Gene Name Error Monthly Report
Mark Ziemann
2022-09-15
Source: https://github.com/markziemann/GeneNameErrors2020
View the reports: http://ziemann-lab.net/public/gene_name_errors/
Intro
Gene name errors result when data are imported improperly into MS Excel and other spreadsheet programs (Zeeberg et al, 2004). Certain gene names like MARCH3, SEPT2 and DEC1 are converted into date format. These errors are surprisingly common in supplementary data files in the field of genomics (Ziemann et al, 2016). This could be considered a small error because it only affects a small number of genes, however it is symptomtic of poor data processing methods. The purpose of this script is to identify gene name errors present in supplementary files of PubMed Central articles in the previous month.
library("XML")
library("jsonlite")
library("xml2")
library("reutils")
library("readxl")
library("RCurl")Get PMC IDs
Here I will be getting PubMed Central IDs for the previous month.
Start with figuring out the date to search PubMed Central.
CURRENT_MONTH=format(Sys.time(), "%m")
CURRENT_YEAR=format(Sys.time(), "%Y")
if (CURRENT_MONTH == "01") {
PREV_YEAR=as.character(as.numeric(format(Sys.time(), "%Y"))-1)
PREV_MONTH="12"
} else {
PREV_YEAR=CURRENT_YEAR
PREV_MONTH=as.character(as.numeric(format(Sys.time(), "%m"))-1)
}
DATE=paste(PREV_YEAR,"/",PREV_MONTH,sep="")
DATE## [1] "2022/8"Let’s see how many PMC IDs we have in the past month.
QUERY ='((genom*[Abstract]))'
ESEARCH_RES <- esearch(term=QUERY, db = "pmc", rettype = "uilist", retmode = "xml", retstart = 0,
retmax = 5000000, usehistory = TRUE, webenv = NULL, querykey = NULL, sort = NULL, field = NULL,
datetype = NULL, reldate = NULL,
mindate = paste(DATE,"/1",sep="") , maxdate = paste(DATE,"/31",sep=""))
pmc <- efetch(ESEARCH_RES,retmode="text",rettype="uilist",outfile="pmcids.txt")## Retrieving UIDs 1 to 500## Retrieving UIDs 501 to 1000## Retrieving UIDs 1001 to 1500## Retrieving UIDs 1501 to 2000## Retrieving UIDs 2001 to 2500## Retrieving UIDs 2501 to 3000## Retrieving UIDs 3001 to 3500pmc <- read.table(pmc)
pmc <- paste("PMC",pmc$V1,sep="")
NUM_ARTICLES=length(pmc)
NUM_ARTICLES## [1] 3117writeLines(pmc,con="pmc.txt")Run the screen
Now run the bash script. Note that false positives can occur (~1.5%) and these results have not been verified by a human.
Here are some definitions:
NUM_XLS = Number of supplementary Excel files in this set of PMC articles.
NUM_XLS_ARTICLES = Number of articles matching the PubMed Central search which have supplementary Excel files.
GENELISTS = The gene lists found in the Excel files. Each Excel file is counted once even it has multiple gene lists.
NUM_GENELISTS = The number of Excel files with gene lists.
NUM_GENELIST_ARTICLES = The number of PMC articles with supplementary Excel gene lists.
ERROR_GENELISTS = Files suspected to contain gene name errors. The dates and five-digit numbers indicate transmogrified gene names.
NUM_ERROR_GENELISTS = Number of Excel gene lists with errors.
NUM_ERROR_GENELIST_ARTICLES = Number of articles with supplementary Excel gene name errors.
ERROR_PROPORTION = This is the proportion of articles with Excel gene lists that have errors.
#system("./gene_names.sh pmc.txt")
results <- readLines("results.txt")
XLS <- results[grep("XLS",results,ignore.case=TRUE)]
NUM_XLS = length(XLS)
NUM_XLS## [1] 5084NUM_XLS_ARTICLES = length(unique(sapply(strsplit(XLS," "),"[[",1)))
NUM_XLS_ARTICLES## [1] 920GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>2]
#GENELISTS
NUM_GENELISTS <- length(unique(sapply(strsplit(GENELISTS," "),"[[",2)))
NUM_GENELISTS## [1] 639NUM_GENELIST_ARTICLES <- length(unique(sapply(strsplit(GENELISTS," "),"[[",1)))
NUM_GENELIST_ARTICLES## [1] 338ERROR_GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>3]
#ERROR_GENELISTS
NUM_ERROR_GENELISTS = length(ERROR_GENELISTS)
NUM_ERROR_GENELISTS## [1] 199GENELIST_ERROR_ARTICLES <- unique(sapply(strsplit(ERROR_GENELISTS," "),"[[",1))
GENELIST_ERROR_ARTICLES## [1] "PMC9427111" "PMC9426231" "PMC9417173" "PMC9413140" "PMC9411202"
## [6] "PMC9410909" "PMC9390996" "PMC9385149" "PMC9402397" "PMC9366661"
## [11] "PMC9393397" "PMC9388569" "PMC9387930" "PMC9386055" "PMC9382581"
## [16] "PMC9382191" "PMC9382085" "PMC9379253" "PMC9376450" "PMC9375324"
## [21] "PMC9374748" "PMC9373797" "PMC9372159" "PMC9371928" "PMC9371440"
## [26] "PMC9369202" "PMC9367689" "PMC9360787" "PMC9360490" "PMC9096232"
## [31] "PMC9355872" "PMC9355568" "PMC9355329" "PMC9351182" "PMC9351085"
## [36] "PMC9351068" "PMC9344228" "PMC9342750" "PMC9339328" "PMC9426548"
## [41] "PMC9426036" "PMC9424213" "PMC9421583" "PMC9418738" "PMC9412201"
## [46] "PMC9409727" "PMC9405706" "PMC9402582" "PMC9402440" "PMC9398501"
## [51] "PMC9396193" "PMC9392717" "PMC9388377" "PMC9387304" "PMC9386474"
## [56] "PMC9380254" "PMC9377152" "PMC9348801" "PMC9380953" "PMC9380471"
## [61] "PMC9374162" "PMC9363578" "PMC9361633" "PMC9361544" "PMC9360553"
## [66] "PMC9357003" "PMC9356431" "PMC9355943" "PMC9354845" "PMC9354623"
## [71] "PMC9353714"NUM_ERROR_GENELIST_ARTICLES <- length(GENELIST_ERROR_ARTICLES)
NUM_ERROR_GENELIST_ARTICLES## [1] 71ERROR_PROPORTION = NUM_ERROR_GENELIST_ARTICLES / NUM_GENELIST_ARTICLES
ERROR_PROPORTION## [1] 0.2100592Look at the errors detected
Here you can have a look at all the gene lists detected in the past month, as well as those with errors. The dates are obvious errors, these are commonly dates in September, March, December and October. The five-digit numbers represent dates as they are encoded in the Excel internal format. The five digit number is the number of days since 1900. If you were to take these numbers and put them into Excel and format the cells as dates, then these will also mostly map to dates in September, March, December and October.
