Gene Name Error Monthly Dashboard
Mark Ziemann
2026-07-20
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("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.
DATE="2026/4"
#2024-06Let’s see how many PMC IDs we have in the past month.
QUERY ='((genom*[Title/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 3500## Retrieving UIDs 3501 to 4000## Retrieving UIDs 4001 to 4500## Retrieving UIDs 4501 to 5000pmc <- read.table(pmc)
pmc <- paste("PMC",pmc$V1,sep="")
NUM_ARTICLES=length(pmc)
NUM_ARTICLES## [1] 4619writeLines(pmc,con="pmc.txt")Run the screen
Now run the bash script. As PMC has changed and restricts scraping journal articles, it is best to use the dedicated utility called pygetpapers for the download.
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] 52832NUM_XLS_ARTICLES = length(unique(sapply(strsplit(XLS," "),"[[",1)))
NUM_XLS_ARTICLES## [1] 1565GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>2]
#GENELISTS
NUM_GENELISTS <- length(unique(sapply(strsplit(GENELISTS," "),"[[",2)))
NUM_GENELISTS## [1] 733NUM_GENELIST_ARTICLES <- length(unique(sapply(strsplit(GENELISTS," "),"[[",1)))
NUM_GENELIST_ARTICLES## [1] 410ERROR_GENELISTS <- XLS[lapply(strsplit(XLS," "),length)>3]
#ERROR_GENELISTS
NUM_ERROR_GENELISTS = length(ERROR_GENELISTS)
NUM_ERROR_GENELISTS## [1] 191GENELIST_ERROR_ARTICLES <- unique(sapply(strsplit(ERROR_GENELISTS," "),"[[",1))
GENELIST_ERROR_ARTICLES## [1] "PMC13122177" "PMC13121032" "PMC13121700" "PMC13121600" "PMC13113180"
## [6] "PMC13113289" "PMC13113871" "PMC13111977" "PMC13105942" "PMC13107986"
## [11] "PMC13106906" "PMC13107910" "PMC13102677" "PMC13104449" "PMC13101725"
## [16] "PMC13100816" "PMC13101241" "PMC13101146" "PMC12863721" "PMC13097569"
## [21] "PMC13097772" "PMC13094536" "PMC13093992" "PMC11380252" "PMC13090386"
## [26] "PMC13088500" "PMC13087478" "PMC13083257" "PMC13083945" "PMC13082837"
## [31] "PMC13080027" "PMC13076202" "PMC13076215" "PMC13078000" "PMC13076896"
## [36] "PMC13073949" "PMC13072936" "PMC13072424" "PMC13068978" "PMC13069869"
## [41] "PMC13067734" "PMC13066385" "PMC13066215" "PMC13063721" "PMC13057473"
## [46] "PMC13057301" "PMC13056565" "PMC13050951" "PMC13050664" "PMC13049855"
## [51] "PMC13049730" "PMC13048226" "PMC13043442" "PMC13043545" "PMC13040077"
## [56] "PMC13041326" "PMC13042304" "PMC13042866" "PMC13042984" "PMC13036604"
## [61] "PMC13037077" "PMC13038215" "PMC13036838" "PMC12487699" "PMC12487700"
## [66] "PMC12036792"NUM_ERROR_GENELIST_ARTICLES <- length(GENELIST_ERROR_ARTICLES)
NUM_ERROR_GENELIST_ARTICLES## [1] 66ERROR_PROPORTION = NUM_ERROR_GENELIST_ARTICLES / NUM_GENELIST_ARTICLES
ERROR_PROPORTION## [1] 0.1609756Look 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] "PMC13122177 zip/Supplementary_Table_8.xlsx Ggallus 8 39692 40422 40057 38961 40787 40057 39326 37500"
## [2] "PMC13121032 PMC_DL/PMC13121032/supplementaryfiles/42255_2026_1498_MOESM14_ESM.xlsx Hsapiens 28 45360 45536 45541 45359 45543 45355 45361 45352 45352 45627 45547 45538 45549 45359 45354 45353 45353 45356 45362 45545 45537 45550 45358 45544 45536 45546 45539 45352"
## [3] "PMC13121032 PMC_DL/PMC13121032/supplementaryfiles/42255_2026_1498_MOESM8_ESM.xlsx Hsapiens 3 44256 44264 44261"
## [4] "PMC13121032 PMC_DL/PMC13121032/supplementaryfiles/42255_2026_1498_MOESM8_ESM.xlsx Hsapiens 11 44259 44263 44265 44260 44257 44262 44256 44256 44264 44257 44261"
## [5] "PMC13121032 PMC_DL/PMC13121032/supplementaryfiles/42255_2026_1498_MOESM15_ESM.xlsx Hsapiens 4 45361 45538 45357 45354"
## [6] "PMC13121700 PMC_DL/PMC13121700/supplementaryfiles/41467_2026_70403_MOESM4_ESM.xlsx Hsapiens 19 44990 45171 45173 45173 44987 44987 44987 44993 44993 44987 44987 45200 45175 45176 45176 44991 45200 44987 45200"
## [7] "PMC13121600 PMC_DL/PMC13121600/supplementaryfiles/41467_2026_70517_MOESM4_ESM.xlsx Hsapiens 24 38047 37681 39508 40422 37316 38596 38412 38777 37500 38961 39326 39142 42248 40787 36951 37316 39692 41883 40057 39873 37135 37865 38231 37226"
## [8] "PMC13121600 PMC_DL/PMC13121600/supplementaryfiles/41467_2026_70517_MOESM4_ESM.xlsx Hsapiens 22 38047 37681 38961 37316 40787 36951 38777 39326 39873 38596 40422 38412 42248 39692 40057 39508 37316 41883 37500 37135 39142 37226"
## [9] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 4 45717 45724 45718 45722"
## [10] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 5 45717 45724 45718 45718 45722"
## [11] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 5 45724 45717 45718 45719 45718"
## [12] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 8 45723 45724 45721 45722 45717 45718 45717 45718"
## [13] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 2 45718 45717"
## [14] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 5 45724 45723 45726 45717 45718"
## [15] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 