vignettes/tidypmc.md
In ropensci/tidypmc: Parse Full Text XML Documents from PubMed Central
Introduction to tidypmc
Chris Stubben
August 6, 2019
The tidypmc package parses XML documents in the Open Access subset of
Pubmed Central. Download the full text using
pmc_xml.
library(tidypmc)
doc <- pmc_xml("PMC2231364")
doc
# {xml_document}
# <article article-type="research-article" xmlns:xlink="http://www.w3.org/1999/xlink">
# [1] <front>\n <journal-meta>\n <journal-id journal-id-type="nlm-ta"> ...
# [2] <body>\n <sec>\n <title>Background</title>\n <p><italic>Yersi ...
# [3] <back>\n <ack>\n <sec>\n <title>Acknowledgements</title>\n ...
The package includes five functions to parse the
xml_document.
| R function | Description |
| :-------------- | :-------------------------------------------------------------------------- |
| pmc_text | Split section paragraphs into sentences with full path to subsection titles |
| pmc_caption | Split figure, table and supplementary material captions into sentences |
| pmc_table | Convert table nodes into a list of tibbles |
| pmc_reference | Format references cited into a tibble |
| pmc_metadata | List journal and article metadata in front node |
pmc_text splits paragraphs into sentences and removes any tables,
figures or formulas that are nested within paragraph tags, replaces
superscripted references with brackets, adds carets and underscores to
other superscripts and subscripts and includes the full path to the
subsection title.
library(dplyr)
txt <- pmc_text(doc)
txt
# # A tibble: 194 x 4
# section paragraph sentence text
# <chr> <int> <int> <chr>
# 1 Title 1 1 Comparative transcriptomics in Yersinia pestis: a global view of e…
# 2 Abstract 1 1 Environmental modulation of gene expression in Yersinia pestis is …
# 3 Abstract 1 2 Using cDNA microarray technology, we have analyzed the global gene…
# 4 Abstract 2 1 To provide us with a comprehensive view of environmental modulatio…
# 5 Abstract 2 2 Almost all known virulence genes of Y. pestis were differentially …
# 6 Abstract 2 3 Clustering enabled us to functionally classify co-expressed genes,…
# 7 Abstract 2 4 Collections of operons were predicted from the microarray data, an…
# 8 Abstract 2 5 Several regulatory DNA motifs, probably recognized by the regulato…
# 9 Abstract 3 1 The comparative transcriptomics analysis we present here not only …
# 10 Background 1 1 Yersinia pestis is the etiological agent of plague, alternatively …
# # … with 184 more rows
count(txt, section)
# # A tibble: 21 x 2
# section n
# <chr> <int>
# 1 Abstract 8
# 2 Authors' contributions 6
# 3 Background 20
# 4 Conclusion 3
# 5 Methods; Clustering analysis 7
# 6 Methods; Collection of microarray expression data 17
# 7 Methods; Discovery of regulatory DNA motifs 8
# 8 Methods; Gel mobility shift analysis of Fur binding 13
# 9 Methods; Operon prediction 5
# 10 Methods; Verification of predicted operons by RT-PCR 7
# # … with 11 more rows
pmc_caption splits figure, table and supplementary material captions
into sentences.
cap1 <- pmc_caption(doc)
# Found 5 figures
# Found 4 tables
# Found 3 supplements
filter(cap1, sentence == 1)
# # A tibble: 12 x 4
# tag label sentence text
# <chr> <chr> <int> <chr>
# 1 figure Figure 1 1 Environmental modulation of expression of virulence genes.
# 2 figure Figure 2 1 RT-PCR analysis of potential operons.
# 3 figure Figure 3 1 Schematic representation of the clustered microarray data.
# 4 figure Figure 4 1 Graphical representation of the consensus patterns by moti…
# 5 figure Figure 5 1 EMSA analysis of the binding of Fur protein to promoter DN…
# 6 table Table 1 1 Stress-responsive operons in Y. pestis predicted from micr…
# 7 table Table 2 1 Classification of the gene members of the cluster II in Fi…
# 8 table Table 3 1 Motif discovery for the clustering genes
# 9 table Table 4 1 Designs for expression profiling of Y. pestis
# 10 supplem… Additional file 1 … 1 Growth curves of Y. pestis strain 201 under different cond…
# 11 supplem… Additional file 2 … 1 All the transcriptional changes of 4005 genes of Y. pestis…
# 12 supplem… Additional file 3 … 1 List of oligonucleotide primers used in this study.
pmc_table formats tables by collapsing multiline headers, expanding
rowspan and colspan attributes and adding subheadings into a new column.
