Gene Name Error Monthly Report
Mark Ziemann
2023-05-03
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")
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] "2023/4"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] 3359writeLines(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] 4944NUM_XLS_ARTICLES = length(unique(sapply(strsplit(XLS," "),"[[",1)))
NUM_XLS_ARTICLES## [1] 1157GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>2]
#GENELISTS
NUM_GENELISTS <- length(unique(sapply(strsplit(GENELISTS," "),"[[",2)))
NUM_GENELISTS## [1] 567NUM_GENELIST_ARTICLES <- length(unique(sapply(strsplit(GENELISTS," "),"[[",1)))
NUM_GENELIST_ARTICLES## [1] 296ERROR_GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>3]
#ERROR_GENELISTS
NUM_ERROR_GENELISTS = length(ERROR_GENELISTS)
NUM_ERROR_GENELISTS## [1] 204GENELIST_ERROR_ARTICLES <- unique(sapply(strsplit(ERROR_GENELISTS," "),"[[",1))
GENELIST_ERROR_ARTICLES## [1] "PMC10148045" "PMC10147683" "PMC10146888" "PMC10140479" "PMC10140356"
## [6] "PMC10140160" "PMC10138339" "PMC10134541" "PMC10134367" "PMC10126422"
## [11] "PMC10124151" "PMC10121420" "PMC10120908" "PMC10119186" "PMC10119024"
## [16] "PMC10104777" "PMC10103417" "PMC10101686" "PMC10101332" "PMC10099834"
## [21] "PMC10098193" "PMC10095438" "PMC10095316" "PMC10092771" "PMC10087516"
## [26] "PMC10086525" "PMC10084920" "PMC10082087" "PMC10080023" "PMC10079878"
## [31] "PMC10078807" "PMC10076939" "PMC10076316" "PMC10073929" "PMC10072823"
## [36] "PMC10070455" "PMC10069891" "PMC10069868" "PMC10067406" "PMC10066595"
## [41] "PMC10066286" "PMC10063665" "PMC10148492" "PMC10146992" "PMC10142227"
## [46] "PMC10141963" "PMC10140399" "PMC10139981" "PMC10132553" "PMC10123121"
## [51] "PMC10120025" "PMC10119396" "PMC10118386" "PMC10115815" "PMC10115646"
## [56] "PMC10108528" "PMC10106861" "PMC10104827" "PMC10103646" "PMC10099689"
## [61] "PMC10097911" "PMC10092010" "PMC10091377" "PMC10087204" "PMC10085592"
## [66] "PMC10082194" "PMC10080956" "PMC10077681" "PMC10071012" "PMC10070495"
## [71] "PMC10067935" "PMC10064647" "PMC10064413"NUM_ERROR_GENELIST_ARTICLES <- length(GENELIST_ERROR_ARTICLES)
NUM_ERROR_GENELIST_ARTICLES## [1] 73ERROR_PROPORTION = NUM_ERROR_GENELIST_ARTICLES / NUM_GENELIST_ARTICLES
ERROR_PROPORTION## [1] 0.2466216Look 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] "PMC10148045 PMC_DL/PMC10148045/supplementaryfiles/mmc6.xlsx Hsapiens 14 44626 44819 44810 44806 44811 44621 44812 44622 44627 44815 44625 44814 44807 44813"
## [2] "PMC10148045 PMC_DL/PMC10148045/supplementaryfiles/mmc4.xlsx Hsapiens 200 45173 45184 45175 45171 45176 44986 45170 45177 44987 45180 44990 45179 45178 45173 45175 45176 44986 45170 45177 44987 45174 45180 44990 45179 45178 45173 44991 45175 45171 45176 44986 45170 45177 44987 45174 45180 45179 45172 45178 44991 45175 45171 45176 44986 45170 45177 44987 45174 45180 44990 45179 45172 45178 45173 45175 45171 45176 44986 45170 45177 44987 45180 44990 45179 45178 45184 45175 45171 45176 44986 45177 44987 45180 44990 45178 45173 45175 45171 45176 44986 45177 45174 45180 45179 45178 45173 45175 45171 45176 45177 45180 45178 45184 45175 45171 45176 44986 45170 45177 44987 45180 44990 45179 45178 45173 45184 45175 45171 45176 44986 45170 45177 45174 45180 45179 45178 45175 45171 45176 44986 45170 45177 44987 45180 45179 45172 45178 45173 45175 45171 45176 45170 45177 44987 45174 45180 45179 45178 45175 45171 45176 44986 45177 44987 45180 44990 45179 45178 45175 45171 45176 44986 45170 45177 44987 45180 45178 45184 45175 45171 45176 44986 45170 45177 44987 45180 44990 45179 45178 45175 45171 45176 45170 45177 45174 45180 44990 45178 45184 45175 45171 45176 44986 45177 44987 45174 45180 45179 45178 44991 45175 45171 45176 45170 45177 45174 45180 44990 45179 45178"
## [3] "PMC10148045 PMC_DL/PMC10148045/supplementaryfiles/mmc4.xlsx Hsapiens 1184 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45171 45171 45171 45171 45171 45171 45171 45171 45171 45175 45170 45170 45170 45178 45175 45178 45176 45175 45170 45178 45175 45176 45175 45170 45178 45175 45176 45175 45170 45178 45175 45176 45175 45170 45178 45175 45176 45175 45170 45178 45175 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## [4] "PMC10148045 PMC_DL/PMC10148045/supplementaryfiles/mmc4.xlsx Hsapiens 15 44808 44626 44819 44810 44806 44811 44621 44812 44622 44627 44815 44625 44814 44807 44813"
## [5] "PMC10148045 PMC_DL/PMC10148045/supplementaryfiles/mmc8.xlsx Hsapiens 14 44621 44622 44625 44805 44814 44815 44806 44807 44808 44809 44810 44811 44812 44813"
## [6] "PMC10147683 PMC_DL/PMC10147683/supplementaryfiles/41467_2023_38132_MOESM5_ESM.xlsx Hsapiens 2 39692 40057"
## [7] "PMC10147683 PMC_DL/PMC10147683/supplementaryfiles/41467_2023_38132_MOESM4_ESM.xlsx Hsapiens 26 36951 37316 36951 40238 37316 37681 38047 38412 38777 39142 39508 39873 42248 37135 40422 40787 41153 41883 37500 37865 38231 38596 38961 39326 39692 40057"
## [8] "PMC10146888 zip/ade2675_Table_S1.xlsx Mmusculus 1 44440"
## [9] "PMC10140479 PMC_DL/PMC10140479/supplementaryfiles/mmc9.xlsx Hsapiens 2 36951 37316"
## [10] "PMC10140479 PMC_DL/PMC10140479/supplementaryfiles/mmc4.xlsx