5 45721 45724 45726 45718 45717"
## [16] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 8 45724 45721 45726 45718 45722 45717 45718 45717"
## [17] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 7 45724 45722 45717 45718 45726 45720 45718"
## [18] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 8 45724 45721 45726 45718 45722 45717 45719 45718"
## [19] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 5 45717 45723 45904 45909 45903"
## [20] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 5 45717 45723 45904 45909 45903"
## [21] "PMC13113180 zip/Supplementary_table.xlsx Ggallus 2 46088 46274"
## [22] "PMC13113180 zip/Supplementary_table.xlsx Ggallus 2 46088 46274"
## [23] "PMC13113180 zip/Supplementary_table.xlsx Hsapiens 195 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45904 45723 45909 45903 45717 45717 45904 45723 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45904 45723 45717 45909 45903 45723 45717 45903 45904 45909 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45717 45723 45904 45909 45903 45904 45903 45723 45909 45717"
## [24] "PMC13113289 zip/Table_S3_Differentially_Expressed_Genes.xlsx Hsapiens 24 46083 46270 46274 46091 46279 46266 46273 46084 46275 46088 46092 46087 46268 46083 46082 46271 46082 46086 46276 46272 46085 46267 46089 46090"
## [25] "PMC13113289 zip/Table_S3_Differentially_Expressed_Genes.xlsx Hsapiens 24 46083 46270 46274 46091 46279 46266 46277 46273 46084 46275 46088 46087 46268 46083 46082 46271 46082 46086 46276 46272 46085 46267 46089 46090"
## [26] "PMC13113289 zip/Table_S3_Differentially_Expressed_Genes.xlsx Hsapiens 25 46083 46270 46274 46091 46279 46266 46277 46273 46084 46275 46088 46092 46087 46268 46083 46082 46271 46082 46086 46276 46272 46085 46267 46089 46090"
## [27] "PMC13113289 zip/Table_S3_Differentially_Expressed_Genes.xlsx Hsapiens 24 46083 46270 46274 46091 46279 46266 46273 46084 46275 46088 46092 46087 46268 46083 46082 46271 46082 46086 46276 46272 46085 46267 46089 46090"
## [28] "PMC13113289 zip/Table_S3_Differentially_Expressed_Genes.xlsx Hsapiens 24 46083 46270 46274 46091 46279 46266 46273 46084 46275 46088 46092 46087 46268 46083 46082 46271 46082 46086 46276 46272 46085 46267 46089 46090"
## [29] "PMC13113289 zip/Table_S3_Differentially_Expressed_Genes.xlsx Hsapiens 25 46083 46270 46274 46091 46279 46266 46277 46273 46084 46275 46088 46092 46087 46268 46083 46082 46271 46082 46086 46276 46272 46085 46267 46089 46090"
## [30] "PMC13113871 zip/Supplementary_Table.xlsx Athaliana 1 46240"
## [31] "PMC13111977 PMC_DL/PMC13111977/supplementaryfiles/MEN-26-e70147-s004.xlsx Hsapiens 4 46267 46269 46087 46269"
## [32] "PMC13105942 PMC_DL/PMC13105942/supplementaryfiles/DataSheet1.xlsx Ggallus 1 2025-03-01"
## [33] "PMC13107986 PMC_DL/PMC13107986/supplementaryfiles/RSSN_A_2662625_SM3839.xlsx Hsapiens 1 45360"
## [34] "PMC13107986 PMC_DL/PMC13107986/supplementaryfiles/RSSN_A_2662625_SM3839.xlsx Hsapiens 1 45353"
## [35] "PMC13106906 zip/S3allproteins.xlsx Mmusculus 6 39326 37500 40787 38961 37865 38596"
## [36] "PMC13107910 PMC_DL/PMC13107910/supplementaryfiles/13073_2026_1624_MOESM5_ESM.xlsx Hsapiens 48 37226 36951 38047 37226 38047 39508 38047 36951 38596 36951 38047 37681 36951 38047 36951 36951 37226 36951 38047 36951 38047 40787 37681 36951 36951 37316 38047 37226 37226 37681 38047 37681 37226 37226 37681 38047 40603 40603 37226 37681 37681 36951 36951 36951 40787 40787 38047 38047"
## [37] "PMC13107910 PMC_DL/PMC13107910/supplementaryfiles/13073_2026_1624_MOESM5_ESM.xlsx Hsapiens 59 36951 36951 38047 37226 37226 39508 38047 36951 36951 36951 38047 36951 36951 36951 38047 36951 37681 40057 36951 36951 36951 40603 38047 37316 36951 37681 36951 40603 36951 36951 38047 36951 36951 36951 36951 39142 37681 38047 36951 37226 37681 40603 40603 36951 38047 40603 38047 36951 36951 36951 36951 36951 36951 36951 36951 36951 36951 37681 36951"
## [38] "PMC13107910 PMC_DL/PMC13107910/supplementaryfiles/13073_2026_1624_MOESM5_ESM.xlsx Hsapiens 65 36951 36951 37226 37226 39508 38047 36951 40057 38047 36951 36951 36951 36951 38047 37681 40057 36951 36951 36951 38047 38047 37316 36951 37681 36951 40787 37681 37681 36951 38047 40603 36951 36951 36951 36951 38047 36951 36951 36951 36951 38047 36951 37226 40603 38047 36951 37681 40603 36951 38047 40787 40603 36951 36951 36951 36951 36951 36951 36951 37226 36951 37681 36951 38047 40787"
## [39] "PMC13102677 PMC_DL/PMC13102677/supplementaryfiles/TRC2-12-e70258-s001.xlsx Hsapiens 9 38961 39142 40603 37681 38047 38777 39873 37316 40238"
## [40] "PMC13102677 PMC_DL/PMC13102677/supplementaryfiles/TRC2-12-e70258-s001.xlsx Hsapiens 2 37288 36923"
## [41] "PMC13102677 PMC_DL/PMC13102677/supplementaryfiles/TRC2-12-e70258-s001.xlsx Hsapiens 7 37316 37316 40057 