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">BMC M ...
# [2] <body>\n <sec>\n <title>Background</title>\n <p><italic>Yersinia p ...
# [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 × 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 × 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 rowspmc_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 × 4
# tag label sentence text
# <chr> <chr> <int> <chr>
# 1 figure Figure 1 1 Environmental modulation of expression of virule…
# 2 figure Figure 2 1 RT-PCR analysis of potential operons.
# 3 figure Figure 3 1 Schematic representation of the clustered microa…
# 4 figure Figure 4 1 Graphical representation of the consensus patter…
# 5 figure Figure 5 1 EMSA analysis of the binding of Fur protein to p…
# 6 table Table 1 1 Stress-responsive operons in Y. pestis predicted…
# 7 table Table 2 1 Classification of the gene members of the cluste…
# 8 table Table 3 1 Motif discovery for the clustering genes
# 9 table Table 4 1 Designs for expression profiling of Y. pestis
# 10 supplement Additional file 1 Figure S1 1 Growth curves of Y. pestis strain 201 under diff…
# 11 supplement Additional file 2 Table S1 1 All the transcriptional changes of 4005 genes of…
# 12 supplement Additional file 3 Table S2 1 List of oligonucleotide primers used in this stu…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 × 5
# subheading `Potential operon (r value)` `Gene ID` Putative or pre…¹ Refer…²
# <chr> <chr> <chr> <chr> <chr>
# 1 Iron uptake or heme synthesis yfeABCD operon* (r > 0.91) YPO2439-2442 Transport/bindin… yfeABC…
# 2 Iron uptake or heme synthesis hmuRSTUV operon (r > 0.90) YPO0279-0283 Transport/bindin… hmuRST…
# 3 Iron uptake or heme synthesis ysuJIHG* (r > 0.95) YPO1529-1532 Iron uptake -
# 4 Iron uptake or heme synthesis sufABCDS* (r > 0.90) YPO2400-2404 Iron-regulated F… -
# 5 Iron uptake or heme synthesis YPO1854-1856* (r > 0.97) YPO1854-1856 Iron uptake or h… -
# 6 Sulfur metabolism tauABCD operon (r > 0.90) YPO0182-0185 Transport/bindin… tauABC…
# 7 Sulfur metabolism ssuEADCB operon (r > 0.97) YPO3623-3627 Sulphur metaboli… ssu op…
# 8 Sulfur metabolism cys operon (r > 0.92) YPO3010-3015 Cysteine synthes… -
# 9 Sulfur metabolism YPO1317-1319 (r > 0.97) YPO1317-1319 Sulfur metabolis… -
# 10 Sulfur metabolism YPO4109-4111 (r > 0.90) YPO4109-4111 Sulfur metabolis… -
# # … with 29 more rows, and abbreviated variable names ¹`Putative or predicted function`,
# # ²`Reference (s)`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 × 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 rowspmc_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 × 9
# id pmid authors year title journal volume pages doi
# <int> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr>
# 1 1 8993858 Perry RD, Fetherston JD 1997 Yers… Clin M… 10 35-66 NA
# 2 2 16053250 Hinnebusch BJ 2005 The … Curr I… 7 197-… NA
# 3 3 6469352 Straley SC, Harmon PA 1984 Yers… Infect… 45 655-… NA
# 4 4 15557646 Huang XZ, Lindler LE 2004 The … Infect… 72 7212… 10.1…
# 5 5 15721832 Pujol C, Bliska JB 2005 Turn… Clin I… 114 216-… 10.1…
# 6 6 12732299 Rhodius VA, LaRossa RA 2003 Uses… Curr O… 6 114-… 10.1…
# 7 7 15342600 Motin VL, Georgescu AM, Fitch JP, Gu PP, N… 2004 Temp… J Bact… 186 6298… 10.1…
# 8 8 15557737 Han Y, Zhou D, Pang X, Song Y, Zhang L, Ba… 2004 Micr… Microb… 48 791-… NA
# 9 9 15777740 Han Y, Zhou D, Pang X, Zhang L, Song Y, To… 2005 DNA … Microb… 7 335-… 10.1…
# 10 10 15808945 Han Y, Zhou D, Pang X, Zhang L, Song Y, To… 2005 Comp… Res Mi… 156 403-… 10.1…
# # … with 66 more rowsFinally, 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 × 5
# match section parag…¹ sente…² text
# <chr> <chr> <int> <int> <chr>
# 1 ACGCAATCGTTTTCNT Results and Discussion; Computational discovery of re… 2 3 A 16…
# 2 AAACGTTTNCGT Results and Discussion; Computational discovery of re… 2 4 It i…
# 3 TGATAATGATTATCATTATCA Results and Discussion; Computational discovery of re… 2 5 A 21…
# 4 GATAATGATAATCATTATC Results and Discussion; Computational discovery of re… 2 6 It i…
# 5 TGANNNNNNTCAA Results and Discussion; Computational discovery of re… 2 7 A 15…
# 6 TTGATN Results and Discussion; Computational discovery of re… 2 8 It i…
# 7 NATCAA Results and Discussion; Computational discovery of re… 2 8 It i…
# 8 GTTAATTAA Results and Discussion; Computational discovery of re… 3 4 The …
# 9 GTTAATTAATGT Results and Discussion; Computational discovery of re… 3 5 An A…
# # … with abbreviated variable names ¹paragraph, ²sentenceA 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 × 6
# id match