidaceae) from Korea: inference of population-establishment history and implications for conservation.
authors: ['Chung MY', 'Lopez-Pujol J', 'Maki M', 'Moon MO', 'Hyun JO', 'Chung MG']
source: J Hered. 2013 Mar;104(2):248-62. doi: 10.1093/jhered/ess088. Epub 2012 Nov 1.
title: Briacavatolides D-F, new briaranes from the Taiwanese octocoral Briareum excavatum.
authors: ['Wang SK', 'Yeh TT', 'Duh CY']
source: Mar Drugs. 2012 Sep;10(9):2103-10. doi: 10.3390/md10092103. Epub 2012 Sep 24.
title: Observational Research in Childhood Infectious Diseases (ORChID): a dynamic birth cohort study.
authors: ['Lambert SB', 'Ware RS', 'Cook AL', 'Maguire FA', 'Whiley DM', 'Bialasiewicz S', 'Mackay IM', 'Wang D', 'Sloots TP', 'Nissen MD', 'Grimwood K']
source: BMJ Open. 2012 Oct 31;2(6). pii: e002134. doi: 10.1136/bmjopen-2012-002134. Print 2012.
title: Microsatellite primers for the neotropical epiphyte Epidendrum firmum (Orchidaceae).
authors: ['Kartzinel TR', 'Trapnell DW', 'Glenn TC']
source: Am J Bot. 2012 Nov;99(11):e450-2. doi: 10.3732/ajb.1200232. Epub 2012 Oct 31.
title: The production of a key floral volatile is dependent on UV light in a sexually deceptive orchid.
authors: ['Falara V', 'Amarasinghe R', 'Poldy J', 'Pichersky E', 'Barrow RA', 'Peakall R']
source: Ann Bot. 2013 Jan;111(1):21-30. doi: 10.1093/aob/mcs228. Epub 2012 Oct 22.
title: Exotic and indigenous viruses infect wild populations and captive collections of temperate terrestrial orchids (Diuris species) in Australia.
authors: ['Wylie SJ', 'Li H', 'Dixon KW', 'Richards H', 'Jones MG']
source: Virus Res. 2013 Jan;171(1):22-32. doi: 10.1016/j.virusres.2012.10.003. Epub 2012 Oct 23.
title: Orchid fleck virus: an unclassified bipartite, negative-sense RNA plant virus.
authors: ['Peng de W', 'Zheng GH', 'Zheng ZZ', 'Tong QX', 'Ming YL']
source: Arch Virol. 2013 Feb;158(2):313-23. doi: 10.1007/s00705-012-1506-5. Epub 2012 Oct 16.
title: Untangling above- and belowground mycorrhizal fungal networks in tropical orchids.
authors: ['Leake JR', 'Cameron DD']
source: Mol Ecol. 2012 Oct;21(20):4921-4. doi: 10.1111/j.1365-294X.2012.05718.x.
title: Pre-adaptations and the evolution of pollination by sexual deception: Cope's rule of specialization revisited.
authors: ['Vereecken NJ', 'Wilson CA', 'Hotling S', 'Schulz S', 'Banketov SA', 'Mardulyn P']
source: Proc Biol Sci. 2012 Dec 7;279(1748):4786-94. doi: 10.1098/rspb.2012.1804. Epub 2012 Oct 10.
title: The mirror crack'd: both pigment and structure contribute to the glossy blue appearance of the mirror orchid, Ophrys speculum.
authors: ['Vignolini S', 'Davey MP', 'Bateman RM', 'Rudall PJ', 'Moyroud E', 'Tratt J', 'Malmgren S', 'Steiner U', 'Glover BJ']
source: New Phytol. 2012 Dec;196(4):1038-47. doi: 10.1111/j.1469-8137.2012.04356.x. Epub 2012 Oct 9.
The output for this looks like:
title: Sex pheromone mimicry in the early spider orchid (ophrys sphegodes):
patterns of hydrocarbons as the key mechanism for pollination by sexual
deception [In Process Citation]
authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C', 'Hansson BS',
'Ibarra F', 'Francke W']
source: J Comp Physiol [A] 2000 Jun;186(6):567-74
Especially interesting to note is the list of authors, which is returned as a standard Python list. This makes it easy to manipulate and search using standard Python tools. For instance, we could loop through a whole bunch of entries searching for a particular author with code like the following:
In [60]:
search_author = "Waits T"
In [61]:
for record in records:
if not "AU" in record:
continue
if search_author in record["AU"]:
print("Author %s found: %s" % (search_author, record["SO"]))
Hopefully this section gave you an idea of the power and flexibility of the Entrez and Medline interfaces and how they can be used together.
Searching, downloading, and parsing Entrez Nucleotide records¶
Here we’ll show a simple example of performing a remote Entrez query. In section [sec:orchids] of the parsing examples, we talked about using NCBI’s Entrez website to search the NCBI nucleotide databases for info on Cypripedioideae, our friends the lady slipper orchids. Now, we’ll look at how to automate that process using a Python script. In this example, we’ll just show how to connect, get the results, and parse them, with the Entrez module doing all of the work.
First, we use EGQuery to find out the number of results we will get before actually downloading them. EGQuery will tell us how many search results were found in each of the databases, but for this example we are only interested in nucleotides:
In [62]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.egquery(term="Cypripedioideae")
record = Entrez.read(handle)
for row in record["eGQueryResult"]:
if row["DbName"]=="nuccore":
print(row["Count"])
4247
So, we expect to find 814 Entrez Nucleotide records (this is the number I obtained in 2008; it is likely to increase in the future). If you find some ridiculously high number of hits, you may want to reconsider if you really want to download all of them, which is our next step:
In [63]:
from Bio import Entrez
handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae", retmax=814)
record = Entrez.read(handle)
Here, record is a Python dictionary containing the search results
and some auxiliary information. Just for information, let’s look at what
is stored in this dictionary:
In [64]:
print(record.keys())
dict_keys(['TranslationStack', 'IdList', 'TranslationSet', 'QueryTranslation', 'Count', 'RetStart', 'RetMax'])
First, let’s check how many results were found:
In [65]:
print(record["Count"])
4247
which is the number we expected. The 814 results are stored in
record['IdList']:
In [66]:
len(record["IdList"])
Out[66]:
814
Let’s look at the first five results:
In [67]:
record["IdList"][:5]
Out[67]:
['874509867', '874509089', '844174433', '937957673', '694174838']
[sec:entrez-batched-efetch] We can download these records using
efetch. While you could download these records one by one, to reduce
the load on NCBI’s servers, it is better to fetch a bunch of records at
the same time, shown below. However, in this situation you should
ideally be using the history feature described later in
Section History and WebEnv.
In [68]:
idlist = ",".join(record["IdList"][:5])
print(idlist)
874509867,874509089,844174433,937957673,694174838
In [69]:
handle = Entrez.efetch(db="nucleotide", id=idlist, retmode="xml")
records = Entrez.read(handle)
len(records)
Out[69]:
5
Each of these records corresponds to one GenBank record.
In [70]:
print(records[0].keys())
dict_keys(['GBSeq_division', 'GBSeq_moltype', 'GBSeq_definition', 'GBSeq_topology', 'GBSeq_locus', 'GBSeq_strandedness', 'GBSeq_source', 'GBSeq_taxonomy', 'GBSeq_create-date', 'GBSeq_accession-version', 'GBSeq_references', 'GBSeq_sequence', 'GBSeq_other-seqids', 'GBSeq_primary-accession', 'GBSeq_length', 'GBSeq_update-date', 'GBSeq_feature-table', 'GBSeq_organism'])
In [71]:
print(records[0]["GBSeq_primary-accession"])
KP644081
In [72]:
print(records[0]["GBSeq_other-seqids"])
['gnl|uoguelph|SCBI449-14.rbcLa', 'gb|KP644081.1|', 'gi|874509867']
In [73]:
print(records[0]["GBSeq_definition"])
Cypripedium calceolus voucher SNP_13_0359 ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) gene, partial cds; chloroplast
In [74]:
print(records[0]["GBSeq_organism"])
Cypripedium calceolus
You could use this to quickly set up searches – but for heavy usage, see Section History and WebEnv.
Searching, downloading, and parsing GenBank records¶
The GenBank record format is a very popular method of holding information about sequences, sequence features, and other associated sequence information. The format is a good way to get information from the NCBI databases at http://www.ncbi.nlm.nih.gov/.
In this example we’ll show how to query the NCBI databases,to retrieve
the records from the query, and then parse them using Bio.SeqIO -
something touched on in Section [sec:SeqIO_GenBank_Online]. For
simplicity, this example does not take advantage of the WebEnv history
feature – see Section History and
WebEnv for this.