tab1 <- pmc_table(doc)
# Parsing 4 tables
# Adding footnotes to Table 1
sapply(tab1, nrow)
# Table 1 Table 2 Table 3 Table 4
# 39 23 4 34
tab1[[1]]
# # A tibble: 39 x 5
# subheading `Potential operon (r … `Gene ID` `Putative or predicted fu… `Reference (s)`
# <chr> <chr> <chr> <chr> <chr>
# 1 Iron uptake or heme… yfeABCD operon* (r > … YPO2439-2… Transport/binding chelate… yfeABCD [54]
# 2 Iron uptake or heme… hmuRSTUV operon (r > … YPO0279-0… Transport/binding hemin hmuRSTUV [55]
# 3 Iron uptake or heme… ysuJIHG* (r > 0.95) YPO1529-1… Iron uptake -
# 4 Iron uptake or heme… sufABCDS* (r > 0.90) YPO2400-2… Iron-regulated Fe-S clust… -
# 5 Iron uptake or heme… YPO1854-1856* (r > 0.… YPO1854-1… Iron uptake or heme synth… -
# 6 Sulfur metabolism tauABCD operon (r > 0… YPO0182-0… Transport/binding taurine tauABCD [56]
# 7 Sulfur metabolism ssuEADCB operon (r > … YPO3623-3… Sulphur metabolism ssu operon [57]
# 8 Sulfur metabolism cys operon (r > 0.92) YPO3010-3… Cysteine synthesis -
# 9 Sulfur metabolism YPO1317-1319 (r > 0.9… YPO1317-1… Sulfur metabolism? -
# 10 Sulfur metabolism YPO4109-4111 (r > 0.9… YPO4109-4… Sulfur metabolism? -
# # … with 29 more rows
Captions and footnotes are added as attributes.
attributes(tab1[[1]])
# $names
# [1] "subheading" "Potential operon (r value)"
# [3] "Gene ID" "Putative or predicted function"
# [5] "Reference (s)"
#
# $row.names
# [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
# [33] 33 34 35 36 37 38 39
#
# $class
# [1] "tbl_df" "tbl" "data.frame"
#
# $caption
# [1] "Stress-responsive operons in Y. pestis predicted from microarray expression data"
#
# $footnotes
# [1] "'r' represents the correlation coefficient of adjacent genes; '*' represent the defined operon has the similar expression pattern in two other published microarray datasets [7, 21]; '?' inferred functions of uncharacterized genes; '-' means the corresponding operons have not been experimentally validated in other bacteria."
Use collapse_rows to join column names and cell values in a semi-colon
delimited string (and then search using functions in the next section).
collapse_rows(tab1, na.string="-")
# # A tibble: 100 x 3
# table row text
# <chr> <int> <chr>
# 1 Table 1 1 subheading=Iron uptake or heme synthesis; Potential operon (r value)=yfeABCD opero…
# 2 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r value)=hmuRSTUV oper…
# 3 Table 1 3 subheading=Iron uptake or heme synthesis; Potential operon (r value)=ysuJIHG* (r >…
# 4 Table 1 4 subheading=Iron uptake or heme synthesis; Potential operon (r value)=sufABCDS* (r …
# 5 Table 1 5 subheading=Iron uptake or heme synthesis; Potential operon (r value)=YPO1854-1856*…
# 6 Table 1 6 subheading=Sulfur metabolism; Potential operon (r value)=tauABCD operon (r > 0.90)…
# 7 Table 1 7 subheading=Sulfur metabolism; Potential operon (r value)=ssuEADCB operon (r > 0.97…
# 8 Table 1 8 subheading=Sulfur metabolism; Potential operon (r value)=cys operon (r > 0.92); Ge…
# 9 Table 1 9 subheading=Sulfur metabolism; Potential operon (r value)=YPO1317-1319 (r > 0.97); …
# 10 Table 1 10 subheading=Sulfur metabolism; Potential operon (r value)=YPO4109-4111 (r > 0.90); …
# # … with 90 more rows
pmc_reference extracts the id, pmid, authors, year, title, journal,
volume, pages, and DOIs from reference tags.
ref1 <- pmc_reference(doc)
# Found 76 citation tags
ref1
# # A tibble: 76 x 9
# id pmid authors year title journal volume pages doi
# <int> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr>
# 1 1 89938… Perry RD, Fetherston JD 1997 Yersinia pestis--eti… Clin Mic… 10 35-66 <NA>
# 2 2 16053… Hinnebusch BJ 2005 The evolution of fle… Curr Iss… 7 197-… <NA>
# 3 3 64693… Straley SC, Harmon PA 1984 Yersinia pestis grow… Infect I… 45 655-… <NA>
# 4 4 15557… Huang XZ, Lindler LE 2004 The pH 6 antigen is … Infect I… 72 7212… 10.1128/…
# 5 5 15721… Pujol C, Bliska JB 2005 Turning Yersinia pat… Clin Imm… 114 216-… 10.1016/…
# 6 6 12732… Rhodius VA, LaRossa RA 2003 Uses and pitfalls of… Curr Opi… 6 114-… 10.1016/…
# 7 7 15342… Motin VL, Georgescu AM… 2004 Temporal global chan… J Bacter… 186 6298… 10.1128/…
# 8 8 15557… Han Y, Zhou D, Pang X,… 2004 Microarray analysis … Microbio… 48 791-… <NA>
# 9 9 15777… Han Y, Zhou D, Pang X,… 2005 DNA microarray analy… Microbes… 7 335-… 10.1016/…
# 10 10 15808… Han Y, Zhou D, Pang X,… 2005 Comparative transcri… Res Micr… 156 403-… 10.1016/…
# # … with 66 more rows
Finally, pmc_metadata saves journal and article metadata to a list.