Mmusculus 16 40422 37500 38596 38231 39692 38777 37316 39326 40787 40057 37135 40238 36951 38412 39508 36951"
## [11] "PMC10140479 PMC_DL/PMC10140479/supplementaryfiles/mmc8.xlsx Hsapiens 20 39692 40422 40057 37681 37865 36951 38961 38231 37316 39508 40787 37500 38412 38777 39326 36951 39873 39142 37316 38596"
## [12] "PMC10140479 PMC_DL/PMC10140479/supplementaryfiles/mmc7.xlsx Hsapiens 3 37226 37316 36951"
## [13] "PMC10140479 PMC_DL/PMC10140479/supplementaryfiles/mmc2.xlsx Hsapiens 1 37288"
## [14] "PMC10140479 PMC_DL/PMC10140479/supplementaryfiles/mmc3.xlsx Mmusculus 4264 40422 11287 11298 11302 11303 11304 11305 11306 11307 11308 11350 11352 11354 11363 11364 11370 11409 11416 11418 11419 11421 11423 11425 11426 11428 11429 11430 11431 11432 11433 11434 11435 11438 11440 11441 11443 11444 11447 11448 11449 11450 11451 11459 11461 11464 11465 11468 11470 11471 11472 11474 11475 11477 11479 11480 11481 11482 11484 11486 11487 11488 11489 11490 11491 11492 11495 11496 11497 11498 11499 11500 11501 11502 11504 11512 11513 11514 11515 11516 11517 11518 11519 11520 11522 11529 11532 11534 11535 11536 11537 11538 11539 11540 11541 11542 11544 11545 11546 11548 11549 11550 11551 11552 11553 11554 11555 11556 11564 11565 11566 11567 11568 11569 11571 11572 11576 11593 11595 11596 11600 11601 11602 11603 11604 11605 11606 11607 11608 11609 11610 11611 11614 11615 11622 11624 11625 11628 11629 11630 11632 11634 11636 11637 11639 11640 11641 11642 11643 11647 11648 11650 11651 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80289 80290 80291 80292 80294 80297 80334 80384 80385 80509 80515 80517 80706 80707 80708 80718 80719 80720 80721 80732 80733 80743 80744 80748 80749 80750 80751 80752 80782 80794 80795 80796 80797 80828 80837 80838 80857 80859 80860 80861 80876 80877 80879 80880 80883 80884 80885 80886 80888 80889 80890 80891 80892 80893 80898 80901 80902 80903 80904 80905 80906 80907 80908 80909 80910 80911 80912 80913 80914 80915 80976 80978 80979 80981 80982 80985 80986 80987 81000 81003 81004 81006 81007 81010 81011 81012 81013 81014 81015 81016 81017 81018 81489 81497 81500 81535 81600 81601 81630 81701 81702 81703 81799 81840 81845 81877 81879 81896 81897 81898 81904 81905 81906 81907 81909 81910 81913 83379 83380 83382 83383 83395 83396 83397 83398 83408 83409 83410"
## [15] "PMC10140356 PMC_DL/PMC10140356/supplementaryfiles/Table_1.xlsx Mmusculus 1 44626"
## [16] "PMC10140356 PMC_DL/PMC10140356/supplementaryfiles/Table_1.xlsx Mmusculus 1 44626"
## [17] "PMC10140160 PMC_DL/PMC10140160/supplementaryfiles/mmc2.xlsx Ggallus 9 44813 44815 44815 44628 44628 44628 44628 44628 44624"
## [18] "PMC10140160 PMC_DL/PMC10140160/supplementaryfiles/mmc2.xlsx Ggallus 7 44813 44811 44628 44628 44628 44628 44628"
## [19] "PMC10140160 PMC_DL/PMC10140160/supplementaryfiles/mmc2.xlsx Ggallus 4 44813 44815 44628 44624"
## [20] "PMC10140160 PMC_DL/PMC10140160/supplementaryfiles/mmc2.xlsx Hsapiens 3 44813 44811 44628"
## [21] "PMC10138339 zip/ijms-2295322-supplementary.xlsx Hsapiens 12 44986 44987 45184 45179 45180 45171 45173 45174 45175 45176 45177 45178"
## [22] "PMC10134541 PMC_DL/PMC10134541/supplementaryfiles/13058_2023_1635_MOESM2_ESM.xlsx Hsapiens 1 40603"
## [23] "PMC10134541 PMC_DL/PMC10134541/supplementaryfiles/13058_2023_1635_MOESM2_ESM.xlsx Hsapiens 1 40603"
## [24] "PMC10134541 PMC_DL/PMC10134541/supplementaryfiles/13058_2023_1635_MOESM2_ESM.xlsx Hsapiens 2 38047 40603"
## [25] "PMC10134367 PMC_DL/PMC10134367/supplementaryfiles/CAM4-12-8956-s005.xlsx Hsapiens 20 44621 44622 44813 44629 44623 44812 44626 44621 44815 44808 44805 44625 44622 44806 44628 44809 44819 44810 44811 44627"
## [26] "PMC10126422 PMC_DL/PMC10126422/supplementaryfiles/Table1.XLSX Hsapiens 1 44624"
## [27] "PMC10124151 PMC_DL/PMC10124151/supplementaryfiles/NIHMS1887289-supplement-2.xlsx Celegans 15 44713 44625 44626 44836 44652 44805 44624 44624 44625 44713 44626 44652 44805 44621 44836"
## [28] "PMC10124151 PMC_DL/PMC10124151/supplementaryfiles/NIHMS1887289-supplement-2.xlsx Celegans 5 44713 44836 44625 44626 44624"
## [29] "PMC10124151 PMC_DL/PMC10124151/supplementaryfiles/NIHMS1887289-supplement-2.xlsx Celegans 5 44713 44836 44625 44626 44624"
## [30] "PMC10121420 PMC_DL/PMC10121420/supplementaryfiles/41420_2023_1420_MOESM3_ESM.xlsx Mmusculus 20 40603 40057 37681 37316 38961 39142 37316 40787 36951 38596 39873 37500 37865 39508 38777 38412 40422 42248 39692 39326"
## [31] "PMC10120908 PMC_DL/PMC10120908/supplementaryfiles/aging-15-204623-s003.xlsx Hsapiens 16 45178 45178 45179 45179 45179 45179 44992 45171 45171 45171 45171 44991 44991 44991 44988 45176"
## [32] "PMC10119186 PMC_DL/PMC10119186/supplementaryfiles/41467_2023_37849_MOESM4_ESM.xlsx Hsapiens 19 44812 44628 44621 44816 44807 44813 44629 44621 44808 44631 44622 44624 44814 44819 44626 44806 44805 44818 44623"
## [33] "PMC10119186 PMC_DL/PMC10119186/supplementaryfiles/41467_2023_37849_MOESM4_ESM.xlsx Hsapiens 20 44627 44813 44812 44621 44816 44814 44622 44631 44624 44623 44808 44629 44805 44818 44807 44819 44806 44626 44621 44628"
## [34] "PMC10119186 PMC_DL/PMC10119186/supplementaryfiles/41467_2023_37849_MOESM4_ESM.xlsx