First, we want to make a query and find out the ids of the records to retrieve. Here we’ll do a quick search for one of our favorite organisms, Opuntia (prickly-pear cacti). We can do quick search and get back the GIs (GenBank identifiers) for all of the corresponding records. First we check how many records there are:
In [75]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.egquery(term="Opuntia AND rpl16")
record = Entrez.read(handle)
for row in record["eGQueryResult"]:
if row["DbName"]=="nuccore":
print(row["Count"])
26
Now we download the list of GenBank identifiers:
In [76]:
handle = Entrez.esearch(db="nuccore", term="Opuntia AND rpl16")
record = Entrez.read(handle)
gi_list = record["IdList"]
gi_list
Out[76]:
['377581039', '330887241', '330887240', '330887239', '330887238', '330887237', '330887236', '330887235', '330887233', '330887232', '330887231', '330887228', '330887227', '330887226', '330887225', '330887224', '330887223', '57240072', '57240071', '6273287']
Now we use these GIs to download the GenBank records - note that with older versions of Biopython you had to supply a comma separated list of GI numbers to Entrez, as of Biopython 1.59 you can pass a list and this is converted for you:
In [77]:
gi_str = ",".join(gi_list)
handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
If you want to look at the raw GenBank files, you can read from this handle and print out the result:
In [78]:
text = handle.read()
print(text)
LOCUS HQ621368 399 bp DNA linear PLN 26-FEB-2012
DEFINITION Opuntia decumbens voucher Martinez & Eggli 146a (ZSS) ribosomal
protein L16 (rpl16) gene, partial cds; chloroplast.
ACCESSION HQ621368
VERSION HQ621368.1 GI:377581039
KEYWORDS .
SOURCE chloroplast Opuntia decumbens
ORGANISM Opuntia decumbens
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 399)
AUTHORS Arakaki,M., Christin,P.A., Nyffeler,R., Lendel,A., Eggli,U.,
Ogburn,R.M., Spriggs,E., Moore,M.J. and Edwards,E.J.
TITLE Contemporaneous and recent radiations of the world's major
succulent plant lineages
JOURNAL Proc. Natl. Acad. Sci. U.S.A. 108 (20), 8379-8384 (2011)
PUBMED 21536881
REFERENCE 2 (bases 1 to 399)
AUTHORS Arakaki,M., Christin,P.-A., Nyffeler,R., Eggli,U., Ogburn,R.M.,
Spriggs,E., Moore,M.J. and Edwards,E.J.
TITLE Direct Submission
JOURNAL Submitted (15-NOV-2010) Department of Ecology and Evolutionary
Biology, Brown University, 80 Waterman St., Providence, RI 02912,
USA
COMMENT ##Assembly-Data-START##
Assembly Method :: MIRA V3rc4; Geneious v. 4.8
Sequencing Technology :: 454
##Assembly-Data-END##
FEATURES Location/Qualifiers
source 1..399
/organism="Opuntia decumbens"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/specimen_voucher="Martinez & Eggli 146a (ZSS)"
/db_xref="taxon:867482"
/tissue_type="stem"
/note="authority: Opuntia decumbens Salm-Dyck"
gene <1..>399
/gene="rpl16"
CDS <1..>399
/gene="rpl16"
/codon_start=1
/transl_table=11
/product="ribosomal protein L16"
/protein_id="AFB70658.1"
/db_xref="GI:377581040"
/translation="NPKRTRFCKQHRGRMKGISYRGNRICFGRYALQALEPAWITSRQ
IEAGRRAMTRNARRGGKIWVRIFPDKPVTVKSAESRMGSGKGSHLYWVVVVKPGRILY
EISGVSENIARRAISIAASKMPVRTQFIISG"
ORIGIN
1 aaccccaaaa gaaccagatt ctgtaaacaa catagaggaa gaatgaaggg aatatcttat
61 cgggggaatc gtatttgttt cggaagatat gctcttcagg cacttgagcc tgcttggatc
121 acgtctagac aaatagaagc aggtcggcga gcaatgacgc gaaatgcacg ccgcggtgga
181 aaaatatggg tacgtatatt tccagacaaa ccagttacag taaaatctgc ggaaagccgt
241 atgggttcgg ggaaaggatc ccacctatat tgggtagttg ttgtcaaacc cggtcgaata
301 ctttatgaaa taagcggagt atcagaaaat atagcccgaa gggctatctc gatagcggca
361 tctaaaatgc ctgtacgaac tcaattcatt atttcagga
//
LOCUS HM041482 1197 bp DNA linear PLN 03-MAY-2011
DEFINITION Cylindropuntia tunicata ribosomal protein L16-like (rpl16) gene,
partial sequence; chloroplast.
ACCESSION HM041482
VERSION HM041482.1 GI:330887241
KEYWORDS .
SOURCE chloroplast Cylindropuntia tunicata
ORGANISM Cylindropuntia tunicata
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Cylindropuntia.
REFERENCE 1 (bases 1 to 1197)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1197)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1197
/organism="Cylindropuntia tunicata"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:766221"
gene <1..>1197
/gene="rpl16"
misc_feature <1094..>1197
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gtgatatacg aaacagtaag agcccatagt atgaagtatg aactaataac tatagaacta
61 ataaccaact catcgcatca cattatctgg atccaaagaa gcagtcaaga taggatattt
121 tggtcctatc attgcagcaa ctgaattttt tttttcataa acaagaaatc gaatgagttg
181 tcaagcaaaa gaaaaaaaaa aaaagaaaaa tatacnttaa aggaggggga tgcggataaa
241 tggaaaggcg aaagaaagaa aaaaatgaat ctaaatgata tacgattcca ctatgtaagg
301 tctttgaatc atatcataaa agacaatgta ataaagcatg aatacagatt cacacataat
361 tatctgatat gaatctattc atagaaaaaa gaaaaaagta agagcctccg gccaataaag
421 actaagaggg gttggctcaa aaacaaagtt cattaagagc tcccattgta gaattcagac
481 ctaatcatta atcaagaagc gatgggaacg atgtaatcca tgaatacaga agattcaatt
541 gaaaaaagaa tcctaatgat tcattgggga ggatggcgga acgaaccaga gaccaattca
601 tctattctga aaagtgataa actaatccta taaaactaaa atagatattg aaagagtaaa
661 tattcgcccg cgaaaattcc ttttttatta aattgctcat attttatttt agcaatgcaa
721 tctaataaaa tatatctata caaaaaaaca tagacaaact atatatataa tatttcaaat
781 tcccttatat atccaaatat aaaaatatct aataaattag atgaatatca aagaatctat
841 tgatttagtg tattattaaa tgtatatctt aattcaatat tattattcta ttcattttta
901 ttcattttca aatttataat atattaatct atatattaat ttagaattct attctaattc
961 gaattcaatt tttaaatatt cattcatatt caattaaaat tgaaattttt tcattcgcga
1021 ggagccggat gagaagaaac tctcatgtcc ggttctgtag tagagatgga attaagaaaa
1081 aaccatcaac tataacccca aaagaaccag attctgtaaa caacatagag gaagaatgaa
1141 gggaatatct tatcggggga atcgtatttg tttcggaaaa tatgctctca ggcacga
//
LOCUS HM041481 1200 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia palmadora ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041481
VERSION HM041481.1 GI:330887240
KEYWORDS .
SOURCE chloroplast Opuntia palmadora
ORGANISM Opuntia palmadora
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1200)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1200)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1200
/organism="Opuntia palmadora"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001118"
gene <1..>1200
/gene="rpl16"
misc_feature <1098..>1200
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 tgatatacga aaagtaagag cccatagtat gaagtatgaa ctaataacta tagaactaat
61 aaccaactca tcgcatcaca ttatctggat ccaaagaagc agtcaagata ggatattttg
121 gtcctatcat tgcagcaact gaattttttt ttcataaaca agaaatcaaa tgagttgtca
181 agcaaaagaa aaaaaaaaga aaaatatacn ttaaaggagg gggatgcgga taaatggaaa
241 ggcgaaagaa agaaaaaaat gaatctaaat gatatacgat tccactatgt aaggtctttg
301 aatcatatca taaaagacaa tgtaataaag catgaataca gattcacaca taattatctg
361 atatgaatct attcatagaa aaaagaaaaa agtaagagcc tccgggccaa taaagactaa
421 gagggttggg ctcaagaaca aagttcatta agagctccat tgtagaattc agacctaatc
481 attaatcaag aagcgatggg aacgatgtaa tccatgaata cagaagattc aattgaaaaa
541 gaatcctaat gattcattgg gaaggatggc ggaacgaacc agagaccaat tcatctattc
601 tgaaaagtga taaactaatc ctataaaact aaaatagata ttgaaagagt aaatattcgc
661 ccgcgaaaat tcctttttta ttaaattgct cacattttat tttagcaatg caatctaata
721 aaatatatct atacaaaaaa atatagacaa actatatata taatatattt caaatttcct
781 tatatatcct aatataaaaa tatctaataa attagatgaa tatcaaagaa tctattgatt
841 tagtgtatta ttaaatgtat atcttaattc aatattatta ttctattcat ttttattatt
901 catttttatt cattttcaaa tttagaatat attaatctat atattaattt agaattctat
961 tctaattcga attcaatttt taaatattca tattcaatta aaattgaaat tttttcattc
1021 gcgaggagcc ggatgagaag aaactctcac gtccggttct gtagtagagg tggaattaag
1081 aaaaaaccat caactataac cccaaaagaa ccagattctg taaacaacat agaggaagaa
1141 tgaagggaat atcttatcgg gggaatcgta tttgtttcgg aagatatgct ctcagcacga
//
LOCUS HM041480 1153 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia microdasys ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041480
VERSION HM041480.1 GI:330887239
KEYWORDS .