38777 39508 36951 39142"
## [42] "PMC13102677 PMC_DL/PMC13102677/supplementaryfiles/TRC2-12-e70258-s001.xlsx Hsapiens 22 36951 40057 38777 38777 39142 36951 37316 39142 37681 38777 39508 36951 37316 38777 39873 40238 36951 36951 38777 36951 40057 36951"
## [43] "PMC13104449 PMC_DL/PMC13104449/supplementaryfiles/40246_2026_919_MOESM1_ESM.xlsx Hsapiens 2 45718 45718"
## [44] "PMC13104449 PMC_DL/PMC13104449/supplementaryfiles/40246_2026_919_MOESM1_ESM.xlsx Hsapiens 1 45726"
## [45] "PMC13104449 PMC_DL/PMC13104449/supplementaryfiles/40246_2026_919_MOESM1_ESM.xlsx Hsapiens 1 45718"
## [46] "PMC13101725 PMC_DL/PMC13101725/supplementaryfiles/mmc11.xlsx Hsapiens 17 45909 45717 45727 45909 45719 45909 45717 45726 45719 45717 45727 45717 45727 45727 45717 45727 45727"
## [47] "PMC13101725 PMC_DL/PMC13101725/supplementaryfiles/mmc6.xlsx Ggallus 10 45717 45717 45717 45717 45717 45717 45719 45719 45719 45992"
## [48] "PMC13101725 PMC_DL/PMC13101725/supplementaryfiles/mmc4.xlsx Hsapiens 27 45717 45720 45717 45717 45717 45717 45717 45717 45717 45717 45717 45717 45717 45717 45717 45727 45727 45727 45992 45992 45992 45992 45992 45992 45901 45726 45903"
## [49] "PMC13101725 PMC_DL/PMC13101725/supplementaryfiles/mmc3.xlsx Hsapiens 7 45717 45727 45727 45719 45907 45724 45903"
## [50] "PMC13100816 zip/Supp_tables.xlsx Scerevisiae 2 37165 45413"
## [51] "PMC13100816 zip/Supp_tables.xlsx Scerevisiae 2 45413 37165"
## [52] "PMC13101241 PMC_DL/PMC13101241/supplementaryfiles/12967_2026_8094_MOESM5_ESM.xlsx Hsapiens 1 40787"
## [53] "PMC13101146 PMC_DL/PMC13101146/supplementaryfiles/12964_2026_2793_MOESM2_ESM.xlsx Hsapiens 1 45355"
## [54] "PMC12863721 PMC_DL/PMC12863721/supplementaryfiles/41380_2025_3392_MOESM2_ESM.xlsx Hsapiens 1 45725"
## [55] "PMC12863721 PMC_DL/PMC12863721/supplementaryfiles/41380_2025_3392_MOESM2_ESM.xlsx Hsapiens 1 45725"
## [56] "PMC13097569 zip/PSP-2025-0223-s04.xlsx Hsapiens 4 45911 45909 45906 45717"
## [57] "PMC13097772 zip/SupplementaryDataset_revised_v2/Supplementary_Sheet3.xlsx Scerevisiae 1 36982"
## [58] "PMC13094536 PMC_DL/PMC13094536/supplementaryfiles/EXP2-6-70160-s009.xlsx Hsapiens 4 45904 45906 45906 45724"
## [59] "PMC13093992 PMC_DL/PMC13093992/supplementaryfiles/12864_2026_12729_MOESM2_ESM.xlsx Hsapiens 16 45719 45931 45902 45902 45721 45906 45907 45724 45722 45722 45721 45721 45721 45904 45904 45904"
## [60] "PMC11380252 PMC_DL/PMC11380252/supplementaryfiles/cbm-40-cbm230517-s001.xlsx Hsapiens 7 44626 44628 44630 44631 44622 44624 44627"
## [61] "PMC11380252 PMC_DL/PMC11380252/supplementaryfiles/cbm-40-cbm230517-s001.xlsx Hsapiens 8 44630 44624 44626 44896 44623 44627 44629 44819"
## [62] "PMC11380252 PMC_DL/PMC11380252/supplementaryfiles/cbm-40-cbm230517-s001.xlsx Hsapiens 12 44627 44621 44628 44806 44623 44819 44631 44813 44809 44621 44814 44625"
## [63] "PMC13090386 PMC_DL/PMC13090386/supplementaryfiles/41467_2026_71758_MOESM14_ESM.xlsx Hsapiens 3 46088 46274 46086"
## [64] "PMC13090386 PMC_DL/PMC13090386/supplementaryfiles/41467_2026_71758_MOESM11_ESM.xlsx Hsapiens 27 46083 46268 46269 46083 46272 46271 46088 46276 46090 46277 46085 46082 46087 46279 46273 46089 46267 46357 46091 46084 46266 46092 46274 46270 46082 46275 46086"
## [65] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 9 40057 40057 40057 40057 40057 40057 40057 40057 40057"
## [66] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 5 40057 40057 40787 40057 40057"
## [67] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 1 40787"
## [68] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 20 40787 40057 40057 40057 40057 40057 40057 40057 40057 40787 40057 40057 40057 40057 39326 40057 40057 40057 38231 38231"
## [69] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 10 40787 40057 40057 40057 40057 40057 40057 40057 40057 40057"
## [70] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 23 40787 40057 40057 40057 40057 40057 40057 40057 40057 40057 40057 38231 40787 40057 39326 40057 38231 40057 40057 40057 40057 40057 40057"
## [71] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 14 40787 40057 40057 40057 40057 40057 40057 40057 40057 39692 40057 40057 38596 42248"
## [72] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 37 40787 40057 40057 40057 40057 40057 40057 40057 40057 40057 40057 40057 38596 40057 40057 40057 38231 40057 38961 38231 40057 40057 40057 40057 40057 38231 39692 40057 40057 42248 40057 40057 40057 40422 40057 38961 40057"
## [73] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 31 40787 40057 40057 40057 40057 40057 40057 40057 40057 40057 40057 40057 38596 40057 40057 40057 38231 40057 38231 40057 40057 40057 38231 39692 40057 40057 42248 40057 40057 40422 40057"
## [74] "PMC13088500 PMC_DL/PMC13088500/supplementaryfiles/13148_2026_2118_MOESM2_ESM.xlsx Hsapiens 6 