MED264: Rentrez Tutorial

NCBI Intro

NCBI has a lot of data in it. As of today, it has:

• 27.7 million papers in PubMed,
• includes 4.7 million full-text records available in PubMed Central
• The NCBI Nucleotide Database (which includes GenBank) has data for 245.5 million different sequences
• dbSNP describes 1070.2 million different genetic variants

All records can be cross-referenced with the 1.3 million species in the NCBI taxonomy or 25.2 thousand disease-associated records in OMIM.

rentrez package

rentrez provides functions that work with the NCBI Eutils API to search, download data from, and otherwise interact with NCBI databases.

library(devtools)
install_github("ropensci/rentrez")

rentrez Uses the EUtils API

• Read more about EUtils
• Working with the EUtils API will often require making multiple calls using the entrez package.
• Python has a module with similar functionality in the Biopython module; http://biopython.org/wiki/Main_Page

Gettting started with rentrez

Install

• Install from the R Cran repository:

install.packages('rentrez')

• From the development branch on GitHub:

install_github("ropensci/rentrez")

• library() tells our R environment to load the package for use.

library(rentrez)

Getting started

• Let’s take a look at the available NCBI databases
• We can use entrez_dbs to get a list of the databases we can search.

entrez_dbs()

#  [1] "pubmed"          "protein"         "nuccore"        
#  [4] "ipg"             "nucleotide"      "nucgss"         
#  [7] "nucest"          "structure"       "sparcle"        
# [10] "genome"          "annotinfo"       "assembly"       
# [13] "bioproject"      "biosample"       "blastdbinfo"    
# [16] "books"           "cdd"             "clinvar"        
# [19] "clone"           "gap"             "gapplus"        
# [22] "grasp"           "dbvar"           "gene"           
# [25] "gds"             "geoprofiles"     "homologene"     
# [28] "medgen"          "mesh"            "ncbisearch"     
# [31] "nlmcatalog"      "omim"            "orgtrack"       
# [34] "pmc"             "popset"          "probe"          
# [37] "proteinclusters" "pcassay"         "biosystems"     
# [40] "pccompound"      "pcsubstance"     "pubmedhealth"   
# [43] "seqannot"        "snp"             "sra"            
# [46] "taxonomy"        "biocollections"  "unigene"        
# [49] "gencoll"         "gtr"

Helper Functions that help you learn about NCBI databases

Function name	Return
entrez_db_summary()	Brief description of what the database is
entrez_db_searchable()	Set of search terms that can used with this database
entrez_db_links()	Set of databases that might contain linked records

• Let’s retrieve a description of on of the databases:

entrez_db_summary('dbvar')

#  DbName: dbvar
#  MenuName: dbVar
#  Description: dbVar records
#  DbBuild: Build170930-2230.1
#  Count: 6621056
#  LastUpdate: 2017/10/01 01:32

#entrez_db_summary('snp')

• We can also use entrez_db_searchable to see what search fields and qualifiers are allowable
• For example, we can see what searchable fields (qualifiers) can be used with the Sequence Read Archive (SRA) database:

entrez_db_searchable("sra")

# Searchable fields for database 'sra'
#   ALL      All terms from all searchable fields 
#   UID      Unique number assigned to publication 
#   FILT     Limits the records 
#   ACCN     Accession number of sequence 
#   TITL     Words in definition line 
#   PROP     Classification by source qualifiers and molecule type 
#   WORD     Free text associated with record 
#   ORGN     Scientific and common names of organism, and all higher levels of taxonomy 
#   AUTH     Author(s) of publication 
#   PDAT     Date sequence added to GenBank 
#   MDAT     Date of last update 
#   GPRJ     BioProject 
#   BSPL     BioSample 
#   PLAT     Platform 
#   STRA     Strategy 
#   SRC      Source 
#   SEL      Selection 
#   LAY      Layout 
#   RLEN     Percent of aligned reads 
#   ACS      Access is public or controlled 
#   ALN      Percent of aligned reads 
#   MBS      Size in megabases

• Check out https://www.ncbi.nlm.nih.gov/entrez/query/static/help/Summary_Matrices.html#Search_Fields_and_Qualifiers for more information on the search term qualifiers.

