Gene Name Error Monthly Dashboard
Mark Ziemann
2022-04-03
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")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] "2022/3"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 = DATE, maxdate = DATE)
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] 3310writeLines(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] 4644NUM_XLS_ARTICLES = length(unique(sapply(strsplit(XLS," "),"[[",1)))
NUM_XLS_ARTICLES## [1] 768GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>2]
#GENELISTS
NUM_GENELISTS <- length(unique(sapply(strsplit(GENELISTS," "),"[[",2)))
NUM_GENELISTS## [1] 582NUM_GENELIST_ARTICLES <- length(unique(sapply(strsplit(GENELISTS," "),"[[",1)))
NUM_GENELIST_ARTICLES## [1] 276ERROR_GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>3]
#ERROR_GENELISTS
NUM_ERROR_GENELISTS = length(ERROR_GENELISTS)
NUM_ERROR_GENELISTS## [1] 211GENELIST_ERROR_ARTICLES <- unique(sapply(strsplit(ERROR_GENELISTS," "),"[[",1))
GENELIST_ERROR_ARTICLES## [1] "PMC8960258" "PMC8958792" "PMC8958363" "PMC8941526" "PMC8938417"
## [6] "PMC8957956" "PMC8947600" "PMC8941758" "PMC8940286" "PMC8938861"
## [11] "PMC8937020" "PMC8925074" "PMC8903709" "PMC8934046" "PMC8931337"
## [16] "PMC8930846" "PMC8883549" "PMC8883538" "PMC8930202" "PMC8927907"
## [21] "PMC8927698" "PMC8891275" "PMC8885711" "PMC8881579" "PMC8863982"
## [26] "PMC8923467" "PMC8921613" "PMC8919059" "PMC8912178" "PMC8892733"
## [31] "PMC8896830" "PMC8831532" "PMC8888678" "PMC8885993" "PMC8884250"
## [36] "PMC8967178" "PMC8966188" "PMC8960263" "PMC8959321" "PMC8942222"
## [41] "PMC8904843" "PMC8916121" "PMC8891017" "PMC8933692" "PMC8931336"
## [46] "PMC8925295" "PMC8924366" "PMC8921511" "PMC8921118" "PMC8888590"
## [51] "PMC8885418" "PMC8873204" "PMC8864016" "PMC8861042" "PMC8923497"
## [56] "PMC8921988" "PMC8915965" "PMC8913945" "PMC8907571" "PMC8891552"
## [61] "PMC8905247" "PMC8902468" "PMC8896005" "PMC8894600" "PMC8892324"
## [66] "PMC8891516" "PMC8889144" "PMC8844066" "PMC8837555" "PMC8887668"
## [71] "PMC8885996" "PMC8884696"NUM_ERROR_GENELIST_ARTICLES <- length(GENELIST_ERROR_ARTICLES)
NUM_ERROR_GENELIST_ARTICLES## [1] 72ERROR_PROPORTION = NUM_ERROR_GENELIST_ARTICLES / NUM_GENELIST_ARTICLES
ERROR_PROPORTION## [1] 0.2608696Look 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] "PMC8960258 /pmc/articles/PMC8960258/bin/Table5.XLSX Hsapiens 2 44263 44448"
## [2] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM2_ESM.xlsx Rnorvegicus 16 44441 44262 44450 44263 44440 44256 44447 44265 44448 44448 44266 44266 44266 44442 44258 44445"
## [3] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM2_ESM.xlsx Rnorvegicus 17 44259 44256 44257 44257 44450 44453 44256 44256 44265 44265 44265 44448 44448 44448 44266 44266 44266"
## [4] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM2_ESM.xlsx Rnorvegicus 28 44259 44259 44256 44257 44257 44262 44262 44450 44440 44256 44256 44256 44446 44443 44265 44448 44448 44266 44266 44266 44261 44444 44258 44258 44258 44258 44258 44445"
## [5] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM2_ESM.xlsx Mmusculus 37 44256 44257 44257 44453 44453 44263 44256 44256 44449 44264 44447 44265 44265 44265 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44266 44266 44266 44266 44261 44451 44444 44444 44258 44258 44258 44445 44445"
## [6] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM2_ESM.xlsx Rnorvegicus 45 44256 44257 44257 44450 44450 44450 44450 44450 44263 44440 44446 44447 44443 44443 44265 44265 44265 44265 44265 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44266 44266 44266 44266 44444 44444 44258 44258 44258 44445"
## [7] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM3_ESM.xlsx Mmusculus 2 44450 44443"
## [8] "PMC8958792 /pmc/articles/PMC8958792/bin/13148_2022_1267_MOESM3_ESM.xlsx Mmusculus 6 44440 44449 44450 44441 44445 44447"
## [9] "PMC8958363 /pmc/articles/PMC8958363/bin/mmc2.xlsx Mmusculus 4 43899 43896 44088 44083"
## [10] "PMC8958363 /pmc/articles/PMC8958363/bin/mmc2.xlsx Mmusculus 1 43896"
## [11] "PMC8958363 /pmc/articles/PMC8958363/bin/mmc3.xlsx Mmusculus 21 43894 43899 43901 44081 44083 43896 44077 44078 43893 43892 43891 43897 44084 44085 44086 43895 44088 43900 44082 44075 43892"
## [12] "PMC8958363 /pmc/articles/PMC8958363/bin/mmc3.xlsx Mmusculus 21 43894 43899 43901 44081 44083 43896 44077 44078 43893 43892 43891 43897 44084 44085 44086 43895 44088 43900 44082 44075 43892"
## [13] "PMC8958363 /pmc/articles/PMC8958363/bin/mmc3.xlsx Mmusculus 21 43894 43899 43901 44081 44083 43896 44077 44078 43893 43892 43891 43897 44084 44085 44086 43895 44088 43900 44082 44075 43892"
## [14] "PMC8958363 /pmc/articles/PMC8958363/bin/mmc3.xlsx Mmusculus 21 43894 43899 43901 44081 44083 43896 44077 44078 43893 43892 43891 43897 44084 44085 44086 43895 44088 43900 44082 44075 43892"
## [15] "PMC8941526 /pmc/articles/PMC8941526/bin/Supplementary_Data2.xlsx Hsapiens 2 44450 44441"
## [16] "PMC8938417 /pmc/articles/PMC8938417/bin/41598_2022_8645_MOESM3_ESM.xlsx Hsapiens 2 40057 38961"
## [17] "PMC8957956 /pmc/articles/PMC8957956/bin/Table_1.xlsx Hsapiens 332 43352 43164 43345 43353 43350 43352 43350 43354 43165 43354 43345 43163 43345 43353 43168 43166 43350 43165 43352 43345 43168 43353 43352 43165 43349 43345 43349 43345 43354 43160 43345 43166 43349 43161 43345 43345 43351 43345 43350 43166 43345 43352 43161 43345 43350 43350 43349 43353 43345 43352 43345 43353 43352 43352 43351 43351 43345 43354 43350 43351 43353 43164 43345 43354 43345 43167 43166 43344 43353 43345 43345 43345 