38961 40057 40057 40057 40057 38961"
## [75] "PMC13087478 zip/tpj16873-sup-0003-TableS1.xlsx Athaliana 5 36982 38626 37712 37347 38565"
## [76] "PMC13087478 zip/tpj16873-sup-0003-TableS1.xlsx Athaliana 5 38231 37712 36982 37347 38565"
## [77] "PMC13087478 zip/tpj16873-sup-0005-TableS3.xlsx Athaliana 1 38231"
## [78] "PMC13087478 zip/tpj16873-sup-0005-TableS3.xlsx Athaliana 1 38231"
## [79] "PMC13087478 zip/tpj16873-sup-0006-TableS4.xlsx Athaliana 7 36982 37347 38565 38626 37712 38231 38930"
## [80] "PMC13083257 PMC_DL/PMC13083257/supplementaryfiles/41586_2026_10235_MOESM4_ESM.xlsx Hsapiens 27 45722 45723 45720 45901 45912 45721 45902 45903 45914 45908 45718 45910 45717 45907 45717 45718 45915 45909 45725 45726 45992 45727 45911 45724 45905 45719 45906"
## [81] "PMC13083257 PMC_DL/PMC13083257/supplementaryfiles/41586_2026_10235_MOESM4_ESM.xlsx Hsapiens 28 45914 45909 45992 45725 45904 45718 45718 45727 45910 45906 45902 45901 45721 45726 45717 45723 45912 45908 45915 45905 45720 45903 45907 45724 45911 45717 45722 45719"
## [82] "PMC13083257 PMC_DL/PMC13083257/supplementaryfiles/41586_2026_10235_MOESM4_ESM.xlsx Hsapiens 28 45725 45723 45722 45906 45901 45908 45720 45724 45905 45907 45721 45910 45904 45912 45992 45717 45915 45903 45902 45914 45718 45717 45909 45726 45727 45719 45911 45718"
## [83] "PMC13083257 PMC_DL/PMC13083257/supplementaryfiles/41586_2026_10235_MOESM4_ESM.xlsx Hsapiens 28 45717 45904 45726 45915 45911 45905 45727 45720 45910 45909 45724 45902 45718 45719 45721 45903 45722 45725 45718 45901 45717 45906 45908 45907 45723 45914 45912 45992"
## [84] "PMC13083257 PMC_DL/PMC13083257/supplementaryfiles/41586_2026_10235_MOESM4_ESM.xlsx Hsapiens 28 45911 45718 45718 45909 45904 45725 45992 45908 45905 45723 45722 45726 45906 45914 45902 45727 45903 45901 45915 45719 45717 45717 45720 45724 45721 45910 45907 45912"
## [85] "PMC13083257 PMC_DL/PMC13083257/supplementaryfiles/41586_2026_10235_MOESM4_ESM.xlsx Hsapiens 28 45904 45726 45717 45721 45720 45905 45910 45723 45724 45912 45915 45914 45727 45725 45909 45911 45718 45722 45719 45906 45992 45901 45717 45902 45718 45903 45908 45907"
## [86] "PMC13083945 PMC_DL/PMC13083945/supplementaryfiles/44321_2026_386_MOESM2_ESM.xlsx Hsapiens 2 46086 46012"
## [87] "PMC13082837 PMC_DL/PMC13082837/supplementaryfiles/NIHMS2159075-supplement-3.xlsx Hsapiens 1 45725"
## [88] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 17 45727 45912 45902 45718 45723 45911 45724 45901 45907 45910 45904 45726 45722 45903 45718 45719 45721"
## [89] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 18 45912 45905 45717 45718 45724 45727 45719 45910 45902 45725 45723 45718 45721 45908 45726 45904 45907 45722"
## [90] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 23 45724 45721 45907 45904 45723 45912 45718 45910 45726 45718 45903 45719 45906 45725 45727 45717 45722 45914 45908 45902 45905 45901 45911"
## [91] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 15 45726 45727 45718 45906 45907 45912 45904 45721 45718 45724 45910 45903 45717 45723 45905"
## [92] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 19 45902 45718 45723 45911 45724 45901 45907 45910 45725 45904 45726 45909 45727 45722 45903 45912 45718 45719 45721"
## [93] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 17 45912 45727 45718 45910 45718 45904 45725 45719 45726 45724 45722 45908 45902 45907 45905 45723 45721"
## [94] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 19 45717 45912 45727 45726 45718 45910 45902 45721 45905 45719 45724 45907 45718 45723 45914 45904 45903 45722 45908"
## [95] "PMC13080027 PMC_DL/PMC13080027/supplementaryfiles/pnas.2517498123.sd08.xlsx Mmusculus 13 45912 45726 45727 45717 45723 45904 45903 45724 45718 45910 45907 45721 45905"
## [96] "PMC13076202 PMC_DL/PMC13076202/supplementaryfiles/44220_2026_608_MOESM3_ESM.xlsx Hsapiens 2 45724 45915"
## [97] "PMC13076202 PMC_DL/PMC13076202/supplementaryfiles/44220_2026_608_MOESM3_ESM.xlsx Hsapiens 1 45915"
## [98] "PMC13076202 PMC_DL/PMC13076202/supplementaryfiles/44220_2026_608_MOESM3_ESM.xlsx Ggallus 1 45717"
## [99] "PMC13076202 PMC_DL/PMC13076202/supplementaryfiles/44220_2026_608_MOESM3_ESM.xlsx Hsapiens 1 45717"
## [100] "PMC13076202 PMC_DL/PMC13076202/supplementaryfiles/44220_2026_608_MOESM3_ESM.xlsx Hsapiens 7 45903 45718 45723 45912 45724 45901 45721"
## [101] "PMC13076202 PMC_DL/PMC13076202/supplementaryfiles/44220_2026_608_MOESM3_ESM.xlsx Hsapiens 8 45903 45718 45723 45911 45912 45724 45901 45721"
## [102] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 8 45538 45540 45354 45355 45353 45545 45543 45541"
## [103] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 3 45354 45362 45545"
## [104] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 6 45719 45720 45903 45904 45905 45906"