SOURCE chloroplast Opuntia microdasys
ORGANISM Opuntia microdasys
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1153)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1153)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1153
/organism="Opuntia microdasys"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:169217"
gene <1..>1153
/gene="rpl16"
misc_feature <1079..>1153
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gcccatagta tgaagtatga actaataact atagaactaa taaccaactc atcgcatcac
61 attatctgga tccaaagaag cagtcaagat aggatatttt ggtcctatca ttgcagcaac
121 tgaatttttt ttttcataaa caagaaatca aatgagttgt caagcaaaag aaaaaaaaaa
181 aaaaaaatat actttaaggg ggggggatgg ggataaaggg aaaggggaaa aaaaaaaaaa
241 aatgaatcta aatgatatac aattccacta tgaaaggtct ttgaatcata tcaaaaaaaa
301 caatgtaata aagcaggaat acagattccc acataattat ctgatatgaa tcttttcata
361 aaaaaaaaaa aaaagtaaga gcctccggcc aataaagact aagagggttg gctcaagaac
421 aaagttcatt aagggctcca ttgtagaatt cagacctaat cattaatcaa gaggcgatgg
481 gaacgatgta atccatgaat acagaagatt caattgaaaa agaatcctaa tgattcattg
541 ggaaggatgg cggaacgaac cagagaccaa ttcatctatt ctgaaaagtg aaaaactaat
601 cctataaaac taaaatagat attgaaagag taaatattcg cccgcgaaaa ttcctttttt
661 attaaattgc tcacatttta ttttagcaat gcaatctaat aaaatatatc tatacaaaaa
721 aatatagaca aactatatat ataatatatt tcaaatttcc ttatatatcc taatataaaa
781 atatctaata aattagatga atatcaaaga atctattgat ttagtgtatt attaaatgta
841 tatcttaatt caatattatt attctattca tttttattat tcatttttat tcattttcaa
901 atttagaata tattaatcta tatattaatt tataattcta ttctaattcg aattcaattt
961 ttaaatattc atattcaatt aaaattgaaa ttttttcatt cgcgaggagc cggatgagaa
1021 gaaactctca cgtccggttc tgtagtagag gtggaattaa gaaaaaacca tcaactataa
1081 ccccaaaaga accagattct gtaaacaaca tagaggaaga atgaagggaa tatcttatcg
1141 ggggatatcg tat
//
LOCUS HM041479 1197 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia megasperma ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041479
VERSION HM041479.1 GI:330887238
KEYWORDS .
SOURCE chloroplast Opuntia megasperma
ORGANISM Opuntia megasperma
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1197)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1197)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1197
/organism="Opuntia megasperma"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001117"
gene <1..>1197
/gene="rpl16"
misc_feature <1098..>1197
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gatatacgaa aagtaagagc ccatagtatg aagtatgaac taataactat agaactaata
61 accaactcat cgcatcacat tatccggatc caaagaagca gtcaagatag gatattttgg
121 tcctatcatt gcagcaactg aatttttttt tcataaacaa gaaatcaaat gagttgtcaa
181 gcaaaagaaa aaaaaaaaag aaaaatatac tttaaaggag ggggatgcgg ataaatggaa
241 aggcgaaaga aagaaaaaaa tgaatctaaa tgatatacga ttccnctatg taaggtcttt
301 gaatcatatc ataaaagaca atgtaataaa gcatgaatac agattcacac ataattatct
361 gatatgaatc tattcataga aaaaagaaaa aagtaagagc ctccgggcca ataaagacta
421 agagggttgg ctcaagaaca aagttcatta agagctccat tgtagaattc agacctaatc
481 attaatcaag aagcgatggg aacgatgtaa tccatgaata cagaagattc aattgaaaaa
541 gaatcctaat gattcattgg gaaggatggc ggaacgaacc agagaccaat tcatctattc
601 tgaaaagtga taaactaatc ctataaaact aaaatagata ttgaaagagt aaatattcgc
661 ccgcgaaaat tcctttttta ttaaattgct cacattttat tttagcaatg caatctaata
721 aaatatatct atacaaaaaa atatagacaa actatatata taatatattt caaatttcct
781 tatatatcct aatataaaaa tatctaataa attagatgaa tatcaaagaa tctattgatt
841 tagtgtatta ttaaatgtat atcttaattc aatattttta ttctattcat ttttattatt
901 catttttatt cattttcaaa tttagaatat attaatctat atattaattt agaattctat
961 tctaattcga attcaatttt taaatattca tattcaatta aaattgaaat tttttcattc
1021 gcgaggagcc ggatgagaag aaactctcac gtccggttct gtagtagagg tggaattaag
1081 aaaaaaccat caactataac cccaaaagaa ccagattctg taaacaacat agaggaagaa
1141 tgaagggaat atcttatcgg gggaatcgta tttgtttcgg aagatatgct ctcagca
//
LOCUS HM041478 1187 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia macbridei ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041478
VERSION HM041478.1 GI:330887237
KEYWORDS .
SOURCE chloroplast Opuntia macbridei
ORGANISM Opuntia macbridei
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1187)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1187)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1187
/organism="Opuntia macbridei"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001116"
gene <1..>1187
/gene="rpl16"
misc_feature <1090..>1187
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 aaaagtaaga gcccatagta tgaagtatga actaataact atagaactaa taaccaactc
61 atcgcatcac attatctgga tccaaagaag cagtcaagat aggatatttt ggtcctatca
121 ttgcagcaac tgaatttttt tttcataaac aagaaatcaa atgagttgtc aagcaaaaga
181 aaaaaaaaaa agaaaaatat acattaaagg agggggatgc ggataaatgg aaaggcgaaa
241 gaaagaaaaa aatgaatcta aatgatatac gattccacta tgtaaggtct ttgaatcata
301 tcataaaaga caatgtaata aagcatgaat acagattcac acataattat ctgatatgaa
361 tctattcata gaaaaaagaa aaaagtaaga gcctccggcc aataaagact aagagggttg
421 gctcaagaac aaagttcatt aagggctcca tttgtagaat tcagacctaa tcattaatca
481 agaagcgatg ggaacgatgt aattccatga atacagaaga ttcaattgaa aaagatccta
541 atgattcatt gggaaggatg gcggacgaac cagagaccaa ttcatctatt ctgaaaagtg
601 ataaactaat cctataaaac taaaatagat attgaaagag taaatattcg cccgcgaaaa
661 ttcctttttt attaaattgc tcacatttta ttttagcaat gcaatctaat aaaatatatc
721 tatacaaaaa aaatatagac aaactatata tataatatat ttcaaatttc cttatatatc
781 ctaatataaa aatatctaat aatttagatg aatatcaaag aatctattga tttagtgtat
841 tattaaatgt atatcttaat tcaatattat tattctattc atttttatta ttcattttta
901 ttcattttca aatttagaat atattaatct atatattaat ttagaattct attctaattc
961 gaattcaatt tttaaatatt catattcaat taaaattgaa attttttcat tcgcgaggag
1021 ccggatgaga agaaactctc acgtccggtt ctgtagtaga ggtggaatta agaaaaaacc
1081 atcaactata accccaaaag aaccagattc tgtaaacaac atagaggaag aatgaaggga
1141 atatcttatc gggggaatcg tatttgtttc ggaagatatg ctctcag
//
LOCUS HM041477 1197 bp DNA linear PLN 03-MAY-2011
DEFINITION Cylindropuntia leptocaulis ribosomal protein L16-like (rpl16) gene,
partial sequence; chloroplast.
ACCESSION HM041477
VERSION HM041477.1 GI:330887236
KEYWORDS .
SOURCE chloroplast Cylindropuntia leptocaulis
ORGANISM Cylindropuntia leptocaulis
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Cylindropuntia.
REFERENCE 1 (bases 1 to 1197)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1197)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1197
/organism="Cylindropuntia leptocaulis"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:866983"
gene <1..>1197
/gene="rpl16"
misc_feature <1096..>1197
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 ttgtgngnct cctgaagagt aggagcccct agtatgaagt atgaactaat aactatagaa
61 ctaataacca actcatcgca tcacattatc cggatccaaa aaagcagtca agataggata
121 ttttggtcct atcattgcag caactgaatt ttttttttca taaacaagaa atcgaatgag
181 ttgtcaagca aaagaaaaaa aaagaaaaat atactttaaa ggagggggat gcggataaat
241 ggaaaggcga aagaaagaaa aaaatgaatc taaatgatat aggattcccc tatgtaaggt
301 ctttgaatca tatcataaaa gacaatgtaa taaagcatga atacagattc ccacataatt
361 atctgatatg aatctattcc tagaaaaaag aaaaaagtaa gagcctccgg ccaataaaga
421 ctaagagggt tggctcaaga acaaagttca ttaaaagctc ccttgtagaa ttcagaccta
481 atcnttaatc aagaagcgat gggaacgatg taatccctga atacagaaga ttcaattgaa
541 aaagaatcct aatgattcat tgggaaggat ggcggaacga accagagacc aattcatcta
601 ttctgaaaag tgataaacta atcctataaa actaaaatag atattgaaag agtaaatatt
661 cgcccgcgaa atttcctttt ttattaaatt gctcatattt ttttttagca atgcaatcta
721 ataaaatata tctctacaaa aaaacataga caaactatat atatatatat atataatatt
781 tcaaattccc ttatatatcc aaatataaaa atatctaata aattagatga atatcaaaga
841 atctattgat ttagtgtatt attaaatgta tatcttaatt caatattatt attctattca
901 tttttattca ttttcaaatt tataatatat taatctatat attaatatag aattctattc
961 taattcgaat tcaattttta aatattcata ttcaattaaa attgaaattt tttcattcgc
1021 gaggagccgg atgagaagaa actctcatgt ccggttctgt agtagagatg gaattaagaa
1081 aaaaccatca actataaccc caaaagaacc ggattctgta aacaacatag aggaagaatg
1141 aagggaatat cttgtcgggg gaatcgatnn gtncggaant natgntcgcn gcgcgcc
//
LOCUS HM041476 1205 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia lasiacantha ribosomal protein L16-like (rpl16) gene,
partial sequence; chloroplast.