43165 43345 43354 43345 43161 43350 43350 43161 43165 43349 43345 43345 43161 43345 43353 43351 43345 43161 43350 43345 43345 43349 43162 43165 43345 43354 43345 43352 43352 43352 43161 43353 43352 43167 43165 43345 43165 43351 43353 43352 43166 43352 43345 43345 43352 43350 43166 43352 43165 43352 43351 43345 43348 43348 43352 43165 43353 43168 43167 43352 43351 43350 43352 43354 43345 43166 43352 43349 43169 43352 43344 43344 43348 43352 43353 43352 43352 43167 43354 43160 43348 43353 43351 43169 43354 43167 43160 43351 43348 43348 43347 43164 43352 43345 43345 43352 43160 43345 43350 43354 43167 43160 43351 43169 43160 43355 43348 43351 43347 43165 43164 43160 43165 43351 43169 43352 43166 43352 43346 43347 43347 43350 43165 43160 43352 43352 43350 43161 43161 43161 43161 43161 43161 43160 43160 43160 43160 43160 43160 43160 43167 43355 43355 43355 43355 43355 43355 43355 43344 43344 43344 43344 43344 43344 43344 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43347 43169 43169 43169 43169 43169 43169 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43352 43161 43353 43166 43345 43345 43345 43345 43345 43345 43345 43348 43348 43348 43348 43348 43348 43346 43346 43346 43346 43346 43346 43354 43354 43354 43354 43354 43160 43160 43160 43160 43160 43160 43160 43165 43165 43170 43170 43170 43162 43162 43162 43162 43351 43351 43351 43350 43350 43350 43350 43357 43357 43435 43435 43435 43435 43349 43349"
## [18] "PMC8947600 /pmc/articles/PMC8947600/bin/pcbi.1009395.s002.xlsx Hsapiens 4 10423 10423 11219 11219"
## [19] "PMC8947600 /pmc/articles/PMC8947600/bin/pcbi.1009395.s002.xlsx Ggallus 6 55276 55276 26289 26289 26289 26289"
## [20] "PMC8947600 /pmc/articles/PMC8947600/bin/pcbi.1009395.s002.xlsx Hsapiens 3 64078 64078 55276"
## [21] "PMC8947600 /pmc/articles/PMC8947600/bin/pcbi.1009395.s003.xlsx Hsapiens 9 10587 10587 10587 10587 11219 11219 11219 10514 10555"
## [22] "PMC8941758 /pmc/articles/PMC8941758/bin/12915_2022_1270_MOESM10_ESM.xlsx Hsapiens 25 37226 36951 37316 36951 40238 40603 37316 37681 38047 38412 38777 39142 39508 39873 37135 40422 40787 37500 37865 38231 38596 38961 39326 39692 40057"
## [23] "PMC8941758 /pmc/articles/PMC8941758/bin/12915_2022_1270_MOESM10_ESM.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"
## [24] "PMC8941758 /pmc/articles/PMC8941758/bin/12915_2022_1270_MOESM10_ESM.xlsx Hsapiens 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"
## [25] "PMC8941758 /pmc/articles/PMC8941758/bin/12915_2022_1270_MOESM10_ESM.xlsx Hsapiens 23 36951 37316 36951 40238 40603 37316 37681 38047 38412 38777 39142 39508 39873 37135 40422 40787 41883 37865 38231 38961 39326 39692 40057"
## [26] "PMC8941758 /pmc/articles/PMC8941758/bin/12915_2022_1270_MOESM10_ESM.xlsx Hsapiens 20 36951 36951 40603 37316 37681 38047 38412 38777 39142 39508 40422 40787 41153 37500 38231 38596 38961 39326 39692 40057"
## [27] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 39142"
## [28] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 38412"
## [29] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 38412"
## [30] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 37500"
## [31] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Ggallus 2 39692 39326"
## [32] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 2 38777 39326"
## [33] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 36951"
## [34] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 40422"
## [35] "PMC8940286 /pmc/articles/PMC8940286/bin/Table_7.xlsx Hsapiens 1 38961"
## [36] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc3.xlsx Hsapiens 1 44411"
## [37] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc6.xlsx Hsapiens 7 44452 44448 44448 44448 44448 44448 44450"
## [38] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc8.xlsx Hsapiens 2 44444 44444"
## [39] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc8.xlsx Hsapiens 7 44445 44444 44448 44448 44448 44444 44444"
## [40] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc9.xlsx Hsapiens 5 44440 44454 44410 44448 44409"
## [41] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc9.xlsx Hsapiens 3 44452 44448 44444"
## [42] "PMC8938861 /pmc/articles/PMC8938861/bin/mmc9.xlsx Hsapiens 23 44445 44448 44445 44448 44448 44448 44452 44445 44448 44448 44448 44443 44448 44448 44445 44448 44448 44444 44448 44444 44444 44448 44448"
## [43] "PMC8937020 /pmc/articles/PMC8937020/bin/Table1.XLSX Hsapiens 5 44621 44631 44896 44813 44628"
## [44] "PMC8925074 /pmc/articles/PMC8925074/bin/13073_2022_1036_MOESM4_ESM.xlsx Hsapiens 47 44256 44256 44256 44451 44260 44263 44260 44263 44263 44440 44260 44260 44257 44257 44257 44257 44257 44257 44257 44256 44257 44257 44257 44257 44257 44263 44257 44257 44257 44257 44257 44257 44263 44263 44260 44449 44256 44257 44257 44257 44257 44257 44257 44257 44257 44449 44449"
## [45] "PMC8903709 /pmc/articles/PMC8903709/bin/11658_2022_306_MOESM3_ESM.xls Hsapiens 26 2021/12/01 2021/03/10 2021/03/11 2021/03/01 2021/03/02 2021/03/03 2021/03/04 2021/03/05 2021/03/06 2021/03/07 2021/03/08 2021/03/09 2021/09/15 2021/09/10 2021/09/11 2021/09/12 2021/09/14 2021/09/01 2021/09/02 2021/09/03 2021/09/04 2021/09/05 2021/09/06 2021/09/07 2021/09/08 2021/09/09"
## [46] "PMC8934046 /pmc/articles/PMC8934046/bin/peerj-10-13138-s002.xlsx Rnorvegicus 25 37500 42248 39692 40787 40422 40057 39508 38596 38231 37316 37316 39326 38412 39142 36951 38961 37681 39873 37135 40603 40238 36951 41153 41883 37865"
## [47] "PMC8931337 /pmc/articles/PMC8931337/bin/Table2.XLSX Hsapiens 7 44265 44443 44265 44257 