## [105] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 8 45540 45538 45541 45355 45353 45354 45543 45546"
## [106] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 7 45540 45538 45355 45354 45546 45541 45353"
## [107] "PMC13076215 zip/Supplementary_Tables.xlsx Rnorvegicus 8 45538 45540 45355 45362 45354 45541 45539 45545"
## [108] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 6 45903 45719 45718 45910 45908 45906"
## [109] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 2 45719 45910"
## [110] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 6 45903 45906 45718 45719 45908 45911"
## [111] "PMC13076215 zip/Supplementary_Tables.xlsx Mmusculus 5 45903 45719 45911 45906 45718"
## [112] "PMC13076215 zip/Supplementary_Tables.xlsx Rnorvegicus 2 45719 45910"
## [113] "PMC13078000 zip/SupplementaryFiles_rev1/SupplementaryTableS1.xlsx Hsapiens 1 45719"
## [114] "PMC13078000 zip/SupplementaryFiles_rev1/SupplementaryTableS3.xlsx Hsapiens 1 45717"
## [115] "PMC13078000 zip/SupplementaryFiles_rev1/SupplementaryTableS5.xlsx Hsapiens 1 45717"
## [116] "PMC13076896 PMC_DL/PMC13076896/supplementaryfiles/41467_2026_69762_MOESM11_ESM.xlsx Hsapiens 26 36951 36951 36951 38231 36951 38231 36951 38231 36951 38231 38231 38231 38231 38231 38231 38231 36951 38231 38231 38231 38231 36951 38231 36951 38231 36951"
## [117] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 4 37865 38596 40057 37316"
## [118] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 3 36951 38047 39142"
## [119] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Ggallus 3 39326 37316 37681"
## [120] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Ggallus 6 37865 39692 40603 37316 36951 38047"
## [121] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 5 40603 38777 36951 38047 37681"
## [122] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 1 36951"
## [123] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 1 36951"
## [124] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 1 36951"
## [125] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 1 36951"
## [126] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Hsapiens 2 36951 37681"
## [127] "PMC13073949 zip/S4_CNVR_CODING_GENES.xlsx Ggallus 1 36951"
## [128] "PMC13072936 zip/ijms-4180207-supplementary.xlsx Hsapiens 4 46083 46082 46268 46091"
## [129] "PMC13072936 zip/ijms-4180207-supplementary.xlsx Hsapiens 4 46083 46082 46268 46091"
## [130] "PMC13072424 zip/animals_Supplementary_Table_V2.xlsx Ggallus 8 46088 46084 46089 46090 46082 46083 46086 46087"
## [131] "PMC13068978 PMC_DL/PMC13068978/supplementaryfiles/42003_2026_10005_MOESM6_ESM.xlsx Hsapiens 13 21448 37712 16163 72364 39208 14985 52299 12031 23670 73729 20275 26596 11515"
## [132] "PMC13069869 PMC_DL/PMC13069869/supplementaryfiles/mmc2.xlsx Hsapiens 27 45719 45718 45725 45915 45723 45914 45912 45721 45908 45724 45907 45911 45901 45904 45727 45717 45905 45720 45909 45717 45722 45910 45718 45903 45992 45726 45906"
## [133] "PMC13069869 PMC_DL/PMC13069869/supplementaryfiles/mmc2.xlsx Hsapiens 27 45901 45914 45718 45915 45718 45723 45724 45912 45905 45726 45909 45720 45722 45904 45903 45992 45721 45906 45725 45727 45717 45911 45910 45717 45907 45908 45719"
## [134] "PMC13069869 PMC_DL/PMC13069869/supplementaryfiles/mmc2.xlsx Hsapiens 27 45719 45724 45718 45901 45723 45911 45915 45725 45718 45717 45720 45907 45904 45992 45903 45914 45909 45717 45908 45910 45726 45727 45912 45722 45905 45721 45906"
## [135] "PMC13067734 PMC_DL/PMC13067734/supplementaryfiles/40364_2026_915_MOESM1_ESM.xlsx Ggallus 5 37469 36892 38200 37104 37834"
## [136] "PMC13066385 PMC_DL/PMC13066385/supplementaryfiles/43856_2026_1436_MOESM4_ESM.xlsx Hsapiens 1 38412"
## [137] "PMC13066385 PMC_DL/PMC13066385/supplementaryfiles/43856_2026_1436_MOESM4_ESM.xlsx Hsapiens 27 37226 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"
## [138] "PMC13066385 PMC_DL/PMC13066385/supplementaryfiles/43856_2026_1436_MOESM7_ESM.xlsx Hsapiens 1 39326"
## [139] "PMC13066215 PMC_DL/PMC13066215/supplementaryfiles/41467_2026_69273_MOESM5_ESM.xlsx Hsapiens 27 40422 40603 41883 39326 36951 39142 40238 39692 38047 37865 38231 37226 40787 39873 38596 37316 38961 41153 37500 39508 40057 38777 36951 38412 37316 37681 37135"
## [140] "PMC13066215 PMC_DL/PMC13066215/supplementaryfiles/41467_2026_69273_MOESM5_ESM.xlsx Hsapiens 2 39326 39692"
## [141] "PMC13063721 PMC_DL/PMC13063721/supplementaryfiles/13046_2026_3699_MOESM3_ESM.xlsx Hsapiens 13 38596 38231 37135 37865 41883 39326 40422 40787 37500 40057 39692 41153 38961"
## [142] "PMC13057473 PMC_DL/PMC13057473/supplementaryfiles/42003_2026_9962_MOESM3_ESM.xlsx Hsapiens 4 46299 46299 46299 46299"
## [143] "PMC13057473 PMC_DL/PMC13057473/supplementaryfiles/42003_2026_9962_MOESM3_ESM.xlsx Hsapiens 1 46299"
## [144] "PMC13057473 