pmc_metadata(doc)
# $PMCID
# [1] "PMC2231364"
#
# $Title
# [1] "Comparative transcriptomics in Yersinia pestis: a global view of environmental modulation of gene expression"
#
# $Authors
# [1] "Yanping Han, Jingfu Qiu, Zhaobiao Guo, He Gao, Yajun Song, Dongsheng Zhou, Ruifu Yang"
#
# $Year
# [1] 2007
#
# $Journal
# [1] "BMC Microbiology"
#
# $Volume
# [1] "7"
#
# $Pages
# [1] "96"
#
# $`Published online`
# [1] "2007-10-29"
#
# $`Date received`
# [1] "2007-6-2"
#
# $DOI
# [1] "10.1186/1471-2180-7-96"
#
# $Publisher
# [1] "BioMed Central"
Searching text
There are a few functions to search within the pmc_text or collapsed
pmc_table output. separate_text uses the
stringr package to extract any
matching regular expression.
separate_text(txt, "[ATCGN]{5,}")
# # A tibble: 9 x 5
# match section paragraph sentence text
# <chr> <chr> <int> <int> <chr>
# 1 ACGCAATCGT… Results and Discussion; Comp… 2 3 A 16 basepair (bp) box (5'-ACGCAATCG…
# 2 AAACGTTTNC… Results and Discussion; Comp… 2 4 It is very similar to the E. coli Pu…
# 3 TGATAATGAT… Results and Discussion; Comp… 2 5 A 21 bp box (5'-TGATAATGATTATCATTATC…
# 4 GATAATGATA… Results and Discussion; Comp… 2 6 It is a 10-1-10 inverted repeat that…
# 5 TGANNNNNNT… Results and Discussion; Comp… 2 7 A 15 bp box (5'-TGANNNNNNTCAA-3') wa…
# 6 TTGATN Results and Discussion; Comp… 2 8 It is a part of the E. coli Fnr box …
# 7 NATCAA Results and Discussion; Comp… 2 8 It is a part of the E. coli Fnr box …
# 8 GTTAATTAA Results and Discussion; Comp… 3 4 The ArcA regulator can recognize a r…
# 9 GTTAATTAAT… Results and Discussion; Comp… 3 5 An ArcA-box-like sequence (5'-GTTAAT…
A few wrappers search pre-defined patterns and add an extra step to
expand matched ranges. separate_refs matches references within
brackets using \\[[0-9, -]+\\] and expands ranges like [7-9].
x <- separate_refs(txt)
x
# # A tibble: 93 x 6
# id match section paragraph sentence text
# <dbl> <chr> <chr> <int> <int> <chr>
# 1 1 [1] Backgrou… 1 1 Yersinia pestis is the etiological agent of plague, alt…
# 2 2 [2] Backgrou… 1 3 To produce a transmissible infection, Y. pestis coloniz…
# 3 3 [3] Backgrou… 1 9 However, a few bacilli are taken up by tissue macrophag…
# 4 4 [4,5] Backgrou… 1 10 Residence in this niche also facilitates the bacteria's…
# 5 5 [4,5] Backgrou… 1 10 Residence in this niche also facilitates the bacteria's…
# 6 6 [6] Backgrou… 2 1 A DNA microarray is able to determine simultaneous chan…
# 7 7 [7-9] Backgrou… 2 2 We and others have measured the gene expression profile…
# 8 8 [7-9] Backgrou… 2 2 We and others have measured the gene expression profile…
# 9 9 [7-9] Backgrou… 2 2 We and others have measured the gene expression profile…
# 10 10 [10] Backgrou… 2 2 We and others have measured the gene expression profile…
# # … with 83 more rows
filter(x, id == 8)
# # A tibble: 5 x 6
# id match section paragraph sentence text
# <dbl> <chr> <chr> <int> <int> <chr>
# 1 8 [7-9] Background 2 2 We and others have measured the…
# 2 8 [8-13,1… Background 2 4 In order to acquire more regula…
# 3 8 [7-13,1… Results and Discussion 2 1 Recently, many signature expres…
# 4 8 [7-9] Results and Discussion; Virule… 3 1 As described previously, expres…
# 5 8 [8-10] Methods; Collection of microar… 1 6 The genome-wide transcriptional…
separate_genes expands microbial gene operons like hmsHFRS into four
separate genes.
separate_genes(txt)
# # A tibble: 103 x 6
# gene match section paragraph sentence text
# <chr> <chr> <chr> <int> <int> <chr>
# 1 purR PurR Abstract 2 5 Several regulatory DNA motifs, p…
# 2 phoP PhoP Background 2 3 We also identified the regulons …
# 3 ompR OmpR Background 2 3 We also identified the regulons …
# 4 oxyR OxyR Background 2 3 We also identified the regulons …
# 5 csrA CsrA Results and Discussion 1 3 After the determination of the C…
# 6 slyA SlyA Results and Discussion 1 3 After the determination of the C…
# 7 phoPQ PhoPQ Results and Discussion 1 3 After the determination of the C…
# 8 hmsH hmsHF… Results and Discussion; Virule… 3 3 For example, the hemin storage l…
# 9 hmsF hmsHF… Results and Discussion; Virule… 3 3 For example, the hemin storage l…
# 10 hmsR hmsHF… Results and Discussion; Virule… 3 3 For example, the hemin storage l…
# # … with 93 more rows
Finally, separate_tags expands locus tag ranges.
