OSCR

Brain Proteomic Responses to Glucocorticoids and Their Relationship With Transcriptome: A Systematic Meta-Analysis.

A correction to this paper has been published: the notice, 42262671, from Europe PMC.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Methods › Updating Data Containing Only Gene Symbols ↔ R_Mouse_Ens_Uni_NCBI.R, lines 1–64 · score 0.80 · mouse ens uni, Ensembl_UniProt_final.csv, official symbols, tsv, ambiguous, readr
  2. [2] § Methods › Updating Data Containing UniProt IDs ↔ R_Human_Uni_NCBI_Ens.R, lines 146–211 · score 0.70 · human uni ncbi, uni ncbi ens, HGNC IDs, UniProt, NCBI gene, accession
  3. [3] § Methods › Databases ↔ R_Mouse_manual_and_databases.R, lines 92–141 · score 0.68 · SwissProt, GeneID, Taxonomic ID, Nomenclature ID, tsv, Accessions
  4. [4] § Methods › Databases ↔ R_test of additional IDs in Ensembl Human.R, lines 41–111 · score 0.66 · committee IDs, HGNC IDs, Ensembl stable, NCBI IDs, human
  5. [5] § Methods › Databases ↔ R_NCBI test.R, lines 1–44 · score 0.63 · mouse genome, human genome, rat genome, tsv, Accessions, downloaded
  6. [6] § Methods › Updating Data Containing GI IDs or Protein Symbols Together With Full Names ↔ R_Mouse_manual_and_databases.R, lines 143–205 · score 0.51 · mouse manual, UniProt IDs, MGI, NCBI gene, biomaRt, database
  7. [7] § Methods › Databases ↔ R_Rat_manual_and_databases.R, lines 1–47 · score 0.50 · GeneID, UniProt, database, organism, tsv, RGD

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 343 lines · 21 KB · MIT · 2 matches

  1. ####################################################################
  2. ####### paper data import #############################################
  3. ####################################################################
  5. Paper_data = read.delim('Paper_data.csv', sep=';', header = TRUE) # data import
  6. head(Paper_data, n = 8) # showing first n rows
  8. Manual_search_IDs = dplyr::select (Paper_data, c(Manual_search_URL_primary, Manual_search_URL_secondary, Organism_manual, NCBI_ID_manual, UniProt_ID_manual)) # selecting columns
  9. head(Manual_search_IDs, n = 10) # showing first n rows
  11. library(dplyr)
  12. Manual_search_IDs_2 <- distinct(Manual_search_IDs)
  13. head(Manual_search_IDs_2, n = 10) # showing first n rows
  14. #readr::write_csv(x = Manual_search_IDs_2, file = "Manual_search_IDs_2.csv")
  15. ####################################################################
  16. ####### UniProt data import and pre-processing ############################
  17. ####################################################################
  19. UniProt_data <-readr::read_tsv("UniProt_data.tsv", col_names = TRUE) # data import
  20. head(UniProt_data, n = 8) # showing first n rows
  22. UniProt_data <- as.data.frame(UniProt_data) # changing data format to data frame.
  23. head(UniProt_data, n = 8) # showing first n rows
  25. UniProt_data <- UniProt_data [, c("From", "Entry", "Entry Name", "Protein names", "Gene Names (primary)", "GeneID", "MGI", "Organism", "Organism (ID)")] # setting column order
  26. head(UniProt_data, n = 8) # showing first n rows
  28. colnames(UniProt_data) <- c("From", "Entry_UniProt", "Entry_Name_UniProt", "Protein_names_UniProt", "Gene_Names_UniProt", "NCBI_Gene_ID_UniProt", "MGI_ID_UniProt", "Organism_UniProt", "Organism_ID_UniProt") # setting column names
  29. head(UniProt_data, n=8) # showing first n rows
  31. UniProt_data2 = dplyr::mutate(UniProt_data, NCBI_Gene_ID_UniProt_clean = substr(NCBI_Gene_ID_UniProt, 1, nchar(NCBI_Gene_ID_UniProt) - 1)) # removing “;” located at the end of NCBI IDs
  32. head(UniProt_data2, n = 5) # showing first n rows
  34. UniProt_data3 = dplyr::mutate(UniProt_data2, MGI_ID_UniProt_clean = substr(MGI_ID_UniProt, 1, nchar(MGI_ID_UniProt) - 1)) # removing “;” located at the end of the IDs
  35. head(UniProt_data3, n = 5) # showing first n rows
  37. UniProt_data3$NCBI_GeneID_UniProt_assigment <- ifelse(grepl(";", UniProt_data3$ NCBI_Gene_ID_UniProt_clean),'Ambiguous','Unique') # testing for the presence of “;” in items in the column GeneID_clean. Presence of the “;” indicates that more than one gene is assigned to the ID.