#GENELISTS
ERROR_GENELISTS## [1] "PMC9427111 PMC_DL/PMC9427111/supplementaryfiles/elife-76095-fig1-figsupp2-data1.xlsx Scerevisiae 1 44105"
## [2] "PMC9426231 PMC_DL/PMC9426231/supplementaryfiles/12885_2022_10007_MOESM9_ESM.xlsx Ggallus 1 44813"
## [3] "PMC9426231 PMC_DL/PMC9426231/supplementaryfiles/12885_2022_10007_MOESM9_ESM.xlsx Hsapiens 1 44813"
## [4] "PMC9426231 PMC_DL/PMC9426231/supplementaryfiles/12885_2022_10007_MOESM9_ESM.xlsx Hsapiens 1 44813"
## [5] "PMC9426231 PMC_DL/PMC9426231/supplementaryfiles/12885_2022_10007_MOESM8_ESM.xlsx Hsapiens 2 44621 44896"
## [6] "PMC9426231 PMC_DL/PMC9426231/supplementaryfiles/12885_2022_10007_MOESM8_ESM.xlsx Hsapiens 1 44621"
## [7] "PMC9417173 zip/sciadv.abo6371_tables_s1_to_s13.xlsx Hsapiens 1 44448"
## [8] "PMC9417173 zip/sciadv.abo6371_tables_s1_to_s13.xlsx Hsapiens 5 38961 38777 41153 38777 37500"
## [9] "PMC9413140 PMC_DL/PMC9413140/supplementaryfiles/DataSheet5.XLS Mmusculus 4 2022/03/08 2022/03/09 2022/09/08 2022/09/10"
## [10] "PMC9413140 PMC_DL/PMC9413140/supplementaryfiles/DataSheet5.XLS Mmusculus 1 2022/09/06"
## [11] "PMC9411202 PMC_DL/PMC9411202/supplementaryfiles/41467_2022_32674_MOESM4_ESM.xlsx Hsapiens 1 43894"
## [12] "PMC9411202 PMC_DL/PMC9411202/supplementaryfiles/41467_2022_32674_MOESM4_ESM.xlsx Hsapiens 1 43894"
## [13] "PMC9411202 PMC_DL/PMC9411202/supplementaryfiles/41467_2022_32674_MOESM4_ESM.xlsx Hsapiens 2 44078 43892"
## [14] "PMC9410909 zip/Table_S4.xlsx Hsapiens 4 43719 43713 43715 43723"
## [15] "PMC9390996 zip/sciadv.abm8466_data_s3.xlsx Hsapiens 23 43162 43167 43352 43160 43170 43164 43354 43160 43166 43161 43347 43350 43349 43346 43161 43358 43345 43351 43165 43348 43355 43168 43353"
## [16] "PMC9390996 zip/sciadv.abm8466_data_s2.xlsx Hsapiens 8 43529 43526 43525 43525 43525 43526 43532 43526"
## [17] "PMC9385149 zip/sciadv.abq4884_table_s2.xlsx Hsapiens 1 44256"
## [18] "PMC9385149 zip/sciadv.abq4884_table_s2.xlsx Hsapiens 1 44442"
## [19] "PMC9385149 zip/sciadv.abq4884_table_s2.xlsx Hsapiens 2 44256 44258"
## [20] "PMC9402397 PMC_DL/PMC9402397/supplementaryfiles/NIHMS1829334-supplement-4.xlsx Hsapiens 1 43161"
## [21] "PMC9402397 PMC_DL/PMC9402397/supplementaryfiles/NIHMS1829334-supplement-3.xlsx Hsapiens 14 43891 43895 43894 43893 43898 43897 44166 43892 43891 43892 43900 43899 43896 43901"
## [22] "PMC9366661 PMC_DL/PMC9366661/supplementaryfiles/jamapsychiatry-e222196-s002.xlsx Hsapiens 23 44450 44257 44256 44256 44449 44454 44263 44454 44443 44454 44443 44263 44447 44441 44443 44441 44256 44260 44257 44256 44265 44447 44257"
## [23] "PMC9366661 PMC_DL/PMC9366661/supplementaryfiles/jamapsychiatry-e222196-s002.xlsx Hsapiens 23 44443 44443 44443 44447 44256 44260 44257 44450 44263 44449 44257 44257 44263 44256 44256 44441 44447 44256 44441 44454 44265 44454 44454"
## [24] "PMC9366661 PMC_DL/PMC9366661/supplementaryfiles/jamapsychiatry-e222196-s002.xlsx Hsapiens 23 44260 44256 44256 44450 44449 44263 44256 44443 44263 44441 44443 44441 44257 44257 44454 44443 44265 44454 44257 44447 44454 44256 44447"
## [25] "PMC9366661 PMC_DL/PMC9366661/supplementaryfiles/jamapsychiatry-e222196-s002.xlsx Hsapiens 23 44256 44257 44256 44450 44441 44447 44449 44443 44256 44454 44260 44443 44443 44257 44447 44454 44265 44454 44257 44263 44441 44263 44256"
## [26] "PMC9393397 PMC_DL/PMC9393397/supplementaryfiles/Table_2.xlsx Scerevisiae 2 36982 39326"
## [27] "PMC9393397 PMC_DL/PMC9393397/supplementaryfiles/Table_2.xlsx Scerevisiae 2 36982 39326"
## [28] "PMC9393397 PMC_DL/PMC9393397/supplementaryfiles/Table_2.xlsx Scerevisiae 2 36982 39326"
## [29] "PMC9388569 PMC_DL/PMC9388569/supplementaryfiles/41598_2022_14977_MOESM2_ESM.xlsx Hsapiens 1 14977"
## [30] "PMC9387930 PMC_DL/PMC9387930/supplementaryfiles/pntd.0010672.s007.xlsx Hsapiens 16 44084 43893 43892 44077 43900 43898 44083 44082 44080 43897 44089 44076 44078 44086 43894 43899"
## [31] "PMC9386055 PMC_DL/PMC9386055/supplementaryfiles/Table_1.xlsx Hsapiens 2 44811 44805"
## [32] "PMC9386055 PMC_DL/PMC9386055/supplementaryfiles/Table_1.xlsx Hsapiens 8 44806 44814 44627 44815 44811 44625 44805 44622"
## [33] "PMC9382581 PMC_DL/PMC9382581/supplementaryfiles/Table_1.xlsx Hsapiens 1 44266"
## [34] "PMC9382191 PMC_DL/PMC9382191/supplementaryfiles/Table5.xlsx Hsapiens 4 44621 44624 44628 44624"
## [35] "PMC9382191 PMC_DL/PMC9382191/supplementaryfiles/Table6.xlsx Hsapiens 3 44624 44621 44628"
## [36] "PMC9382191 PMC_DL/PMC9382191/supplementaryfiles/Table6.xlsx Hsapiens 3 44624 44621 44628"
## [37] "PMC9382085 PMC_DL/PMC9382085/supplementaryfiles/Table5.XLSX Hsapiens 5 44626 44625 44896 44627 44629"
## [38] "PMC9382085 PMC_DL/PMC9382085/supplementaryfiles/Table4.XLSX Hsapiens 2 43900 44166"
## [39] "PMC9379253 PMC_DL/PMC9379253/supplementaryfiles/Table_3.xlsx Hsapiens 28 38596 40422 37135 37135 38961 37865 38777 39692 38047 38231 42248 37500 37226 39326 39142 40787 36951 37316 37681 41883 36951 41153 39873 40603 39508 40057 40238 37316"
## [40] "PMC9379253 PMC_DL/PMC9379253/supplementaryfiles/Table_3.xlsx Hsapiens 26 40422 42248 38777 37865 40603 37226 36951 40238 37316 37681 38047 38412 39142 39508 39873 37135 40787 41153 41883 37500 38231 38596 38961 39326 39692 40057"
## [41] "PMC9379253 PMC_DL/PMC9379253/supplementaryfiles/Table_3.xlsx Hsapiens 28 40603 40422 37865 40238 41883 39508 40057 38777 41153 36951 37135 38231 38047 39692 39142 38596 39873 37316 36951 37316 37681 37500 38961 40787 39326 38412 42248 37226"
## [42] "PMC9376450 PMC_DL/PMC9376450/supplementaryfiles/Table_3.xlsx Hsapiens 13 43525 43525 43526 43532 43526 43529 43533 43534 43528 43531 43800 43530 43535"
## [43] "PMC9376450 PMC_DL/PMC9376450/supplementaryfiles/Table_3.xlsx Hsapiens 2 43525 43525"
## [44] "PMC9375324 zip/Supplementary_table_3.xls Hsapiens 2 2022/09/09 2022/09/03"
## [45] "PMC9374748 PMC_DL/PMC9374748/supplementaryfiles/41467_2022_32485_MOESM6_ESM.xlsx Mmusculus 94 36951 40057 39142 39326 38047 37865 38047 39692 41153 36951 36951 38412 37135 37135 38961 