collapse_rows(tab1, na="-") %>%
separate_tags("YPO")
# # A tibble: 270 x 5
# id match table row text
# <chr> <chr> <chr> <int> <chr>
# 1 YPO2439 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 2 YPO2440 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 3 YPO2441 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 4 YPO2442 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 5 YPO0279 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 6 YPO0280 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 7 YPO0281 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 8 YPO0282 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 9 YPO0283 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 10 YPO1529 YPO1529-1… Table… 3 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# # … with 260 more rows
Using xml2
The pmc_* functions use the xml2
package for parsing and may fail in some situations, so it helps to know
how to parse xml_documents. Use cat and as.character to view nodes
returned by xml_find_all.
library(xml2)
refs <- xml_find_all(doc, "//ref")
refs[1]
# {xml_nodeset (1)}
# [1] <ref id="B1">\n <citation citation-type="journal">\n <person-group person-group-type="aut ...
cat(as.character(refs[1]))
# <ref id="B1">
# <citation citation-type="journal">
# <person-group person-group-type="author">
# <name>
# <surname>Perry</surname>
# <given-names>RD</given-names>
# </name>
# <name>
# <surname>Fetherston</surname>
# <given-names>JD</given-names>
# </name>
# </person-group>
# <article-title>Yersinia pestis--etiologic agent of plague</article-title>
# <source>Clin Microbiol Rev</source>
# <year>1997</year>
# <volume>10</volume>
# <fpage>35</fpage>
# <lpage>66</lpage>
# <pub-id pub-id-type="pmid">8993858</pub-id>
# </citation>
# </ref>
Many journals use superscripts for references cited so they usually
appear after words like results9 below.
# doc1 <- pmc_xml("PMC6385181")
doc1 <- read_xml(system.file("extdata/PMC6385181.xml", package = "tidypmc"))
gsub(".*\\. ", "", xml_text( xml_find_all(doc1, "//sec/p"))[2])
# [1] "RNA-seq identifies the most relevant genes and RT-qPCR validates its results9, especially in the field of environmental and host adaptation10,11 and antimicrobial response12."
Find the tags using xml_find_all and then update the nodes by adding
brackets or other text.
bib <- xml_find_all(doc1, "//xref[@ref-type='bibr']")
bib[1]
# {xml_nodeset (1)}
# [1] <xref ref-type="bibr" rid="CR1">1</xref>
xml_text(bib) <- paste0(" [", xml_text(bib), "]")
bib[1]
# {xml_nodeset (1)}
# [1] <xref ref-type="bibr" rid="CR1"> [1]</xref>
The text is now separated from the reference. Note the pmc_text
function adds the brackets by default.
gsub(".*\\. ", "", xml_text( xml_find_all(doc1, "//sec/p"))[2])
# [1] "RNA-seq identifies the most relevant genes and RT-qPCR validates its results [9], especially in the field of environmental and host adaptation [10], [11] and antimicrobial response [12]."
Genes, species and many other terms are often included within italic
tags. You can mark these nodes using the same code above or simply list
all the names in italics and search text or tables for matches, for
example three letter gene names in text below.
library(tibble)
x <- xml_name(xml_find_all(doc, "//*"))
tibble(tag=x) %>%
count(tag, sort=TRUE)
# # A tibble: 84 x 2
# tag n
# <chr> <int>
# 1 td 398
# 2 given-names 388
# 3 name 388
# 4 surname 388
# 5 italic 235
# 6 pub-id 129
# 7 tr 117
# 8 xref 108
# 9 year 80
# 10 article-title 77
# # … with 74 more rows
it <- xml_text(xml_find_all(doc, "//sec//p//italic"), trim=TRUE)
it2 <- tibble(italic=it) %>%
count(italic, sort=TRUE)
it2
# # A tibble: 53 x 2
# italic n
# <chr> <int>
# 1 Y. pestis 46
# 2 in vitro 5
# 3 E. coli 4
# 4 psaEFABC 3
# 5 r 3
# 6 cis 2
# 7 fur 2
# 8 n 2
# 9 nrdHIEF 2
# 10 sufABCDSE 2
# # … with 43 more rows
filter(it2, nchar(italic) == 3)
# # A tibble: 8 x 2
# italic n
# <chr> <int>
# 1 cis 2
# 2 fur 2
# 3 cys 1
# 4 hmu 1
# 5 ybt 1
# 6 yfe 1
# 7 yfu 1
# 8 ymt 1
separate_text(txt, c("fur", "cys", "hmu", "ybt", "yfe", "yfu", "ymt"))
# # A tibble: 9 x 5
# match section paragraph sentence text
# <chr> <chr> <int> <int> <chr>
# 1 ymt Results and Discussion; Virulence ge… 3 4 The ymt gene encoding Yersinia mur…
# 2 fur Results and Discussion; Clustering a… 3 2 It is noticeable that almost all o…
# 3 yfe Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 4 hmu Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 5 yfu Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 6 ybt Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 7 cys Results and Discussion; Clustering a… 4 2 Genes responsible for sulfur uptak…
# 8 cys Results and Discussion; Clustering a… 4 3 Cluster III contains members of th…
# 9 fur Methods; Gel mobility shift analysis… 1 1 The entire coding region of the fu…
ropensci/tidypmc documentation built on Dec. 14, 2019, 11:42 p.m.