Hsapiens 19 44812 44628 44807 44622 44621 44816 44621 44626 44629 44623 44624 44808 44819 44806 44631 44813 44818 44805 44814"
## [35] "PMC10119186 PMC_DL/PMC10119186/supplementaryfiles/41467_2023_37849_MOESM4_ESM.xlsx Hsapiens 19 44627 44628 44812 44808 44814 44813 44816 44626 44624 44819 44622 44818 44806 44631 44621 44621 44629 44805 44623"
## [36] "PMC10119186 PMC_DL/PMC10119186/supplementaryfiles/41467_2023_37849_MOESM4_ESM.xlsx Hsapiens 1 44627"
## [37] "PMC10119024 PMC_DL/PMC10119024/supplementaryfiles/41477_2023_1377_MOESM3_ESM.xlsx Athaliana 1 38261"
## [38] "PMC10104777 PMC_DL/PMC10104777/supplementaryfiles/41417_2022_577_MOESM1_ESM.xls Hsapiens 1 44440"
## [39] "PMC10104777 PMC_DL/PMC10104777/supplementaryfiles/41417_2022_577_MOESM1_ESM.xls Hsapiens 1 44258"
## [40] "PMC10104777 PMC_DL/PMC10104777/supplementaryfiles/41417_2022_577_MOESM1_ESM.xls Hsapiens 1 44446"
## [41] "PMC10103417 PMC_DL/PMC10103417/supplementaryfiles/40104_2023_855_MOESM1_ESM.xlsx Hsapiens 1 44805"
## [42] "PMC10103417 PMC_DL/PMC10103417/supplementaryfiles/40104_2023_855_MOESM1_ESM.xlsx Hsapiens 2 44630 44628"
## [43] "PMC10103417 PMC_DL/PMC10103417/supplementaryfiles/40104_2023_855_MOESM1_ESM.xlsx Hsapiens 26 44806 44813 44811 44815 44626 44627 44814 44812 44808 44622 44810 44809 44628 44622 44625 44629 44623 44807 44805 44631 44621 44621 44630 44624 44818 44816"
## [44] "PMC10101686 zip/Carruthers_et_al.-Supplementary_Materials-Tables_S1-S15-ACCEPTED.xlsx Drerio 4 39326 39692 38961 37865"
## [45] "PMC10101332 PMC_DL/PMC10101332/supplementaryfiles/Table_1.XLSX Ggallus 1 44713"
## [46] "PMC10099834 PMC_DL/PMC10099834/supplementaryfiles/12915_2023_1581_MOESM1_ESM.xls Dmelanogaster 1 2022/09/01"
## [47] "PMC10098193 PMC_DL/PMC10098193/supplementaryfiles/Table1.XLSX Hsapiens 1 44630"
## [48] "PMC10098193 PMC_DL/PMC10098193/supplementaryfiles/Table1.XLSX Hsapiens 1 44623"
## [49] "PMC10095438 zip/FASD_SupplementaryTableS1_010223.xlsx Hsapiens 10 37500 38231 40422 40057 39326 40787 38961 37012 37135 39692"
## [50] "PMC10095316 zip/Table_S3._Drug_Z_score_ranking_of_ETO_co-essential_genes_in_ZATT_KO_cells.xlsx Hsapiens 28 44264 44441 44440 44258 44265 44451 44259 44445 44256 44256 44446 44257 44531 44257 44449 44262 44442 44454 44447 44260 44453 44263 44450 44443 44448 44261 44266 44444"
## [51] "PMC10095316 zip/Table_S1._Normalized_sgRNA_counts_in_the_CRISPR_screens.xlsx Hsapiens 109 44988 45175 45171 44987 45184 45170 45178 45170 44994 44989 44995 44991 44989 45175 45173 44992 45177 44987 45173 44996 44992 45173 44986 45172 45181 44990 44987 45181 44994 45261 45180 45175 44987 45174 45174 45261 45170 44989 44996 45181 44991 44987 45183 44995 45184 45179 45178 45184 45177 44986 45175 45183 45183 44986 45180 44986 45174 44993 44995 44988 44993 45170 45261 45177 44994 44990 44994 45173 45178 45172 45179 45172 45171 45177 45172 44996 44989 44995 45180 45171 44988 44992 44996 44987 45178 45176 45183 45184 45179 44990 45171 44991 44990 44991 45180 44986 44988 45179 45181 45261 45174 44993 44987 44992 44986 44986 44993 44986 44987"
## [52] "PMC10095316 zip/Table_S2._Drug_Z_score_ranking_of_ETO_co-essential_genes_in_293A_WT_cells.xlsx Hsapiens 28 44446 44453 44264 44450 44261 44531 44451 44445 44442 44443 44441 44266 44440 44262 44263 44260 44454 44448 44258 44259 44265 44444 44257 44449 44256 44447 44257 44256"
## [53] "PMC10092771 zip/Supplementary_Tables/Supplementary_table_1_TA_number.xlsx Mmusculus 27 38047 37500 37500 36951 37316 40422 39873 39692 38231 40238 40057 40603 38777 37865 41153 38596 37316 37681 38412 39142 40787 41883 39508 37135 36951 39326 38961"
## [54] "PMC10087516 PMC_DL/PMC10087516/supplementaryfiles/ALL-78-522-s002.xlsx Hsapiens 1 44622"
## [55] "PMC10087516 PMC_DL/PMC10087516/supplementaryfiles/ALL-78-522-s002.xlsx Hsapiens 1 44622"
## [56] "PMC10087516 PMC_DL/PMC10087516/supplementaryfiles/ALL-78-522-s002.xlsx Hsapiens 1 44622"
## [57] "PMC10086525 PMC_DL/PMC10086525/supplementaryfiles/ACEL-22-e13792-s017.xlsx Mmusculus 2 44815 44622"
## [58] "PMC10084920 zip/Table_S5.xlsx Hsapiens 24 44626 44813 44623 44816 44814 44630 44805 44628 44810 44812 44806 44896 44622 44808 44625 44622 44621 44815 44807 44624 44621 44627 44629 44811"
## [59] "PMC10084920 zip/Table_S2.xlsx Hsapiens 11 43899 43900 43892 44076 43897 43891 43891 43901 44081 44166 44080"
## [60] "PMC10082087 PMC_DL/PMC10082087/supplementaryfiles/41536_2023_293_MOESM2_ESM.xlsx Drerio 5 44810 44816 44806 44628 44814"
## [61] "PMC10080023 PMC_DL/PMC10080023/supplementaryfiles/Table5.XLS Hsapiens 31 2022/03/07 2022/03/07 2022/03/06 2022/03/01 2022/03/01 2022/03/07 2022/03/01 2022/09/15 2022/03/05 2022/03/09 2022/03/06 2022/03/05 2022/03/03 2022/03/07 2022/03/10 2022/03/06 2022/03/03 2022/03/04 2022/12/01 2022/03/08 2022/03/07 2022/03/08 2022/03/01 2022/03/10 2022/03/06 2022/03/06 2022/03/02 2022/03/06 2022/03/08 2022/03/11 2022/03/09"
## [62] "PMC10079878 PMC_DL/PMC10079878/supplementaryfiles/DataSheet_3.xlsx Hsapiens 6 44621 44625 44626 44627 44627 44628"
## [63] "PMC10079878 PMC_DL/PMC10079878/supplementaryfiles/DataSheet_1.xlsx Hsapiens 1 44621"
## [64] "PMC10079878 