37316 40787 37500 38777 36951 37500 40603 39142 36951 36951 41153 38231 39508 37316 40057 36951 40238 39508 37135 36951 40787 39508 38231 39692 38961 39326 36951 39873 40787 38596 38961 38596 39142 40603 40422 40238 38047 39692 39873 39873 38596 40603 38777 37681 37316 40422 37316 38412 41153 41883 40787 38412 41153 36951 38231 39508 37681 40238 41883 37316 39873 37500 39142 40603 37316 37865 40057 37316 39326 39692 40057 38596 38047 38961 37316 37865 41883 39142 40422"
## [46] "PMC9374748 PMC_DL/PMC9374748/supplementaryfiles/41467_2022_32485_MOESM6_ESM.xlsx Mmusculus 9 39873 41153 38231 39692 39692 38777 37865 40238 41153"
## [47] "PMC9373797 PMC_DL/PMC9373797/supplementaryfiles/DataSheet_3.xls Hsapiens 27 2022/09/03 2022/09/10 2022/09/02 2022/09/04 2022/03/11 2022/03/05 2022/09/05 2022/03/02 2022/03/02 2022/03/10 2022/09/07 2022/12/01 2022/09/08 2022/03/01 2022/03/07 2022/03/08 2022/03/01 2022/03/04 2022/09/12 2022/09/06 2022/09/14 2022/03/03 2022/09/11 2022/09/01 2022/09/09 2022/03/09 2022/03/06"
## [48] "PMC9372159 PMC_DL/PMC9372159/supplementaryfiles/41467_2022_31375_MOESM4_ESM.xlsx Hsapiens 146 37226 37226 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 38961 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 37500 37500 37500 38777 39508 38777 39508 38777 39508 38777 39508 38777 39508 38777 39508 36951 38047 36951 36951 38777 37681 38777 37681 38777 37681 38777 37681 38777 37681 38777 37681 38777 37681 38777 37681 38777 37681 36951 36951 36951 40603 36951 36951 36951 40603 36951 36951 36951 36951 36951 36951 36951 36951 36951 39326 39326 39326 39326 37226 38412 37226 42248 42248 42248 42248 42248 42248 42248 42248 42248 39508 36951 37316 36951 40603 36951 40603 36951 36951 36951 36951 36951 39508 36951 40603 37226 36951 39692 40238 37681 40057 38961 36951 39508"
## [49] "PMC9372159 PMC_DL/PMC9372159/supplementaryfiles/41467_2022_31375_MOESM4_ESM.xlsx Hsapiens 2 39508 40422"
## [50] "PMC9372159 PMC_DL/PMC9372159/supplementaryfiles/41467_2022_31375_MOESM4_ESM.xlsx Hsapiens 1 40422"
## [51] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Mmusculus 1 36951"
## [52] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Hsapiens 1 40787"
## [53] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Mmusculus 4 36951 39326 38412 38412"
## [54] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Mmusculus 26 36951 37316 36951 40238 40603 37316 37681 38047 38412 38777 39142 39508 39873 37135 40422 40787 41153 41883 37500 37865 38231 38596 38961 39326 39692 40057"
## [55] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Mmusculus 26 36951 37316 36951 40238 40603 37316 37681 38047 38412 38777 39142 39508 39873 37135 40422 40787 41153 41883 37500 37865 38231 38596 38961 39326 39692 40057"
## [56] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Hsapiens 1 40057"
## [57] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Hsapiens 1 36951"
## [58] "PMC9371928 zip/Supplementary_Tables_revised.xlsx Mmusculus 1 36951"
## [59] "PMC9371440 zip/science.abn5800_table_s6.xlsx Dmelanogaster 2 44808 44806"
## [60] "PMC9371440 zip/science.abn5800_table_s3.xlsx Dmelanogaster 51 44809 44809 44805 44806 44805 44808 44806 44805 44805 44806 44806 44805 44805 44806 44809 44805 44809 44806 44805 44809 44805 44809 44805 44809 44806 44805 44806 44809 44805 44806 44806 44809 44805 44805 44806 44809 44805 44806 44805 44805 44809 44806 44805 44805 44806 44805 44805 44805 44806 44805 44806"
## [61] "PMC9369202 zip/Supplementary_Table-ijms-0-.xlsx Athaliana 4 42614 42616 42617 42615"
## [62] "PMC9369202 zip/Supplementary_Table-ijms-0-.xlsx Athaliana 7 42462 42615 42615 42650 42461 42463 42614"
## [63] "PMC9367689 PMC_DL/PMC9367689/supplementaryfiles/Table_1.xlsx Hsapiens 1 44623"
## [64] "PMC9360787 PMC_DL/PMC9360787/supplementaryfiles/Table_2.xlsx Hsapiens 1 44623"
## [65] "PMC9360490 PMC_DL/PMC9360490/supplementaryfiles/Table_1.xlsx Hsapiens 1 44808"
## [66] "PMC9360490 PMC_DL/PMC9360490/supplementaryfiles/Table_1.xlsx Hsapiens 1 44805"
## [67] "PMC9360490 PMC_DL/PMC9360490/supplementaryfiles/Table_1.xlsx Hsapiens 2 44810 44621"
## [68] "PMC9360490 PMC_DL/PMC9360490/supplementaryfiles/Table_1.xlsx Hsapiens 3 44624 44807 44809"
## [69] "PMC9096232 PMC_DL/PMC9096232/supplementaryfiles/med-2022-0475-sm.xlsx Hsapiens 1 44259"
## [70] "PMC9355872 PMC_DL/PMC9355872/supplementaryfiles/41588_2022_1131_MOESM3_ESM.xlsx Hsapiens 1 44166"
## [71] "PMC9355872 PMC_DL/PMC9355872/supplementaryfiles/41588_2022_1131_MOESM3_ESM.xlsx Hsapiens 1 44166"
## [72] "PMC9355872 PMC_DL/PMC9355872/supplementaryfiles/41588_2022_1131_MOESM3_ESM.xlsx Hsapiens 1 44166"
## [73] "PMC9355568 PMC_DL/PMC9355568/supplementaryfiles/elife-77999-fig1-data6.xls Hsapiens 10 37135 40238 36951 37316 38047 38412 38777 41153 39142 40422"
## [74] "PMC9355568 PMC_DL/PMC9355568/supplementaryfiles/elife-77999-fig1-data5.xls Hsapiens 11 37135 41153 40238 37316 38047 38412 38777 39142 36951 40057 40422"
## [75] "PMC9355568 PMC_DL/PMC9355568/supplementaryfiles/elife-77999-fig1-data4.xls Hsapiens 17 41153 40422 38047 37865 37316 38777 38231 39326 38596 38961 38412 41883 39142 36951 40057 40238 37135"
## [76] "PMC9355568 PMC_DL/PMC9355568/supplementaryfiles/elife-77999-fig1-data2.xls Hsapiens 10 40238 37316 38777 38412 38047 36951 40057 39142 40422 41153"
## [77] "PMC9355568 PMC_DL/PMC9355568/supplementaryfiles/elife-77999-fig1-data7.xls Hsapiens 16 37135 40238 38596 37865 37316 38777 38231 39326 38047 36951 41883 40057 40422 39142 41153 38412"
## [78] "PMC9355329 PMC_DL/PMC9355329/supplementaryfiles/Table1.xlsx Hsapiens 1 44810"
## [79] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM5_ESM.xlsx Athaliana 1 37165"
## [80] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM2_ESM.xlsx Athaliana 2 44473 44416"
## [81] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM2_ESM.xlsx Athaliana 6 44470 44474 44476 44287 44416 44470"
## [82] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM2_ESM.xlsx Athaliana 5 