Search Terms: entrez_search()

• More often what you’ll want to do with rentrez is search a given NCBI database to find records that match some keywords
• We use the function entrez_search() to do this
• Simplest case you just need to provide a database name (db) and a search term (term) so let’s search PubMed for articles about the Natural Language Processing:

r_search <- entrez_search(db="pubmed", term="Natural Language Processing")

• The object returned by a search acts like a list, and you can get a summary of its contents by printing it.

r_search

# Entrez search result with 4811 hits (object contains 20 IDs and no web_history object)
#  Search term (as translated):  "natural language processing"[MeSH Terms] OR ("nat ...

• NCBI’s server has worked out that we meant NPL, and so included the ‘MeSH’ term
• there are many more ‘hits’ for this search than there are unique IDs contained in this object. That’s because the optional argument retmax, which controls the maximum number of returned values has a default value of 20.
• The IDs are the most important thing returned here. They allow us to fetch records matching those IDs, gather summary data about them or find cross-referenced records in other databases.
• We access the IDs as a vector using the $ operator:

r_search$ids

#  [1] "28971429" "28967001" "28964503" "28962645" "28954419" "28952936"
#  [7] "28950906" "28945588" "28940969" "28938912" "28936186" "28935617"
# [13] "28933506" "28932767" "28924815" "28924564" "28919830" "28916254"
# [19] "28906424" "28905434"

• let’s set the retmax up to retrieve more ids

another_r_search <- entrez_search(db="pubmed", term="Natural Language Processing", retmax=40)
another_r_search

# Entrez search result with 4811 hits (object contains 40 IDs and no web_history object)
#  Search term (as translated):  "natural language processing"[MeSH Terms] OR ("nat ...

• If we want to get IDs for all of the thousands of records that match this search, we can use the NCBI’s web history feature.

A Simple Example: Find by DOI using entrez_search()

Use a DOI to return the PMID of an article using entrez_search

Use an article DOI: Cancer risk reduction and reproductive concerns in female BRCA1/2 mutation carriers. DOI of 10.1007/s10689-007-9171-7.

wcancer_paper <- entrez_search(db="pubmed", term="10.1002/ijc.21536[doi]")

wcancer_paper$ids

# [1] "16331614"

Get some summary info

wcan_summary <- entrez_summary(db="pubmed", wcancer_paper$ids)
wcan_summary$title

# [1] "Tamoxifen and contralateral breast cancer in BRCA1 and BRCA2 carriers: an update."

wcan_summary$authors

#                                              name       authtype clusterid
# 1                                      Gronwald J         Author          
# 2                                          Tung N         Author          
# 3                                      Foulkes WD         Author          
# 4                                         Offit K         Author          
# 5                                      Gershoni R         Author          
# 6                                          Daly M         Author          
# 7                                      Kim-Sing C         Author          
# 8                                        Olsson H         Author          
# 9                                     Ainsworth P         Author          
# 10                                        Eisen A         Author          
# 11                                         Saal H         Author          
# 12                                     Friedman E         Author          
# 13                                      Olopade O         Author          
# 14                                      Osborne M         Author          
# 15                                      Weitzel J         Author          
# 16                                        Lynch H         Author          
# 17                                    Ghadirian P         Author          
# 18                                     Lubinski J         Author          
# 19                                          Sun P         Author          
# 20                                       Narod SA         Author          
# 21 Hereditary Breast Cancer Clinical Study Group. CollectiveName

Using search field operators

• NCBI has search field operators that we can add to queries query[search field].

• For instance, we can find next generation sequence datasets for the (amazing…) ciliate Tetrahymena thermophila by using the organism (‘ORGN’) search field:

entrez_search(db="sra",
              term="Tetrahymena thermophila[ORGN]",
              retmax=0)

# Entrez search result with 253 hits (object contains 0 IDs and no web_history object)
#  Search term (as translated):  "Tetrahymena thermophila"[Organism]

• We can narrow our focus to only those records that have been added recently (using the colon to specify a range of values):

entrez_search(db="sra",
              term="Tetrahymena thermophila[ORGN] AND 2013:2015[PDAT]",
              retmax=0)

# Entrez search result with 75 hits (object contains 0 IDs and no web_history object)
#  Search term (as translated):  "Tetrahymena thermophila"[Organism] AND 2013[PDAT] ...