44447 44443 44446"
## [48] "PMC8930846 /pmc/articles/PMC8930846/bin/Table1.XLSX Rnorvegicus 5 44256 44446 44259 44266 44256"
## [49] "PMC8930846 /pmc/articles/PMC8930846/bin/Table1.XLSX Mmusculus 3 44448 44446 44454"
## [50] "PMC8930846 /pmc/articles/PMC8930846/bin/Table1.XLSX Mmusculus 3 44446 44454 44266"
## [51] "PMC8930846 /pmc/articles/PMC8930846/bin/Table1.XLSX Mmusculus 6 44258 44442 44447 44445 44446 44256"
## [52] "PMC8883549 /pmc/articles/PMC8883549/bin/2020_274258_MA_TABLES_SUPPL.xlsx Hsapiens 3 43168 43168 43345"
## [53] "PMC8883549 /pmc/articles/PMC8883549/bin/2020_274258_MA_TABLES_SUPPL.xlsx Hsapiens 2 43164 43164"
## [54] "PMC8883549 /pmc/articles/PMC8883549/bin/2020_274258_MA_TABLES_SUPPL.xlsx Hsapiens 1 43345"
## [55] "PMC8883549 /pmc/articles/PMC8883549/bin/2020_274258_MA_TABLES_SUPPL.xlsx Hsapiens 1 43168"
## [56] "PMC8883549 /pmc/articles/PMC8883549/bin/2020_274258_MA_TABLES_SUPPL.xlsx Hsapiens 1 43164"
## [57] "PMC8883538 /pmc/articles/PMC8883538/bin/2020_257246_WEINREB_TABLES_SUPPL.xlsx Drerio 1 44077"
## [58] "PMC8930202 /pmc/articles/PMC8930202/bin/Table_1.xlsx Hsapiens 37 43712 43710 43710 43722 43531 43710 43717 43717 43717 43526 43532 43712 43532 43531 43525 43716 43526 43528 43526 43528 43717 43532 43532 43525 43719 43717 43717 43711 43800 43717 43531 43531 43531 43531 43714 43722 43532"
## [59] "PMC8930202 /pmc/articles/PMC8930202/bin/Table_1.xlsx Hsapiens 4 43710 43525 43526 43723"
## [60] "PMC8930202 /pmc/articles/PMC8930202/bin/Table_1.xlsx Hsapiens 26 43710 43712 43526 43722 43532 43717 43531 43800 43525 43534 43535 43527 43528 43529 43530 43533 43723 43709 43718 43719 43720 43711 43713 43714 43715 43716"
## [61] "PMC8930202 /pmc/articles/PMC8930202/bin/Table_1.xlsx Hsapiens 2 43526 43712"
## [62] "PMC8930202 /pmc/articles/PMC8930202/bin/Table_1.xlsx Hsapiens 3 43800 43525 43526"
## [63] "PMC8927907 /pmc/articles/PMC8927907/bin/mmc6.xlsx Hsapiens 1 39701"
## [64] "PMC8927698 /pmc/articles/PMC8927698/bin/Table2.XLSX Hsapiens 26 44811 44806 44627 44626 44896 44807 44623 44814 44625 44805 44631 44810 44818 44815 44813 44808 44630 44809 44629 44622 44816 44624 44628 44812 44819 44621"
## [65] "PMC8927698 /pmc/articles/PMC8927698/bin/Table2.XLSX Hsapiens 26 44811 44806 44627 44626 44896 44807 44623 44814 44625 44805 44631 44810 44818 44815 44813 44808 44630 44809 44629 44622 44816 44624 44628 44812 44819 44621"
## [66] "PMC8927698 /pmc/articles/PMC8927698/bin/Table3.XLS Hsapiens 26 44811 44806 44627 44626 44896 44807 44623 44814 44625 44805 44631 44810 44818 44815 44813 44808 44630 44809 44629 44622 44816 44624 44628 44812 44819 44621"
## [67] "PMC8927698 /pmc/articles/PMC8927698/bin/Table3.XLS Hsapiens 26 44811 44806 44627 44626 44896 44807 44623 44814 44625 44805 44631 44810 44818 44815 44813 44808 44630 44809 44629 44622 44816 44624 44628 44812 44819 44621"
## [68] "PMC8891275 /pmc/articles/PMC8891275/bin/41467_2022_28719_MOESM5_ESM.xlsx Hsapiens 1 44448"
## [69] "PMC8885711 /pmc/articles/PMC8885711/bin/41467_2022_28661_MOESM39_ESM.xlsx Hsapiens 32 44811 44810 44805 44815 44627 44623 44813 44806 44621 44626 44625 44811 44810 44815 44805 44627 44626 44621 44813 44806 44625 44626 44622 44621 44627 44815 44811 44810 44625 44805 44806 44813"
## [70] "PMC8885711 /pmc/articles/PMC8885711/bin/41467_2022_28661_MOESM39_ESM.xlsx Hsapiens 46 44806 44809 44810 44626 44628 44815 44623 44625 44621 44818 44819 44627 44621 44813 44896 44622 44807 44630 44622 44814 44812 44811 44624 44806 44809 44810 44626 44628 44815 44623 44625 44621 44818 44819 44627 44621 44813 44896 44622 44807 44630 44622 44814 44812 44811 44624"
## [71] "PMC8881579 /pmc/articles/PMC8881579/bin/41467_2022_28682_MOESM4_ESM.xlsx Hsapiens 3 43526 43526 43528"
## [72] "PMC8863982 /pmc/articles/PMC8863982/bin/41467_2022_28554_MOESM3_ESM.xlsx Mmusculus 2 43898 44079"
## [73] "PMC8923467 /pmc/articles/PMC8923467/bin/pgen.1010106.s012.xlsx Scerevisiae 1 44470"
## [74] "PMC8921613 /pmc/articles/PMC8921613/bin/supplementary_table_s4_bbab548.xlsx Hsapiens 1 44256"
## [75] "PMC8919059 /pmc/articles/PMC8919059/bin/DataSheet_2.xlsx Hsapiens 1 44448"
## [76] "PMC8919059 /pmc/articles/PMC8919059/bin/DataSheet_5.xlsx Hsapiens 2 44442 44259"
## [77] "PMC8919059 /pmc/articles/PMC8919059/bin/DataSheet_5.xlsx Hsapiens 2 44442 44259"
## [78] "PMC8912178 /pmc/articles/PMC8912178/bin/pone.0265008.s011.xlsx Hsapiens 20 44259 44262 44441 44447 44451 44444 44448 44443 44257 44449 44261 44266 44446 44256 44445 44256 44442 44450 44263 44260"
## [79] "PMC8912178 /pmc/articles/PMC8912178/bin/pone.0265008.s011.xlsx Hsapiens 2 44263 44260"
## [80] "PMC8912178 /pmc/articles/PMC8912178/bin/pone.0265008.s012.xlsx Hsapiens 20 44259 44262 44441 44447 44451 44444 44448 44443 44257 44449 44261 44266 44446 44256 44445 44256 44442 44450 44263 44260"
## [81] "PMC8892733 /pmc/articles/PMC8892733/bin/12885_2022_9328_MOESM6_ESM.xlsx Hsapiens 1 44263"
## [82] "PMC8896830 /pmc/articles/PMC8896830/bin/elife-69476-supp1.xlsx Mmusculus 1 37104"
## [83] "PMC8831532 /pmc/articles/PMC8831532/bin/41467_2022_28434_MOESM12_ESM.xlsx Drerio 24 38412 37865 39508 39873 38047 37530 37316 39508 38961 38777 38961 39508 39508 38777 41153 39142 41153 41153 39142 40422 38231 39508 37865 39142"
## [84] "PMC8831532 /pmc/articles/PMC8831532/bin/41467_2022_28434_MOESM4_ESM.xlsx Drerio 8 37316 38961 38961 38777 41153 40422 38231 39508"
## [85] "PMC8831532 /pmc/articles/PMC8831532/bin/41467_2022_28434_MOESM4_ESM.xlsx Drerio 5 38961 38961 41153 40422 38231"
## [86] "PMC8831532 /pmc/articles/PMC8831532/bin/41467_2022_28434_MOESM4_ESM.xlsx