ACCESSION HM041476
VERSION HM041476.1 GI:330887235
KEYWORDS .
SOURCE chloroplast Opuntia lasiacantha
ORGANISM Opuntia lasiacantha
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1205)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1205)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1205
/organism="Opuntia lasiacantha"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:547104"
gene <1..>1205
/gene="rpl16"
misc_feature <1103..>1205
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gggcccnnna ngangaaaag tagagcccat agtatgaagt atgaactaat aactatagaa
61 ctaataacca actcatcgca tcacattatc tggatccaaa gaagcagtca agataggata
121 ttttggtcct atcattgcag caactgaatt ttttttttca taaacaagaa atcaaatgag
181 ttgtcaagca aaagaaaaaa aaaaagaaaa atatccttta aaggaggggg atgcggataa
241 atggaaaggc gaaagaaaga aaaaaatgaa tctaaatgat atacgattcc cctatgtaag
301 gtctttgaat catatcataa aagacaatgt aataaagcat gaatacagat tcccccataa
361 ttatctgata tgaatctatt cctagaaaaa agaaaaaagt aagagcctcc ggccaataaa
421 gactaagagg gttggctcaa gaacaaagtt cattaagggc tccattgtag aattcagacc
481 taatcattaa tcaagaggcg atgggaacga tgtaatccat gaatacagaa gattcaattg
541 aaaaagaatc ctaatgattc attgggaagg atggcggaac gaaccagaga ccaattcatc
601 tattctgaaa agtgataaac taatcctata aaactaaaat agatattgaa agagtaaata
661 ttcgcccgcg aaaattcctt ttttattaaa ttgctcacat tttattttag caatgcaatc
721 taataaaatc tatctataca aaaaaatata gacaaactat atatataata tatttcaaat
781 ttccttatat atcctaatat aaaaatatct aataaattag atgaatatca aagaatctat
841 tgatttagtg tattattaaa tgtatatctt aattcaatat tattattcta ttcattttta
901 ttattcattt ttattcattt tcaaatttag aatatattaa tctatatatt aatttataat
961 tctattctaa ttcgaattca atttttaaat attcatattc aattaaaatt gaaatttttt
1021 cattcgcgag gagccggatg agaagaaact ctcacgtccg gttctgtagt agaggtggaa
1081 ttaagaaaaa accatcaact ataaccccaa aagaaccaga ttctgtaaac aacatagagg
1141 aagaatgaag ggaatatctt atcgagggaa tcgtatttgt ttcggaagat agtnctngcn
1201 nggtg
//
LOCUS HM041474 1163 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia helleri ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041474
VERSION HM041474.1 GI:330887233
KEYWORDS .
SOURCE chloroplast Opuntia helleri
ORGANISM Opuntia helleri
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1163)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1163)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1163
/organism="Opuntia helleri"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001115"
gene <1..>1163
/gene="rpl16"
misc_feature <1081..>1163
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gagcccatag tatgaagtat gaactaataa ctatagaact aataaccaac tcatcgcatc
61 acattatccg gatccaaaga agcagtcaag ataggatatt ttggtcctat cattgcagca
121 actgaatttt tttttcataa acaagaaatc aaatgagttg tcaagcaaaa gaaaaaaaaa
181 aaagaaaaat atacattaaa ggagggggat gcggataaat ggaaaggcga aagaaagaaa
241 aaaatgaatc taaatgatat acgattccnc tatgtaaggt ctttgaatca tatcataaaa
301 gacaatgtaa taaagcatga atacagattc acacataatt atctgatatg aatctattca
361 tagaaaaaag aaaaaagtaa gagcctccgg ccaataaaga ctaagagggt tggctcaaga
421 acaaagttca ttaagggctc cattgtagaa ttcagaccta atcattaatc aagaagcgat
481 gggaacgatg taatccatga atacagaaga ttcaattgaa aaagaatcct aatgattcat
541 tgggaaggat ggcggaacga accagagacc aattcatcta ttctgaaaag tgataaacta
601 atcctataaa actaaaatag atattgaaag agtaaatatt cgcccgcgaa aattcctttt
661 ttattaaatt gctcacattt tattttagca atgcaatcta ataaaatata tctatacaaa
721 aaaatataga caaactatat atataatata tttaaaattt ccttatatat cctaatataa
781 aaatatctaa taaattagat gaatatcaaa gaatctattg atttagtgta ttattaaatg
841 tatatcttaa ttcaatattt ttattctatt catttttatt attcattttt attcattttc
901 aaatttagaa tatattaatc tatatattaa tttagaattc tattctaatt cgaattcaat
961 ttttaaatat tcatattcaa ttaaaattga aattttttca ttcgcgagga gccggatgag
1021 aagaaactct cacgtccggt tctgtagtag aggtggaatt aagaaaaaac catcaactat
1081 aaccccaaaa gaaccagatt ctgtaaacaa catagaggaa gaatgaaggg aatatcttat
1141 cgggggaatc gtatttgttt cgg
//
LOCUS HM041473 1203 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia excelsa ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041473
VERSION HM041473.1 GI:330887232
KEYWORDS .
SOURCE chloroplast Opuntia excelsa
ORGANISM Opuntia excelsa
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1203)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1203)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1203
/organism="Opuntia excelsa"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:867487"
gene <1..>1203
/gene="rpl16"
misc_feature <1103..>1203
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 ccgnncnttg nnanacagaa nagtagagcc cnttntntga agtatgaact aatcactatt
61 gaactaatcc ccnactcatc gcatcacatt atctggatcc aaagaagcag tcaagatagg
121 atattttggt cctatcattg cagcaactga attttttttt tcctaaacaa gaaatcaaat
181 gagttgtcaa gcaaaagaaa aaaaagaaaa atatacatta aaggaggggg atgcggataa
241 atggaaaggc gaaagaaaga aaaaaatgaa tctaaatgat atacgattcc cctatgtaag
301 gtctttgaat catatcataa aagacaatgt aataaagcat gaatacagat tcccacataa
361 ttatctgata tgaatctatt catagaaaaa agaaaaaagt aagagcctcc ggccaataaa
421 gactaaaagg gttggctcaa gaacaaagtt cattaagggc tccattgtaa aattcagacc
481 taatcattaa tcaagaggcg atgggaacga tgtaatccat gaatacagaa gattcaattg
541 aaaaagaatc ctaatgattc attgggaagg atggcggaac gaaccagaga ccaattcatc
601 tattctgaaa agtgataaac taatcctata aaactaaaat agatattgaa agagtaaata
661 ttcgcccgcg aaaattcctt ttttattaaa ttgctcacat tttattttag caatgcaatc
721 taataaaatc tatctataca aaaaaatata gacaaactat atatataata tatttcaaat
781 ttccttatat atcctaatat aaaaatatct aataaattag atgaatatca aagaatctat
841 tgatttagtg tattattaaa tgtatatctt aattcaatat tattattcta ttcattttta
901 ttattcattt ttattcattt tcaaatttag aatatattaa tctatatatt aatttagaat
961 tctattctaa ttcgaattca atttttaaat attcatattc aattaaaatt gaaatttttt
1021 cattcgcgag gagccggatg agaagaaact ctcacgtccg gttctgtagt agaggtggaa
1081 ttaagaaaaa accatcaact ataaccccaa aagaaccaga ttctgtaaac aacatagagg
1141 aagaatgaag ggaatatctt atcgggggaa tcgtatngtg cnggctngtg cancgcgggc
1201 nng
//
LOCUS HM041472 1182 bp DNA linear PLN 03-MAY-2011
DEFINITION Opuntia echios ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041472
VERSION HM041472.1 GI:330887231
KEYWORDS .