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Introduction to tidypmc
Chris Stubben August 6, 2019
The tidypmc package parses XML documents in the Open Access subset of
Pubmed Central. Download the full text using
pmc_xml.
library(tidypmc)
doc <- pmc_xml("PMC2231364")
doc
# {xml_document}
# <article article-type="research-article" xmlns:xlink="http://www.w3.org/1999/xlink">
# [1] <front>\n <journal-meta>\n <journal-id journal-id-type="nlm-ta"> ...
# [2] <body>\n <sec>\n <title>Background</title>\n <p><italic>Yersi ...
# [3] <back>\n <ack>\n <sec>\n <title>Acknowledgements</title>\n ...
The package includes five functions to parse the
xml_document.
| R function | Description |
| :-------------- | :-------------------------------------------------------------------------- |
| pmc_text | Split section paragraphs into sentences with full path to subsection titles |
| pmc_caption | Split figure, table and supplementary material captions into sentences |
| pmc_table | Convert table nodes into a list of tibbles |
| pmc_reference | Format references cited into a tibble |
| pmc_metadata | List journal and article metadata in front node |
pmc_text splits paragraphs into sentences and removes any tables,
figures or formulas that are nested within paragraph tags, replaces
superscripted references with brackets, adds carets and underscores to
other superscripts and subscripts and includes the full path to the
subsection title.
library(dplyr)
txt <- pmc_text(doc)
txt
# # A tibble: 194 x 4
# section paragraph sentence text
# <chr> <int> <int> <chr>
# 1 Title 1 1 Comparative transcriptomics in Yersinia pestis: a global view of e…
# 2 Abstract 1 1 Environmental modulation of gene expression in Yersinia pestis is …
# 3 Abstract 1 2 Using cDNA microarray technology, we have analyzed the global gene…
# 4 Abstract 2 1 To provide us with a comprehensive view of environmental modulatio…
# 5 Abstract 2 2 Almost all known virulence genes of Y. pestis were differentially …
# 6 Abstract 2 3 Clustering enabled us to functionally classify co-expressed genes,…
# 7 Abstract 2 4 Collections of operons were predicted from the microarray data, an…
# 8 Abstract 2 5 Several regulatory DNA motifs, probably recognized by the regulato…
# 9 Abstract 3 1 The comparative transcriptomics analysis we present here not only …
# 10 Background 1 1 Yersinia pestis is the etiological agent of plague, alternatively …
# # … with 184 more rows
count(txt, section)
# # A tibble: 21 x 2
# section n
# <chr> <int>
# 1 Abstract 8
# 2 Authors' contributions 6
# 3 Background 20
# 4 Conclusion 3
# 5 Methods; Clustering analysis 7
# 6 Methods; Collection of microarray expression data 17
# 7 Methods; Discovery of regulatory DNA motifs 8
# 8 Methods; Gel mobility shift analysis of Fur binding 13
# 9 Methods; Operon prediction 5
# 10 Methods; Verification of predicted operons by RT-PCR 7
# # … with 11 more rows
pmc_caption splits figure, table and supplementary material captions
into sentences.
cap1 <- pmc_caption(doc)
# Found 5 figures
# Found 4 tables
# Found 3 supplements
filter(cap1, sentence == 1)
# # A tibble: 12 x 4
# tag label sentence text
# <chr> <chr> <int> <chr>
# 1 figure Figure 1 1 Environmental modulation of expression of virulence genes.
# 2 figure Figure 2 1 RT-PCR analysis of potential operons.
# 3 figure Figure 3 1 Schematic representation of the clustered microarray data.
# 4 figure Figure 4 1 Graphical representation of the consensus patterns by moti…
# 5 figure Figure 5 1 EMSA analysis of the binding of Fur protein to promoter DN…
# 6 table Table 1 1 Stress-responsive operons in Y. pestis predicted from micr…
# 7 table Table 2 1 Classification of the gene members of the cluster II in Fi…
# 8 table Table 3 1 Motif discovery for the clustering genes
# 9 table Table 4 1 Designs for expression profiling of Y. pestis
# 10 supplem… Additional file 1 … 1 Growth curves of Y. pestis strain 201 under different cond…
# 11 supplem… Additional file 2 … 1 All the transcriptional changes of 4005 genes of Y. pestis…
# 12 supplem… Additional file 3 … 1 List of oligonucleotide primers used in this study.
pmc_table formats tables by collapsing multiline headers, expanding
rowspan and colspan attributes and adding subheadings into a new column.