  38. head(UniProt_data3, n=5) # showing first n rows
  40. UniProt_data3$GeneName_UniProt_assigment <- ifelse(grepl(";", UniProt_data3$Gene_Names_UniProt),'Ambiguous','Unique') # testing for the presence of “;” in items in the column Gene_Names_UniProt. Presence of the “;” indicates that more than one gene is assigned to the ID.
  41. head(UniProt_data3, n=10) # showing first n rows
  43. Ambiguous <- dplyr::filter(UniProt_data3, NCBI_GeneID_UniProt_assigment == "Ambiguous" | GeneName_UniProt_assigment == "Ambiguous") # showing rows with ambiguous IDs
  44. head(Ambiguous, n=10) # showing first n rows
  46. #### Testing duplicated UniPror IDs ####
  48. duplicates_from_first = duplicated(UniProt_data3$From, fromLast = FALSE) # identification of duplicated gene names (forward)
  49. head(duplicates_from_first, n=4) # showing first n rows
  51. duplicates_from_last = duplicated(UniProt_data3$From, fromLast = TRUE) # identification of duplicated gene names (reverse)
  52. head(duplicates_from_first, n=4) # showing first n rows
  54. UniProt_data4 <- cbind(UniProt_data3, duplicates_from_first, duplicates_from_last) # joining the dataset with results of duplication testing
  55. head(UniProt_data4, n=4) # showing first n rows
  57. UniProt_data4_not_duplicatted = dplyr::filter(UniProt_data4, duplicates_from_first == "FALSE" & duplicates_from_last == "FALSE") # selection of duplicated gene symbols
  58. head(UniProt_data4_not_duplicatted, n=10) # showing first n rows
  60. UniProt_data4_duplicatted = dplyr::filter(UniProt_data4, duplicates_from_first == "TRUE" | duplicates_from_last == "TRUE") # selection of duplicated gene symbols
  61. head(UniProt_data4_duplicatted, n=10) # showing first n rows
  63. #* Check data frame UniProt_data4_duplicatted for duplicated UniProt IDs in the “From” column. If necessary, filter required species with the next command (silenced with #). Otherwise, go to the “UniProt_data5 = dplyr:: bind_rows” action.
  65. #UniProt_data4_duplicatted = dplyr::filter(UniProt_data4_duplicatted, Organism_UniProt == "Mus musculus (Mouse)") # selection of mouse identifiers
  66. #head(UniProt_data4_duplicatted, n=10) # showing first n rows
  68. UniProt_data5 = dplyr:: bind_rows(UniProt_data4_not_duplicatted, UniProt_data4_duplicatted) # binding rows
  69. head(UniProt_data5, n=100) # showing first n rows
  71. UniProt_data6 = dplyr::mutate(UniProt_data5, duplicates_from_first = NULL, duplicates_from_last = NULL) # removing unnecessary columns
  72. head(UniProt_data6, n=5) # showing first n rows
  74. library(tidyr)
  75. UniProt_data7 = UniProt_data6 %>% separate_longer_delim(NCBI_Gene_ID_UniProt_clean, delim = ';') # splits by a delimiter into separate rows
  76. head(UniProt_data7, n=10) # showing first n rows
  78. library(tidyr)
  79. UniProt_data8 = UniProt_data7 %>% separate_wider_delim(Entry_Name_UniProt, delim = '_', names = c("Entry_Name2_UniProt", NA), cols_remove = FALSE) # splits by a delimiter into separate columns