SOURCE chloroplast Opuntia echios
ORGANISM Opuntia echios
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1182)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1182)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1182
/organism="Opuntia echios"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:412453"
gene <1..>1182
/gene="rpl16"
misc_feature <1085..>1182
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gtaagagccc atagtatgaa gtatgaacta ataactatag aactaataac caactcatcg
61 catcacatta tccggatcca aagaagcagt caagatagga tattttggtc ctatcattgc
121 agcaactgaa tttttttttc ataaacaaga aatcaaatga gttgtcaagc aaaagaaaaa
181 aaaaaaaaaa aaatatacat taaaggaggg ggatgcggat aaatggaaag gcgaaagaaa
241 gaaaaaaatg aatctaaatg atatacgatt ccactatgta aggtctttga atcatatcat
301 aaaagacaat gtaataaagc atgaatacag attcacacat aattatctga tatgaatcta
361 ttcatagaaa aaagaaaaaa gtaagagcct ccggccaata aagactaaga ggttgggctc
421 aagaacaaag ttcattaagg gctccattgt agaattcaga cctaatcatt aatcaagaag
481 cgatgggaac gatgtaatcc atgaatacag aagattcaat tgaaaaagaa tcctaatgat
541 tcattgggaa ggatggcgga acgaaccaga gaccaattca tctattctga aaagtgataa
601 actaatccta taaaactaaa atagatattg aaagagtaaa tattcgcccg cgaaaattcc
661 ttttttatta aattgctcac attttatttt agcaatgcaa tctaataaaa tatatctata
721 caaaaaaata tagacaaact atatatataa tatatttcaa atttccttat atatcctaat
781 ataaaaatat ctaataaatt agatgaatat caaagaatct attgatttag tgtattatta
841 aatgtatatc ttaattcaat attattattc tattcatttt tattattcat ttttattcat
901 tttcaaattt agaatatatt aatctatata ttaatttaga attctattct aattcgaatt
961 caatttttaa atattcatat tcaattaaaa ttgaaatttt ttcattcgcg aggagccgga
1021 tgagaagaaa ctctcacgtc cggttctgta gtagaggtgg aattaagaaa aaaccatcaa
1081 ctataacccc aaaagaacca gattctgtaa acaacataga ggaagaatga agggaatatc
1141 ttatcggggg aatcgtattt gtttcggaag atggctacta ta
//
LOCUS HM041469 1189 bp DNA linear PLN 03-MAY-2011
DEFINITION Nopalea sp. THH-2011 ribosomal protein L16-like (rpl16) gene,
partial sequence; chloroplast.
ACCESSION HM041469
VERSION HM041469.1 GI:330887228
KEYWORDS .
SOURCE chloroplast Opuntia sp. THH-2011
ORGANISM Opuntia sp. THH-2011
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1189)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1189)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1189
/organism="Opuntia sp. THH-2011"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001114"
gene <1..>1189
/gene="rpl16"
misc_feature <1096..>1189
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 atatacgaaa aagtagagcc catagtatga agtatgaact aataactata gaactaataa
61 ccaactcatc gcatcacatt atctggatcc aaagaagcag tcaagatagg atattttggt
121 cctatcattg cagcaactga attttttttt cataaacaag aaatcaaatg agttgtcaag
181 caaaagaaaa aaaaaaaaga aaaatatact ttaagggagg gggatgcgga taaatggaaa
241 ggcgaaagaa agaaaaaaat gaatctaaat gatatacgat tccactatgt aaggtctttg
301 aatcatatca taaaagacaa tgtaataaag catgaataca gattcacaca taattatctg
361 gtatgaatct attcatagaa aaaagaaaaa agtaagaccc tccggccaat aaagactaag
421 agggttggct caagaacaaa gttcattaag ggctccattg tagaattcag acctaatcat
481 taatcaagaa gcgatgggaa cgatgtaatc catgaataca gaagattcaa ttgaaaaaga
541 atcctaatga ttcattggga aggatggcgg aacgaaccag agaccaattc atctattctg
601 aaaagtgata aactaatcct ataaaactaa aatagatatt gaaagagtaa atattcgccc
661 gcgaaaattc cttttttatt aaattgctca cattttattt tagcaatgca atctaataaa
721 atatatctat acaaaaaaat atagacaaac tatatatata atatatttca aatttcctta
781 tatatcctaa tataaaaata tctaataaat tagatgaata tcaaagaatc tattgattta
841 gtgtattatt aaatgtatat cttaattcaa tattattatt ctattcattt ttattattca
901 tttttattca ttttcaaatt tagaatatat taatctatat attaatttag aattctattc
961 taattcgaat tcaattttta aatattcata ttcaattaaa attgaaattt tttcattcgc
1021 gaggagccgg atgagaagaa actctcacgt ccggttctgt agtagaggtg gaattaagaa
1081 aaaaccatca actataaccc caaaagaacc agattctgta aacaacatag aggaagaatg
1141 aagggaatat cttatcgggg gaatcgtatt tgtttcggaa gatatgctc
//
LOCUS HM041468 1202 bp DNA linear PLN 03-MAY-2011
DEFINITION Nopalea lutea ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041468
VERSION HM041468.1 GI:330887227
KEYWORDS .
SOURCE chloroplast Opuntia lutea
ORGANISM Opuntia lutea
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1202)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1202)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1202
/organism="Opuntia lutea"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001113"
gene <1..>1202
/gene="rpl16"
misc_feature <1099..>1202
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gatatacgaa aaagtaagag cccatagtat gaagtatgaa ctaataacta tagaactaat
61 aaccaactca tcgcatcaca ttatctggat ccaaagaagc agtcaagata ggatattttg
121 gtcctatcat tgcagcaact gaattttttt ttcataaaca agaaatcaaa tgagttgtca
181 agcaaaagaa aaaaaaaaaa gaaaaatata ctttaaggga gggggatgcg gataaatgga
241 aaggcgaaag aaagaaaaaa atgaatctaa atgatatacg attcccccta tgtaaggtct
301 ttgaatcata tcataaaaga caatgtaata aagcatgaat acagattcac acataattat
361 ctgatatgaa tctattcata gaaaaaagaa aaaagtaaga ccctccggcc aataaagact
421 aagagggttg gctcaagaac aaagttcatt aagggctcca ttgtagaatt cagacctaat
481 cattaatcaa gaagcgatgg gaacgatgta atccatgaat acagaagatt caattgaaaa
541 agaatcctaa tgattcattg ggaaggatgg cggaacgaac cagagaccaa ttcatctatt
601 ctgaaaagtg ataaactaat cctataaaac taaaatagat attgaaagag taaatattcg
661 cccgcgaaaa ttcctttttt attaaattgc tcacatttta ttttagcaat gcaatctaat
721 aaaatatatc tatacaaaaa aatatagaca aactatatat ataatatatt tcaaatttcc
781 ttatatatcc taatataaaa atatctaata aattagatga atatcaaaga atctattgat
841 ttagtgtatt attaaatgta tatcttaatt caatattatt attctattca tttttattat
901 tcatttttat tcattttcaa atttagaata tattaatcta tatattaatt tagaattcta
961 ttctaattcg aattcaattt ttaaatattc atattcaatt aaaattgaaa ttttttcatt
1021 cgcgaggagc cggatgagaa gaaactctca cgtccggttc tgtagtagag gtggaattaa
1081 gaaaaaacca tcaactataa ccccaaaaga accagattct gtaaacaaca tagaggaaga
1141 atgaagggaa tatcttatcg ggggaatcgt atttgtttcg gaagatatgc tctcaggcac
1201 ga
//
LOCUS HM041467 1199 bp DNA linear PLN 03-MAY-2011
DEFINITION Nopalea karwinskiana ribosomal protein L16-like (rpl16) gene,
partial sequence; chloroplast.
ACCESSION HM041467
VERSION HM041467.1 GI:330887226
KEYWORDS .
SOURCE chloroplast Opuntia karwinskiana
ORGANISM Opuntia karwinskiana
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1199)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1199)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1199
/organism="Opuntia karwinskiana"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001112"
gene <1..>1199
/gene="rpl16"
misc_feature <1098..>1199
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gtgatatcga aaaagtagag cccatagtat gaagtatgaa ctaataacta tagaactaat
61 aaccaactca tcgcatcaca ttatctggat ccaaagaagc agtcaagata ggatattttg
121 gtcctatcat tgcagcaact gaattttttt ttcataaaca agaaatcaaa tgagttgtca
181 agcaaaagaa aaaaaaaaaa gaaaaattta ctttaaggga gggggatgcg gataaatgga
241 aaggcgaaag aaagaaaaaa atgaatctaa atgatatacg attcccctat gtagggtctt
301 tgaatcatat cataaaaaac aatgtaataa agcatgaata cagattcccc cataattatc
361 tggtatgaat cttttcatag aaaaaaaaaa aaagtaagag cctccggcca ataaaaacta
421 aaagggttgg ctcaagaaca aagttcatta agggctccat tgtagaattc agacctaatc
481 nttaatcaag aagcgatggg aacgatgtaa tccatgaata cagaagattc aattgaaaaa
541 gaatcctaat gattcattgg gaaggatggc ggaacgaacc agagaccaat tcatctattc
601 tgaaaagtga taaactaatc ctataaaact aaaatagata ttgaaagagt aaatattcgc
661 ccgcgaaaat tcctttttta ttaaattgct cacattttat tttagcaatg caatctaata
721 aaatatatct atacaaaaaa atatagacaa actatatata taatatattt caaatttcct
781 tatatatcct aatataaaaa tatctaataa attagatgaa tatcaaagaa tctattgatt
841 tagtgtatta ttaaatgtat atcttaattc aatattatta ttctattcat ttttattatt
901 catttttatt cattttcaaa tttagaatat attaatctat atattaattt agaattctat
961 tctaattcga attcaatttt taaatattca tattcaatta aaattgaaat tttttcattc
1021 gcgaggagcc ggatgagaag aaactctcac gtccggttct gtagtagagg tggaattaag
1081 aaaaaaccat caactataac cccaaaagaa ccagattctg taaacaacat agaggaagaa
1141 tgaagggaat atcttatgcg ggggaatcgt attgtttcgg aagatatgct ctgcggccc
//
LOCUS HM041466 1205 bp DNA linear PLN 03-MAY-2011
DEFINITION Nopalea gaumeri ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041466
VERSION HM041466.1 GI:330887225
KEYWORDS .