Drerio 3 37316 38777 39508"
## [87] "PMC8831532 /pmc/articles/PMC8831532/bin/41467_2022_28434_MOESM6_ESM.xlsx Drerio 28 38961 38961 38961 38961 38961 38961 38231 38231 38231 38231 38231 38231 38231 38231 38231 38231 41153 41153 41153 41153 41153 41153 41153 41153 40422 40422 40422 40422"
## [88] "PMC8888678 /pmc/articles/PMC8888678/bin/Table1.XLSX Hsapiens 96 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 1-Dec 1-Dec 1-Dec 1-Dec 7-Mar 7-Mar 7-Mar 7-Mar 14-Sep 14-Sep 14-Sep 14-Sep 12-Sep 12-Sep 12-Sep 12-Sep 6-Sep 6-Sep 6-Sep 6-Sep 4-Mar 4-Mar 4-Mar 4-Mar 11-Mar 11-Mar 11-Mar 11-Mar 2-Mar 2-Mar 2-Mar 2-Mar 10-Mar 10-Mar 10-Mar 10-Mar 11-Sep 11-Sep 11-Sep 11-Sep 8-Mar 8-Mar 8-Mar 8-Mar 1-Sep 1-Sep 1-Sep 1-Sep 2-Sep 2-Sep 2-Sep 2-Sep 15-Sep 15-Sep 15-Sep 15-Sep 3-Mar 3-Mar 3-Mar 3-Mar 10-Sep 10-Sep 10-Sep 10-Sep 1-Mar 1-Mar 1-Mar 1-Mar 3-Sep 3-Sep 3-Sep 3-Sep 5-Sep 5-Sep 5-Sep 5-Sep 6-Mar 6-Mar 6-Mar 6-Mar 7-Sep 7-Sep 7-Sep 7-Sep 9-Sep 9-Sep 9-Sep 9-Sep"
## [89] "PMC8888678 /pmc/articles/PMC8888678/bin/Table1.XLSX Hsapiens 96 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 8-Mar 1-Dec 1-Dec 1-Dec 1-Dec 7-Mar 7-Mar 7-Mar 7-Mar 14-Sep 14-Sep 14-Sep 14-Sep 12-Sep 12-Sep 12-Sep 12-Sep 6-Sep 6-Sep 6-Sep 6-Sep 4-Mar 4-Mar 4-Mar 4-Mar 11-Mar 11-Mar 11-Mar 11-Mar 2-Mar 2-Mar 2-Mar 2-Mar 10-Mar 10-Mar 10-Mar 10-Mar 11-Sep 11-Sep 11-Sep 11-Sep 8-Mar 8-Mar 8-Mar 8-Mar 1-Sep 1-Sep 1-Sep 1-Sep 2-Sep 2-Sep 2-Sep 2-Sep 15-Sep 15-Sep 15-Sep 15-Sep 3-Mar 3-Mar 3-Mar 3-Mar 10-Sep 10-Sep 10-Sep 10-Sep 1-Mar 1-Mar 1-Mar 1-Mar 3-Sep 3-Sep 3-Sep 3-Sep 5-Sep 5-Sep 5-Sep 5-Sep 6-Mar 6-Mar 6-Mar 6-Mar 7-Sep 7-Sep 7-Sep 7-Sep 9-Sep 9-Sep 9-Sep 9-Sep"
## [90] "PMC8888678 /pmc/articles/PMC8888678/bin/Table1.XLSX Hsapiens 22 8-Mar 1-Dec 7-Mar 14-Sep 12-Sep 6-Sep 4-Mar 11-Mar 2-Mar 10-Mar 11-Sep 1-Sep 2-Sep 15-Sep 3-Mar 10-Sep 1-Mar 3-Sep 5-Sep 6-Mar 7-Sep 9-Sep"
## [91] "PMC8885993 /pmc/articles/PMC8885993/bin/Table3.XLSX Hsapiens 5 44445 44450 44440 44443 44266"
## [92] "PMC8885993 /pmc/articles/PMC8885993/bin/Table3.XLSX Ggallus 4 44445 44443 44440 44259"
## [93] "PMC8885993 /pmc/articles/PMC8885993/bin/Table3.XLSX Hsapiens 2 44266 44259"
## [94] "PMC8885993 /pmc/articles/PMC8885993/bin/Table3.XLSX Hsapiens 5 44266 44449 44450 44445 44531"
## [95] "PMC8885993 /pmc/articles/PMC8885993/bin/Table5.XLSX Hsapiens 8 44449 44450 44531 44266 44440 44443 44445 44259"
## [96] "PMC8884250 /pmc/articles/PMC8884250/bin/Table_2.xls Hsapiens 2 2021/03/04 2021/09/14"
## [97] "PMC8967178 /pmc/articles/PMC8967178/bin/Table_2.XLSX Hsapiens 1 44264"
## [98] "PMC8967178 /pmc/articles/PMC8967178/bin/Table_3.XLSX Hsapiens 5 44261 44261 44260 44260 44262"
## [99] "PMC8966188 /pmc/articles/PMC8966188/bin/12915_2022_1269_MOESM9_ESM.xlsx Hsapiens 15 43897 44076 44081 43899 44085 43892 44082 43893 44084 43892 44079 44078 44083 43896 44080"
## [100] "PMC8960263 /pmc/articles/PMC8960263/bin/Table6.XLSX Hsapiens 2 44621 44805"
## [101] "PMC8959321 /pmc/articles/PMC8959321/bin/Table_1.xlsx Hsapiens 1 44256"
## [102] "PMC8959321 /pmc/articles/PMC8959321/bin/Table_1.xlsx Hsapiens 1 44896"
## [103] "PMC8959321 /pmc/articles/PMC8959321/bin/Table_1.xlsx Hsapiens 1 44623"
## [104] "PMC8959321 /pmc/articles/PMC8959321/bin/Table_1.xlsx Hsapiens 2 44626 44623"
## [105] "PMC8959321 /pmc/articles/PMC8959321/bin/Table_1.xlsx Hsapiens 6 44629 44621 44819 44624 44629 44623"
## [106] "PMC8942222 /pmc/articles/PMC8942222/bin/pone.0264776.s021.xlsx Drerio 7 44257 44445 44262 44261 44441 44449 44260"
## [107] "PMC8942222 /pmc/articles/PMC8942222/bin/pone.0264776.s022.xlsx Drerio 1 44257"
## [108] "PMC8942222 /pmc/articles/PMC8942222/bin/pone.0264776.s023.xlsx Drerio 4 44445 44261 44263 44441"
## [109] "PMC8904843 /pmc/articles/PMC8904843/bin/42003_2022_3117_MOESM7_ESM.xlsx Hsapiens 3 44257 44442 44256"
## [110] "PMC8916121 /pmc/articles/PMC8916121/bin/JEM_20211121_TableS6.xlsx Hsapiens 10 37316 39873 39142 37681 36951 36951 39508 38412 38777 37316"
## [111] "PMC8916121 /pmc/articles/PMC8916121/bin/JEM_20211121_TableS6.xlsx Hsapiens 7 38777 38412 36951 37681 39142 39873 37316"
## [112] "PMC8916121 /pmc/articles/PMC8916121/bin/JEM_20211121_TableS6.xlsx Hsapiens 7 37316 37681 38412 38777 39873 36951 39142"
## [113] "PMC8916121 /pmc/articles/PMC8916121/bin/JEM_20211121_TableS6.xlsx Hsapiens 7 38777 38412 36951 37681 39142 39873 37316"
## [114] "PMC8916121 /pmc/articles/PMC8916121/bin/JEM_20211121_TableS6.xlsx Hsapiens 6 39142 36951 39873 38412 37316 38777"
## [115] "PMC8916121 /pmc/articles/PMC8916121/bin/JEM_20211121_TableS6.xlsx Hsapiens 6 39142 38412 37316 38777 39873 36951"
## [116] "PMC8891017 /pmc/articles/PMC8891017/bin/41586_2022_4394_MOESM4_ESM.xlsx Hsapiens 2 39508 39508"
## [117] "PMC8891017 /pmc/articles/PMC8891017/bin/41586_2022_4394_MOESM4_ESM.xlsx Hsapiens 2 39508 39508"
## [118] "PMC8891017 /pmc/articles/PMC8891017/bin/41586_2022_4394_MOESM4_ESM.xlsx Hsapiens 2 39508 39508"
## [119] "PMC8891017 /pmc/articles/PMC8891017/bin/41586_2022_4394_MOESM4_ESM.xlsx Hsapiens 2 39508 39508"
## [120] "PMC8891017 /pmc/articles/PMC8891017/bin/41586_2022_4394_MOESM4_ESM.xlsx Hsapiens 23 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508 39508"
## [121] "PMC8933692 /pmc/articles/PMC8933692/bin/mmc2.xlsx Mmusculus 78 44256 44256 44256 44257 44257 44450 44450 44450 44450 44450 44450 44450 44263 44440 44447 44447 44447 44447 44447 44443 44265 44265 44265 44265 44265 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44261 44442 44451 44444 44444 44444 44258 44258 44258 44258 44258"
## [122] "PMC8933692 /pmc/articles/PMC8933692/bin/mmc4.xlsx Mmusculus 8 44448 44448 44448 44448 44448 44448 44258 44258"
## [123] "PMC8933692 /pmc/articles/PMC8933692/bin/mmc4.xlsx Mmusculus 44 44256 44256 44257 44257 44450 44450 44450 44450 44450 44263 44440 44447 44447 44443 44265 44265 44265 44265 44265 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44442 44451 44444 44444 44258 44258"
## [124] "PMC8933692 /pmc/articles/PMC8933692/bin/mmc4.xlsx Mmusculus 70 44256 44256 44256 44257 44257 44450 44450 44450 44450 44450 44450 44450 44263 44440 44447 44447 44447 44447 44447 44443 44265 44265 44265 44265 44265 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44261 44442 44451 44444 44444 44444 44258 44258 44258"
## [125] "PMC8933692 /pmc/articles/PMC8933692/bin/mmc4.xlsx Mmusculus 34 44256 44450 44450 44447 44447 44447 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44448 44261 44444 44258 44258 44258"
## [126] "PMC8931336 /pmc/articles/PMC8931336/bin/pnas.2119415119.sd01.xlsx Mmusculus 18 44076 43892 44084 43899 44082 44078 44083 44077 43896 43892 43893 43895 43897 44085 43898 44075 44081 44080"
## [127] "PMC8925295 /pmc/articles/PMC8925295/bin/41598_2022_8619_MOESM2_ESM.xlsx Hsapiens 1 44819"
## [128] "PMC8925295 /pmc/articles/PMC8925295/bin/41598_2022_8619_MOESM2_ESM.xlsx Hsapiens 2 44631 44624"
## [129] "PMC8925295 /pmc/articles/PMC8925295/bin/41598_2022_8619_MOESM2_ESM.xlsx Hsapiens 3 44624 44629 44818"
## [130] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_3.xlsx Hsapiens 20 44440 44449 44448 44441 44449 44440 44449 44449 44446 44446 44446 44444 44445 44444 44446 44444 44444 44256 44444 44442"
## [131] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_3.xlsx Hsapiens 4 44444 44450 44442 44447"
## [132] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 4 44264 44258 44257 44446"
## [133] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 8 44264 44256 44257 44445 44258 44440 44261 44448"
## [134] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 8 44445 44257 44256 44446 44258 44443 44262 44259"
## [135] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 2 44256 44262"
## [136] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 18 44445 44448 44449 44257 44446 44453 44265 44441 44256 44261 44443 44447 44260 44266 44263 44444 44259 44442"
## [137] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 15 44257 44258 44441 44447 44446 44259 44444 44443 44440 44256 44450 44262 44263 44260 44442"
## [138] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 10 44450 44447 44258 44261 44445 44440 44446 44260 44443 44451"
## [139] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 14 44440 44445 44443 44257 44263 44261 44447 44444 44262 44450 44260 44264 44531 44256"
## [140] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 3 44256 44258 44259"
## [141] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 10 44450 44258 44447 44443 44256 44445 44263 44442 44261 44265"
## [142] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 16 44445 44440 44443 44263 44446 44449 44450 44259 44257 44531 44265 44261 44444 44447 44264 44442"
## [143] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 17 44450 44449 44265 44266 44447 44262 44259 44445 44264 44446 44261 44444 44263 44442 44256 44443 44448"
## [144] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 6 44264 44448 44451 44258 44449 44440"
## [145] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 14 44264 44450 44449 44446 44447 44444 44257 44443 44260 44256 44451 44262 44263 44266"
## [146] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 5 44443 44445 44450 44448 44264"
## [147] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 9 44444 44442 44256 44440 44258 44261 44449 44262 44266"
## [148] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 11 44256 44440 44445 44448 44262 44258 44442 44444 44263 44443 44259"
## [149] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 8 44440 44445 44443 44450 44263 44256 44261 44447"
## [150] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 6 44440 44442 44256 44451 44257 44260"
## [151] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 11 44445 44440 44258 44261 44264 44257 44444 44260 44441 44262 44446"
## [152] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 16 44449 44264 44441 44447 44450 44263 44261 44256 44448 44440 44265 44446 44445 44260 44262 44443"
## [153] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 13 44445 44440 44450 44256 44449 44446 44451 44265 44261 44260 44257 44264 44444"
## [154] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 10 44264 44449 44262 44263 44257 44446 44265 44450 44256 44440"
## [155] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 13 44257 44264 44263 44449 44446 44262 44450 44260 44261 44447 44441 44448 44443"
## [156] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 11 44446 44264 44449 44263 44447 44262 44444 44260 44261 44453 44445"
## [157] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 13 44443 44445 44264 44440 44448 44450 44444 44258 44442 44441 44256 44446 44262"
## [158] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 10 44449 44448 44450 44261 44440 44262 44442 44264 44265 44256"
## [159] "PMC8924366 /pmc/articles/PMC8924366/bin/Table_4.xlsx Hsapiens 5 44440 44443 44445 44260 44441"
## [160] "PMC8921511 /pmc/articles/PMC8921511/bin/41598_2022_8356_MOESM2_ESM.xlsx Hsapiens 1 37226"
## [161] "PMC8921118 /pmc/articles/PMC8921118/bin/345_2022_3936_MOESM2_ESM.xlsx Hsapiens 11 44454 44445 44441 44446 44256 44447 44257 44450 44260 44449 