tab1 <- pmc_table(doc)
# Parsing 4 tables
# Adding footnotes to Table 1
sapply(tab1, nrow)
# Table 1 Table 2 Table 3 Table 4
# 39 23 4 34
tab1[[1]]
# # A tibble: 39 x 5
# subheading `Potential operon (r … `Gene ID` `Putative or predicted fu… `Reference (s)`
# <chr> <chr> <chr> <chr> <chr>
# 1 Iron uptake or heme… yfeABCD operon* (r > … YPO2439-2… Transport/binding chelate… yfeABCD [54]
# 2 Iron uptake or heme… hmuRSTUV operon (r > … YPO0279-0… Transport/binding hemin hmuRSTUV [55]
# 3 Iron uptake or heme… ysuJIHG* (r > 0.95) YPO1529-1… Iron uptake -
# 4 Iron uptake or heme… sufABCDS* (r > 0.90) YPO2400-2… Iron-regulated Fe-S clust… -
# 5 Iron uptake or heme… YPO1854-1856* (r > 0.… YPO1854-1… Iron uptake or heme synth… -
# 6 Sulfur metabolism tauABCD operon (r > 0… YPO0182-0… Transport/binding taurine tauABCD [56]
# 7 Sulfur metabolism ssuEADCB operon (r > … YPO3623-3… Sulphur metabolism ssu operon [57]
# 8 Sulfur metabolism cys operon (r > 0.92) YPO3010-3… Cysteine synthesis -
# 9 Sulfur metabolism YPO1317-1319 (r > 0.9… YPO1317-1… Sulfur metabolism? -
# 10 Sulfur metabolism YPO4109-4111 (r > 0.9… YPO4109-4… Sulfur metabolism? -
# # … with 29 more rows
Captions and footnotes are added as attributes.
attributes(tab1[[1]])
# $names
# [1] "subheading" "Potential operon (r value)"
# [3] "Gene ID" "Putative or predicted function"
# [5] "Reference (s)"
#
# $row.names
# [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
# [33] 33 34 35 36 37 38 39
#
# $class
# [1] "tbl_df" "tbl" "data.frame"
#
# $caption
# [1] "Stress-responsive operons in Y. pestis predicted from microarray expression data"
#
# $footnotes
# [1] "'r' represents the correlation coefficient of adjacent genes; '*' represent the defined operon has the similar expression pattern in two other published microarray datasets [7, 21]; '?' inferred functions of uncharacterized genes; '-' means the corresponding operons have not been experimentally validated in other bacteria."
Use collapse_rows to join column names and cell values in a semi-colon
delimited string (and then search using functions in the next section).
collapse_rows(tab1, na.string="-")
# # A tibble: 100 x 3
# table row text
# <chr> <int> <chr>
# 1 Table 1 1 subheading=Iron uptake or heme synthesis; Potential operon (r value)=yfeABCD opero…
# 2 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r value)=hmuRSTUV oper…
# 3 Table 1 3 subheading=Iron uptake or heme synthesis; Potential operon (r value)=ysuJIHG* (r >…
# 4 Table 1 4 subheading=Iron uptake or heme synthesis; Potential operon (r value)=sufABCDS* (r …
# 5 Table 1 5 subheading=Iron uptake or heme synthesis; Potential operon (r value)=YPO1854-1856*…
# 6 Table 1 6 subheading=Sulfur metabolism; Potential operon (r value)=tauABCD operon (r > 0.90)…
# 7 Table 1 7 subheading=Sulfur metabolism; Potential operon (r value)=ssuEADCB operon (r > 0.97…
# 8 Table 1 8 subheading=Sulfur metabolism; Potential operon (r value)=cys operon (r > 0.92); Ge…
# 9 Table 1 9 subheading=Sulfur metabolism; Potential operon (r value)=YPO1317-1319 (r > 0.97); …
# 10 Table 1 10 subheading=Sulfur metabolism; Potential operon (r value)=YPO4109-4111 (r > 0.90); …
# # … with 90 more rows
pmc_reference extracts the id, pmid, authors, year, title, journal,
volume, pages, and DOIs from reference tags.
ref1 <- pmc_reference(doc)
# Found 76 citation tags
ref1
# # A tibble: 76 x 9
# id pmid authors year title journal volume pages doi
# <int> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr>
# 1 1 89938… Perry RD, Fetherston JD 1997 Yersinia pestis--eti… Clin Mic… 10 35-66 <NA>
# 2 2 16053… Hinnebusch BJ 2005 The evolution of fle… Curr Iss… 7 197-… <NA>
# 3 3 64693… Straley SC, Harmon PA 1984 Yersinia pestis grow… Infect I… 45 655-… <NA>
# 4 4 15557… Huang XZ, Lindler LE 2004 The pH 6 antigen is … Infect I… 72 7212… 10.1128/…
# 5 5 15721… Pujol C, Bliska JB 2005 Turning Yersinia pat… Clin Imm… 114 216-… 10.1016/…
# 6 6 12732… Rhodius VA, LaRossa RA 2003 Uses and pitfalls of… Curr Opi… 6 114-… 10.1016/…
# 7 7 15342… Motin VL, Georgescu AM… 2004 Temporal global chan… J Bacter… 186 6298… 10.1128/…
# 8 8 15557… Han Y, Zhou D, Pang X,… 2004 Microarray analysis … Microbio… 48 791-… <NA>
# 9 9 15777… Han Y, Zhou D, Pang X,… 2005 DNA microarray analy… Microbes… 7 335-… 10.1016/…
# 10 10 15808… Han Y, Zhou D, Pang X,… 2005 Comparative transcri… Res Micr… 156 403-… 10.1016/…
# # … with 66 more rows
Finally, pmc_metadata saves journal and article metadata to a list.