SOURCE chloroplast Nopalea gaumeri
ORGANISM Nopalea gaumeri
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1205)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1205)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1205
/organism="Nopalea gaumeri"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001111"
gene <1..>1205
/gene="rpl16"
misc_feature <1103..>1205
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gctgtgatat acgaaanagt aagagcccat agtatgaagt atgaactaat aactatagaa
61 ctaataacca actcatcgca tcacattatc tggatccaaa gaagcagtca agataggata
121 ttttggtcct atcattgcag caactgaatt tttttttcat aaacaagaaa tcaaatgagt
181 tgtcaagcaa aagaaaaaaa aaaaaaaaaa tatacattaa aggaggggga tgcggataaa
241 tggaaaggcg aaagaaagaa aaaaatgaat ctaaatgata tacgattcca ctatgtaagg
301 tctttgaatc atatcataaa agacaatgta ataaagcatg aatacagatt cacacataat
361 tatctgaata tgaatctatt catagaaaaa agaaaaaagt aagaccctcc ggccaataaa
421 gactaaaggg gttggctcaa gaacaaagtt cattaagggc tccattgtag aattcagacc
481 taatcattaa tcaagaagcg atgggaacga tgtaatccat gaatacagaa gattcaattg
541 aaaaagaatc ctaatgattc attgggaagg atggcggaac gaaccagaga ccaattcatc
601 tattctgaaa agtgataaac taatcctata aaactaaaat agatattgaa agagtaaata
661 ttcgcccgcg aaaattcctt ttttattaaa ttgctcacat tttattttag caatgcaatc
721 taataaaata tatctataca aaaaaatata gacaaactat atatataata tatttcaaat
781 ttccttatat atcctaatat aaaaatatct aataaattag atgaatatca aagaatctat
841 tgatttagtg tattattaaa tgtatatctt aattcaatat tattattcta ttcattttta
901 ttattcattt ttattcattt tcaaatttag aatatattaa tctatatatt aatttagaat
961 tctattctaa ttcgaattca atttttaaat attcatattc aattaaaatt gaaatttttt
1021 cattcgcgag gagccggatg agaagaaact ctcacgtccg gttctgtagt agaggtggaa
1081 ttaagaaaaa accatcaact ataaccccaa aagaaccaga ttctgtaaac aacatagagg
1141 aagaatgaag ggaatatctt atcgggggaa tcgtatttgt ttcggaagat atgctctcag
1201 cacga
//
LOCUS HM041465 1190 bp DNA linear PLN 03-MAY-2011
DEFINITION Nopalea dejecta ribosomal protein L16-like (rpl16) gene, partial
sequence; chloroplast.
ACCESSION HM041465
VERSION HM041465.1 GI:330887224
KEYWORDS .
SOURCE chloroplast Opuntia dejecta
ORGANISM Opuntia dejecta
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1190)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1190)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1190
/organism="Opuntia dejecta"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:1001110"
gene <1..>1190
/gene="rpl16"
misc_feature <1096..>1190
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 tgatatacga aanagtaaga gcccatagta tgaagtatga actaataact atagaactaa
61 taaccaactc atcgcatcac attatctgga tccaaagaag cagtcaagat aggatatttt
121 ggtcctatca ttgcagcaac tgaatttttt tttcataaac aagaaatcaa atgagttgtc
181 aagcaaaaga aaaaaaaaaa aaaaaatata ctttaangga gggggatgcg gataaatgga
241 aaggcgaaag aaagaaaaaa atgaatctaa atgatatacg attccactat gtaaggtctt
301 tgaatcatat cataaaagac aatgtaataa agcatgaata cagattcaca cataattatc
361 tgtatgatct attcatagaa aaaagaaaaa agtaagagcc tccggccaat aaagactaag
421 agggttggct caagaacaaa gttcattaag ggctccattg tagaattcag acctaatcat
481 taatcaagaa gcgatgggaa cgatgtaatc catgaataca gaagattcaa ttgaaaaaga
541 atcctaatga ttcattggga aggatggcgg aacgaaccag agaccaattc atctattctg
601 aaaagtgata aactaatcct ataaaactaa aatagatatt gaaagagtaa atattcgccc
661 gcgaaaattc cttttttatt aaattgctca cattttattt tagcaatgca atctaataaa
721 atatatctat acaaaaaaat atagacaaac tatatatata atatatttca aatttcctta
781 tatatcctaa tataaaaata tctaataaat tagatgaata tcaaagaatc tattgattta
841 gtgtattatt aaatgtatat cttaattcaa tattattatt ctattcattt ttattattca
901 tttttattca ttttcaaatt tagaatatat taatctatat attaatttag aattctattc
961 taattcgaat tcaattttta aatattcata ttcaattaaa attgaaattt tttcattcgc
1021 gaggagccgg atgagaagaa actctcacgt ccggttctgt agtagaggtg gaattaagaa
1081 aaaaccatca actataaccc caaaagaacc agattctgta aacaacatag aggaagaatg
1141 aagggaatat cttatcgggg gaatcgtatt tgtttcggaa gatatgctct
//
LOCUS HM041464 1184 bp DNA linear PLN 03-MAY-2011
DEFINITION Nopalea cochenillifera ribosomal protein L16-like (rpl16) gene,
partial sequence; chloroplast.
ACCESSION HM041464
VERSION HM041464.1 GI:330887223
KEYWORDS .
SOURCE chloroplast Opuntia cochenillifera
ORGANISM Opuntia cochenillifera
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 1184)
AUTHORS Hernandez-Hernandez,T., Hernandez,H.M., De-Nova,J.A., Puente,R.,
Eguiarte,L.E. and Magallon,S.
TITLE Phylogenetic relationships and evolution of growth form in
Cactaceae (Caryophyllales, Eudicotyledoneae)
JOURNAL Am. J. Bot. 98 (1), 44-61 (2011)
PUBMED 21613084
REFERENCE 2 (bases 1 to 1184)
AUTHORS Hernandez-Hernandez,T., Magallon,S.A., Hernandez,H.M., De-Nova,A.,
Puente,R. and Eguiarte,L.E.
TITLE Direct Submission
JOURNAL Submitted (17-MAR-2010) Departamento de Botanica, Instituto de
Biologia, Universidad Nacional Autonoma de Mexico, 3er Circuito de
Ciudad Universitaria, Ciudad Universitaria, Coyoacan, Distrito
Federal C.P. 04510, Mexico
FEATURES Location/Qualifiers
source 1..1184
/organism="Opuntia cochenillifera"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:338184"
gene <1..>1184
/gene="rpl16"
misc_feature <1102..>1184
/gene="rpl16"
/note="similar to ribosomal protein L16"
ORIGIN
1 gctgtgatat acgaaaaagt aagagcccat agtatgaagt atgaactaac aactatagaa
61 ctaataacca actcatcgca tcacattatc tggatccaaa gaagcagtca agataggata
121 ttttggtcct atcattgcag caactgaatt tttttttcat aaacaagaaa tcaaatgagt
181 tgtcaagcaa aagaaaaaaa aaaaaaaaaa tatactttaa aggaggggga tgcggataaa
241 tggaaaggcg aaagaaagaa aaaaatgaat ctaaatgata tacgattcca ctatgtaagg
301 tctttgaatc atatcataaa agacaatgta ataaagcatg aatacagatt cccacataat
361 tatctgatat gaatctattc atagaaaaaa gaaaaaagta agagcctccg gccaataaag
421 actaagaggg ttggctcaag aacaaagttc attaagggct ccattgtaga attcagacct
481 aatcattaat caagaagcga tgggaacgat gtaatccatg aatacagaag attcaattga
541 aaaagaatcc taatgattca ttgggaagga tggcggaacg aaccagagac caattcatct
601 attctgaaaa gtgataaact aatcctataa aactaaaata gatattgaaa gagtaaatat
661 tcgcccgcga aaattccttt tttattaaat tgctcacatt ttattttagc aatgcaatct
721 aataaaatat atctatacaa aaaaatatag acaaactata tatataatat atttcaaatt
781 tccttatata tcctaatata aaaatatcta ataaattaga tgaatatcaa agaatctatt
841 gatttagtgt attattaaat gtatatctta attcaatatt attattctat tcatttttat
901 tattcatttt tattcatttt caaatttaga atatattaat ctatatatta atttagaatt
961 ctattctaat tcgaattcaa tttttaaata ttcatattca attaaaattg aaattttttc
1021 attcgcgagg agccggatga gaagaaactc tcacgtccgg ttctgtagta gaggtggaat
1081 taagaaaaaa ccatcaacta taaccccaaa agaaccagat tctgtaaaca acatagagga
1141 agaatgaagg gaatatctta tcgggggaat cgtatttgtt tcgg
//
LOCUS AY851612 892 bp DNA linear PLN 10-APR-2007
DEFINITION Opuntia subulata rpl16 gene, intron; chloroplast.
ACCESSION AY851612
VERSION AY851612.1 GI:57240072
KEYWORDS .