  80. head(UniProt_data8, n=10) # showing first n rows
  82. UniProt_data8 <- as.data.frame(UniProt_data8) # changing data format to data frame.
  83. head(UniProt_data8, n = 8) # showing first n rows
  85. ####################################################################
  86. ####### Joining the paper and UniProt data #################################
  87. ####################################################################
  89. Pap_Uni = dplyr:: full_join(Manual_search_IDs_2, UniProt_data8, by = c("UniProt_ID_manual" = "From"), keep = FALSE, na_matches = "never") # joining the data
  90. head(Pap_Uni, n=10) # showing first n rows
  92. ####################################################################
  93. ####### NCBI data import and pre-processing ############################
  94. ####################################################################
  96. ncbi_dataset <-readr::read_tsv("ncbi_dataset.tsv", col_names = TRUE) # data import
  97. head(ncbi_dataset, n = 8) # showing first n rows
  99. ncbi_dataset <- as.data.frame(ncbi_dataset) # changing data format to data frame.
  100. head(ncbi_dataset, n = 8) # showing first n rows
  101. ncbi_dataset <- ncbi_dataset [, c("NCBI GeneID", "Symbol", "Description", "Taxonomic Name", "Common Name", "Gene Type", "Transcripts", "Gene Group Identifier", "Gene Group Method", "Chromosomes", "Nomenclature ID", "Ensembl GeneIDs", "Annotation Genomic Range Accession", "Annotation Genomic Range Start", "Annotation Genomic Range Stop", "OMIM IDs", "Orientation", "Proteins", "Synonyms", "Taxonomic ID", "SwissProt Accessions")] # setting column order
  102. head(ncbi_dataset, n = 8) # showing first n rows
  105. colnames(ncbi_dataset) <- c("NCBI_GeneID", "Symbol_NCBI", "Description_NCBI", "Taxonomic_Name", "Common_Name", "NCBI_Gene_Type", "Transcripts", "Gene_Group_Identifier", "Gene_Group_Method", "Chromosomes", "Nomenclature_ID_NCBI", "Ensembl_GeneIDs", "Annotation_Genomic_Range_Accession", "Annotation_Genomic_Range_Start", "Annotation_Genomic_Range_Stop", "OMIM_IDs", "Orientation", "Proteins", "Synonyms", "Taxonomic_ID_NCBI", "SwissProt_Accessions_NCBI") # setting column names
  106. head(ncbi_dataset, n = 8) # showing first n rows
  108. ncbi_dataset2 = dplyr::mutate(ncbi_dataset,
  109. Taxonomic_Name = NULL,
  110. Common_Name = NULL,
  111. Transcripts = NULL,
  112. Gene_Group_Identifier = NULL,
  113. Gene_Group_Method = NULL,
  114. Chromosomes = NULL,
  115. Ensembl_GeneIDs = NULL,
  116. Annotation_Genomic_Range_Accession = NULL,
  117. Annotation_Genomic_Range_Start = NULL,
  118. Annotation_Genomic_Range_Stop = NULL,
  119. OMIM_IDs = NULL,
  120. Orientation = NULL,
  121. Proteins = NULL,
  122. Synonyms = NULL) # removing unnecessary columns
  123. head(ncbi_dataset2, n = 8) # showing first n rows
  125. ncbi_dataset2$NCBI_GeneID <-as.character(ncbi_dataset2$NCBI_GeneID) # changing data type in selected column
  126. ncbi_dataset2$Nomenclature_ID_NCBI <-as.character(ncbi_dataset2$Nomenclature_ID_NCBI) # changing data type in selected column
  127. head(ncbi_dataset2, n = 8) # showing first n rows
  129. ####################################################################
  130. ####### joining paper, UniProt and NCBI data based on NCBI IDs################