pmc_metadata(doc)
# $PMCID
# [1] "PMC2231364"
#
# $Title
# [1] "Comparative transcriptomics in Yersinia pestis: a global view of environmental modulation of gene expression"
#
# $Authors
# [1] "Yanping Han, Jingfu Qiu, Zhaobiao Guo, He Gao, Yajun Song, Dongsheng Zhou, Ruifu Yang"
#
# $Year
# [1] 2007
#
# $Journal
# [1] "BMC Microbiology"
#
# $Volume
# [1] "7"
#
# $Pages
# [1] "96"
#
# $`Published online`
# [1] "2007-10-29"
#
# $`Date received`
# [1] "2007-6-2"
#
# $DOI
# [1] "10.1186/1471-2180-7-96"
#
# $Publisher
# [1] "BioMed Central"
Searching text
There are a few functions to search within the pmc_text or collapsed
pmc_table output. separate_text uses the
stringr package to extract any
matching regular expression.
separate_text(txt, "[ATCGN]{5,}")
# # A tibble: 9 x 5
# match section paragraph sentence text
# <chr> <chr> <int> <int> <chr>
# 1 ACGCAATCGT… Results and Discussion; Comp… 2 3 A 16 basepair (bp) box (5'-ACGCAATCG…
# 2 AAACGTTTNC… Results and Discussion; Comp… 2 4 It is very similar to the E. coli Pu…
# 3 TGATAATGAT… Results and Discussion; Comp… 2 5 A 21 bp box (5'-TGATAATGATTATCATTATC…
# 4 GATAATGATA… Results and Discussion; Comp… 2 6 It is a 10-1-10 inverted repeat that…
# 5 TGANNNNNNT… Results and Discussion; Comp… 2 7 A 15 bp box (5'-TGANNNNNNTCAA-3') wa…
# 6 TTGATN Results and Discussion; Comp… 2 8 It is a part of the E. coli Fnr box …
# 7 NATCAA Results and Discussion; Comp… 2 8 It is a part of the E. coli Fnr box …
# 8 GTTAATTAA Results and Discussion; Comp… 3 4 The ArcA regulator can recognize a r…
# 9 GTTAATTAAT… Results and Discussion; Comp… 3 5 An ArcA-box-like sequence (5'-GTTAAT…
A few wrappers search pre-defined patterns and add an extra step to
expand matched ranges. separate_refs matches references within
brackets using \\[[0-9, -]+\\] and expands ranges like [7-9].
x <- separate_refs(txt)
x
# # A tibble: 93 x 6
# id match section paragraph sentence text
# <dbl> <chr> <chr> <int> <int> <chr>
# 1 1 [1] Backgrou… 1 1 Yersinia pestis is the etiological agent of plague, alt…
# 2 2 [2] Backgrou… 1 3 To produce a transmissible infection, Y. pestis coloniz…
# 3 3 [3] Backgrou… 1 9 However, a few bacilli are taken up by tissue macrophag…
# 4 4 [4,5] Backgrou… 1 10 Residence in this niche also facilitates the bacteria's…
# 5 5 [4,5] Backgrou… 1 10 Residence in this niche also facilitates the bacteria's…
# 6 6 [6] Backgrou… 2 1 A DNA microarray is able to determine simultaneous chan…
# 7 7 [7-9] Backgrou… 2 2 We and others have measured the gene expression profile…
# 8 8 [7-9] Backgrou… 2 2 We and others have measured the gene expression profile…
# 9 9 [7-9] Backgrou… 2 2 We and others have measured the gene expression profile…
# 10 10 [10] Backgrou… 2 2 We and others have measured the gene expression profile…
# # … with 83 more rows
filter(x, id == 8)
# # A tibble: 5 x 6
# id match section paragraph sentence text
# <dbl> <chr> <chr> <int> <int> <chr>
# 1 8 [7-9] Background 2 2 We and others have measured the…
# 2 8 [8-13,1… Background 2 4 In order to acquire more regula…
# 3 8 [7-13,1… Results and Discussion 2 1 Recently, many signature expres…
# 4 8 [7-9] Results and Discussion; Virule… 3 1 As described previously, expres…
# 5 8 [8-10] Methods; Collection of microar… 1 6 The genome-wide transcriptional…
separate_genes expands microbial gene operons like hmsHFRS into four
separate genes.
separate_genes(txt)
# # A tibble: 103 x 6
# gene match section paragraph sentence text
# <chr> <chr> <chr> <int> <int> <chr>
# 1 purR PurR Abstract 2 5 Several regulatory DNA motifs, p…
# 2 phoP PhoP Background 2 3 We also identified the regulons …
# 3 ompR OmpR Background 2 3 We also identified the regulons …
# 4 oxyR OxyR Background 2 3 We also identified the regulons …
# 5 csrA CsrA Results and Discussion 1 3 After the determination of the C…
# 6 slyA SlyA Results and Discussion 1 3 After the determination of the C…
# 7 phoPQ PhoPQ Results and Discussion 1 3 After the determination of the C…
# 8 hmsH hmsHF… Results and Discussion; Virule… 3 3 For example, the hemin storage l…
# 9 hmsF hmsHF… Results and Discussion; Virule… 3 3 For example, the hemin storage l…
# 10 hmsR hmsHF… Results and Discussion; Virule… 3 3 For example, the hemin storage l…
# # … with 93 more rows
Finally, separate_tags expands locus tag ranges.