• Or include recent records for either T. thermophila or it’s close relative T. borealis (using parentheses to make ANDs and ORs explicit).

entrez_search(db="sra",
              term="(Tetrahymena thermophila[ORGN] OR Tetrahymena borealis[ORGN]) AND 2013:2015[PDAT]",
              retmax=0)

# Entrez search result with 75 hits (object contains 0 IDs and no web_history object)
#  Search term (as translated):  ("Tetrahymena thermophila"[Organism] OR "Tetrahyme ...

• The set of search terms available varies between databases. You can get a list of available terms or any given data base with entrez_db_searchable()

entrez_db_searchable("sra")

# Searchable fields for database 'sra'
#   ALL      All terms from all searchable fields 
#   UID      Unique number assigned to publication 
#   FILT     Limits the records 
#   ACCN     Accession number of sequence 
#   TITL     Words in definition line 
#   PROP     Classification by source qualifiers and molecule type 
#   WORD     Free text associated with record 
#   ORGN     Scientific and common names of organism, and all higher levels of taxonomy 
#   AUTH     Author(s) of publication 
#   PDAT     Date sequence added to GenBank 
#   MDAT     Date of last update 
#   GPRJ     BioProject 
#   BSPL     BioSample 
#   PLAT     Platform 
#   STRA     Strategy 
#   SRC      Source 
#   SEL      Selection 
#   LAY      Layout 
#   RLEN     Percent of aligned reads 
#   ACS      Access is public or controlled 
#   ALN      Percent of aligned reads 
#   MBS      Size in megabases

Precise queries using MeSH terms

• NCBI allows searches using Medical Subject Heading (MeSH) terms
• these terms create a ‘controlled vocabulary’, and allow users to make very finely controlled queries of databases.
• CASE: if you were interested in reviewing studies on how a class of anti-malarial drugs called Folic Acid Antagonists work against Plasmodium vivax (a particular species of malarial parasite), you could use this search:

entrez_search(db   = "pubmed",
              term = "(vivax malaria[MeSH]) AND (folic acid antagonists[MeSH])")

# Entrez search result with 12 hits (object contains 12 IDs and no web_history object)
#  Search term (as translated):  "malaria, vivax"[MeSH Terms] AND "folic acid antag ...

• MeSH terms are available as a database from the NCBI
• You can download detailed information about each term and findthe ways in which terms relate to each other using rentrez.
• Search specific terms with entrez_search(db="mesh", term =...) and learn about the results of your search using the tools described below.

Challenge (5min)

* Section 2 - Search and plot trendy topics in genetics example

Linking docs: entrez_link()

• One of the strengths of the NCBI databases is that records of one type are connected to other records within the NCBI or to external data sources

*entrez_link() allows users to discover these links between records.

• Let’s find all NCBI data associated with a single gene (in this case the Amyloid Beta Precursor gene, the product of which is associated with the plaques that form in the brains of Alzheimer’s Disease patients).

• we need to provide an ID (id), the database from which this ID comes (dbfrom) and the name of a database in which to find linked records (db)

• If we set this last argument to ‘all’ we can find links in multiple databases:

all_the_links <- entrez_link(dbfrom='gene', id=351, db='all')
all_the_links

# elink object with contents:
#  $links: IDs for linked records from NCBI
# 

• return object behaves like a list

all_the_links$links

# elink result with information from 54 databases:
#  [1] gene_bioconcepts               gene_biosystems               
#  [3] gene_biosystems_all            gene_clinvar                  
#  [5] gene_clinvar_specific          gene_dbvar                    
#  [7] gene_genome                    gene_gtr                      
#  [9] gene_homologene                gene_medgen_diseases          
# [11] gene_pcassay_alltarget_list    gene_pcassay_alltarget_summary
# [13] gene_pcassay_rnai              gene_pcassay_target           
# [15] gene_probe                     gene_structure                
# [17] gene_bioproject                gene_books                    
# [19] gene_cdd                       gene_gene_h3k4me3             
# [21] gene_gene_neighbors            gene_genereviews              
# [23] gene_genome2                   gene_geoprofiles              
# [25] gene_nuccore                   gene_nuccore_mgc              
# [27] gene_nuccore_pos               gene_nuccore_refseqgene       
# [29] gene_nuccore_refseqrna         gene_nucest                   
# [31] gene_nucest_clust              gene_nucleotide               
# [33] gene_nucleotide_clust          gene_nucleotide_mgc           
# [35] gene_nucleotide_mgc_url        gene_nucleotide_pos           
# [37] gene_omim                      gene_pcassay_proteintarget    
# [39] gene_pccompound                gene_pcsubstance              
# [41] gene_pmc                       gene_pmc_nucleotide           
# [43] gene_protein                   gene_protein_refseq           
# [45] gene_pubmed                    gene_pubmed_citedinomim       
# [47] gene_pubmed_pmc_nucleotide     gene_pubmed_rif               
# [49] gene_snp                       gene_snp_geneview             
# [51] gene_sparcle                   gene_taxonomy                 
# [53] gene_unigene                   gene_varview

• names of the list elements are in the format [source_database]_[linked_database] and the elements themselves contain a vector of linked-IDs

• if we want to find open access publications associated with this gene we could get linked records in PubMed Central:

all_the_links$links$gene_pmc[1:10]

#  [1] "5561919" "5560349" "5559291" "5548265" "5540602" "5434815" "5395029"
#  [8] "5360245" "5233555" "5104494"

• Or if were interested in this gene’s role in diseases we could find links to clinVar:

all_the_links$links$gene_clinvar

#   [1] "397432" "396808" "396332" "396150" "394309" "391496" "369359"
#   [8] "339659" "339658" "339657" "339656" "339655" "339654" "339653"
#  [15] "339652" "339651" "339650" "339649" "339648" "339647" "339646"
#  [22] "339645" "339644" "339643" "339642" "339641" "339640" "339639"
#  [29] "339638" "339637" "339636" "339635" "339634" "339633" "339632"
#  [36] "339631" "339630" "339629" "339628" "339627" "339626" "339625"
#  [43] "339624" "339623" "339622" "339621" "339620" "339619" "253512"
#  [50] "253403" "236549" "236548" "236547" "221889" "160886" "155682"
#  [57] "155309" "155093" "155053" "154360" "154063" "153438" "152839"
#  [64] "151388" "150018" "149551" "149418" "149160" "149035" "148411"
#  [71] "148262" "148180" "146125" "145984" "145474" "145468" "145332"
#  [78] "145107" "144677" "144194" "127268" "98242"  "98241"  "98240" 
#  [85] "98239"  "98238"  "98237"  "98236"  "98235"  "59247"  "59246" 
#  [92] "59245"  "59243"  "59226"  "59224"  "59223"  "59222"  "59221" 
#  [99] "59010"  "59005"  "59004"  "37145"  "32099"  "18106"  "18105" 
# [106] "18104"  "18103"  "18102"  "18101"  "18100"  "18099"  "18098" 
# [113] "18097"  "18096"  "18095"  "18094"  "18093"  "18092"  "18091" 
# [120] "18090"  "18089"  "18088"  "18087"

original lesson ended here - go to lesson 2 to show plotting if less time

Narrowing our focus

• If we know beforehand what sort of links we’d like to find, we can to use the db argument to narrow the focus of a call to entrez_link().

• For instance, say we are interested in knowing about all of the RNA transcripts associated with the Amyloid Beta Precursor gene in humans.

• Transcript sequences are stored in the nucleotide database (referred to as nuccore in EUtils), so to find transcripts associated with a given gene we need to set dbfrom=gene and db=nuccore.

nuc_links <- entrez_link(dbfrom='gene', id=351, db='nuccore')
nuc_links

# elink object with contents:
#  $links: IDs for linked records from NCBI
# 

nuc_links$links

# elink result with information from 5 databases:
# [1] gene_nuccore            gene_nuccore_mgc        gene_nuccore_pos       
# [4] gene_nuccore_refseqgene gene_nuccore_refseqrna

• The object we get back contains links to the nucleotide database generally, but also to special subsets of that database like refseq.

• We can take advantage of this narrower set of links to find IDs that match unique transcripts from our gene of interest.

nuc_links$links$gene_nuccore_refseqrna

#  [1] "324021747" "324021746" "324021739" "324021737" "324021735"
#  [6] "228008405" "228008404" "228008403" "228008402" "228008401"

• We can use these ids in calls to entrez_fetch() or entrez_summary() to learn more about the transcripts they represent.