  131. ####################################################################
  133. Pap_Uni$NCBI_ID_manual <-as.character(Pap_Uni$NCBI_ID_manual) # changing data type in selected column
  134. head(Pap_Uni, n=4) # showing first n rows
  137. Pap_Uni_NCBI = dplyr:: left_join(Pap_Uni, ncbi_dataset2, by = c("NCBI_ID_manual" = "NCBI_GeneID"), keep = TRUE, na_matches = "never") # joining the data
  138. head(Pap_Uni_NCBI, n=10) # showing first n rows
  140. Pap_Uni_NCBI_matched <- dplyr::filter(Pap_Uni_NCBI, NCBI_GeneID != "NA") # selecting rows with matched data
  141. head(Pap_Uni_NCBI_matched, n=10) # showing first n rows
  143. ####################################################################
  144. ####### joining unmatched paper / UniProt and NCBI data based on Uniprot IDs####
  145. ####################################################################
  147. Pap_Uni_NCBI_unmatched = na_rows <- Pap_Uni_NCBI[is.na(Pap_Uni_NCBI$NCBI_GeneID), ] # selecting rows with unmatched data
  148. head(Pap_Uni_NCBI_unmatched, n=10) # showing first n rows
  150. Pap_Uni_NCBI_unmatched2 = dplyr::mutate(Pap_Uni_NCBI_unmatched,
  151. NCBI_GeneID = NULL,
  152. Symbol_NCBI = NULL,
  153. Description_NCBI = NULL,
  154. NCBI_Gene_Type = NULL,
  155. Nomenclature_ID_NCBI = NULL,
  156. Taxonomic_ID_NCBI = NULL,
  157. SwissProt_Accessions_NCBI = NULL) # removing NCBI columns from previous joining
  158. head(Pap_Uni_NCBI_unmatched2, n = 8) # showing first n rows
  160. Pap_Uni_NCBI_V2 = dplyr:: left_join(Pap_Uni_NCBI_unmatched2, ncbi_dataset2, by = c("Entry_UniProt" = "SwissProt_Accessions_NCBI"), keep = TRUE, na_matches = "never") # joining the data
  161. head(Pap_Uni_NCBI_V2, n=10) # showing first n rows
  163. Pap_Uni_NCBI_matched$Nomenclature_ID_NCBI <-as.character(Pap_Uni_NCBI_matched$Nomenclature_ID_NCBI) # changing data type in selected column
  164. head(Pap_Uni_NCBI_matched, n=4) # showing first n rows
  166. Pap_Uni_NCBI_V3 = dplyr:: bind_rows(Pap_Uni_NCBI_matched, Pap_Uni_NCBI_V2) # joining data
  167. head(Pap_Uni_NCBI_V3, n=10) # showing first n rows
  169. Pap_Uni_NCBI_V3$Gene_Symbol_congruence_UniProt_NCBI <- ifelse(
  170. Pap_Uni_NCBI_V3$Gene_Names_UniProt == Pap_Uni_NCBI_V3$Symbol_NCBI,
  171. 'TheSame',
  172. 'Different') # testing gene symbol congruence
  173. head(Pap_Uni_NCBI_V3, n=10) # showing first n rows
  174. ##################################################################
  175. ####### Downloading data from Ensembl ################################
  176. ##################################################################
  177. library(biomaRt) # package selection
  179. ensembl = useEnsembl(biomart="genes", dataset="mmusculus_gene_ensembl") # selection of Ensembl biomart database and dataset
  181. Attributes = listAttributes(mart = ensembl) # list of available attribute codes
  182. head(Attributes, n = 4) # showing first n rows
  183. readr::write_csv(x = Attributes, file = "EnsemblAttributes.csv") # data export
  185. Filters = listFilters(mart = ensembl) # list of available filter codes
  186. head(Filters, n = 4) # showing first n rows
  187. readr::write_csv(x = Filters, file = "EnsemblFilters.csv") # data export
  189. EnsemblDatabases = listEnsembl() # list of Ensembl databases
  190. head(EnsemblDatabases, n = 20) # showing first n rows