44470 44474 44476 44473 44470"
## [83] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM2_ESM.xlsx Athaliana 3 44470 44414 44470"
## [84] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM4_ESM.xlsx Athaliana 2 44288 44475"
## [85] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM4_ESM.xlsx Athaliana 1 44475"
## [86] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM4_ESM.xlsx Athaliana 1 44835"
## [87] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM4_ESM.xlsx Athaliana 2 44836 44781"
## [88] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM4_ESM.xlsx Athaliana 3 44805 44837 44840"
## [89] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM3_ESM.xlsx Athaliana 1 44473"
## [90] "PMC9351182 PMC_DL/PMC9351182/supplementaryfiles/13059_2022_2731_MOESM3_ESM.xlsx Athaliana 1 44473"
## [91] "PMC9351085 PMC_DL/PMC9351085/supplementaryfiles/12903_2022_2358_MOESM2_ESM.xlsx Hsapiens 1 44531"
## [92] "PMC9351068 PMC_DL/PMC9351068/supplementaryfiles/13073_2022_1083_MOESM3_ESM.xlsx Hsapiens 105 43891 43896 43896 44166 43891 43891 43896 43896 43896 43896 43894 44081 44088 43891 43891 44088 43891 43891 43901 44087 43891 43898 43898 44083 43893 43900 43891 44084 43891 44088 44089 43898 43893 43891 44088 44089 43891 43891 43893 44082 44084 44166 43893 44088 44085 43891 43894 43891 43891 43891 43891 43891 43891 43891 43891 44088 43896 43893 44166 44083 44085 43891 43900 43896 43900 43900 44083 43891 44083 43891 43893 44083 43892 43891 44089 43897 44076 43891 43891 43891 43891 44166 43891 43891 43894 43891 43898 43891 43896 44085 43891 44083 43891 43891 43901 43893 44083 43891 44080 43893 43892 44084 43891 43891 43898"
## [93] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 1 44808"
## [94] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44808 44811 44806 44813 44819 44627 44626 44896"
## [95] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44811 44896 44626 44808 44806 44627 44813 44819"
## [96] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44808 44896 44806 44819 44811 44626 44813 44627"
## [97] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44808 44627 44626 44813 44806 44811 44896 44819"
## [98] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44808 44811 44806 44896 44813 44627 44626 44819"
## [99] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44808 44813 44896 44626 44811 44819 44806 44627"
## [100] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44626 44896 44819 44806 44813 44627 44811 44808"
## [101] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44808 44813 44806 44811 44896 44626 44819 44627"
## [102] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44813 44808 44811 44806 44626 44627 44819 44896"
## [103] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44626 44813 44808 44806 44627 44811 44819 44896"
## [104] "PMC9344228 PMC_DL/PMC9344228/supplementaryfiles/12985_2022_1853_MOESM12_ESM.xlsx Hsapiens 8 44806 44808 44811 44813 44819 44627 44896 44626"
## [105] "PMC9342750 PMC_DL/PMC9342750/supplementaryfiles/pone.0272384.s002.xlsx Hsapiens 9 43720 43719 43710 43711 43716 43712 43714 43715 43525"
## [106] "PMC9342750 PMC_DL/PMC9342750/supplementaryfiles/pone.0272384.s002.xlsx Hsapiens 5 43719 43710 43716 43714 43715"
## [107] "PMC9339328 PMC_DL/PMC9339328/supplementaryfiles/jkac132_supplementary_data.xlsx Drerio 4 43355 43355 43355 43355"
## [108] "PMC9426548 PMC_DL/PMC9426548/supplementaryfiles/Table1.xlsx Hsapiens 1 44622"
## [109] "PMC9426036 PMC_DL/PMC9426036/supplementaryfiles/13073_2022_1084_MOESM1_ESM.xls Hsapiens 49 40057 36951 40057 40787 40057 39692 39142 40057 40238 37865 40057 40057 40057 40057 40057 40057 40422 36951 40057 40422 36951 40057 40057 40057 36951 40422 40057 37865 40057 40057 38231 40057 37681 40238 42248 37135 40057 40057 40422 36951 40057 40057 40057 39692 40238 40057 40238 41153 36951"
## [110] "PMC9426036 PMC_DL/PMC9426036/supplementaryfiles/13073_2022_1084_MOESM1_ESM.xls Hsapiens 3 38047 38047 36951"
## [111] "PMC9424213 PMC_DL/PMC9424213/supplementaryfiles/41467_2022_32537_MOESM5_ESM.xlsx Hsapiens 9 44449 44265 44263 44260 44261 44262 44440 44256 44256"
## [112] "PMC9424213 PMC_DL/PMC9424213/supplementaryfiles/41467_2022_32537_MOESM5_ESM.xlsx Hsapiens 6 44257 44445 44440 44256 44444 44256"
## [113] "PMC9424213 PMC_DL/PMC9424213/supplementaryfiles/41467_2022_32537_MOESM5_ESM.xlsx Hsapiens 1 44265"
## [114] "PMC9424213 PMC_DL/PMC9424213/supplementaryfiles/41467_2022_32537_MOESM5_ESM.xlsx Hsapiens 4 44259 44265 44440 44256"
## [115] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 5 43892 44081 44081 44084 44084"
## [116] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 5 43892 44081 44081 44084 44084"
## [117] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 1 Tex14-Sept4"
## [118] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Rnorvegicus 1 Tex14-Sept4"
## [119] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 3 43892 43900 43900"
## [120] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 3 43892 43900 43900"
## [121] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 2 44081 44081"
## [122] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 2 44081 44081"
## [123] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 2 44084 44084"
## [124] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 2 44084 44084"
## [125] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 1 43900"
## [126] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 1 43900"
## [127] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 1 43897"
## [128] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 1 43897"
## [129] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 3 44075 44084 44084"
## [130] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 3 44075 44084 44084"
## [131] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 3 44085 44085 44085"
## [132] "PMC9421583 PMC_DL/PMC9421583/supplementaryfiles/mmc1.xlsx Mmusculus 3 44085 44085 44085"