PMC_DL/PMC13057473/supplementaryfiles/42003_2026_9962_MOESM3_ESM.xlsx Hsapiens 4 46299 46299 46299 46299"
## [145] "PMC13057301 PMC_DL/PMC13057301/supplementaryfiles/41467_2026_70699_MOESM6_ESM.xlsx Hsapiens 20 4-Sep 6-Mar 7-Mar 5-Sep 2-Mar 8-Mar 10-Sep 3-Sep 2-Mar 8-Sep 6-Sep 9-Mar 5-Mar 15-Sep 7-Sep 11-Sep 9-Sep 2-Sep 1-Sep 1-Mar"
## [146] "PMC13057301 PMC_DL/PMC13057301/supplementaryfiles/41467_2026_70699_MOESM6_ESM.xlsx Hsapiens 22 1-Mar 8-Mar 6-Mar 6-Sep 7-Mar 5-Sep 2-Mar 8-Sep 1-Mar 11-Sep 2-Sep 9-Sep 15-Sep 5-Mar 9-Mar 7-Sep 4-Sep 3-Mar 1-Sep 2-Mar 10-Sep 3-Sep"
## [147] "PMC13057301 PMC_DL/PMC13057301/supplementaryfiles/41467_2026_70699_MOESM6_ESM.xlsx Hsapiens 23 46090 46274 46087 46088 46271 46083 46089 46273 46280 46276 46272 46270 46082 46269 46086 46268 46083 46267 46085 46275 46082 46084 46266"
## [148] "PMC13057301 PMC_DL/PMC13057301/supplementaryfiles/41467_2026_70699_MOESM6_ESM.xlsx Hsapiens 21 9-Sep 5-Sep 6-Mar 8-Mar 11-Sep 4-Mar 9-Mar 1-Mar 5-Mar 4-Sep 8-Sep 2-Sep 6-Sep 2-Mar 1-Sep 3-Mar 3-Sep 10-Sep 7-Sep 15-Sep 7-Mar"
## [149] "PMC13057301 PMC_DL/PMC13057301/supplementaryfiles/41467_2026_70699_MOESM6_ESM.xlsx Hsapiens 20 9-Sep 3-Sep 8-Mar 5-Mar 9-Mar 8-Sep 10-Sep 5-Sep 4-Sep 2-Mar 4-Mar 6-Mar 7-Sep 2-Sep 3-Mar 15-Sep 6-Sep 1-Sep 11-Sep 7-Mar"
## [150] "PMC13056565 PMC_DL/PMC13056565/supplementaryfiles/41564_2026_2295_MOESM4_ESM.xls Mmusculus 1 45909"
## [151] "PMC13056565 PMC_DL/PMC13056565/supplementaryfiles/41564_2026_2295_MOESM4_ESM.xls Mmusculus 4 45909 45719 45909 45909"
## [152] "PMC13050951 PMC_DL/PMC13050951/supplementaryfiles/Table5.xlsx Hsapiens 6 45719 45718 45717 45720 45992 45722"
## [153] "PMC13050951 PMC_DL/PMC13050951/supplementaryfiles/Table4.xlsx Hsapiens 5 45719 45718 45717 45720 45992"
## [154] "PMC13050664 PMC_DL/PMC13050664/supplementaryfiles/Table1.xlsx Hsapiens 3 44812 44806 44815"
## [155] "PMC13049855 PMC_DL/PMC13049855/supplementaryfiles/13148_2026_2094_MOESM2_ESM.xlsx Hsapiens 1 45359"
## [156] "PMC13049730 PMC_DL/PMC13049730/supplementaryfiles/12888_2026_7895_MOESM2_ESM.xlsx Hsapiens 1 45721"
## [157] "PMC13049730 PMC_DL/PMC13049730/supplementaryfiles/12888_2026_7895_MOESM2_ESM.xlsx Hsapiens 1 45721"
## [158] "PMC13049730 PMC_DL/PMC13049730/supplementaryfiles/12888_2026_7895_MOESM2_ESM.xlsx Hsapiens 2 45723 45721"
## [159] "PMC13049730 PMC_DL/PMC13049730/supplementaryfiles/12888_2026_7895_MOESM2_ESM.xlsx Hsapiens 2 45723 45721"
## [160] "PMC13049730 PMC_DL/PMC13049730/supplementaryfiles/12888_2026_7895_MOESM2_ESM.xlsx Hsapiens 3 45539 45541 45547"
## [161] "PMC13049730 PMC_DL/PMC13049730/supplementaryfiles/12888_2026_7895_MOESM2_ESM.xlsx Hsapiens 3 45355 45546 45352"
## [162] "PMC13048226 PMC_DL/PMC13048226/supplementaryfiles/peerj-14-20666-s009.xlsx Hsapiens 33 45357 45357 45358 45357 45358 45357 45355 45358 45357 45357 45360 45356 45358 45550 45353 45362 45357 45550 45354 45357 45359 45352 45358 45358 45358 45357 45358 45627 45357 45354 45358 45361 45358"
## [163] "PMC13048226 PMC_DL/PMC13048226/supplementaryfiles/peerj-14-20666-s009.xlsx Hsapiens 6 45545 45546 45548 45537 45542 45544"
## [164] "PMC13043442 PMC_DL/PMC13043442/supplementaryfiles/Table1.xlsx Hsapiens 2 36951 37316"
## [165] "PMC13043442 PMC_DL/PMC13043442/supplementaryfiles/Table1.xlsx Hsapiens 2 36951 37316"
## [166] "PMC13043545 PMC_DL/PMC13043545/supplementaryfiles/204_2025_4294_MOESM2_ESM.xlsx Hsapiens 4 45717 45717 45718 45718"
## [167] "PMC13040077 PMC_DL/PMC13040077/supplementaryfiles/41419_2026_8652_MOESM3_ESM.xlsx Mmusculus 4 37469 37104 37834 38200"
## [168] "PMC13041326 PMC_DL/PMC13041326/supplementaryfiles/13148_2026_2090_MOESM1_ESM.xlsx Hsapiens 19 45719 45723 45719 45721 45719 45719 45722 45718 45726 45727 45726 45719 45726 45723 45718 45722 45724 45717 45726"
## [169] "PMC13041326 PMC_DL/PMC13041326/supplementaryfiles/13148_2026_2090_MOESM1_ESM.xlsx Hsapiens 19 45719 45723 45719 45721 45719 45719 45722 45718 45726 45727 45726 45719 45726 45723 45718 45722 45724 45717 45726"
## [170] "PMC13042304 zip/File_S4_G3-2026-406569.xlsx Dmelanogaster 10 38322 37469 37104 37500 39692 37956 37834 37135 37226 37591"
## [171] "PMC13042304 zip/File_S4_G3-2026-406569.xlsx Dmelanogaster 10 37104 39692 37591 37500 37469 37834 38322 37956 37135 37226"
## [172] "PMC13042304 zip/File_S4_G3-2026-406569.xlsx Dmelanogaster 10 38322 37500 37469 37591 37956 39692 37834 37226 37135 37104"
## [173] "PMC13042866 PMC_DL/PMC13042866/supplementaryfiles/MOL2-20-709-s002.xlsx Hsapiens 14 43713 43715 43530 43714 43717 43526 43723 43718 43719 43710 43716 43525 43529 43709"
## [174] "PMC13042984 PMC_DL/PMC13042984/supplementaryfiles/ADVS-13-e11925-s003.xlsx Hsapiens 2 45542 45537"
## [175] "PMC13042984 PMC_DL/PMC13042984/supplementaryfiles/ADVS-13-e11925-s003.xlsx Mmusculus 3 45540 45542 45543"