SOURCE chloroplast Austrocylindropuntia subulata
ORGANISM Austrocylindropuntia subulata
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Austrocylindropuntia.
REFERENCE 1 (bases 1 to 892)
AUTHORS Butterworth,C.A. and Wallace,R.S.
TITLE Molecular Phylogenetics of the Leafy Cactus Genus Pereskia
(Cactaceae)
JOURNAL Syst. Bot. 30 (4), 800-808 (2005)
REFERENCE 2 (bases 1 to 892)
AUTHORS Butterworth,C.A. and Wallace,R.S.
TITLE Direct Submission
JOURNAL Submitted (10-DEC-2004) Desert Botanical Garden, 1201 North Galvin
Parkway, Phoenix, AZ 85008, USA
FEATURES Location/Qualifiers
source 1..892
/organism="Austrocylindropuntia subulata"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:106982"
gene <1..>892
/gene="rpl16"
intron <1..>892
/gene="rpl16"
ORIGIN
1 cattaaagaa gggggatgcg gataaatgga aaggcgaaag aaagaaaaaa atgaatctaa
61 atgatatacg attccactat gtaaggtctt tgaatcatat cataaaagac aatgtaataa
121 agcatgaata cagattcaca cataattatc tgatatgaat ctattcatag aaaaaagaaa
181 aaagtaagag cctccggcca ataaagacta agagggttgg ctcaagaaca aagttcatta
241 agagctccat tgtagaattc agacctaatc attaatcaag aagcgatggg aacgatgtaa
301 tccatgaata cagaagattc aattgaaaaa gatcctaatg atcattggga aggatggcgg
361 aacgaaccag agaccaattc atctattctg aaaagtgata aactaatcct ataaaactaa
421 aatagatatt gaaagagtaa atattcgccc gcgaaaattc cttttttatt aaattgctca
481 tattttattt tagcaatgca atctaataaa atatatctat acaaaaaaat atagacaaac
541 tatatatata taatatattt caaatttcct tatataccca aatataaaaa tatctaataa
601 attagatgaa tatcaaagaa tctattgatt tagtgtatta ttaaatgtat atcttaattc
661 aatattatta ttctattcat ttttattcat tttcaaattt ataatatatt aatctatata
721 ttaatttata attctattct aattcgaatt caatttttaa atattcatat tcaattaaaa
781 ttgaaatttt ttcattcgcg aggagccgga tgagaagaaa ctctcatgtc cggttctgta
841 gtagagatgg aattaagaaa aaaccatcaa ctataacccc aagagaacca ga
//
LOCUS AY851611 881 bp DNA linear PLN 10-APR-2007
DEFINITION Opuntia polyacantha rpl16 gene, intron; chloroplast.
ACCESSION AY851611
VERSION AY851611.1 GI:57240071
KEYWORDS .
SOURCE chloroplast Opuntia polyacantha
ORGANISM Opuntia polyacantha
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Opuntia.
REFERENCE 1 (bases 1 to 881)
AUTHORS Butterworth,C.A. and Wallace,R.S.
TITLE Molecular Phylogenetics of the Leafy Cactus Genus Pereskia
(Cactaceae)
JOURNAL Syst. Bot. 30 (4), 800-808 (2005)
REFERENCE 2 (bases 1 to 881)
AUTHORS Butterworth,C.A. and Wallace,R.S.
TITLE Direct Submission
JOURNAL Submitted (10-DEC-2004) Desert Botanical Garden, 1201 North Galvin
Parkway, Phoenix, AZ 85008, USA
FEATURES Location/Qualifiers
source 1..881
/organism="Opuntia polyacantha"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:307728"
gene <1..>881
/gene="rpl16"
intron <1..>881
/gene="rpl16"
ORIGIN
1 cattaaagga gggggatgcg gataaatgga aaggcgaaag aaagaaaaaa atgaatctaa
61 atgatatacg attccactat gtaaggtctt tgaatcatat cataaaagac aatgtaataa
121 agcatgaata cagattcaca cataattatc tgatatgaat ctattcatag aaaaaagaaa
181 aaagtaagag cctccggcca ataaagacta agagggttgg ctcaagaaca aagttcatta
241 agggctccat tgtagaattc agacctaatc attaatcaag aagcgatggg aacgatgtaa
301 tccatgaata cagaagattc aattgaaaaa gatcctaatg atcattggga aggatggcgg
361 aacgaaccag agaccaattc atctattctg aaaagtgata aactaatcct ataaaactaa
421 aatagatatt gaaagagtaa atattcgccc gcgaaaattc cttttttatt aaattgctca
481 cattttattt tagcaatgca atctaataaa atatatctat acaaaaaaat atagacaaac
541 tctatatata atatatttca aatttcctta tatatcctaa tataaaaata tctaataaat
601 tagatgaata tcaaagaatc tattgattta gtgtattatt aaatgtatat cttaattcaa
661 tattattatt ctattcattt tcaaatttag aatatattaa tctatatatt aatttagaat
721 tctattctaa ttcgaattca atttttaaat attcatattc aattaaaatt gaaatttttt
781 cattcgcgag gagccggatg agaagaaact ctcacgtccg gttactgtag tagaggtgga
841 attaagaaaa aaccatcaac tataacccca aaagaaccag a
//
LOCUS AF191661 895 bp DNA linear PLN 07-NOV-1999
DEFINITION Opuntia kuehnrichiana rpl16 gene; chloroplast gene for chloroplast
product, partial intron sequence.
ACCESSION AF191661
VERSION AF191661.1 GI:6273287
KEYWORDS .
SOURCE chloroplast Cumulopuntia sphaerica
ORGANISM Cumulopuntia sphaerica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; Gunneridae;
Pentapetalae; Caryophyllales; Cactineae; Cactaceae; Opuntioideae;
Cumulopuntia.
REFERENCE 1 (bases 1 to 895)
AUTHORS Dickie,S.L. and Wallace,R.S.
TITLE Phylogeny of the subfamily Opuntioideae (Cactaceae)
JOURNAL Unpublished
REFERENCE 2 (bases 1 to 895)
AUTHORS Dickie,S.L. and Wallace,R.S.
TITLE Direct Submission
JOURNAL Submitted (28-SEP-1999) Botany, Iowa State University, 353 Bessey
Hall, Ames, IA 50011-1020, USA
FEATURES Location/Qualifiers
source 1..895
/organism="Cumulopuntia sphaerica"
/organelle="plastid:chloroplast"
/mol_type="genomic DNA"
/db_xref="taxon:106979"
/note="subfamily Opuntioideae; synonym: Cumulopuntia
kuenrichiana"
gene <1..>895
/gene="rpl16"
intron <1..>895
/gene="rpl16"
ORIGIN
1 tatacattaa agaaggggga tgcggataaa tggaaaggcg aaagaaagaa aaaaatgaat
61 ctaaatgata tacgattcca ctatgtaagg tctttgaatc atatcataaa agacaatgta
121 ataaagcatg aatacagatt cacacataat tatctgatat gaatctattc atagaaaaaa
181 gaaaaaagta agagcctccg gccaataaag actaagaggg ttggctcaag aacaaagttc
241 attaagagct ccattgtaga attcagacct aatcattaat caagaagcga tgggaacgat
301 gtaatccatg aatacagaag attcaattga aaaagatcct atgatccatt gggaaggatg
361 gcggaacgaa ccagagacca attcatctat tctgaaaagt gataaactaa tcctataaaa
421 ctaaaataga tattgaaaga gtaaatattc gcccgcgaaa attccttttt tttttaaatt
481 gctcatattt tattttagca atgcaatcta ataaaatata tctatacaaa aaaataaaga
541 caaactatat atataatata tttcaaattt ccttatatat ccaaatataa aaatatctaa
601 taaattagat gaatatcaaa gaatctattg atttagtgta ttattaaatg tatatcttaa
661 ttcaatatta ttattctatt catttttatt cattttcaat tttataatat attaatctat
721 atattaattt ataattctat tctaattcga attcaatttt taaatattca tattcaatta
781 aaattgaaat tttttcattc gcgaggagcc ggatgagaag aaactctcat gtccggttct
841 gtagtagaga tggaattaag aaaaaaccat caactataac cccaagagaa ccaga
//
In this case, we are just getting the raw records. To get the records in
a more Python-friendly form, we can use Bio.SeqIO to parse the
GenBank data into SeqRecord objects, including SeqFeature
objects (see Chapter [chapter:Bio.SeqIO]):
In [79]:
from Bio import SeqIO
handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
records = SeqIO.parse(handle, "gb")
We can now step through the records and look at the information we are interested in:
In [80]:
for record in records:
print("%s, length %i, with %i features" \
% (record.name, len(record), len(record.features)))
HQ621368, length 399, with 3 features
HM041482, length 1197, with 3 features
HM041481, length 1200, with 3 features
HM041480, length 1153, with 3 features
HM041479, length 1197, with 3 features
HM041478, length 1187, with 3 features
HM041477, length 1197, with 3 features
HM041476, length 1205, with 3 features
HM041474, length 1163, with 3 features
HM041473, length 1203, with 3 features
HM041472, length 1182, with 3 features
HM041469, length 1189, with 3 features
HM041468, length 1202, with 3 features
HM041467, length 1199, with 3 features
HM041466, length 1205, with 3 features
HM041465, length 1190, with 3 features
HM041464, length 1184, with 3 features
AY851612, length 892, with 3 features
AY851611, length 881, with 3 features
AF191661, length 895, with 3 features
Using these automated query retrieval functionality is a big plus over doing things by hand. Although the module should obey the NCBI’s max three queries per second rule, the NCBI have other recommendations like avoiding peak hours. See Section [sec:entrez-guidelines]. In particular, please note that for simplicity, this example does not use the WebEnv history feature. You should use this for any non-trivial search and download work, see Section History and WebEnv.