## [133] "PMC9418738 PMC_DL/PMC9418738/supplementaryfiles/mmc2.xlsx Hsapiens 1 44818"
## [134] "PMC9418738 PMC_DL/PMC9418738/supplementaryfiles/mmc2.xlsx Hsapiens 1 44625"
## [135] "PMC9412201 PMC_DL/PMC9412201/supplementaryfiles/Table10.XLSX Mmusculus 6 44807 44813 44628 44629 44621 44806"
## [136] "PMC9412201 PMC_DL/PMC9412201/supplementaryfiles/Table10.XLSX Mmusculus 2 44815 44808"
## [137] "PMC9412201 PMC_DL/PMC9412201/supplementaryfiles/Table10.XLSX Mmusculus 6 44807 44813 44628 44629 44621 44806"
## [138] "PMC9412201 PMC_DL/PMC9412201/supplementaryfiles/Table10.XLSX Mmusculus 2 44815 44808"
## [139] "PMC9412201 PMC_DL/PMC9412201/supplementaryfiles/Table6.XLSX Mmusculus 2 44815 44808"
## [140] "PMC9412201 PMC_DL/PMC9412201/supplementaryfiles/Table12.XLSX Rnorvegicus 27 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [141] "PMC9409727 zip/Table_S2-Annotations_of_Diaporthe_eres.xlsx Scerevisiae 2 38596 38596"
## [142] "PMC9405706 zip/Supplementary_tables.xlsx Hsapiens 26 44896 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [143] "PMC9402582 PMC_DL/PMC9402582/supplementaryfiles/mmc1.xlsx Hsapiens 34 40422 TSA: Gallus Gallus. breed J-Line. contig 15_25_c5960/24/415. transcribed RNA sequence 41153 38596 40057 40603 TSA: Gallus Gallus. breed J-Line. contig 1_14_c470/27/2140. transcribed RNA sequence 37316 36951 38961 38412 38047 39142 42248 37500 37500"
## [144] "PMC9402440 PMC_DL/PMC9402440/supplementaryfiles/41586_2022_5072_MOESM4_ESM.xlsx Hsapiens 1 43800"
## [145] "PMC9402440 PMC_DL/PMC9402440/supplementaryfiles/41586_2022_5072_MOESM4_ESM.xlsx Hsapiens 1 43800"
## [146] "PMC9398501 PMC_DL/PMC9398501/supplementaryfiles/13071_2022_5405_MOESM5_ESM.xlsx Hsapiens 2 44623 44816"
## [147] "PMC9398501 PMC_DL/PMC9398501/supplementaryfiles/13071_2022_5405_MOESM5_ESM.xlsx Hsapiens 2 44623 44816"
## [148] "PMC9398501 PMC_DL/PMC9398501/supplementaryfiles/13071_2022_5405_MOESM6_ESM.xlsx Ggallus 6 44816 44816 44816 44816 44816 44816"
## [149] "PMC9398501 PMC_DL/PMC9398501/supplementaryfiles/13071_2022_5405_MOESM6_ESM.xlsx Ggallus 4 44816 44816 44816 44816"
## [150] "PMC9398501 PMC_DL/PMC9398501/supplementaryfiles/13071_2022_5405_MOESM8_ESM.xlsx Hsapiens 141 44623 44623 44816 44623 44623 44623 44623 44623 44623 44623 44623 44623 44623 44623 44816 44623 44623 44623 44623 44623 44623 44623 44623 44816 44623 44623 44623 44623 44623 44623 44623 44623 44623 44816 44623 44623 44623 44623 44816 44816 44623 44816 44623 44816 44816 44623 44816 44623 44623 44623 44623 44816 44816 44816 44816 44816 44623 44816 44623 44816 44816 44623 44816 44816 44623 44816 44816 44623 44816 44623 44816 44816 44623 44816 44816 44623 44816 44816 44623 44623 44623 44623 44623 44816 44816 44816 44623 44623 44623 44816 44816 44623 44816 44816 44816 44816 44816 44816 44623 44623 44816 44816 44816 44623 44623 44816 44816 44816 44816 44816 44623 44816 44816 44623 44623 44816 44816 44623 44816 44623 44816 44623 44816 44816 44623 44623 44623 44816 44816 44816 44816 44816 44816 44623 44816 44623 44816 44623 44816 44816 44816"
## [151] "PMC9398501 PMC_DL/PMC9398501/supplementaryfiles/13071_2022_5405_MOESM8_ESM.xlsx Hsapiens 174 44623 44623 44623 44816 44623 44623 44623 44623 44623 44623 44816 44623 44623 44623 44623 44623 44623 44623 44623 44623 44623 44623 44623 44816 44623 44816 44816 44623 44816 44623 44623 44623 44623 44623 44623 44623 44623 44623 44623 44816 44623 44816 44623 44816 44816 44623 44816 44623 44816 44816 44816 44623 44623 44816 44623 44623 44623 44816 44816 44623 44623 44816 44816 44816 44623 44623 44816 44816 44623 44816 44816 44816 44816 44816 44623 44816 44816 44623 44816 44623 44623 44623 44623 44816 44623 44623 44623 44816 44623 44623 44816 44623 44623 44816 44816 44623 44816 44623 44623 44623 44623 44816 44816 44623 44816 44816 44623 44623 44623 44623 44816 44816 44816 44816 44816 44816 44623 44816 44623 44816 44816 44816 44816 44623 44816 44816 44816 44623 44816 44816 44816 44816 44623 44816 44816 44816 44623 44816 44623 44816 44623 44816 44816 44623 44816 44816 44623 44816 44623 44623 44816 44816 44816 44816 44623 44816 44623 44623 44816 44816 44623 44623 44816 44623 44816 44816 44623 44816 44623 44816 44816 44623 44816 44623"
## [152] "PMC9396193 PMC_DL/PMC9396193/supplementaryfiles/JCLA-36-e24582-s001.xlsx Hsapiens 1 44623"
## [153] "PMC9392717 PMC_DL/PMC9392717/supplementaryfiles/18_2022_4519_MOESM2_ESM.xlsx Hsapiens 2 38231 38200"
## [154] "PMC9388377 PMC_DL/PMC9388377/supplementaryfiles/41591_2022_1927_MOESM3_ESM.xlsx Hsapiens 4 40057 37865 37226 37316"
## [155] "PMC9387304 zip/Supp_Data_1.xlsx Hsapiens 1 44261"
## [156] "PMC9387304 zip/Supp_Data_1.xlsx Hsapiens 1 44626"
## [157] "PMC9387304 zip/Supp_Data_1.xlsx Hsapiens 1 44623"
## [158] "PMC9386474 zip/Figure.data/Fig.7.xlsx Hsapiens 28 44815 44621 44626 44631 44623 44812 44819 44811 44818 44896 44628 44625 44629 44816 44805 44622 44621 44808 44630 44813 44622 44809 44807 44810 44814 44627 44624 44806"
## [159] "PMC9380254 PMC_DL/PMC9380254/supplementaryfiles/mmc2.xlsx Mmusculus 1 42248"
## [160] "PMC9377152 PMC_DL/PMC9377152/supplementaryfiles/CTM2-12-e982-s002.xlsx Mmusculus 1 44265"
## [161] "PMC9348801 zip/sciadv.abj7176_table_s2.xlsx Hsapiens 4 44531 44265 44262 44264"
## [162] "PMC9348801 zip/sciadv.abj7176_table_s2.xlsx Hsapiens 3 44256 44266 44263"
## [163] "PMC9348801 zip/sciadv.abj7176_table_s6.xlsx Hsapiens 1 44262"
## [164] "PMC9348801 zip/sciadv.abj7176_table_s6.xlsx Hsapiens 1 44256"
## [165] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s015.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [166] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s015.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [167] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s015.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [168] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s013.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [169] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s013.