## [176] "PMC13042984 PMC_DL/PMC13042984/supplementaryfiles/ADVS-13-e11925-s003.xlsx Mmusculus 1 45543"
## [177] "PMC13036604 PMC_DL/PMC13036604/supplementaryfiles/NIHMS2146427-supplement-5.xlsx Hsapiens 1 44627"
## [178] "PMC13036604 PMC_DL/PMC13036604/supplementaryfiles/NIHMS2146427-supplement-5.xlsx Hsapiens 1 44621"
## [179] "PMC13036604 PMC_DL/PMC13036604/supplementaryfiles/NIHMS2146427-supplement-5.xlsx Hsapiens 1 44622"
## [180] "PMC13037077 PMC_DL/PMC13037077/supplementaryfiles/12915_2026_2559_MOESM1_ESM.xlsx Hsapiens 39 45359 45543 45542 45545 45545 45537 45543 45546 45546 45546 45546 45546 45546 45546 45546 45546 45537 45549 45549 45549 45544 45544 45544 45544 45544 45544 45544 45544 45544 45537 45537 45537 45537 45537 45537 45537 45539 45543 45539"
## [181] "PMC13038215 PMC_DL/PMC13038215/supplementaryfiles/jci-136-199497-s296.xlsx Hsapiens 1 45550"
## [182] "PMC13038215 PMC_DL/PMC13038215/supplementaryfiles/jci-136-199497-s297.xlsx Hsapiens 27 45550 45353 45352 45545 45358 45355 45537 45546 45352 45357 45362 45354 45543 45542 45549 45627 45359 45356 45360 45547 45536 45539 45361 45544 45353 45540 45538"
## [183] "PMC13036838 PMC_DL/PMC13036838/supplementaryfiles/table_s6_ddaf200.xlsx Hsapiens 1 14977"
## [184] "PMC12487699 PMC_DL/PMC12487699/supplementaryfiles/Supplemental_Table_S9.xlsx Hsapiens 6 45718 45909 45909 45906 45909 45909"
## [185] "PMC12487700 PMC_DL/PMC12487700/supplementaryfiles/NIHMS2142358-supplement-Supp_Table_S5.xlsx Mmusculus 11 43712 43529 43526 43715 43719 43717 43710 43718 43713 43716 43714"
## [186] "PMC12487700 PMC_DL/PMC12487700/supplementaryfiles/NIHMS2142358-supplement-Supp_Table_S6.xlsx Mmusculus 27 42619 42615 42623 42618 42616 42617 42620 42614 42622 42625 42624 42432 42440 42621 42433 42436 42439 42437 42438 42435 42434 42430 42628 42431 42627 42430 42431"
## [187] "PMC12487700 PMC_DL/PMC12487700/supplementaryfiles/NIHMS2142358-supplement-Supp_Table_S4.xlsx Mmusculus 9 44079 44083 44084 44085 44082 44081 44080 43892 43350"
## [188] "PMC12487700 PMC_DL/PMC12487700/supplementaryfiles/NIHMS2142358-supplement-Supp_Table_S2.xlsx Mmusculus 1 43350"
## [189] "PMC12487700 PMC_DL/PMC12487700/supplementaryfiles/NIHMS2142358-supplement-Supp_Table_S3.xlsx Mmusculus 9 44083 44076 44081 44085 44082 44079 44080 43892 44084"
## [190] "PMC12036792 PMC_DL/PMC12036792/supplementaryfiles/NIHMS2069401-supplement-Supplemental_Tables.xlsx Hsapiens 1 44896"
## [191] "PMC12036792 PMC_DL/PMC12036792/supplementaryfiles/NIHMS2069401-supplement-Supplemental_Tables.xlsx Hsapiens 1 44896"In depth look at the errors
Let’s investigate the errors in more detail.
# By species
SPECIES <- sapply(strsplit(ERROR_GENELISTS," "),"[[",3)
table(SPECIES)## SPECIES
## Athaliana Dmelanogaster Ggallus Hsapiens Mmusculus
## 6 3 11 138 28
## Rnorvegicus Scerevisiae
## 2 3par(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##
## PMC11380252 PMC12036792 PMC12487699 PMC12487700 PMC12863721 PMC13036604
## 3 2 1 5 2 3
## PMC13036838 PMC13037077 PMC13038215 PMC13040077 PMC13041326 PMC13042304
## 1 1 2 1 2 3
## PMC13042866 PMC13042984 PMC13043442 PMC13043545 PMC13048226 PMC13049730
## 1 3 2 1 2 6
## PMC13049855 PMC13050664 PMC13050951 PMC13056565 PMC13057301 PMC13057473
## 1 1 2 2 5 3
## PMC13063721 PMC13066215 PMC13066385 PMC13067734 PMC13068978 PMC13069869
## 1 2 3 1 1 3
## PMC13072424 PMC13072936 PMC13073949 PMC13076202 PMC13076215 PMC13076896
## 1 2 11 6 11 1
## PMC13078000 PMC13080027 PMC13082837 PMC13083257 PMC13083945 PMC13087478
## 3 8 1 6 1 5
## PMC13088500 PMC13090386 PMC13093992 PMC13094536 PMC13097569 PMC13097772
## 10 2 1 1 1 1
## PMC13100816 PMC13101146 PMC13101241 PMC13101725 PMC13102677 PMC13104449
## 2 1 1 4 4 3
## PMC13105942 PMC13106906 PMC13107910 PMC13107986 PMC13111977 PMC13113180
## 1 1 3 2 1 15
## PMC13113289 PMC13113871 PMC13121032 PMC13121600 PMC13121700 PMC13122177
## 6 1 4 2 1 1summary(as.numeric(DIST))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.894 3.000 15.000hist(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
## 60 PMC13113180 15
## 33 PMC13073949 11
## 35 PMC13076215 11
## 43 PMC13088500 10
## 38 PMC13080027 8
## 18 PMC13049730 6
## 34 PMC13076202 6
## 40 PMC13083257 6
## 61 PMC13113289 6
## 4 PMC12487700 5
## 23 PMC13057301 5
## 42 PMC13087478 5
## 52 PMC13101725 4
## 53 PMC13102677 4
## 63 PMC13121032 4
## 1 PMC11380252 3
## 6 PMC13036604 3
## 12 PMC13042304 3
## 14 PMC13042984 3
## 24 PMC13057473 3MOST_ERR_FILES = as.character(DIST_DF[1,1])
MOST_ERR_FILES## [1] "PMC13113180"# 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## PMC11380252 PMC12036792 PMC12487699 PMC12487700 PMC12863721 PMC13036604