44448"
## [162] "PMC8888590 /pmc/articles/PMC8888590/bin/41467_2022_28705_MOESM4_ESM.xlsx Hsapiens 2 38412 38961"
## [163] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM11_ESM.xlsx Mmusculus 128 37135 37135 37135 40422 40422 40422 40787 40787 40787 41153 41153 41153 41883 41883 41883 42248 42248 42248 37500 37500 37500 37865 37865 37865 38231 38231 38231 38596 38596 38596 38961 38961 38961 39326 39326 39326 39692 39692 39692 40057 40057 40057 37012 37012 37012 38108 38108 38108 1-Mar 1-Mar 1-Mar 44075 44075 44075 10-Mar 10-Mar 10-Mar 44084 44084 44084 11-Mar 11-Mar 11-Mar 44085 44085 44085 44086 44086 44086 44088 44088 44088 44089 44089 44089 2-Mar 2-Mar 2-Mar 44076 44076 44076 3-Mar 3-Mar 3-Mar 44077 44077 44077 4-Mar 4-Mar 4-Mar 44078 44078 44078 5-Mar 5-Mar 5-Mar 44079 44079 44079 6-Mar 6-Mar 44080 44080 44080 7-Mar 7-Mar 7-Mar 44081 44081 44081 8-Mar 8-Mar 8-Mar 44082 44082 44082 9-Mar 9-Mar 9-Mar 44083 44083 44083 37012 37012 37012 38108 38108 38108"
## [164] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM12_ESM.xlsx Mmusculus 128 37135 37135 37135 40422 40422 40422 40787 40787 40787 41153 41153 41153 41883 41883 41883 42248 42248 42248 37500 37500 37500 37865 37865 37865 38231 38231 38231 38596 38596 38596 38961 38961 38961 39326 39326 39326 39692 39692 39692 40057 40057 40057 37012 37012 37012 38108 38108 38108 1-Mar 1-Mar 1-Mar 44075 44075 44075 10-Mar 10-Mar 10-Mar 44084 44084 44084 11-Mar 11-Mar 11-Mar 44085 44085 44085 44086 44086 44086 44088 44088 44088 44089 44089 44089 2-Mar 2-Mar 2-Mar 44076 44076 44076 3-Mar 3-Mar 3-Mar 44077 44077 44077 4-Mar 4-Mar 4-Mar 44078 44078 44078 5-Mar 5-Mar 5-Mar 44079 44079 44079 6-Mar 6-Mar 44080 44080 44080 7-Mar 7-Mar 7-Mar 44081 44081 44081 8-Mar 8-Mar 8-Mar 44082 44082 44082 9-Mar 9-Mar 9-Mar 44083 44083 44083 37012 37012 37012 38108 38108 38108"
## [165] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM12_ESM.xlsx Mmusculus 40 37135 40422 44080 11-Mar 37012 37500 37865 38108 39326 39692 40787 44075 44077 44083 44084 10-Mar 1-Mar 4-Mar 6-Mar 9-Mar 38231 38596 38961 40057 41153 41883 42248 44076 44078 44079 44081 44082 44085 44086 44088 2-Mar 3-Mar 5-Mar 7-Mar 8-Mar"
## [166] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM8_ESM.xlsx Mmusculus 7 39692 39326 37135 38231 40422 38108 37500"
## [167] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM8_ESM.xlsx Mmusculus 7 39692 39326 37135 38231 40422 38108 37500"
## [168] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM8_ESM.xlsx Mmusculus 7 39692 39326 37135 38231 40422 38108 37500"
## [169] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM8_ESM.xlsx Mmusculus 7 39692 39326 37135 38231 40422 38108 37500"
## [170] "PMC8885418 /pmc/articles/PMC8885418/bin/41375_2021_1461_MOESM9_ESM.xlsx Mmusculus 10 37500 40787 37135 39326 40422 39692 38231 40057 38596 38961"
## [171] "PMC8873204 /pmc/articles/PMC8873204/bin/41467_2022_28569_MOESM5_ESM.xlsx Hsapiens 1 39692"
## [172] "PMC8873204 /pmc/articles/PMC8873204/bin/41467_2022_28569_MOESM5_ESM.xlsx Hsapiens 1 40057"
## [173] "PMC8864016 /pmc/articles/PMC8864016/bin/41467_2022_28657_MOESM4_ESM.xlsx Mmusculus 2 43348 43349"
## [174] "PMC8864016 /pmc/articles/PMC8864016/bin/41467_2022_28657_MOESM5_ESM.xlsx Mmusculus 1 43167"
## [175] "PMC8861042 /pmc/articles/PMC8861042/bin/41467_2022_28634_MOESM4_ESM.xlsx Hsapiens 8 37500 38777 38961 39508 38412 39326 37316 38777"
## [176] "PMC8923497 /pmc/articles/PMC8923497/bin/pone.0264645.s002.xls Hsapiens 3 2021/03/10 2021/12/01 2021/03/11"
## [177] "PMC8921988 /pmc/articles/PMC8921988/bin/DataSheet_3.xlsx Hsapiens 1 44450"
## [178] "PMC8915965 /pmc/articles/PMC8915965/bin/pnas.2119676119.sd01.xlsx Hsapiens 26 41335 41527 41608 41343 41528 41334 41519 41525 41526 41531 41517 41336 41520 41338 41341 41333 41340 41521 41530 41524 41337 41518 41522 41523 41342 41339"
## [179] "PMC8913945 /pmc/articles/PMC8913945/bin/DataSheet_6.xls Hsapiens 2 44443 44265"
## [180] "PMC8913945 /pmc/articles/PMC8913945/bin/DataSheet_7.xls Hsapiens 23 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44443 44443 44443 44443 44443"
## [181] "PMC8913945 /pmc/articles/PMC8913945/bin/DataSheet_7.xls Ggallus 23 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44445 44443 44443 44443 44443 44443"
## [182] "PMC8907571 /pmc/articles/PMC8907571/bin/Table_1.xlsx Hsapiens 13 44454 44257 44454 44261 44263 44257 44257 44262 44261 44258 44263 44260 44257"
## [183] "PMC8891552 /pmc/articles/PMC8891552/bin/CPR-55-e13195-s002.xlsx Mmusculus 5 44446 44441 44262 44454 44449"
## [184] "PMC8891552 /pmc/articles/PMC8891552/bin/CPR-55-e13195-s002.xlsx Mmusculus 4 44261 44265 44257 44263"
## [185] "PMC8891552 /pmc/articles/PMC8891552/bin/CPR-55-e13195-s004.xlsx Mmusculus 2 44447 44448"
## [186] "PMC8891552 /pmc/articles/PMC8891552/bin/CPR-55-e13195-s004.xlsx Mmusculus 4 44262 44266 44258 44256"
## [187] "PMC8891552 /pmc/articles/PMC8891552/bin/CPR-55-e13195-s009.xlsx Mmusculus 6 43891 43891 43893 43897 43891 43901"
## [188] "PMC8905247 /pmc/articles/PMC8905247/bin/DataSheet1.XLSX Hsapiens 2 44256 44257"
## [189] "PMC8902468 /pmc/articles/PMC8902468/bin/Table_2.xlsx Athaliana 6 44476 44476 44416 44476 44476 44473"
## [190] "PMC8896005 /pmc/articles/PMC8896005/bin/jkac009_table_s3.xlsx Celegans 8 44440 44260 44440 44260 44440 44260 44440 44260"
## [191] "PMC8894600 /pmc/articles/PMC8894600/bin/DataSheet_1.xls Hsapiens 3 2021/03/05 2021/03/05 2021/03/05"
## [192] "PMC8892324 /pmc/articles/PMC8892324/bin/pnas.2111404119.sd02.xlsx Hsapiens 24 \"LJP007 HA1E 24H-HG-9-91-01-10\" \"LJP005 