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [170] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s013.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [171] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 2 44621 44626"
## [172] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 6 44814 44622 44625 44813 44628 44629"
## [173] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 7 44815 44814 44819 44627 44813 44629 44812"
## [174] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [175] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [176] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [177] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [178] "PMC9380953 PMC_DL/PMC9380953/supplementaryfiles/pbio.3001741.s014.xlsx Hsapiens 28 44896 44621 44622 44621 44630 44631 44622 44623 44624 44625 44626 44627 44628 44629 44819 44805 44814 44815 44816 44818 44806 44807 44808 44809 44810 44811 44812 44813"
## [179] "PMC9380471 PMC_DL/PMC9380471/supplementaryfiles/NIHMS1812939-supplement-11.xlsx Hsapiens 6 44258 44260 44261 44262 44263 44264"
## [180] "PMC9374162 PMC_DL/PMC9374162/supplementaryfiles/NIHMS1798680-supplement-6.xlsx Hsapiens 2 43891 44166"
## [181] "PMC9374162 PMC_DL/PMC9374162/supplementaryfiles/NIHMS1798680-supplement-6.xlsx Hsapiens 6 43894 44085 43901 44080 44078 44083"
## [182] "PMC9374162 PMC_DL/PMC9374162/supplementaryfiles/NIHMS1798680-supplement-6.xlsx Hsapiens 3 44084 43891 44077"
## [183] "PMC9374162 PMC_DL/PMC9374162/supplementaryfiles/NIHMS1798680-supplement-6.xlsx Hsapiens 3 44085 43901 43898"
## [184] "PMC9363578 PMC_DL/PMC9363578/supplementaryfiles/Table_1.xlsx Hsapiens 2 44809 44622"
## [185] "PMC9361633 PMC_DL/PMC9361633/supplementaryfiles/12863_2022_1070_MOESM1_ESM.xlsx Hsapiens 13 44630 44624 44896 44631 44622 44623 44626 44629 44628 44819 44621 44627 44625"
## [186] "PMC9361544 PMC_DL/PMC9361544/supplementaryfiles/13059_2022_2740_MOESM2_ESM.xlsx Hsapiens 1 44082"
## [187] "PMC9361544 PMC_DL/PMC9361544/supplementaryfiles/13059_2022_2740_MOESM2_ESM.xlsx Hsapiens 4 44076 44079 44080 44083"
## [188] "PMC9361544 PMC_DL/PMC9361544/supplementaryfiles/13059_2022_2740_MOESM2_ESM.xlsx Hsapiens 4 44076 44079 44080 44083"
## [189] "PMC9360553 PMC_DL/PMC9360553/supplementaryfiles/Table1.XLSX Hsapiens 3 44631 44631 44815"
## [190] "PMC9360553 PMC_DL/PMC9360553/supplementaryfiles/Table1.XLSX Hsapiens 2 44805 44805"
## [191] "PMC9357003 PMC_DL/PMC9357003/supplementaryfiles/41467_2022_32295_MOESM5_ESM.xls Hsapiens 2 2022/03/07 2022/03/03"
## [192] "PMC9357003 PMC_DL/PMC9357003/supplementaryfiles/41467_2022_32295_MOESM4_ESM.xls Hsapiens 2 2022/03/04 2022/12/01"
## [193] "PMC9356431 PMC_DL/PMC9356431/supplementaryfiles/12885_2022_9909_MOESM3_ESM.xlsx Hsapiens 43 44627 44628 44814 44806 44627 44814 44627 44627 44626 44806 44626 44814 44627 44626 44622 44622 44815 44815 44621 44810 44810 44814 44626 44814 44806 44806 44806 44627 44627 44627 44815 44814 44627 44627 44806 44627 44623 44814 44813 44815 44810 44810 44810"
## [194] "PMC9355943 PMC_DL/PMC9355943/supplementaryfiles/41598_2022_16754_MOESM2_ESM.xlsx Hsapiens 2 39326 40787"
## [195] "PMC9354845 zip/Supplementary_file/S7-mRNA-miRNA-differention.xlsx Hsapiens 1 44266"
## [196] "PMC9354845 zip/Supplementary_file/S7-mRNA-miRNA-differention.xlsx Hsapiens 1 44266"
## [197] "PMC9354845 zip/Supplementary_file/S2-mRNA-fpkm.xlsx Hsapiens 11 44265 44266 44261 44262 44259 44260 44263 44264 44257 44258 44256"
## [198] "PMC9354623 zip/supplementary_table_2.xlsx Hsapiens 6 44811 44811 44811 44819 44811 44811"
## [199] "PMC9353714 zip/Supplementary_material/Supplementary_Table_1.xlsx Hsapiens 6 44622 44809 44623 44808 44621 44621"In depth look at the errors
Let’s investigate the errors in more detail.
# By species
SPECIES <- sapply(strsplit(ERROR_GENELISTS," "),"[[",3)
table(SPECIES)## SPECIES
## Athaliana Dmelanogaster Drerio Ggallus Hsapiens
## 14 2 1 3 139
## Mmusculus Rnorvegicus Scerevisiae
## 33 2 5par(mar=c(5,12,4,2))
barplot(table(SPECIES),horiz=TRUE,las=1)
par(mar=c(5,5,4,2))
# Number of affected Excel files per paper
DIST <- table(sapply(strsplit(ERROR_GENELISTS," "),"[[",1))
DIST##
## PMC9096232 PMC9339328 PMC9342750 PMC9344228 PMC9348801 PMC9351068 PMC9351085
## 1 1 2 12 4 1 1
## PMC9351182 PMC9353714 PMC9354623 PMC9354845 PMC9355329 PMC9355568 PMC9355872
## 12 1 1 3 1 5 3
## PMC9355943 PMC9356431 PMC9357003 PMC9360490 PMC9360553 PMC9360787 PMC9361544
## 1 1 2 4 2 1 3
## PMC9361633 PMC9363578 PMC9366661 PMC9367689 PMC9369202 PMC9371440 PMC9371928
## 1 1 4 1 2 2 8
## PMC9372159 PMC9373797 PMC9374162 PMC9374748 PMC9375324 PMC9376450 PMC9377152
## 3 1 4 2 1 2 1
## PMC9379253 PMC9380254 PMC9380471 PMC9380953 PMC9382085 PMC9382191 PMC9382581
## 3 1 1 14 2 3 1
## PMC9385149 PMC9386055 PMC9386474 PMC9387304 PMC9387930 PMC9388377 PMC9388569
## 3 2 1 3 1 1 1
## PMC9390996 PMC9392717 PMC9393397 PMC9396193 PMC9398501 PMC9402397 PMC9402440
## 2 1 3 1 6 2 2
## PMC9402582 PMC9405706 PMC9409727 PMC9410909 PMC9411202 PMC9412201 PMC9413140
## 1 1 1 1 3 6 2
## PMC9417173 PMC9418738 PMC9421583 PMC9424213 PMC9426036 PMC9426231 PMC9426548
## 2 2 18 4 2 5 1
## PMC9427111
## 1summary(as.numeric(DIST))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.803 3.000 18.000hist(DIST,main="Number of affected Excel files per paper")
# PMC Articles with the most errors
DIST_DF <- as.data.frame(DIST)
DIST_DF <- DIST_DF[order(-DIST_DF$Freq),,drop=FALSE]
head(DIST_DF,20)## Var1 Freq
## 66 PMC9421583 18
## 39 PMC9380953 14
## 4 PMC9344228 12
## 8 PMC9351182 12
## 28 PMC9371928 8
## 54 PMC9398501 6
## 62 PMC9412201 6
## 13 PMC9355568 5
## 69 PMC9426231 5
## 5 PMC9348801 4
## 18 PMC9360490 4
## 24 PMC9366661 4
## 31 PMC9374162 4
## 67 PMC9424213 4
## 11 PMC9354845 3
## 14 PMC9355872 3
## 21 PMC9361544 3
## 29 PMC9372159 3
## 36 PMC9379253 3
## 41 PMC9382191 3MOST_ERR_FILES = as.character(DIST_DF[1,1])
MOST_ERR_FILES## [1] "PMC9421583"# Number of errors per paper