  191. readr::write_csv(x = EnsemblDatabases, file = "EnsemblDatabases.csv") # data export
  193. EnsemblDatasets = listDatasets(mart = ensembl) # listing available dataset codes
  194. head(EnsemblDatasets, n = 20) # showing first n rows
  195. readr::write_csv(x = EnsemblDatasets, file = "EnsemblDatasets.csv") # data export
  197. Ensembl = getBM(attributes=c('external_gene_name', 'description', 'ensembl_gene_id', 'entrezgene_id', 'mgi_id', 'gene_biotype'), mart = ensembl) # import of data from Ensembl
  198. head(Ensembl, n = 20) # showing first n rows
  199. readr::write_csv(x = Ensembl, file = "Ensembl.csv") # data export
  201. colnames(Ensembl) <- c("Ensembl_gene_symbol", "Ensembl_description", "Ensembl_gene_id", "NCBI_ID_Ensembl", "MGI_ID_Ensembl", "Ensembl_gene_type") # setting column names
  202. head(Ensembl, n = 8) # showing first n rows
  204. Ensembl$NCBI_ID_Ensembl <-as.character(Ensembl$NCBI_ID_Ensembl) # changing data type in selected column
  205. Ensembl$MGI_ID_Ensembl <-as.character(Ensembl$MGI_ID_Ensembl) # changing data type in selected column
  207. ##################################################################
  208. ####### Joining data #################################################
  209. ##################################################################
  211. Pap_Uni_NCBI_Ensembl = dplyr:: left_join(Pap_Uni_NCBI_V3, Ensembl, by = c("NCBI_GeneID" = "NCBI_ID_Ensembl"), keep = TRUE, na_matches = "never") # joining the data
  212. head(Pap_Uni_NCBI_Ensembl, n=10) # showing first n rows
  215. Pap_Uni_NCBI_Ensembl_matched <- dplyr::filter(Pap_Uni_NCBI_Ensembl, Ensembl_gene_id != "NA") # selecting rows with matched data
  216. head(Pap_Uni_NCBI_Ensembl_matched, n=10) # showing first n rows
  218. ####################################################################
  219. ####### joining unmatched data based on MGI ID####
  220. ####################################################################
  222. Pap_Uni_NCBI_Ensembl_unmatched = na_rows <- Pap_Uni_NCBI_Ensembl [is.na(Pap_Uni_NCBI_Ensembl$Ensembl_gene_id), ] # selecting rows with unmatched data
  223. head(Pap_Uni_NCBI_Ensembl_unmatched, n=10) # showing first n rows
  225. Pap_Uni_NCBI_Ensembl_unmatched2 = dplyr::mutate(Pap_Uni_NCBI_Ensembl_unmatched,
  226. Ensembl_gene_symbol = NULL,
  227. Ensembl_description = NULL,
  228. Ensembl_gene_id = NULL,
  229. NCBI_ID_Ensembl = NULL,
  230. MGI_ID_Ensembl = NULL,
  231. Ensembl_gene_type = NULL) # removing NCBI columns from previous joining
  232. head(Pap_Uni_NCBI_Ensembl_unmatched2, n = 8) # showing first n rows
  234. Pap_Uni_NCBI_Ensembl_V2 = dplyr:: left_join(Pap_Uni_NCBI_Ensembl_unmatched2, Ensembl, by = c("Nomenclature_ID_NCBI" = "MGI_ID_Ensembl"), keep = TRUE, na_matches = "never") # joining the data
  235. head(Pap_Uni_NCBI_Ensembl_V2, n=10) # showing first n rows
  237. Pap_Uni_NCBI_Ensembl_V2_matched <- dplyr::filter(Pap_Uni_NCBI_Ensembl_V2, Ensembl_gene_id != "NA") # selecting rows with matched data
  238. head(Pap_Uni_NCBI_Ensembl_V2_matched, n=10) # showing first n rows
  240. Pap_Uni_NCBI_Ensembl_V2_unmatched = na_rows <- Pap_Uni_NCBI_Ensembl_V2 [is.na(Pap_Uni_NCBI_Ensembl_V2$Ensembl_gene_id), ] # selecting rows with unmatched data
  241. head(Pap_Uni_NCBI_Ensembl_V2_unmatched, n=10) # showing first n rows