External links

• In addition to finding data within the NCBI, entrez_link can turn up connections to external databases. Perhaps the most interesting example is finding links to the full text of papers in PubMed.

• For example, when I wrote this document the first paper linked to Amyloid Beta Precursor had a unique ID of 25500142. We can find links to the full text of that paper with entrez_link by setting the cmd argument to ‘llinks’:

paper_links <- entrez_link(dbfrom="pubmed", id=25500142, cmd="llinks")
paper_links

# elink object with contents:
#  $linkouts: links to external websites

• Each element of the linkouts object contains information about an external source of data on this paper:

paper_links$linkouts

# $ID_25500142
# $ID_25500142[[1]]
# Linkout from Elsevier Science 
#  $Url: https://linkinghub.elsevie ...
# 
# $ID_25500142[[2]]
# Linkout from Europe PubMed Central 
#  $Url: http://europepmc.org/abstr ...
# 
# $ID_25500142[[3]]
# Linkout from Ovid Technologies, Inc. 
#  $Url: http://ovidsp.ovid.com/ovi ...
# 
# $ID_25500142[[4]]
# Linkout from PubMed Central 
#  $Url: https://www.ncbi.nlm.nih.g ...
# 
# $ID_25500142[[5]]
# Linkout from PubMed Central Canada 
#  $Url: http://pubmedcentralcanada ...
# 
# $ID_25500142[[6]]
# Linkout from MedlinePlus Health Information 
#  $Url: https://medlineplus.gov/al ...
# 
# $ID_25500142[[7]]
# Linkout from Mouse Genome Informatics (MGI) 
#  $Url: http://www.informatics.jax ...

• Each of those linkout objects contains quite a lot of information, but the URL is probably the most useful.

• For that reason, rentrez provides the function linkout_urls to make extracting just the URL simple:

linkout_urls(paper_links)

# $ID_25500142
# [1] "https://linkinghub.elsevier.com/retrieve/pii/S0014-4886(14)00393-8"     
# [2] "http://europepmc.org/abstract/MED/25500142"                             
# [3] "http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=linkout&SEARCH=25500142.ui"
# [4] "https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25500142/"               
# [5] "http://pubmedcentralcanada.ca/pmcc/articles/pmid/25500142"              
# [6] "https://medlineplus.gov/alzheimersdisease.html"                         
# [7] "http://www.informatics.jax.org/reference/25500142"

• The full list of options for the cmd argument are given in in-line documentation (?entrez_link).

• If you are interested in finding full text records for a large number of articles checkout the package fulltext which makes use of multiple sources (including the NCBI) to discover the full text articles.

Using more than one ID

• It is possible to pass more than one ID to entrez_link().

• By default, doing so will give you a single elink object containing the complete set of links for all of the IDs that you specified.

• So, if you were looking for protein IDs related to specific genes you could do:

all_links_together <- entrez_link(db="protein", dbfrom="gene", id=c("93100", "223646"))
all_links_together

# elink object with contents:
#  $links: IDs for linked records from NCBI
# 

all_links_together$links$gene_protein

#  [1] "1034662002" "1034662000" "1034661998" "1034661996" "1034661994"
#  [6] "1034661992" "558472750"  "545685826"  "194394158"  "166221824" 
# [11] "154936864"  "148697547"  "148697546"  "122346659"  "119602646" 
# [16] "119602645"  "119602644"  "119602643"  "119602642"  "81899807"  
# [21] "74215266"   "74186774"   "37787317"   "37787309"   "37787307"  
# [26] "37787305"   "37589273"   "33991172"   "31982089"   "26339824"  
# [31] "26329351"   "21619615"   "10834676"

• Although this behaviour might sometimes be useful, it means we’ve lost track of which protein ID is linked to which gene ID.