## 27 2 6 57 2 3
## PMC13036838 PMC13037077 PMC13038215 PMC13040077 PMC13041326 PMC13042304
## 1 39 28 4 38 30
## PMC13042866 PMC13042984 PMC13043442 PMC13043545 PMC13048226 PMC13049730
## 14 6 4 4 39 12
## PMC13049855 PMC13050664 PMC13050951 PMC13056565 PMC13057301 PMC13057473
## 1 3 11 5 106 9
## PMC13063721 PMC13066215 PMC13066385 PMC13067734 PMC13068978 PMC13069869
## 13 29 29 5 13 81
## PMC13072424 PMC13072936 PMC13073949 PMC13076202 PMC13076215 PMC13076896
## 8 8 28 20 61 26
## PMC13078000 PMC13080027 PMC13082837 PMC13083257 PMC13083945 PMC13087478
## 3 141 1 167 2 19
## PMC13088500 PMC13090386 PMC13093992 PMC13094536 PMC13097569 PMC13097772
## 156 30 16 4 4 1
## PMC13100816 PMC13101146 PMC13101241 PMC13101725 PMC13102677 PMC13104449
## 4 1 1 61 40 4
## PMC13105942 PMC13106906 PMC13107910 PMC13107986 PMC13111977 PMC13113180
## 1 6 172 2 4 266
## PMC13113289 PMC13113871 PMC13121032 PMC13121600 PMC13121700 PMC13122177
## 146 1 46 46 19 8hist(NERR,main="number of errors per PMC article")
NERR_DF <- as.data.frame(NERR)
NERR_DF <- NERR_DF[order(-NERR_DF$NERR),,drop=FALSE]
head(NERR_DF,20)## NERR
## PMC13113180 266
## PMC13107910 172
## PMC13083257 167
## PMC13088500 156
## PMC13113289 146
## PMC13080027 141
## PMC13057301 106
## PMC13069869 81
## PMC13076215 61
## PMC13101725 61
## PMC12487700 57
## PMC13121032 46
## PMC13121600 46
## PMC13102677 40
## PMC13037077 39
## PMC13048226 39
## PMC13041326 38
## PMC13042304 30
## PMC13090386 30
## PMC13066215 29MOST_ERR = rownames(NERR_DF)[1]
MOST_ERR## [1] "PMC13113180"Journals affected
GENELIST_ERROR_ARTICLES <- gsub("PMC","",GENELIST_ERROR_ARTICLES)
### JSON PARSING is more reliable than XML
ARTICLES <- esummary( GENELIST_ERROR_ARTICLES , db="pmc" , retmode = "json" )
ARTICLE_DATA <- reutils::content(ARTICLES,as= "parsed")
ARTICLE_DATA <- ARTICLE_DATA$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] 51par(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] "13113180"
##
## $result$`13113180`
## $result$`13113180`$uid
## [1] "13113180"
##
## $result$`13113180`$pubdate
## [1] "2026 Apr 2"
##
## $result$`13113180`$epubdate
## [1] "2026 Apr 2"
##
## $result$`13113180`$printpubdate
## [1] ""
##
## $result$`13113180`$source
## [1] "Biomedicines"
##
## $result$`13113180`$authors
## name authtype
## 1 Miao D Author
## 2 Ge Y Author
## 3 Liu Z Author
## 4 Wan Z Author
## 5 Chen H Author
## 6 Bai X Author
## 7 Mi J Author
##
## $result$`13113180`$title
## [1] "Genome-Wide Dissection of the Neutrophil-to-Lymphocyte Ratio Uncovers Polygenic Determinants Linked to Inflammatory Gastrointestinal Disorder Susceptibility."
##
## $result$`13113180`$volume
## [1] "14"
##
## $result$`13113180`$issue
## [1] "4"
##
## $result$`13113180`$pages
## [1] ""
##
## $result$`13113180`$articleids
## idtype value
## 1 pmid 42072355
## 2 pmcid PMC13113180
## 3 doi 10.3390/biomedicines14040814
## 4 pii biomedicines14040814
##
## $result$`13113180`$fulljournalname
## [1] "Biomedicines"
##
## $result$`13113180`$sortdate
## [1] "2026/04/02 00:00"
##
## $result$`13113180`$pmclivedate
## [1] "2026/04/28"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] "13113180"
##
## $result$`13113180`
## $result$`13113180`$uid
## [1] "13113180"
##
## $result$`13113180`$pubdate
## [1] "2026 Apr 2"
##
## $result$`13113180`$epubdate
## [1] "2026 Apr 2"
##
## $result$`13113180`$printpubdate
## [1] ""
##
## $result$`13113180`$source
## [1] "Biomedicines"
##
## $result$`13113180`$authors
## name authtype
## 1 Miao D Author
## 2 Ge Y Author
## 3 Liu Z Author
## 4 Wan Z Author
## 5 Chen H Author
## 6 Bai X Author
## 7 Mi J Author
##
## $result$`13113180`$title
## [1] "Genome-Wide Dissection of the Neutrophil-to-Lymphocyte Ratio Uncovers Polygenic Determinants Linked to Inflammatory Gastrointestinal Disorder Susceptibility."
##
## $result$`13113180`$volume
## [1] "14"
##
## $result$`13113180`$issue
## [1] "4"
##
## $result$`13113180`$pages
## [1] ""
##
## $result$`13113180`$articleids
## idtype value
## 1 pmid 42072355
## 2 pmcid PMC13113180
## 3 doi 10.3390/biomedicines14040814
## 4 pii biomedicines14040814
##
## $result$`13113180`$fulljournalname
## [1] "Biomedicines"
##
## $result$`13113180`$sortdate
## [1] "2026/04/02 00:00"
##
## $result$`13113180`$pmclivedate
## [1] "2026/04/28"Trend info
TODO: To plot the trend over the past 6 months.
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 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## 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] readxl_1.5.0 reutils_0.2.3 xml2_1.5.2 jsonlite_2.0.0
##
## loaded via a namespace (and not attached):
## [1] assertthat_0.2.1 digest_0.6.39 XML_3.99-0.23 R6_2.6.1
## [5] fastmap_1.2.0 cellranger_1.1.0 xfun_0.57 cachem_1.1.0
## [9] knitr_1.51 RCurl_1.98-1.18 htmltools_0.5.9 rmarkdown_2.31
## [13] lifecycle_1.0.5 bitops_1.0-9 cli_3.6.6 sass_0.4.10
## [17] jquerylib_0.1.4 compiler_4.6.1 tools_4.6.1 evaluate_1.0.5
## [21] bslib_0.11.0 yaml_2.3.12 otel_0.2.0 rlang_1.2.0