  243. Pap_Uni_NCBI_Ensembl_V3 = dplyr:: bind_rows(Pap_Uni_NCBI_Ensembl_matched, Pap_Uni_NCBI_Ensembl_V2_matched) # joining data
  244. head(Pap_Uni_NCBI_Ensembl_V3, n=10) # showing first n rows
  246. Pap_Uni_NCBI_Ensembl_V3$Gene_Symbol_congruence_NCBI_Ensembl <- ifelse(
  247. Pap_Uni_NCBI_Ensembl_V3$Symbol_NCBI == Pap_Uni_NCBI_Ensembl_V3$Ensembl_gene_symbol,
  248. 'TheSame',
  249. 'Different') # testing gene symbol congruence
  250. head(Pap_Uni_NCBI_Ensembl_V3, n=10) # showing first n rows
  252. grouped <- tidyr::nest( dplyr::group_by(Pap_Uni_NCBI_Ensembl_V3, `Ensembl_gene_symbol`) ) # grouping data according to the items in Ensembl_gene_symbol column
  253. integrated <- purrr::map_dfr(
  254. .x = grouped$data,
  255. .f = function(data_for_given_probe) {
  256. purrr::map_chr(
  257. .x = data_for_given_probe,
  258. .f = function(column) {
  259. paste(unique(column), collapse = ", ")
  260. })
  261. } )
  262. grouped <- cbind(grouped, integrated)
  263. grouped$data <- NULL
  265. head(grouped, n = 10) # showing first n rows
  267. Pap_Uni_NCBI_Ensembl_V4 <- as.data.frame(grouped) # changing data format to data frame
  269. Pap_Uni_NCBI_Ensembl_V2_unmatched = dplyr::mutate(Pap_Uni_NCBI_Ensembl_V2_unmatched, Gene_Symbol_congruence_NCBI_Ensembl = 'NA') # adding new column to enable joining by row
  271. Pap_Uni_NCBI_Ensembl_V2_unmatched$Organism_ID_UniProt <-as.character(Pap_Uni_NCBI_Ensembl_V2_unmatched$Organism_ID_UniProt) # changing data type in selected column
  273. Pap_Uni_NCBI_Ensembl_V2_unmatched$Taxonomic_ID_NCBI <-as.character(Pap_Uni_NCBI_Ensembl_V2_unmatched$Taxonomic_ID_NCBI) # changing data type in selected column
  275. Pap_Uni_NCBI_Ensembl_V5 = dplyr:: bind_rows(Pap_Uni_NCBI_Ensembl_V4, Pap_Uni_NCBI_Ensembl_V2_unmatched) # joining data
  276. head(Pap_Uni_NCBI_Ensembl_V5, n=10) # showing first n rows
  278. ######################################################################
  279. ######## Ambiguity_test #################################################
  280. ######################################################################
  282. Ambiguity_test = dplyr::select (Pap_Uni_NCBI_Ensembl_V5, c(Ensembl_gene_symbol, UniProt_ID_manual)) # selecting columns
  283. head(Ambiguity_test, n=10) # showing first n rows
  285. grouped <- tidyr::nest( dplyr::group_by(Ambiguity_test, `UniProt_ID_manual`) ) # grouping data according to the items in UniProt_ID_manual column
  286. integrated <- purrr::map_dfr(
  287. .x = grouped$data,
  288. .f = function(data_for_given_probe) {
  289. purrr::map_chr(
  290. .x = data_for_given_probe,
  291. .f = function(column) {
  292. paste(unique(column), collapse = ", ")
  293. })
  294. } )
  295. grouped <- cbind(grouped, integrated)
  296. grouped$data <- NULL
  297. head(grouped, n = 10) # showing first n rows
  299. Ambiguity_test2 <- as.data.frame(grouped) # changing data format to data frame
  300. head(Ambiguity_test2, n = 10) # showing first n rows
  302. Ambiguity_test2$Ambiguity_UniProtID_EnsemblSymbol <- ifelse(grepl(",", Ambiguity_test2$Ensembl_gene_symbol),'Ambiguous','Unique') # testing for the presence of “,” in items in the column Ensembl_gene_symbol. Presence of the “,” indicates that more than one gene is assigned to the ID.