PMC_DL/PMC10079878/supplementaryfiles/DataSheet_2.xlsx Hsapiens 4 44623 44625 44629 44630"
## [65] "PMC10078807 PMC_DL/PMC10078807/supplementaryfiles/Table_2.xlsx Hsapiens 5 42251 42073 42073 42064 42256"
## [66] "PMC10078807 PMC_DL/PMC10078807/supplementaryfiles/Table_2.xlsx Hsapiens 26 42065 42064 42251 42251 42251 42251 42251 42251 42251 42251 42251 42073 42073 42073 42073 42073 42073 42073 42073 42256 42256 42256 42256 42065 42065 42065"
## [67] "PMC10078807 PMC_DL/PMC10078807/supplementaryfiles/Table_2.xlsx Hsapiens 6 42256 42256 42251 42073 42256 42073"
## [68] "PMC10078807 PMC_DL/PMC10078807/supplementaryfiles/Table_2.xlsx Hsapiens 35 42251 42251 42251 42251 42251 42251 42251 42251 42251 42073 42073 42073 42073 42073 42073 42073 42073 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42065 42065 42065"
## [69] "PMC10078807 PMC_DL/PMC10078807/supplementaryfiles/Table_2.xlsx Hsapiens 37 42256 42073 42339 42064 42256 42256 42073 42065 42339 42258 42065 42073 42257 42339 42067 42066 42259 42256 42065 42064 42262 42339 42073 42073 42256 42251 42256 42071 42256 42073 42339 42339 42064 42257 42258 42064 42256"
## [70] "PMC10078807 PMC_DL/PMC10078807/supplementaryfiles/Table_2.xlsx Hsapiens 124 42065 42065 42064 42071 42071 42068 42259 42259 42248 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42251 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42073 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42256 42065 42065 42065 42065 42065 42065 42065 42065 42065 42065 42065 42065 42065 42065 42065 42067 42258 42258 42064 42064 42064 42069 42069 42069 42066 42254 42254 42254 42339"
## [71] "PMC10076939 PMC_DL/PMC10076939/supplementaryfiles/noac220_suppl_supplementary_table_s3.xlsx Ggallus 23 41883 38777 39873 39873 39692 38231 40057 40603 38412 38596 37316 38047 39873 39142 41883 37865 40787 41883 39508 40057 38596 39692 40057"
## [72] "PMC10076939 PMC_DL/PMC10076939/supplementaryfiles/noac220_suppl_supplementary_table_s3.xlsx Hsapiens 13 39692 40057 39142 41883 37865 38596 40603 38412 39692 38231 40057 41883 40787"
## [73] "PMC10076316 PMC_DL/PMC10076316/supplementaryfiles/41467_2023_37438_MOESM4_ESM.xlsx Hsapiens 17 44627 44623 44629 44806 44811 44815 44622 44622 44813 44626 44626 44629 44629 44810 44625 44806 44806"
## [74] "PMC10076316 PMC_DL/PMC10076316/supplementaryfiles/41467_2023_37438_MOESM4_ESM.xlsx Hsapiens 9 44627 44813 44813 44809 44631 44813 44818 44818 44806"
## [75] "PMC10076316 PMC_DL/PMC10076316/supplementaryfiles/41467_2023_37438_MOESM4_ESM.xlsx Hsapiens 1 44819"
## [76] "PMC10073929 PMC_DL/PMC10073929/supplementaryfiles/CJP2-9-165-s002.xlsx Hsapiens 34 44265 44440 44263 44261 44445 44449 44449 44447 44256 44450 44257 44265 44445 44257 44263 44261 44261 44447 44256 44264 44454 44448 44257 44258 44448 44448 44446 44445 44441 44441 44441 44262 44257 44259"
## [77] "PMC10072823 zip/DataS4_Mutation_annotations.xlsx Scerevisiae 1 44835"
## [78] "PMC10070455 PMC_DL/PMC10070455/supplementaryfiles/41598_2023_32568_MOESM4_ESM.xlsx Hsapiens 2 44838 44838"
## [79] "PMC10070455 PMC_DL/PMC10070455/supplementaryfiles/41598_2023_32568_MOESM4_ESM.xlsx Hsapiens 2 44838 44838"
## [80] "PMC10070455 PMC_DL/PMC10070455/supplementaryfiles/41598_2023_32568_MOESM1_ESM.xlsx Hsapiens 7 44816 44630 44806 44808 44818 44815 44626"
## [81] "PMC10069891 zip/Supplemental_Tables_v8.xlsx Athaliana 15 38991 37895 37865 37347 37165 37530 38626 37135 37500 39356 38261 37500 36982 37712 37135"
## [82] "PMC10069868 PMC_DL/PMC10069868/supplementaryfiles/elife-79925-supp2.xlsx Hsapiens 1 44808"
## [83] "PMC10067406 PMC_DL/PMC10067406/supplementaryfiles/GBB-22-e12844-s013.xlsx Mmusculus 26 44076 43894 43891 43892 43897 44089 44085 44088 43898 44075 43891 44081 44080 44084 43899 44082 44083 43900 44078 43901 44077 43896 44086 43892 43893 43895"
## [84] "PMC10067406 PMC_DL/PMC10067406/supplementaryfiles/GBB-22-e12844-s013.xlsx Mmusculus 25 44076 43894 43891 43892 43897 44085 44088 43898 44075 43891 44081 44080 44084 43899 44082 44083 43900 44078 43901 44077 43896 44086 43892 43893 43895"
## [85] "PMC10066595 PMC_DL/PMC10066595/supplementaryfiles/mmc2.xlsx Mmusculus 17 44808 44807 44807 44626 44621 44621 44621 44621 44621 44621 44621 44621 44621 44621 44621 44811 44811"
## [86] "PMC10066286 PMC_DL/PMC10066286/supplementaryfiles/42003_2023_4732_MOESM6_ESM.xlsx Hsapiens 7 38412 38777 39142 39873 39508 36951 37316"
## [87] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 2 41341 41532"
## [88] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 4 41528 41528 41528 41528"
## [89] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 6 41528 41528 41528 41528 41334 41334"
## [90] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 14 41339 41339 41339 41339 41344 41344 41336 41336 41525 41525 41525 41525 41525 41525"
## [91] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 5 41338 41338 41338 41341 41532"
## [92] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 6 41528 41529 41528 41337 41528 41337"
## [93] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 2 41609 41609"
## [94] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 6 41341 41338 41338 