PC3 24H-HG-5-113-01-10\" \"LJP006 HA1E 24H-THZ-2-98-01-10\" \"LJP006 HME1 24H-THZ-2-98-01-10\" \"CPC007 A375 24H-ncgc00188535-01-10.0\" \"LJP005 HEPG2 24H-HG-5-113-01-10\" \"LJP005 MCF7 3H-HG-5-113-01-10\" \"LJP006 HS578T 3H-HG-6-64-01-10\""
## [193] "PMC8891516 /pmc/articles/PMC8891516/bin/Table5.xlsx Hsapiens 34 44258 44257 44531 44262 44261 44256 44531 44531 44259 44257 44263 44257 44531 44531 44258 44531 44256 44257 44259 44263 44265 44531 44261 44266 44256 44531 44259 44262 44259 44256 44261 44264 44261 44259"
## [194] "PMC8891516 /pmc/articles/PMC8891516/bin/Table5.xlsx Hsapiens 36 44258 44262 44531 44257 44261 44531 44257 44531 44259 44257 44256 44531 44263 44531 44531 44256 44264 44263 44256 44261 44258 44265 44256 44257 44531 44261 44531 44257 44256 44259 44258 44261 44259 44256 44259 44265"
## [195] "PMC8891516 /pmc/articles/PMC8891516/bin/Table5.xlsx Hsapiens 33 44258 44257 44531 44531 44257 44265 44256 44263 44531 44258 44261 44531 44259 44262 44259 44259 44259 44531 44531 44531 44256 44256 44258 44256 44256 44258 44258 44531 44258 44257 44256 44256 44531"
## [196] "PMC8891516 /pmc/articles/PMC8891516/bin/Table5.xlsx Hsapiens 36 44258 44531 44258 44259 44257 44531 44531 44263 44257 44261 44259 44256 44256 44256 44531 44531 44257 44257 44259 44265 44258 44256 44531 44258 44256 44531 44259 44257 44262 44531 44531 44256 44256 44256 44262 44531"
## [197] "PMC8891516 /pmc/articles/PMC8891516/bin/Table6.xlsx Hsapiens 6 44259 44256 44258 44531 44258 44256"
## [198] "PMC8891516 /pmc/articles/PMC8891516/bin/Table6.xlsx Hsapiens 2 44263 44261"
## [199] "PMC8889144 /pmc/articles/PMC8889144/bin/Table1.XLSX Hsapiens 4 Histone Methylation: https://www.nature.com/articles/s12276-019-0230-6 https://clincancerres.aacrjournals.org/content/clincanres/early/2016/02/26/1078-0432.CCR-13-1333.full.pdf"
## [200] "PMC8844066 /pmc/articles/PMC8844066/bin/41421_2021_366_MOESM3_ESM.xlsx Hsapiens 15 43901 43891 43901 43901 43891 43901 43901 43891 43901 43891 43901 43891 43901 43891 43901"
## [201] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 4 43165 43165 43165 43165"
## [202] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 3 44075 44083 44077"
## [203] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 2 43896 43898"
## [204] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 2 44077 44079"
## [205] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 2 41888 41893"
## [206] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 1 44257"
## [207] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 1 44258"
## [208] "PMC8837555 /pmc/articles/PMC8837555/bin/41588_2021_1008_MOESM3_ESM.xlsx Mmusculus 2 44261 44264"
## [209] "PMC8887668 /pmc/articles/PMC8887668/bin/Table5.xlsx Drerio 1 43892"
## [210] "PMC8885996 /pmc/articles/PMC8885996/bin/Table_2.xlsx Mmusculus 2 43892 44078"
## [211] "PMC8884696 /pmc/articles/PMC8884696/bin/Table3.xlsx Hsapiens 2 44256 44257"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 Celegans Drerio Ggallus Hsapiens Mmusculus
## 1 1 10 4 143 45
## Rnorvegicus Scerevisiae
## 6 1par(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##
## PMC8831532 PMC8837555 PMC8844066 PMC8861042 PMC8863982 PMC8864016 PMC8873204
## 5 8 1 1 1 2 2
## PMC8881579 PMC8883538 PMC8883549 PMC8884250 PMC8884696 PMC8885418 PMC8885711
## 1 1 5 1 1 8 2
## PMC8885993 PMC8885996 PMC8887668 PMC8888590 PMC8888678 PMC8889144 PMC8891017
## 5 1 1 1 3 1 5
## PMC8891275 PMC8891516 PMC8891552 PMC8892324 PMC8892733 PMC8894600 PMC8896005
## 1 6 5 1 1 1 1
## PMC8896830 PMC8902468 PMC8903709 PMC8904843 PMC8905247 PMC8907571 PMC8912178
## 1 1 1 1 1 1 3
## PMC8913945 PMC8915965 PMC8916121 PMC8919059 PMC8921118 PMC8921511 PMC8921613
## 3 1 6 3 1 1 1
## PMC8921988 PMC8923467 PMC8923497 PMC8924366 PMC8925074 PMC8925295 PMC8927698
## 1 1 1 30 1 3 4
## PMC8927907 PMC8930202 PMC8930846 PMC8931336 PMC8931337 PMC8933692 PMC8934046
## 1 5 4 1 1 5 1
## PMC8937020 PMC8938417 PMC8938861 PMC8940286 PMC8941526 PMC8941758 PMC8942222
## 1 1 7 9 1 5 3
## PMC8947600 PMC8957956 PMC8958363 PMC8958792 PMC8959321 PMC8960258 PMC8960263
## 4 1 6 7 5 1 1
## PMC8966188 PMC8967178
## 1 2summary(as.numeric(DIST))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 2.931 4.250 30.000hist(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
## 46 PMC8924366 30
## 60 PMC8940286 9
## 2 PMC8837555 8
## 13 PMC8885418 8
## 59 PMC8938861 7
## 67 PMC8958792 7
## 23 PMC8891516 6
## 38 PMC8916121 6
## 66 PMC8958363 6
## 1 PMC8831532 5
## 10 PMC8883549 5
## 15 PMC8885993 5
## 21 PMC8891017 5
## 24 PMC8891552 5
## 51 PMC8930202 5
## 55 PMC8933692 5
## 62 PMC8941758 5
## 68 PMC8959321 5
## 49 PMC8927698 4
## 52 PMC8930846 4MOST_ERR_FILES = as.character(DIST_DF[1,1])
MOST_ERR_FILES## [1] "PMC8924366"# 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## PMC8831532 PMC8837555 PMC8844066 PMC8861042 PMC8863982 PMC8864016 PMC8873204
## 68 17 15 8 2 3 2
## PMC8881579 PMC8883538 PMC8883549 PMC8884250 PMC8884696 PMC8885418 PMC8885711
## 3 1 8 2 2 334 78
## PMC8885993 PMC8885996 PMC8887668 PMC8888590 PMC8888678 PMC8889144 PMC8891017
## 24 2 1 2 214 4 31
## PMC8891275 PMC8891516 PMC8891552 PMC8892324 PMC8892733 PMC8894600 PMC8896005
## 1 147 21 24 1 3 8
## PMC8896830 PMC8902468 PMC8903709 PMC8904843 PMC8905247 PMC8907571 PMC8912178
## 1 6 26 3 2 13 42
## PMC8913945 PMC8915965 PMC8916121 PMC8919059 PMC8921118 PMC8921511 PMC8921613
## 48 26 43 5 11 1 1
## PMC8921988 PMC8923467 PMC8923497 PMC8924366 PMC8925074 PMC8925295 PMC8927698
## 1 1 3 310 47 6 104
## PMC8927907 PMC8930202 PMC8930846 PMC8931336 PMC8931337 PMC8933692 PMC8934046
## 1 72 17 18 7 234 25
## PMC8937020 PMC8938417 PMC8938861 PMC8940286 PMC8941526 PMC8941758 PMC8942222
## 5 2 48 11 2 120 12
## PMC8947600 PMC8957956 PMC8958363 PMC8958792 PMC8959321 PMC8960258 PMC8960263
## 22 332 89 151 11 2 2
## PMC8966188 PMC8967178
## 15 6hist(NERR,main="number of errors per PMC article")
NERR_DF <- as.data.frame(NERR)