section paragraph sentence text
# <dbl> <chr> <chr> <int> <int> <chr>
# 1 1 [1] Background 1 1 Yersinia pestis is the etiological agent of plague, al…
# 2 2 [2] Background 1 3 To produce a transmissible infection, Y. pestis coloni…
# 3 3 [3] Background 1 9 However, a few bacilli are taken up by tissue macropha…
# 4 4 [4,5] Background 1 10 Residence in this niche also facilitates the bacteria'…
# 5 5 [4,5] Background 1 10 Residence in this niche also facilitates the bacteria'…
# 6 6 [6] Background 2 1 A DNA microarray is able to determine simultaneous cha…
# 7 7 [7-9] Background 2 2 We and others have measured the gene expression profil…
# 8 8 [7-9] Background 2 2 We and others have measured the gene expression profil…
# 9 9 [7-9] Background 2 2 We and others have measured the gene expression profil…
# 10 10 [10] Background 2 2 We and others have measured the gene expression profil…
# # … with 83 more rows
filter(x, id == 8)
# # A tibble: 5 × 6
# id match section parag…¹ sente…² text
# <dbl> <chr> <chr> <int> <int> <chr>
# 1 8 [7-9] Background 2 2 We a…
# 2 8 [8-13,15] Background 2 4 In o…
# 3 8 [7-13,15,19-21] Results and Discussion 2 1 Rece…
# 4 8 [7-9] Results and Discussion; Virulence genes in response t… 3 1 As d…
# 5 8 [8-10] Methods; Collection of microarray expression data 1 6 The …
# # … with abbreviated variable names ¹paragraph, ²sentenceseparate_genes expands microbial gene operons like hmsHFRS into four separate genes.
separate_genes(txt)
# # A tibble: 103 × 6
# gene match section parag…¹ sente…² text
# <chr> <chr> <chr> <int> <int> <chr>
# 1 purR PurR Abstract 2 5 Seve…
# 2 phoP PhoP Background 2 3 We a…
# 3 ompR OmpR Background 2 3 We a…
# 4 oxyR OxyR Background 2 3 We a…
# 5 csrA CsrA Results and Discussion 1 3 Afte…
# 6 slyA SlyA Results and Discussion 1 3 Afte…
# 7 phoPQ PhoPQ Results and Discussion 1 3 Afte…
# 8 hmsH hmsHFRS Results and Discussion; Virulence genes in response to multi… 3 3 For …
# 9 hmsF hmsHFRS Results and Discussion; Virulence genes in response to multi… 3 3 For …
# 10 hmsR hmsHFRS Results and Discussion; Virulence genes in response to multi… 3 3 For …
# # … with 93 more rows, and abbreviated variable names ¹paragraph, ²sentenceFinally, separate_tags expands locus tag ranges.
collapse_rows(tab1, na="-") %>%
separate_tags("YPO")
# # A tibble: 270 × 5
# id match table row text
# <chr> <chr> <chr> <int> <chr>
# 1 YPO2439 YPO2439-2442 Table 1 1 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 2 YPO2440 YPO2439-2442 Table 1 1 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 3 YPO2441 YPO2439-2442 Table 1 1 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 4 YPO2442 YPO2439-2442 Table 1 1 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 5 YPO0279 YPO0279-0283 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 6 YPO0280 YPO0279-0283 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 7 YPO0281 YPO0279-0283 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 8 YPO0282 YPO0279-0283 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 9 YPO0283 YPO0279-0283 Table 1 2 subheading=Iron uptake or heme synthesis; Potential operon (r…
# 10 YPO1529 YPO1529-1532 Table 1 3 subheading=Iron uptake or heme synthesis; Potential operon (r…
# # … with 260 more rowsUsing 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 × 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 × 2
# italic n
# <chr> <int>
# 1 Y. pestis 46
# 2 in vitro 5
# 3 E. coli 4
# 4 psaEFABC 3
# 5 r 3
# 6 Yersinia 2
# 7 Yersinia pestis 2
# 8 cis 2
# 9 fur 2
# 10 n 2
# # … with 43 more rows
filter(it2, nchar(italic) == 3)
# # A tibble: 8 × 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 × 5
# match section parag…¹ sente…² text
# <chr> <chr> <int> <int> <chr>
# 1 ymt Results and Discussion; Virulence genes in response to multiple envir… 3 4 The …
# 2 fur Results and Discussion; Clustering analysis and functional classifica… 3 2 It i…
# 3 yfe Results and Discussion; Clustering analysis and functional classifica… 3 4 Gene…
# 4 hmu Results and Discussion; Clustering analysis and functional classifica… 3 4 Gene…
# 5 yfu Results and Discussion; Clustering analysis and functional classifica… 3 4 Gene…
# 6 ybt Results and Discussion; Clustering analysis and functional classifica… 3 4 Gene…
# 7 cys Results and Discussion; Clustering analysis and functional classifica… 4 2 Gene…
# 8 cys Results and Discussion; Clustering analysis and functional classifica… 4 3 Clus…
# 9 fur Methods; Gel mobility shift analysis of Fur binding 1 1 The …
# # … with abbreviated variable names ¹paragraph, ²sentence