NERR <- as.numeric(sapply(strsplit(ERROR_GENELISTS," "),"[[",4))
names(NERR) <- sapply(strsplit(ERROR_GENELISTS," "),"[[",1)
NERR <-tapply(NERR, names(NERR), sum)
NERR## PMC9096232 PMC9339328 PMC9342750 PMC9344228 PMC9348801 PMC9351068 PMC9351085
## 1 4 14 89 9 105 1
## PMC9351182 PMC9353714 PMC9354623 PMC9354845 PMC9355329 PMC9355568 PMC9355872
## 28 6 6 13 1 64 3
## PMC9355943 PMC9356431 PMC9357003 PMC9360490 PMC9360553 PMC9360787 PMC9361544
## 2 43 4 7 5 1 9
## PMC9361633 PMC9363578 PMC9366661 PMC9367689 PMC9369202 PMC9371440 PMC9371928
## 13 2 92 1 11 53 61
## PMC9372159 PMC9373797 PMC9374162 PMC9374748 PMC9375324 PMC9376450 PMC9377152
## 149 27 14 103 2 15 1
## PMC9379253 PMC9380254 PMC9380471 PMC9380953 PMC9382085 PMC9382191 PMC9382581
## 82 1 6 323 7 10 1
## PMC9385149 PMC9386055 PMC9386474 PMC9387304 PMC9387930 PMC9388377 PMC9388569
## 4 10 28 3 16 4 1
## PMC9390996 PMC9392717 PMC9393397 PMC9396193 PMC9398501 PMC9402397 PMC9402440
## 31 2 6 1 329 15 2
## PMC9402582 PMC9405706 PMC9409727 PMC9410909 PMC9411202 PMC9412201 PMC9413140
## 34 26 2 4 4 45 5
## PMC9417173 PMC9418738 PMC9421583 PMC9424213 PMC9426036 PMC9426231 PMC9426548
## 6 2 42 20 52 6 1
## PMC9427111
## 1hist(NERR,main="number of errors per PMC article")
NERR_DF <- as.data.frame(NERR)
NERR_DF <- NERR_DF[order(-NERR_DF$NERR),,drop=FALSE]
head(NERR_DF,20)## NERR
## PMC9398501 329
## PMC9380953 323
## PMC9372159 149
## PMC9351068 105
## PMC9374748 103
## PMC9366661 92
## PMC9344228 89
## PMC9379253 82
## PMC9355568 64
## PMC9371928 61
## PMC9371440 53
## PMC9426036 52
## PMC9412201 45
## PMC9356431 43
## PMC9421583 42
## PMC9402582 34
## PMC9390996 31
## PMC9351182 28
## PMC9386474 28
## PMC9373797 27MOST_ERR = rownames(NERR_DF)[1]
MOST_ERR## [1] "PMC9398501"Journals affected
GENELIST_ERROR_ARTICLES <- gsub("PMC","",GENELIST_ERROR_ARTICLES)
### JSON PARSING is more reliable than XML
ARTICLES <- esummary( GENELIST_ERROR_ARTICLES , db="pmc" , retmode = "json" )
ARTICLE_DATA <- reutils::content(ARTICLES,as= "parsed")
ARTICLE_DATA <- ARTICLE_DATA$result
ARTICLE_DATA <- ARTICLE_DATA[2:length(ARTICLE_DATA)]
JOURNALS <- unlist(lapply(ARTICLE_DATA,function(x) {x$fulljournalname} ))
JOURNALS_TABLE <- table(JOURNALS)
JOURNALS_TABLE <- JOURNALS_TABLE[order(-JOURNALS_TABLE)]
length(JOURNALS_TABLE)## [1] 44NUM_JOURNALS=length(JOURNALS_TABLE)
par(mar=c(5,25,4,2))
barplot(head(JOURNALS_TABLE,10), horiz=TRUE, las=1,
xlab="Articles with gene name errors in supp files",
main="Top journals this month")
Journal of the month winner
Congrats to our Journal of the Month winner!
JOURNAL_WINNER <- names(head(JOURNALS_TABLE,1))
JOURNAL_WINNER## [1] "Frontiers in Immunology"Paper of the month winners
There are two categories:
Paper with the most suplementary files affected by gene name errors (MOST_ERR_FILES)
Paper with the most gene names converted to dates (MOST_ERR)
Sometimes, one paper can win both categories. Congrats to our winners.
Paper with most files affected
MOST_ERR_FILES <- gsub("PMC","",MOST_ERR_FILES)
ARTICLES <- esummary( MOST_ERR_FILES , db="pmc" , retmode = "json" )
ARTICLE_DATA <- reutils::content(ARTICLES,as= "parsed")
ARTICLE_DATA <- ARTICLE_DATA[2]
ARTICLE_DATA## $result
## $result$uids
## [1] "9421583"
##
## $result$`9421583`
## $result$`9421583`$uid
## [1] "9421583"
##
## $result$`9421583`$pubdate
## [1] "2022 Jun 7"
##
## $result$`9421583`$epubdate
## [1] "2022 Jun 7"
##
## $result$`9421583`$printpubdate
## [1] ""
##
## $result$`9421583`$source
## [1] "Cell Mol Gastroenterol Hepatol"
##
## $result$`9421583`$authors
## name authtype
## 1 Tosti E Author
## 2 Almeida AS Author
## 3 Tran TT Author
## 4 Barbachan e Silva M Author
## 5 Broin PÓ Author
## 6 Dubin R Author
## 7 Chen K Author
## 8 Beck AP Author
## 9 Mclellan AS Author
## 10 Vilar E Author
## 11 Golden A Author
## 12 O’Toole PW Author
## 13 Edelmann W Author
##
## $result$`9421583`$title
## [1] "Loss of MMR and TGFBR2 Increases the Susceptibility to Microbiota-Dependent Inflammation-Associated Colon Cancer"
##
## $result$`9421583`$volume
## [1] "14"
##
## $result$`9421583`$issue
## [1] "3"
##
## $result$`9421583`$pages
## [1] "693-717"
##
## $result$`9421583`$articleids
## idtype value
## 1 pmid 35688320
## 2 doi 10.1016/j.jcmgh.2022.05.010
## 3 pmcid PMC9421583
##
## $result$`9421583`$fulljournalname
## [1] "Cellular and Molecular Gastroenterology and Hepatology"
##
## $result$`9421583`$sortdate
## [1] "2022/06/07 00:00"
##
## $result$`9421583`$pmclivedate
## [1] "2022/08/30"Paper with most date conversions
MOST_ERR <- gsub("PMC","",MOST_ERR)
ARTICLE_DATA <- esummary(MOST_ERR,db = "pmc" , retmode = "json" )
ARTICLE_DATA <- reutils::content(ARTICLE_DATA,as= "parsed")
ARTICLE_DATA## $header
## $header$type
## [1] "esummary"
##
## $header$version
## [1] "0.3"
##
##
## $result
## $result$uids
## [1] "9398501"
##
## $result$`9398501`
## $result$`9398501`$uid
## [1] "9398501"
##
## $result$`9398501`$pubdate
## [1] "2022 Aug 24"
##
## $result$`9398501`$epubdate
## [1] "2022 Aug 24"
##
## $result$`9398501`$printpubdate
## [1] ""
##
## $result$`9398501`$source
## [1] "Parasit Vectors"
##
## $result$`9398501`$authors
## name authtype
## 1 Zhao SS Author
## 2 Tao DL Author
## 3 Chen JM Author
## 4 Wu JP Author
## 5 Yang X Author
## 6 Song JK Author
## 7 Zhu XQ Author
## 8 Zhao GH Author
##
## $result$`9398501`$title
## [1] "RNA sequencing reveals dynamic expression of lncRNAs and mRNAs in caprine endometrial epithelial cells induced by Neospora caninum infection"
##
## $result$`9398501`$volume
## [1] "15"
##
## $result$`9398501`$issue
## [1] ""
##
## $result$`9398501`$pages
## [1] "297"
##
## $result$`9398501`$articleids
## idtype value
## 1 pmid 35999576
## 2 doi 10.1186/s13071-022-05405-5
## 3 pmcid PMC9398501
##
## $result$`9398501`$fulljournalname
## [1] "Parasites & Vectors"
##
## $result$`9398501`$sortdate
## [1] "2022/08/24 00:00"
##
## $result$`9398501`$pmclivedate
## [1] "2022/08/24"Trend info
To plot the trend over the past 6-12 months.