collapse_rows(tab1, na="-") %>%
separate_tags("YPO")
# # A tibble: 270 x 5
# id match table row text
# <chr> <chr> <chr> <int> <chr>
# 1 YPO2439 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 2 YPO2440 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 3 YPO2441 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 4 YPO2442 YPO2439-2… Table… 1 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 5 YPO0279 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 6 YPO0280 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 7 YPO0281 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 8 YPO0282 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 9 YPO0283 YPO0279-0… Table… 2 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# 10 YPO1529 YPO1529-1… Table… 3 subheading=Iron uptake or heme synthesis; Potential operon (r va…
# # … with 260 more rows
Using xml2
The pmc_* functions use the xml2
package for parsing and may fail in some situations, so it helps to know
how to parse xml_documents. Use cat and as.character to view nodes
returned by xml_find_all.
library(xml2)
refs <- xml_find_all(doc, "//ref")
refs[1]
# {xml_nodeset (1)}
# [1] <ref id="B1">\n <citation citation-type="journal">\n <person-group person-group-type="aut ...
cat(as.character(refs[1]))
# <ref id="B1">
# <citation citation-type="journal">
# <person-group person-group-type="author">
# <name>
# <surname>Perry</surname>
# <given-names>RD</given-names>
# </name>
# <name>
# <surname>Fetherston</surname>
# <given-names>JD</given-names>
# </name>
# </person-group>
# <article-title>Yersinia pestis--etiologic agent of plague</article-title>
# <source>Clin Microbiol Rev</source>
# <year>1997</year>
# <volume>10</volume>
# <fpage>35</fpage>
# <lpage>66</lpage>
# <pub-id pub-id-type="pmid">8993858</pub-id>
# </citation>
# </ref>
Many journals use superscripts for references cited so they usually
appear after words like results9 below.
# doc1 <- pmc_xml("PMC6385181")
doc1 <- read_xml(system.file("extdata/PMC6385181.xml", package = "tidypmc"))
gsub(".*\\. ", "", xml_text( xml_find_all(doc1, "//sec/p"))[2])
# [1] "RNA-seq identifies the most relevant genes and RT-qPCR validates its results9, especially in the field of environmental and host adaptation10,11 and antimicrobial response12."
Find the tags using xml_find_all and then update the nodes by adding
brackets or other text.
bib <- xml_find_all(doc1, "//xref[@ref-type='bibr']")
bib[1]
# {xml_nodeset (1)}
# [1] <xref ref-type="bibr" rid="CR1">1</xref>
xml_text(bib) <- paste0(" [", xml_text(bib), "]")
bib[1]
# {xml_nodeset (1)}
# [1] <xref ref-type="bibr" rid="CR1"> [1]</xref>
The text is now separated from the reference. Note the pmc_text
function adds the brackets by default.
gsub(".*\\. ", "", xml_text( xml_find_all(doc1, "//sec/p"))[2])
# [1] "RNA-seq identifies the most relevant genes and RT-qPCR validates its results [9], especially in the field of environmental and host adaptation [10], [11] and antimicrobial response [12]."
Genes, species and many other terms are often included within italic tags. You can mark these nodes using the same code above or simply list all the names in italics and search text or tables for matches, for example three letter gene names in text below.
library(tibble)
x <- xml_name(xml_find_all(doc, "//*"))
tibble(tag=x) %>%
count(tag, sort=TRUE)
# # A tibble: 84 x 2
# tag n
# <chr> <int>
# 1 td 398
# 2 given-names 388
# 3 name 388
# 4 surname 388
# 5 italic 235
# 6 pub-id 129
# 7 tr 117
# 8 xref 108
# 9 year 80
# 10 article-title 77
# # … with 74 more rows
it <- xml_text(xml_find_all(doc, "//sec//p//italic"), trim=TRUE)
it2 <- tibble(italic=it) %>%
count(italic, sort=TRUE)
it2
# # A tibble: 53 x 2
# italic n
# <chr> <int>
# 1 Y. pestis 46
# 2 in vitro 5
# 3 E. coli 4
# 4 psaEFABC 3
# 5 r 3
# 6 cis 2
# 7 fur 2
# 8 n 2
# 9 nrdHIEF 2
# 10 sufABCDSE 2
# # … with 43 more rows
filter(it2, nchar(italic) == 3)
# # A tibble: 8 x 2
# italic n
# <chr> <int>
# 1 cis 2
# 2 fur 2
# 3 cys 1
# 4 hmu 1
# 5 ybt 1
# 6 yfe 1
# 7 yfu 1
# 8 ymt 1
separate_text(txt, c("fur", "cys", "hmu", "ybt", "yfe", "yfu", "ymt"))
# # A tibble: 9 x 5
# match section paragraph sentence text
# <chr> <chr> <int> <int> <chr>
# 1 ymt Results and Discussion; Virulence ge… 3 4 The ymt gene encoding Yersinia mur…
# 2 fur Results and Discussion; Clustering a… 3 2 It is noticeable that almost all o…
# 3 yfe Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 4 hmu Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 5 yfu Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 6 ybt Results and Discussion; Clustering a… 3 4 Genes in category A (yfe, hmu, yfu…
# 7 cys Results and Discussion; Clustering a… 4 2 Genes responsible for sulfur uptak…
# 8 cys Results and Discussion; Clustering a… 4 3 Cluster III contains members of th…
# 9 fur Methods; Gel mobility shift analysis… 1 1 The entire coding region of the fu…
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