41338 41338 41338"
## [95] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 7 41341 41341 41341 41341 41341 41338 41338"
## [96] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 2 41341 41532"
## [97] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 1 41529"
## [98] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 5 41529 41529 41529 41518 41518"
## [99] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 25 41521 41521 41521 41521 41521 41521 41521 41521 41521 41343 41343 41343 41343 41526 41526 41526 41526 41526 41526 41526 41526 41526 41526 41526 41526"
## [100] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 1 41532"
## [101] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 3 41335 41335 41335"
## [102] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 8 41523 41523 41523 41523 41523 41523 41523 41523"
## [103] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Hsapiens 3 41337 41337 41337"
## [104] "PMC10063665 PMC_DL/PMC10063665/supplementaryfiles/42003_2023_4683_MOESM7_ESM.xls Ggallus 21 41527 41527 41527 41527 41527 41527 41527 41527 41340 41340 41340 41340 41340 41337 41519 41519 41519 41519 41519 41519 41519"
## [105] "PMC10148492 PMC_DL/PMC10148492/supplementaryfiles/12866_2023_2865_MOESM7_ESM.xlsx Scerevisiae 3 44805 44807 44807"
## [106] "PMC10146992 zip/Table_S6.xlsx Athaliana 6 2-Oct, ATOCT2 1-Oct, AtOCT1 1-Oct, AtOCT1"
## [107] "PMC10146992 zip/Table_S6.xlsx Athaliana 6 2-Oct, ATOCT2 1-Oct, AtOCT1 1-Oct, AtOCT1"
## [108] "PMC10142227 PMC_DL/PMC10142227/supplementaryfiles/12967_2023_4126_MOESM5_ESM.xlsx Hsapiens 3 38047 37226 37681"
## [109] "PMC10141963 zip/Table_S1.xlsx Hsapiens 1 44625"
## [110] "PMC10140399 PMC_DL/PMC10140399/supplementaryfiles/Table_2.XLSX Mmusculus 2 44994 45175"
## [111] "PMC10139981 PMC_DL/PMC10139981/supplementaryfiles/mmc7.xlsx Hsapiens 2 44626 44626"
## [112] "PMC10132553 PMC_DL/PMC10132553/supplementaryfiles/pone.0284820.s002.xlsx Hsapiens 26 44266 44440 44443 44451 44444 44257 44453 44259 44265 44447 44448 44454 44450 44258 44531 44256 44445 44261 44263 44449 44262 44441 44442 44446 44260 44264"
## [113] "PMC10132553 PMC_DL/PMC10132553/supplementaryfiles/pone.0284820.s002.xlsx Hsapiens 22 44266 44443 44257 44440 44440 44454 44454 44447 44261 44261 44448 44445 44445 44444 44450 44450 44263 44263 44262 44262 44451 44451"
## [114] "PMC10132553 PMC_DL/PMC10132553/supplementaryfiles/pone.0284820.s002.xlsx Hsapiens 4 44266 44440 44443 44451"
## [115] "PMC10132553 PMC_DL/PMC10132553/supplementaryfiles/pone.0284820.s002.xlsx Hsapiens 2 44266 44443"
## [116] "PMC10132553 PMC_DL/PMC10132553/supplementaryfiles/pone.0284820.s002.xlsx Hsapiens 2 44440 44451"
## [117] "PMC10132553 PMC_DL/PMC10132553/supplementaryfiles/pone.0284820.s002.xlsx Hsapiens 2 44266 44443"
## [118] "PMC10123121 zip/Table_S1.xlsx Hsapiens 19 39326 39692 40422 38231 37865 38412 39508 37500 39142 37681 42248 38777 39873 38961 40787 40057 38596 37316 37316"
## [119] "PMC10123121 zip/Table_S1.xlsx Mmusculus 2 37135 38047"
## [120] "PMC10123121 zip/table_S2.xlsx Mmusculus 19 39326 39692 40422 38231 37865 38412 39508 37500 39142 37681 42248 38777 39873 38961 40787 40057 38596 37316 37316"
## [121] "PMC10120025 PMC_DL/PMC10120025/supplementaryfiles/pnas.2220134120.sd01.xlsx Hsapiens 26 43896 44078 43894 44079 44083 43900 44086 44085 44089 44088 43891 44076 44080 43901 43899 43898 44077 44081 44166 44082 43897 43892 44084 43893 44075 43895"
## [122] "PMC10120025 PMC_DL/PMC10120025/supplementaryfiles/pnas.2220134120.sd01.xlsx Hsapiens 26 44624 44896 44812 44809 44813 44811 44818 44819 44626 44816 44622 44628 44808 44805 44630 44815 44631 44807 44621 44810 44627 44629 44814 44806 44623 44625"
## [123] "PMC10120025 PMC_DL/PMC10120025/supplementaryfiles/pnas.2220134120.sd01.xlsx Hsapiens 26 44811 44808 44819 44625 44624 44807 44818 44626 44805 44812 44896 44630 44622 44809 44815 44806 44814 44813 44810 44628 44631 44621 44623 44627 44629 44816"
## [124] "PMC10120025 PMC_DL/PMC10120025/supplementaryfiles/pnas.2220134120.sd01.xlsx Hsapiens 10 44621 44629 44622 44626 44624 44631 44625 44623 44630 44627"
## [125] "PMC10119396 PMC_DL/PMC10119396/supplementaryfiles/Table1.XLSX Hsapiens 1 44622"
## [126] "PMC10119396 zip/Rawdata/Figure_S1/Figure_S1A.xlsx Hsapiens 1 44896"
## [127] "PMC10119396 zip/Rawdata/Figure_S1/Figure_S1B.xlsx Hsapiens 13 44621 44629 44627 44896 44625 44630 44631 44819 44628 44626 44623 44622 44624"
## [128] "PMC10119396 zip/Rawdata/Figure_S2/Figure_S2B.xlsx Hsapiens 1 44622"
## [129] "PMC10119396 zip/Rawdata/Figure_3/Figure_3E.xlsx Hsapiens 1 44622"
## [130] "PMC10119396 zip/Rawdata/Figure_3/Figure_3F.xlsx Hsapiens 1 44622"
## [131] "PMC10119396 zip/Rawdata/Figure_2/Figure_2A/mRNA.xlsx Hsapiens 1 44622"
## [132] "PMC10118386 PMC_DL/PMC10118386/supplementaryfiles/elife-83118-supp1.xlsx Hsapiens 7 44627 44808 44815 44621 44626 44814 44819"
## [133] "PMC10118386 PMC_DL/PMC10118386/supplementaryfiles/elife-83118-supp1.xlsx Hsapiens 1 44622"
## [134] "PMC10115815 PMC_DL/PMC10115815/supplementaryfiles/41467_2023_37691_MOESM3_ESM.xlsx Hsapiens 1 44896"
## [135] "PMC10115646 PMC_DL/PMC10115646/supplementaryfiles/41586_2023_5868_MOESM3_ESM.xlsx