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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 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 × 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 v…` `Gene ID` `Putative or p…` `Reference (s)`
#     <chr>                         <chr>                    <chr>     <chr>            <chr>          
#   1 Iron uptake or heme synthesis yfeABCD operon* (r > 0.… YPO2439-… Transport/bindi… yfeABCD [54]   
#   2 Iron uptake or heme synthesis hmuRSTUV operon (r > 0.… YPO0279-… Transport/bindi… hmuRSTUV [55]  
#   3 Iron uptake or heme synthesis ysuJIHG* (r > 0.95)      YPO1529-… Iron uptake      -              
#   4 Iron uptake or heme synthesis sufABCDS* (r > 0.90)     YPO2400-… Iron-regulated … -              
#   5 Iron uptake or heme synthesis YPO1854-1856* (r > 0.97) YPO1854-… Iron uptake or … -              
#   6 Sulfur metabolism             tauABCD operon (r > 0.9… YPO0182-… Transport/bindi… tauABCD [56]   
#   7 Sulfur metabolism             ssuEADCB operon (r > 0.… YPO3623-… Sulphur metabol… ssu operon [57]
#   8 Sulfur metabolism             cys operon (r > 0.92)    YPO3010-… Cysteine synthe… -              
#   9 Sulfur metabolism             YPO1317-1319 (r > 0.97)  YPO1317-… Sulfur metaboli… -              
#  10 Sulfur metabolism             YPO4109-4111 (r > 0.90)  YPO4109-… Sulfur metaboli… -              
#  # … 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 × 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 × 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 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 × 5
#    match                 section                                             paragraph sentence text 
#    <chr>                 <chr>                                                   <int>    <int> <chr>
#  1 ACGCAATCGTTTTCNT      Results and Discussion; Computational discovery of…         2        3 A 16…
#  2 AAACGTTTNCGT          Results and Discussion; Computational discovery of…         2        4 It i…
#  3 TGATAATGATTATCATTATCA Results and Discussion; Computational discovery of…         2        5 A 21…
#  4 GATAATGATAATCATTATC   Results and Discussion; Computational discovery of…         2        6 It i…
#  5 TGANNNNNNTCAA         Results and Discussion; Computational discovery of…         2        7 A 15…
#  6 TTGATN                Results and Discussion; Computational discovery of…         2        8 It i…
#  7 NATCAA                Results and Discussion; Computational discovery of…         2        8 It i…
#  8 GTTAATTAA             Results and Discussion; Computational discovery of…         3        4 The …
#  9 GTTAATTAATGT          Results and Discussion; Computational discovery of…         3        5 An A…

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 × 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                                             paragraph sentence 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 respons…         3        1 As d…
#  5     8 [8-10]          Methods; Collection of microarray expression data           1        6 The …

separate_genes expands microbial gene operons like hmsHFRS into four separate genes.

separate_genes(txt)
#  # A tibble: 103 × 6
#     gene  match   section                                                    paragraph sentence 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 mu…         3        3 For …
#   9 hmsF  hmsHFRS Results and Discussion; Virulence genes in response to mu…         3        3 For …
#  10 hmsR  hmsHFRS Results and Discussion; Virulence genes in response to mu…         3        3 For …
#  # … with 93 more rows

Finally, 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 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 × 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 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 × 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                                                             paragraph sentence text 
#    <chr> <chr>                                                                   <int>    <int> <chr>
#  1 ymt   Results and Discussion; Virulence genes in response to multiple en…         3        4 The …
#  2 fur   Results and Discussion; Clustering analysis and functional classif…         3        2 It i…
#  3 yfe   Results and Discussion; Clustering analysis and functional classif…         3        4 Gene…
#  4 hmu   Results and Discussion; Clustering analysis and functional classif…         3        4 Gene…
#  5 yfu   Results and Discussion; Clustering analysis and functional classif…         3        4 Gene…
#  6 ybt   Results and Discussion; Clustering analysis and functional classif…         3        4 Gene…
#  7 cys   Results and Discussion; Clustering analysis and functional classif…         4        2 Gene…
#  8 cys   Results and Discussion; Clustering analysis and functional classif…         4        3 Clus…
#  9 fur   Methods; Gel mobility shift analysis of Fur binding                         1        1 The …