Finally, if plan to repeat your analysis, rather than downloading the files from the NCBI and parsing them immediately (as shown in this example), you should just download the records once and save them to your hard disk, and then parse the local file.
Finding the lineage of an organism¶
Staying with a plant example, let’s now find the lineage of the Cypripedioideae orchid family. First, we search the Taxonomy database for Cypripedioideae, which yields exactly one NCBI taxonomy identifier:
In [81]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
handle = Entrez.esearch(db="Taxonomy", term="Cypripedioideae")
record = Entrez.read(handle)
record["IdList"]
Out[81]:
['158330']
In [82]:
record["IdList"][0]
Out[82]:
'158330'
Now, we use efetch to download this entry in the Taxonomy database,
and then parse it:
In [83]:
handle = Entrez.efetch(db="Taxonomy", id="158330", retmode="xml")
records = Entrez.read(handle)
Again, this record stores lots of information:
In [84]:
records[0].keys()
Out[84]:
dict_keys(['PubDate', 'ScientificName', 'Division', 'MitoGeneticCode', 'GeneticCode', 'CreateDate', 'Rank', 'ParentTaxId', 'LineageEx', 'TaxId', 'Lineage', 'UpdateDate', 'OtherNames'])
We can get the lineage directly from this record:
In [85]:
records[0]["Lineage"]
Out[85]:
'cellular organisms; Eukaryota; Viridiplantae; Streptophyta; Streptophytina; Embryophyta; Tracheophyta; Euphyllophyta; Spermatophyta; Magnoliophyta; Mesangiospermae; Liliopsida; Petrosaviidae; Asparagales; Orchidaceae'
The record data contains much more than just the information shown here
- for example look under LineageEx instead of Lineage and you’ll
get the NCBI taxon identifiers of the lineage entries too.
Using the history and WebEnv¶
Often you will want to make a series of linked queries. Most typically, running a search, perhaps refining the search, and then retrieving detailed search results. You can do this by making a series of separate calls to Entrez. However, the NCBI prefer you to take advantage of their history support - for example combining ESearch and EFetch.
Another typical use of the history support would be to combine EPost and EFetch. You use EPost to upload a list of identifiers, which starts a new history session. You then download the records with EFetch by referring to the session (instead of the identifiers).
Searching for and downloading sequences using the history¶
Suppose we want to search and download all the Opuntia rpl16
nucleotide sequences, and store them in a FASTA file. As shown in
Section [sec:entrez-search-fetch-genbank], we can naively combine
Bio.Entrez.esearch() to get a list of GI numbers, and then call
Bio.Entrez.efetch() to download them all.
However, the approved approach is to run the search with the history feature. Then, we can fetch the results by reference to the search results - which the NCBI can anticipate and cache.
To do this, call Bio.Entrez.esearch() as normal, but with the
additional argument of usehistory="y",
In [86]:
from Bio import Entrez
Entrez.email = "history.user@example.com"
search_handle = Entrez.esearch(db="nucleotide",term="Opuntia[orgn] and rpl16", usehistory="y")
search_results = Entrez.read(search_handle)
search_handle.close()
When you get the XML output back, it will still include the usual search
results. However, you also get given two additional pieces of
information, the WebEnv session cookie, and the QueryKey:
In [87]:
gi_list = search_results["IdList"]
count = int(search_results["Count"])
assert count == len(gi_list)
print("The WebEnv is {}".format(search_results["WebEnv"]))
print("The QueryKey is {}".format(search_results["QueryKey"]))
The WebEnv is NCID_1_946410500_130.14.18.34_9001_1452651901_1799213676_0MetA0_S_MegaStore_F_1
The QueryKey is 1
Having stored these values in variables session_cookie and query_key
we can use them as parameters to Bio.Entrez.efetch() instead of
giving the GI numbers as identifiers.
While for small searches you might be OK downloading everything at once, it is better to download in batches. You use the retstart and retmax parameters to specify which range of search results you want returned (starting entry using zero-based counting, and maximum number of results to return). Sometimes you will get intermittent errors from Entrez, HTTPError 5XX, we use a try except pause retry block to address this. For example,
from Bio import Entrez
import time
try:
from urllib.error import HTTPError # for Python 3
except ImportError:
from urllib2 import HTTPError # for Python 2
batch_size = 3
out_handle = open("orchid_rpl16.fasta", "w")
for start in range(0, count, batch_size):
end = min(count, start+batch_size)
print("Going to download record %i to %i" % (start+1, end))
attempt = 1
while attempt <= 3:
try:
fetch_handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text",
retstart=start, retmax=batch_size,
webenv=webenv, query_key=query_key)
except HTTPError as err:
if 500 <= err.code <= 599:
print("Received error from server %s" % err)
print("Attempt %i of 3" % attempt)
attempt += 1
time.sleep(15)
else:
raise
data = fetch_handle.read()
fetch_handle.close()
out_handle.write(data)
out_handle.close()
For illustrative purposes, this example downloaded the FASTA records in batches of three. Unless you are downloading genomes or chromosomes, you would normally pick a larger batch size.
Searching for and downloading abstracts using the history¶
Here is another history example, searching for papers published in the last year about the Opuntia, and then downloading them into a file in MedLine format:
from Bio import Entrez
import time
try:
from urllib.error import HTTPError # for Python 3
except ImportError:
from urllib2 import HTTPError # for Python 2
Entrez.email = "history.user@example.com"
search_results = Entrez.read(Entrez.esearch(db="pubmed",
term="Opuntia[ORGN]",
reldate=365, datetype="pdat",
usehistory="y"))
count = int(search_results["Count"])
print("Found %i results" % count)
batch_size = 10
out_handle = open("recent_orchid_papers.txt", "w")
for start in range(0,count,batch_size):
end = min(count, start+batch_size)
print("Going to download record %i to %i" % (start+1, end))
attempt = 1
while attempt <= 3:
try:
fetch_handle = Entrez.efetch(db="pubmed",rettype="medline",
retmode="text",retstart=start,
retmax=batch_size,
webenv=search_results["WebEnv"],
query_key=search_results["QueryKey"])
except HTTPError as err:
if 500 <= err.code <= 599:
print("Received error from server %s" % err)
print("Attempt %i of 3" % attempt)
attempt += 1
time.sleep(15)
else:
raise
data = fetch_handle.read()
fetch_handle.close()
out_handle.write(data)
out_handle.close()
At the time of writing, this gave 28 matches - but because this is a
date dependent search, this will of course vary. As described in
Section [subsec:entrez-and-medline] above, you can then use
Bio.Medline to parse the saved records.
Searching for citations¶
Back in Section [sec:elink] we mentioned ELink can be used to search for citations of a given paper. Unfortunately this only covers journals indexed for PubMed Central (doing it for all the journals in PubMed would mean a lot more work for the NIH). Let’s try this for the Biopython PDB parser paper, PubMed ID 14630660:
In [88]:
from Bio import Entrez
Entrez.email = "A.N.Other@example.com"
pmid = "14630660"
results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
LinkName="pubmed_pmc_refs", from_uid=pmid))
pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]]
pmc_ids
Out[88]:
['4595337',
'4584387',
'4520291',
'4301084',
'4173008',
'4155247',
'3879085',
'3661355',
'3531324',
'3461403',
'3394275',
'3394243',
'3312550',
'3253748',
'3161047',
'3144279',
'3122320',
'3102222',
'3096039',
'3057020',
'3036045',
'2944279',
'2902450',
'2744707',
'2705363',
'2682512',
'2483496',
'2447749',
'2098836',
'2072961',
'1933154',
'1848001',
'1192790',
'1190160']
Great - eleven articles. But why hasn’t the Biopython application note been found (PubMed ID 19304878)? Well, as you might have guessed from the variable names, there are not actually PubMed IDs, but PubMed Central IDs. Our application note is the third citing paper in that list, PMCID 2682512.
So, what if (like me) you’d rather get back a list of PubMed IDs? Well we can call ELink again to translate them. This becomes a two step process, so by now you should expect to use the history feature to accomplish it (Section History and WebEnv).
But first, taking the more straightforward approach of making a second (separate) call to ELink:
In [89]:
results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed",
from_uid=",".join(pmc_ids)))
pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]]
pubmed_ids
Out[89]:
['26439842',
'26306092',
'26288158',
'25420233',
'24930144',
'24849577',
'24267035',
'23717802',
'23300419',
'22784991',
'22564897',
'22553365',
'22479120',
'22361291',
'21801404',
'21637529',
'21521828',
'21500218',
'21471012',
'21467571',
'20813068',
'20607693',
'20525384',
'19698094',
'19450287',
'19304878',
'18645234',
'18502776',
'18052533',
'17888163',
'17567620',
'17397254',
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