Mmusculus 6 44621 44713 44716 44814 44809 44715"
## [136] "PMC10108528 PMC_DL/PMC10108528/supplementaryfiles/12864_2023_9293_MOESM10_ESM.xlsx Mmusculus 1 44440"
## [137] "PMC10106861 PMC_DL/PMC10106861/supplementaryfiles/PBI-21-961-s002.xlsx Athaliana 1 44263"
## [138] "PMC10104827 zip/Supplementary_Data5.xlsx Mmusculus 6 44259 44453 44256 44265 44451 44258"
## [139] "PMC10104827 zip/Supplementary_Data5.xlsx Mmusculus 11 44441 44262 44454 44450 44440 44446 44445 44449 44448 44261 44260"
## [140] "PMC10104827 zip/Supplementary_Data5.xlsx Mmusculus 4 43895 44260 44264 44446"
## [141] "PMC10104827 zip/Supplementary_Data4.xlsx Mmusculus 5 44453 44256 44265 44451 44258"
## [142] "PMC10104827 zip/Supplementary_Data4.xlsx Mmusculus 1 44260"
## [143] "PMC10104827 zip/Supplementary_Data6.xlsx Mmusculus 1 43895"
## [144] "PMC10103646 PMC_DL/PMC10103646/supplementaryfiles/Data_Sheet_1.xlsx Ggallus 1 44994"
## [145] "PMC10099689 PMC_DL/PMC10099689/supplementaryfiles/13059_2023_2912_MOESM1_ESM.xlsx Hsapiens 2 44443 44266"
## [146] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 4 40422 38231 41153 37500"
## [147] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 1 38231"
## [148] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 1 41153"
## [149] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 1 39326"
## [150] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 3 39692 37500 41153"
## [151] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 1 38231"
## [152] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 2 38231 41153"
## [153] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 1 41153"
## [154] "PMC10097911 PMC_DL/PMC10097911/supplementaryfiles/Table1.XLSX Hsapiens 2 37500 40422"
## [155] "PMC10092010 PMC_DL/PMC10092010/supplementaryfiles/BDR2-115-205-s001.xlsx Ggallus 2 44266 44266"
## [156] "PMC10091377 PMC_DL/PMC10091377/supplementaryfiles/IID3-11-e836-s008.xlsx Hsapiens 1 44810"
## [157] "PMC10091377 PMC_DL/PMC10091377/supplementaryfiles/IID3-11-e836-s008.xlsx Hsapiens 1 44621"
## [158] "PMC10091377 PMC_DL/PMC10091377/supplementaryfiles/IID3-11-e836-s008.xlsx Hsapiens 13 44810 44810 44815 44810 44626 44811 44810 44810 44813 44622 44621 44628 44621"
## [159] "PMC10091377 PMC_DL/PMC10091377/supplementaryfiles/IID3-11-e836-s008.xlsx Hsapiens 8 44622 44810 44810 44621 44810 44813 44810 44628"
## [160] "PMC10091377 PMC_DL/PMC10091377/supplementaryfiles/IID3-11-e836-s008.xlsx Hsapiens 3 44810 44810 44810"
## [161] "PMC10087204 PMC_DL/PMC10087204/supplementaryfiles/IJC-152-511-s002.xlsx Rnorvegicus 2 39326 37500"
## [162] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 6 44450 44451 44441 44444 44448 44443"
## [163] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 13 44440 44449 44531 44444 44264 44442 44262 44447 44441 44257 44454 44446 44450"
## [164] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 5 44258 44440 44441 44259 44444"
## [165] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 16 44263 44257 44261 44440 44449 44450 44260 44262 44444 44448 44441 44264 44256 44443 44454 44446"
## [166] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 9 44440 44449 44260 44265 44257 44261 44258 44263 44448"
## [167] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 4 44450 44445 44256 44261"
## [168] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Ggallus 10 44258 44444 44261 44264 44445 44440 44266 44259 44450 44262"
## [169] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 12 44260 44453 44454 44263 44448 44257 44446 44440 44449 44262 44444 44264"
## [170] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 6 44443 44449 44265 44263 44442 44440"
## [171] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 4 44263 44453 44447 44266"
## [172] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 8 44262 44441 44261 44263 44448 44443 44446 44440"
## [173] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 13 44442 44441 44259 44445 44262 44260 44454 44258 44443 44450 44265 44440 44264"
## [174] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 10 44258 44448 44257 44260 44263 44441 44454 44447 44266 44442"
## [175] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 4 44266 44257 44261 44443"
## [176] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 16 44443 44260 44263 44264 44440 44444 44259 44266 44448 44449 44265 44442 44450 44261 44257 44451"
## [177] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 6 44450 44446 44257 44448 44449 44262"
## [178] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 9 44265 44259 44264 44445 44531 44441 44449 44260 44447"
## [179] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 12 44446 44262 44450 44449 44440 44454 44257 44261 44447 44448 44442 44264"
## [180] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 8 44442 44447 44263 44259 44256 44444 44450 44262"
## [181] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 7 44264 44440 44257 44262 44263 44453 44256"
## [182] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 16 44440 44266 44448 44445 44256 44447 44263 44450 44261 44258 44454 44449 44260 44453 