• To retain that information we can set by_id to TRUE. This gives us a list of elink objects, each once containing links from a single gene ID:

all_links_sep <- entrez_link(db="protein", dbfrom="gene", id=c("93100", "223646"), by_id = TRUE)
all_links_sep

# List of 2 elink objects,each containing
#   $links: IDs for linked records from NCBI
# 

lapply(all_links_sep, function(x) x$links$gene_protein)

# [[1]]
#  [1] "1034662002" "1034662000" "1034661998" "1034661996" "1034661994"
#  [6] "1034661992" "558472750"  "545685826"  "194394158"  "166221824" 
# [11] "154936864"  "122346659"  "119602646"  "119602645"  "119602644" 
# [16] "119602643"  "119602642"  "37787309"   "37787307"   "37787305"  
# [21] "33991172"   "21619615"   "10834676"  
# 
# [[2]]
#  [1] "148697547" "148697546" "81899807"  "74215266"  "74186774" 
#  [6] "37787317"  "37589273"  "31982089"  "26339824"  "26329351"

Getting summary data: entrez_summary()

• Having found the unique IDs for some records via entrez_search or entrez_link(), you are probably going to want to learn something about them.

• The Eutils API has two ways to get information about a record.

• entrez_fetch() returns ‘full’ records in varying formats and entrez_summary() returns less information about each record, but in relatively simple format.

• Very often the summary records have the information you are after, so rentrez provides functions to parse and summarise summary records.

The summary record

• entrez_summary() takes a vector of unique IDs for the samples you want to get summary information from.

• Let’s start by finding out something about the paper describing [Taxize](https://github.com/ropensci/taxize), using its PubMed ID:

taxize_summ <- entrez_summary(db="pubmed", id=24555091)
taxize_summ

# esummary result with 42 items:
#  [1] uid               pubdate           epubdate         
#  [4] source            authors           lastauthor       
#  [7] title             sorttitle         volume           
# [10] issue             pages             lang             
# [13] nlmuniqueid       issn              essn             
# [16] pubtype           recordstatus      pubstatus        
# [19] articleids        history           references       
# [22] attributes        pmcrefcount       fulljournalname  
# [25] elocationid       doctype           srccontriblist   
# [28] booktitle         medium            edition          
# [31] publisherlocation publishername     srcdate          
# [34] reportnumber      availablefromurl  locationlabel    
# [37] doccontriblist    docdate           bookname         
# [40] chapter           sortpubdate       sortfirstauthor

• Once again, the object returned by entrez_summary behaves like a list, so you can extract elements using $.

• For instance, we could convert our PubMed ID to another article identifier…

taxize_summ$articleids

#       idtype idtypen                           value
# 1     pubmed       1                        24555091
# 2        doi       3 10.12688/f1000research.2-191.v2
# 3        pmc       8                      PMC3901538
# 4        rid       8                        24563765
# 5        eid       8                        24555091
# 6    version       8                               2
# 7 version-id       8                               2
# 8      pmcid       5             pmc-id: PMC3901538;

• …or see how many times the article has been cited in PubMed Central papers

taxize_summ$pmcrefcount

# [1] 13

Dealing with many records

• If you give entrez_summary() a vector with more than one ID you’ll get a list of summary records back.

• Let’s get those Plasmodium vivax papers we found in the entrez_search() section back, and fetch some summary data on each paper:

vivax_search <- entrez_search(db = "pubmed", 
                              term = "(vivax malaria[MeSH]) AND (folic acid antagonists[MeSH])")
multi_summs <- entrez_summary(db="pubmed", id=vivax_search$ids)

• rentrez provides a helper function, extract_from_esummary() that takes one or more elements from every summary record in one of these lists.

• Here it is working with one…

extract_from_esummary(multi_summs, "fulljournalname")

#                                                                                                                 24861816 
# "Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases" 
#                                                                                                                 24145518 
#                                                                                  "Antimicrobial agents and chemotherapy" 
#                                                                                                                 24007534 
#                                                                                                        "Malaria journal" 
#                                                                                                                 23230341 
#                                                                                     "The Korean journal of parasitology" 
#                                                                                                                 23043980 
#                                                                                              "Experimental parasitology" 
#                                                                                                                 20810806 
#                                                                  "The American journal of tropical medicine and hygiene" 
#                                                                                                                 20412783 
#                                                                                                           "Acta tropica" 
#                                                                                                                 19597012 
#                                                                                          "Clinical microbiology reviews" 
#                                                                                                                 17556611 
#                                                                  "The American journal of tropical medicine and hygiene" 
#                                                                                                                 17519409 
#                                                                                                                   "JAMA" 
#                                                                                                                 17368986 
#                                                                                                 "Trends in parasitology" 
#                                                                                                                 12374849 
#                                        "Proceedings of the National Academy of Sciences of the United States of America"