NERR_DF <- NERR_DF[order(-NERR_DF$NERR),,drop=FALSE]
head(NERR_DF,20)## NERR
## PMC8885418 334
## PMC8957956 332
## PMC8924366 310
## PMC8933692 234
## PMC8888678 214
## PMC8958792 151
## PMC8891516 147
## PMC8941758 120
## PMC8927698 104
## PMC8958363 89
## PMC8885711 78
## PMC8930202 72
## PMC8831532 68
## PMC8913945 48
## PMC8938861 48
## PMC8925074 47
## PMC8916121 43
## PMC8912178 42
## PMC8891017 31
## PMC8903709 26MOST_ERR = rownames(NERR_DF)[1]
MOST_ERR## [1] "PMC8885418"Journals affected
GENELIST_ERROR_ARTICLES <- gsub("PMC","",GENELIST_ERROR_ARTICLES)
### JSON PARSING is more reliable than XML
ARTICLES <- esummary( GENELIST_ERROR_ARTICLES , db="pmc" , retmode = "json" )
ARTICLE_DATA <- reutils::content(ARTICLES,as= "parsed")
ARTICLE_DATA <- ARTICLE_DATA$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] 35NUM_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] "Nature Communications"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] "8924366"
##
## $result$`8924366`
## $result$`8924366`$uid
## [1] "8924366"
##
## $result$`8924366`$pubdate
## [1] "2022 Mar 2"
##
## $result$`8924366`$epubdate
## [1] "2022 Mar 2"
##
## $result$`8924366`$printpubdate
## [1] ""
##
## $result$`8924366`$source
## [1] "Front Immunol"
##
## $result$`8924366`$authors
## name authtype
## 1 Zhao R Author
## 2 Pan Z Author
## 3 Li B Author
## 4 Zhao S Author
## 5 Zhang S Author
## 6 Qi Y Author
## 7 Qiu J Author
## 8 Gao Z Author
## 9 Fan Y Author
## 10 Guo Q Author
## 11 Qiu W Author
## 12 Wang S Author
## 13 Wang Q Author
## 14 Zhang P Author
## 15 Guo X Author
## 16 Deng L Author
## 17 Xue H Author
## 18 Li G Author
##
## $result$`8924366`$title
## [1] "Comprehensive Analysis of the Tumor Immune Microenvironment Landscape in Glioblastoma Reveals Tumor Heterogeneity and Implications for Prognosis and Immunotherapy"
##
## $result$`8924366`$volume
## [1] "13"
##
## $result$`8924366`$issue
## [1] ""
##
## $result$`8924366`$pages
## [1] "820673"
##
## $result$`8924366`$articleids
## idtype value
## 1 pmid 35309323
## 2 doi 10.3389/fimmu.2022.820673
## 3 pmcid PMC8924366
##
## $result$`8924366`$fulljournalname
## [1] "Frontiers in Immunology"
##
## $result$`8924366`$sortdate
## [1] "2022/03/02 00:00"
##
## $result$`8924366`$pmclivedate
## [1] "2022/03/17"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] "8885418"
##
## $result$`8885418`
## $result$`8885418`$uid
## [1] "8885418"
##
## $result$`8885418`$pubdate
## [1] "2021 Nov 5"
##
## $result$`8885418`$epubdate
## [1] "2021 Nov 5"
##
## $result$`8885418`$printpubdate
## [1] "2022"
##
## $result$`8885418`$source
## [1] "Leukemia"
##
## $result$`8885418`$authors
## name authtype
## 1 Stavast CJ Author
## 2 van Zuijen I Author
## 3 Karkoulia E Author
## 4 Özçelik A Author
## 5 van Hoven-Beijen A Author
## 6 Leon LG Author
## 7 Voerman JS Author
## 8 Janssen GM Author
## 9 van Veelen PA Author
## 10 Burocziova M Author
## 11 Brouwer RW Author
## 12 van IJcken WF Author
## 13 Maas A Author
## 14 Bindels EM Author
## 15 van der Velden VH Author
## 16 Schliehe C Author
## 17 Katsikis PD Author
## 18 Alberich-Jorda M Author
## 19 Erkeland SJ Author
##
## $result$`8885418`$title
## [1] "The tumor suppressor MIR139 is silenced by POLR2M to promote AML oncogenesis"
##
## $result$`8885418`$volume
## [1] "36"
##
## $result$`8885418`$issue
## [1] "3"
##
## $result$`8885418`$pages
## [1] "687-700"
##
## $result$`8885418`$articleids
## idtype value
## 1 pmid 34741119
## 2 doi 10.1038/s41375-021-01461-5
## 3 pmcid PMC8885418
##
## $result$`8885418`$fulljournalname
## [1] "Leukemia"
##
## $result$`8885418`$sortdate
## [1] "2021/11/05 00:00"
##
## $result$`8885418`$pmclivedate
## [1] "2022/03/17"Trend info
To plot the trend over the past 6-12 months.
url <- "http://ziemann-lab.net/public/gene_name_errors/"
doc <- htmlParse(url)
links <- xpathSApply(doc, "//a/@href")
links <- links[grep("html",links)]
links## href href href
## "Report_2021-02.html" "Report_2021-03.html" "Report_2021-04.html"
## href href href
## "Report_2021-05.html" "Report_2021-06.html" "Report_2021-07.html"
## href href href
## "Report_2021-08.html" "Report_2021-09.html" "Report_2021-10.html"
## href href href
## "Report_2021-11.html" "Report_2021-12.html" "Report_2022-01.html"
## href href
## "Report_2022-02.html" "Report_2022-03.html"unlink("online_files/",recursive=TRUE)
dir.create("online_files")
sapply(links, 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
## 0 0 0 0 0 0 0 0 0 0 0 0 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] "2022-04"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 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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] readxl_1.3.1 reutils_0.2.3 xml2_1.3.3 jsonlite_1.8.0 XML_3.99-0.9
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.8.3 knitr_1.37 magrittr_2.0.2 R6_2.5.1
## [5] rlang_1.0.2 fastmap_1.1.0 highr_0.9 stringr_1.4.0
## [9] tools_4.1.3 xfun_0.30 cli_3.2.0 jquerylib_0.1.4
## [13] htmltools_0.5.2 yaml_2.3.5 digest_0.6.29 assertthat_0.2.1
## [17] sass_0.4.1 bitops_1.0-7 RCurl_1.98-1.6 evaluate_0.15
## [21] rmarkdown_2.13 stringi_1.7.6 compiler_4.1.3 bslib_0.3.1
## [25] cellranger_1.1.0