44441 44262"
## [183] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 21 44263 44261 44262 44440 44441 44260 44442 44448 44443 44449 44444 44257 44264 44445 44265 44454 44446 44531 44447 44256 44258"
## [184] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 7 44441 44257 44262 44446 44447 44261 44256"
## [185] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 8 44260 44449 44446 44441 44265 44442 44258 44443"
## [186] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 8 44450 44446 44447 44449 44266 44262 44263 44442"
## [187] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 2 44445 44265"
## [188] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 10 44263 44261 44441 44257 44450 44445 44440 44447 44266 44444"
## [189] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 1 44259"
## [190] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 9 44257 44443 44262 44448 44263 44450 44444 44449 44261"
## [191] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 1 44263"
## [192] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 7 44449 44262 44259 44265 44448 44258 44261"
## [193] "PMC10085592 PMC_DL/PMC10085592/supplementaryfiles/aging-15-204601-s003.xlsx Hsapiens 9 44264 44448 44256 44451 44442 44450 44441 44258 44260"
## [194] "PMC10082194 PMC_DL/PMC10082194/supplementaryfiles/41467_2023_37537_MOESM6_ESM.xlsx Hsapiens 3 38596 38961 40057"
## [195] "PMC10080956 PMC_DL/PMC10080956/supplementaryfiles/12935_2023_2912_MOESM3_ESM.xlsx Hsapiens 23 45176 44991 44986 44989 45179 45175 45184 45181 44987 45174 44987 44988 45171 45178 44996 44990 44995 44992 45180 45261 45183 44993 45173"
## [196] "PMC10077681 PMC_DL/PMC10077681/supplementaryfiles/12864_2023_9248_MOESM1_ESM.xlsx Hsapiens 1 44264"
## [197] "PMC10077681 PMC_DL/PMC10077681/supplementaryfiles/12864_2023_9248_MOESM1_ESM.xlsx Hsapiens 3 44449 44266 44266"
## [198] "PMC10071012 PMC_DL/PMC10071012/supplementaryfiles/Table_13.xlsx Hsapiens 1 44531"
## [199] "PMC10070495 PMC_DL/PMC10070495/supplementaryfiles/41418_2023_1113_MOESM8_ESM.xlsx Hsapiens 1 SOX2-OCT4-NANOG"
## [200] "PMC10070495 PMC_DL/PMC10070495/supplementaryfiles/41418_2023_1113_MOESM8_ESM.xlsx Hsapiens 1 SOX2-OCT4-NANOG"
## [201] "PMC10067935 PMC_DL/PMC10067935/supplementaryfiles/41598_2023_32569_MOESM1_ESM.xlsx Hsapiens 1 43345"
## [202] "PMC10067935 PMC_DL/PMC10067935/supplementaryfiles/41598_2023_32569_MOESM1_ESM.xlsx Hsapiens 3 43525 43717 44075"
## [203] "PMC10064647 zip/Data_S3.xlsx Hsapiens 4 01-Mar 09-Mar 03-Mar 05-Mar"
## [204] "PMC10064413 zip/BSR-2022-2174_supps3.xls Hsapiens 27 2022/09/09 2022/09/04 2022/03/07 2022/09/02 2022/03/09 2022/03/11 2022/03/10 2022/12/01 2022/09/11 2022/09/07 2022/03/02 2022/03/06 2022/09/10 2022/03/05 2022/03/02 2022/03/01 2022/09/05 2022/09/01 2022/03/04 2022/03/01 2022/03/08 2022/09/12 2022/03/03 2022/09/03 2022/09/14 2022/09/06 2022/09/08"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 Dmelanogaster Drerio Ggallus
## 5 3 1 2 9
## Hsapiens Mmusculus Rnorvegicus Scerevisiae
## 159 22 1 2par(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##
## PMC10063665 PMC10064413 PMC10064647 PMC10066286 PMC10066595 PMC10067406
## 18 1 1 1 1 2
## PMC10067935 PMC10069868 PMC10069891 PMC10070455 PMC10070495 PMC10071012
## 2 1 1 3 2 1
## PMC10072823 PMC10073929 PMC10076316 PMC10076939 PMC10077681 PMC10078807
## 1 1 3 2 2 6
## PMC10079878 PMC10080023 PMC10080956 PMC10082087 PMC10082194 PMC10084920
## 3 1 1 1 1 2
## PMC10085592 PMC10086525 PMC10087204 PMC10087516 PMC10091377 PMC10092010
## 32 1 1 3 5 1
## PMC10092771 PMC10095316 PMC10095438 PMC10097911 PMC10098193 PMC10099689
## 1 3 1 9 2 1
## PMC10099834 PMC10101332 PMC10101686 PMC10103417 PMC10103646 PMC10104777
## 1 1 1 3 1 3
## PMC10104827 PMC10106861 PMC10108528 PMC10115646 PMC10115815 PMC10118386
## 6 1 1 1 1 2
## PMC10119024 PMC10119186 PMC10119396 PMC10120025 PMC10120908 PMC10121420
## 1 5 7 4 1 1
## PMC10123121 PMC10124151 PMC10126422 PMC10132553 PMC10134367 PMC10134541
## 3 3 1 6 1 3
## PMC10138339 PMC10139981 PMC10140160 PMC10140356 PMC10140399 PMC10140479
## 1 1 4 2 1 6
## PMC10141963 PMC10142227 PMC10146888 PMC10146992 PMC10147683 PMC10148045
## 1 1 1 2 2 5
## PMC10148492
## 1summary(as.numeric(DIST))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 2.795 3.000 32.000hist(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
## 25 PMC10085592 32
## 1 PMC10063665 18
## 34 PMC10097911 9
## 51 PMC10119396 7
## 18 PMC10078807 6
## 43 PMC10104827 6
## 58 PMC10132553 6
## 66 PMC10140479 6
## 29 PMC10091377 5
## 50 PMC10119186 5
## 72 PMC10148045 5
## 52 PMC10120025 4
## 63 PMC10140160 4
## 10 PMC10070455 3
## 15 PMC10076316 3
## 19 PMC10079878 3
## 28 PMC10087516 3
## 32 PMC10095316 3
## 40 PMC10103417 3
## 42 PMC10104777 3MOST_ERR_FILES = as.character(DIST_DF[1,1])
MOST_ERR_FILES## [1] "PMC10085592"# 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## PMC10063665 PMC10064413 PMC10064647 PMC10066286 PMC10066595 PMC10067406