url <- "https://ziemann-lab.net/public/gene_name_errors/"
doc <- htmlParse(url)
links <- xpathSApply(doc, "//a/@href")
links <- links[grep("html",links)]
listing <- htmlParse( getURL(url, ftp.use.epsv = FALSE, dirlistonly = TRUE) )
listing <- xpathSApply(listing, "//a/@href")
listing <- listing[grep("html",listing)]
unlink("online_files/",recursive=TRUE)
dir.create("online_files")
sapply(listing, function(mylink) {
download.file(paste(url,mylink,sep=""),destfile=paste("online_files/",mylink,sep=""))
} )## href href href href href href href href href href href href href href href href
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## href href
## 0 0myfilelist <- list.files("online_files/",full.names=TRUE)
trends <- sapply(myfilelist, function(myfilename) {
x <- readLines(myfilename)
# Num XL gene list articles
NUM_GENELIST_ARTICLES <- x[grep("NUM_GENELIST_ARTICLES",x)[3]+1]
NUM_GENELIST_ARTICLES <- sapply(strsplit(NUM_GENELIST_ARTICLES," "),"[[",3)
NUM_GENELIST_ARTICLES <- sapply(strsplit(NUM_GENELIST_ARTICLES,"<"),"[[",1)
NUM_GENELIST_ARTICLES <- as.numeric(NUM_GENELIST_ARTICLES)
# number of affected articles
NUM_ERROR_GENELIST_ARTICLES <- x[grep("NUM_ERROR_GENELIST_ARTICLES",x)[3]+1]
NUM_ERROR_GENELIST_ARTICLES <- sapply(strsplit(NUM_ERROR_GENELIST_ARTICLES," "),"[[",3)
NUM_ERROR_GENELIST_ARTICLES <- sapply(strsplit(NUM_ERROR_GENELIST_ARTICLES,"<"),"[[",1)
NUM_ERROR_GENELIST_ARTICLES <- as.numeric(NUM_ERROR_GENELIST_ARTICLES)
# Error proportion
ERROR_PROPORTION <- x[grep("ERROR_PROPORTION",x)[3]+1]
ERROR_PROPORTION <- sapply(strsplit(ERROR_PROPORTION," "),"[[",3)
ERROR_PROPORTION <- sapply(strsplit(ERROR_PROPORTION,"<"),"[[",1)
ERROR_PROPORTION <- as.numeric(ERROR_PROPORTION)
# number of journals
NUM_JOURNALS <- x[grep('JOURNALS_TABLE',x)[3]+1]
NUM_JOURNALS <- sapply(strsplit(NUM_JOURNALS," "),"[[",3)
NUM_JOURNALS <- sapply(strsplit(NUM_JOURNALS,"<"),"[[",1)
NUM_JOURNALS <- as.numeric(NUM_JOURNALS)
NUM_JOURNALS
res <- c(NUM_GENELIST_ARTICLES,NUM_ERROR_GENELIST_ARTICLES,ERROR_PROPORTION,NUM_JOURNALS)
return(res)
})
colnames(trends) <- sapply(strsplit(colnames(trends),"_"),"[[",3)
colnames(trends) <- gsub(".html","",colnames(trends))
trends <- as.data.frame(trends)
rownames(trends) <- c("NUM_GENELIST_ARTICLES","NUM_ERROR_GENELIST_ARTICLES","ERROR_PROPORTION","NUM_JOURNALS")
trends <- t(trends)
trends <- as.data.frame(trends)
CURRENT_RES <- c(NUM_GENELIST_ARTICLES,NUM_ERROR_GENELIST_ARTICLES,ERROR_PROPORTION,NUM_JOURNALS)
trends <- rbind(trends,CURRENT_RES)
paste(CURRENT_YEAR,CURRENT_MONTH,sep="-")## [1] "2022-09"rownames(trends)[nrow(trends)] <- paste(CURRENT_YEAR,CURRENT_MONTH,sep="-")
plot(trends$NUM_GENELIST_ARTICLES, xaxt = "n" , type="b" , main="Number of articles with Excel gene lists per month",
ylab="number of articles", xlab="month")
axis(1, at=1:nrow(trends), labels=rownames(trends))
plot(trends$NUM_ERROR_GENELIST_ARTICLES, xaxt = "n" , type="b" , main="Number of articles with gene name errors per month",
ylab="number of articles", xlab="month")
axis(1, at=1:nrow(trends), labels=rownames(trends))
plot(trends$ERROR_PROPORTION, xaxt = "n" , type="b" , main="Proportion of articles with Excel gene list affected by errors",
ylab="proportion", xlab="month")
axis(1, at=1:nrow(trends), labels=rownames(trends))
plot(trends$NUM_JOURNALS, xaxt = "n" , type="b" , main="Number of journals with affected articles",
ylab="number of journals", xlab="month")
axis(1, at=1:nrow(trends), labels=rownames(trends))
unlink("online_files/",recursive=TRUE)References
Zeeberg, B.R., Riss, J., Kane, D.W. et al. Mistaken Identifiers: Gene name errors can be introduced inadvertently when using Excel in bioinformatics. BMC Bioinformatics 5, 80 (2004). https://doi.org/10.1186/1471-2105-5-80
Ziemann, M., Eren, Y. & El-Osta, A. Gene name errors are widespread in the scientific literature. Genome Biol 17, 177 (2016). https://doi.org/10.1186/s13059-016-1044-7
SessionInfo
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RCurl_1.98-1.7 readxl_1.4.0 reutils_0.2.3 xml2_1.3.3 jsonlite_1.8.0
## [6] XML_3.99-0.10
##
## loaded via a namespace (and not attached):
## [1] knitr_1.39 magrittr_2.0.3 R6_2.5.1 rlang_1.0.4
## [5] fastmap_1.1.0 stringr_1.4.0 highr_0.9 tools_4.2.1
## [9] xfun_0.31 cli_3.3.0 jquerylib_0.1.4 htmltools_0.5.3
## [13] yaml_2.3.5 digest_0.6.29 assertthat_0.2.1 sass_0.4.2
## [17] bitops_1.0-7 cachem_1.0.6 evaluate_0.15 rmarkdown_2.14
## [21] stringi_1.7.8 compiler_4.2.1 bslib_0.4.0 cellranger_1.1.0