• and several elements (using knitr package used for dynamic report generation to display output in R)

date_and_cite <- extract_from_esummary(multi_summs, c("pubdate", "pmcrefcount", "title"))
knitr::kable(head(t(date_and_cite)), row.names = FALSE)

pubdate	pmcrefcount	title
2014 Aug		Prevalence of mutations in the antifolates resistance-associated genes (dhfr and dhps) in Plasmodium vivax parasites from Eastern and Central Sudan.
2014	5	Prevalence of polymorphisms in antifolate drug resistance molecular marker genes pvdhfr and pvdhps in clinical isolates of Plasmodium vivax from Kolkata, India.
2013 Sep 5	2	Prevalence and patterns of antifolate and chloroquine drug resistance markers in Plasmodium vivax across Pakistan.
2012 Dec	13	Prevalence of drug resistance-associated gene mutations in Plasmodium vivax in Central China.
2012 Dec	7	Novel mutations in the antifolate drug resistance marker genes among Plasmodium vivax isolates exhibiting severe manifestations.
2010 Sep	17	Mutations in the antifolate-resistance-associated genes dihydrofolate reductase and dihydropteroate synthase in Plasmodium vivax isolates from malaria-endemic countries.

Fetching full records: entrez_fetch()

• As useful as the summary records are, sometimes they just don’t have the information that you need.

• If you want a complete representation of a record you can use entrez_fetch, using the argument rettype to specify the format you’d like the record in.

Fetch DNA sequences in fasta format

gene_ids <- c(351, 11647)
linked_seq_ids <- entrez_link(dbfrom="gene", id=gene_ids, db="nuccore")
linked_transcripts <- linked_seq_ids$links$gene_nuccore_refseqrna
head(linked_transcripts)

# [1] "1039766414" "1039766413" "1039766411" "1039766410" "1039766409"
# [6] "563317856"

• Now we can get our sequences with entrez_fetch, setting rettype to “fasta” (the list of formats available for each database is give in this table):

all_recs <- entrez_fetch(db="nuccore", id=linked_transcripts, rettype = "fasta")
class(all_recs)

# [1] "character"

nchar(all_recs)

# [1] 55183

• We now have a really huge character vector!

• Rather than printing all those thousands of bases we can take a peak at the top of the file:

cat(strwrap(substr(all_recs, 1, 500)), sep = "\n")

# >XM_006538500.2 PREDICTED: Mus musculus alkaline phosphatase,
# liver/bone/kidney (Alpl), transcript variant X5, mRNA
# GCGCCCGTGGCTTGCGCGACTCCCACGCGCGCGCTCCGCCGGTCCCGCAGTGACTGTCCCAGCCACGGTG
# GGGACACGTGGAAGGTCAGGCTCCCTGGGGACCCACGACCTCCCGCTCCGGACTCCGCGCGCATCTCTTG
# TGGCCTGGCAGGATGATGGACGTGGCGCCCGCTGAGCCGCTACCCAGGACCTCACCCTCGTGCTAAGCAC
# CTGCTCCCGGTGCCCACGCGCCTCCGTAGTCCACAGCTGCGCCCTTCGTGGTCCCTTGGCACTCTGTCCC
# GTTGGTGTCTAAAGTAGTTGGGGAGCAGCAGGAAGAAGGCACGTGCTGCGATCTTTGGCGGGAGAGATCG
# GAGACCGCGTGCTAGTGTCTGTCTGAGAG

• If we wanted to use these sequences in some other application we could write them to file:

write(all_recs, file = "my_transcripts.fasta")

• file will be saved in your current RStudio working directory

End Lesson examples.

• exercise answers

Topics not covered in this lesson:

• Fetch a parsed XML document
• Using NCBI’s Web History features

Where to learn more???

• Most of this tutorial was based on the ROpenscience tutorial on rentrez.
• I also used some of Ted Hart’s Rentrez demo as part of SIO14.