## 121 27 4 7 17 51
## PMC10067935 PMC10069868 PMC10069891 PMC10070455 PMC10070495 PMC10071012
## 4 1 15 11 2 1
## PMC10072823 PMC10073929 PMC10076316 PMC10076939 PMC10077681 PMC10078807
## 1 34 27 36 4 233
## PMC10079878 PMC10080023 PMC10080956 PMC10082087 PMC10082194 PMC10084920
## 11 31 23 5 3 35
## PMC10085592 PMC10086525 PMC10087204 PMC10087516 PMC10091377 PMC10092010
## 277 2 2 3 26 2
## PMC10092771 PMC10095316 PMC10095438 PMC10097911 PMC10098193 PMC10099689
## 27 165 10 16 2 2
## PMC10099834 PMC10101332 PMC10101686 PMC10103417 PMC10103646 PMC10104777
## 1 1 4 29 1 3
## PMC10104827 PMC10106861 PMC10108528 PMC10115646 PMC10115815 PMC10118386
## 28 1 1 6 1 8
## PMC10119024 PMC10119186 PMC10119396 PMC10120025 PMC10120908 PMC10121420
## 1 78 19 88 16 20
## PMC10123121 PMC10124151 PMC10126422 PMC10132553 PMC10134367 PMC10134541
## 40 25 1 58 20 4
## PMC10138339 PMC10139981 PMC10140160 PMC10140356 PMC10140399 PMC10140479
## 12 2 23 2 2 4306
## PMC10141963 PMC10142227 PMC10146888 PMC10146992 PMC10147683 PMC10148045
## 1 3 1 12 28 1427
## PMC10148492
## 3hist(NERR,main="number of errors per PMC article")
NERR_DF <- as.data.frame(NERR)
NERR_DF <- NERR_DF[order(-NERR_DF$NERR),,drop=FALSE]
head(NERR_DF,20)## NERR
## PMC10140479 4306
## PMC10148045 1427
## PMC10085592 277
## PMC10078807 233
## PMC10095316 165
## PMC10063665 121
## PMC10120025 88
## PMC10119186 78
## PMC10132553 58
## PMC10067406 51
## PMC10123121 40
## PMC10076939 36
## PMC10084920 35
## PMC10073929 34
## PMC10080023 31
## PMC10103417 29
## PMC10104827 28
## PMC10147683 28
## PMC10064413 27
## PMC10076316 27MOST_ERR = rownames(NERR_DF)[1]
MOST_ERR## [1] "PMC10140479"Journals affected
GENELIST_ERROR_ARTICLES <- gsub("PMC","",GENELIST_ERROR_ARTICLES)
### JSON PARSING is more reliable than XML
ARTICLES <- esummary( GENELIST_ERROR_ARTICLES , db="pmc" , retmode = "json" )
ARTICLE_DATA <- reutils::content(ARTICLES,as= "parsed")
ARTICLE_DATA <- ARTICLE_DATA$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] 55NUM_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] "10085592"
##
## $result$`10085592`
## $result$`10085592`$uid
## [1] "10085592"
##
## $result$`10085592`$pubdate
## [1] "2023 Mar 23"
##
## $result$`10085592`$epubdate
## [1] "2023 Mar 23"
##
## $result$`10085592`$printpubdate
## [1] ""
##
## $result$`10085592`$source
## [1] "Aging (Albany NY)"
##
## $result$`10085592`$authors
## name authtype
## 1 Tu Z Author
## 2 Wang X Author
## 3 Cai H Author
## 4 Sheng Y Author
## 5 Wu L Author
## 6 Huang K Author
## 7 Zhu X Author
##
## $result$`10085592`$title
## [1] "The cell senescence regulator p16 is a promising cancer prognostic and immune check-point inhibitor (ICI) therapy biomarker"
##
## $result$`10085592`$volume
## [1] "15"
##
## $result$`10085592`$issue
## [1] "6"
##
## $result$`10085592`$pages
## [1] "2136-2157"
##
## $result$`10085592`$articleids
## idtype value
## 1 pmid 36961395
## 2 doi 10.18632/aging.204601
## 3 pmcid PMC10085592
##
## $result$`10085592`$fulljournalname
## [1] "Aging (Albany NY)"
##
## $result$`10085592`$sortdate
## [1] "2023/03/23 00:00"
##
## $result$`10085592`$pmclivedate
## [1] "2023/04/11"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] "10140479"
##
## $result$`10140479`
## $result$`10140479`$uid
## [1] "10140479"
##
## $result$`10140479`$pubdate
## [1] "2023 Apr 11"
##
## $result$`10140479`$epubdate
## [1] "2023 Apr 11"
##
## $result$`10140479`$printpubdate
## [1] ""
##
## $result$`10140479`$source
## [1] "Cell Rep Med"
##
## $result$`10140479`$authors
## name authtype
## 1 Auf der Maur P Author
## 2 Trefny MP Author
## 3 Baumann Z Author
## 4 Vulin M Author
## 5 Correia AL Author
## 6 Diepenbruck M Author
## 7 Kramer N Author
## 8 Volkmann K Author
## 9 Preca BT Author
## 10 Ramos P Author
## 11 Leroy C Author
## 12 Eichlisberger T Author
## 13 Buczak K Author
## 14 Zilli F Author
## 15 Okamoto R Author
## 16 Rad R Author
## 17 Jensen MR Author
## 18 Fritsch C Author
## 19 Zippelius A Author
## 20 Stadler MB Author
## 21 Bentires-Alj M Author
##
## $result$`10140479`$title
## [1] "N-acetylcysteine overcomes NF1 loss-driven resistance to PI3Kα inhibition in breast cancer"
##
## $result$`10140479`$volume
## [1] "4"
##
## $result$`10140479`$issue
## [1] "4"
##
## $result$`10140479`$pages
## [1] "101002"
##
## $result$`10140479`$articleids
## idtype value
## 1 pmid 37044095
## 2 doi 10.1016/j.xcrm.2023.101002
## 3 pmcid PMC10140479
##
## $result$`10140479`$fulljournalname
## [1] "Cell Reports Medicine"
##
## $result$`10140479`$sortdate
## [1] "2023/04/11 00:00"
##
## $result$`10140479`$pmclivedate
## [1] "2023/04/29"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 href href href href href href href href
## 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] "2023-05"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 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RCurl_1.98-1.12 readxl_1.4.2 reutils_0.2.3 xml2_1.3.4
## [5] jsonlite_1.8.4 XML_3.99-0.14
##
## loaded via a namespace (and not attached):
## [1] assertthat_0.2.1 digest_0.6.31 R6_2.5.1 fastmap_1.1.1
## [5] cellranger_1.1.0 xfun_0.39 cachem_1.0.7 knitr_1.42
## [9] htmltools_0.5.5 rmarkdown_2.21 bitops_1.0-7 cli_3.6.1
## [13] sass_0.4.5 jquerylib_0.1.4 compiler_4.3.0 highr_0.10
## [17] tools_4.3.0 evaluate_0.20 bslib_0.4.2 yaml_2.3.7
## [21] rlang_1.1.1