  303. head(Ambiguity_test2, n=20) # showing first n rows
  305. Ambiguity_test3 = dplyr::select (Ambiguity_test2, c(UniProt_ID_manual, Ambiguity_UniProtID_EnsemblSymbol)) # selecting columns
  306. head(Ambiguity_test3, n=10) # showing first n rows
  308. ######################################################################
  309. ######## Joining the dataset with results of ambiguity_test ######################
  310. ######################################################################
  312. Pap_Uni_NCBI_Ensembl_V6 = dplyr:: full_join(Ambiguity_test3, Pap_Uni_NCBI_Ensembl_V5, by = c("UniProt_ID_manual" = "UniProt_ID_manual"), keep = FALSE, na_matches = "never") # joining the data
  313. head(Pap_Uni_NCBI_Ensembl_V6, n=10) # showing first n rows
  315. ######################################################################
  316. ######## Joining the dataset with paper data #################################
  317. ######################################################################
  319. Paper_data = read.delim('Paper_data.csv', sep=';', header = TRUE) # data import
  320. head(Paper_data, n = 8) # showing first n rows
  322. Paper_data_2 = dplyr::mutate(Paper_data, Manual_search_URL_primary = NULL, Manual_search_URL_secondary = NULL, Organism_manual = NULL, NCBI_ID_manual = NULL) # removing unnecessary columns
  323. head(Paper_data_2, n = 10) # showing first n rows
  325. Pap_Uni_NCBI_Ensembl_V7 = dplyr:: full_join(Paper_data_2, Pap_Uni_NCBI_Ensembl_V6, by = c("UniProt_ID_manual" = "UniProt_ID_manual"), keep = FALSE, na_matches = "never") # joining the data
  326. head(Pap_Uni_NCBI_Ensembl_V7, n=10) # showing first n rows
  328. ######################################################################
  329. ######## Final editing ###################################################
  330. ######################################################################
  332. Pap_Uni_NCBI_Ensembl_V8 = dplyr::mutate(Pap_Uni_NCBI_Ensembl_V7, TemporaryCol = 'Gene') # Adding temporary column with the word “gene”
  333. head(Pap_Uni_NCBI_Ensembl_V8, n=4) # showing first n rows
  335. Pap_Uni_NCBI_Ensembl_V9 = tidyr::unite(Pap_Uni_NCBI_Ensembl_V8, 'Anchored_NCBI_gene_symbol', c("TemporaryCol","Symbol_NCBI"), sep = "_", remove = FALSE, na.rm = FALSE) # adding new column with joint items from two other columns
  336. head(Pap_Uni_NCBI_Ensembl_V9, n=4) # showing first n rows
  338. Pap_Uni_NCBI_Ensembl_V10 = dplyr::mutate(Pap_Uni_NCBI_Ensembl_V9, TemporaryCol = NULL) # removing temporary column
  339. head(Pap_Uni_NCBI_Ensembl_V10, n=4) # showing first n rows
  341. readr::write_csv(x = Pap_Uni_NCBI_Ensembl_V10, file = "Pap_Uni_NCBI_Ensembl_final.csv") # data export
  343. ######## The End #######

R_Mouse_manual_and_databases.R at commit d0716ce, under MIT · at the source

Overview

Authors: Grzegorz R Juszczak1
ORCID iDs: Grzegorz R Juszczak
  1. Department of Animal Behavior and Welfare, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, Jastrzebiec, Poland
Institutions: Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences (Poland); Polish Academy of Sciences (Poland)
Journal: FASEB journal : official publication of the Federation of American Societies for Experimental Biology, volume 40, issue 8, article e71772
Dates: received 2 November 2025; accepted 30 March 2026; published online 17 April 2026; in print 30 April 2026
Type: Review · Language: English
License: CC BY-NC
Identifiers: DOI 10.1096/fj.202504113r · PMID 41996197 · PMCID PMC13089601 · OpenAlex W7154763320
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), rat (organism)
Methods: Connectivity
Keywords: brain, glucocorticoids, proteome, transcriptome
MeSH: Brain*, Glucocorticoids*, Proteome*, Proteomics*, Transcriptome*, Animals, Humans, Mice, Rats (* major topic)
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: IGAB PAN Intramural grant (STAT/GRZJUS/2025/01); Narodowe Centrum Nauki (NCN) (2017/27/B/NZ2/02796)
Citations: not cited yet (Europe PMC); 72 references in the paper
Notices: A correction to this paper has been published (42262671, from Europe PMC)

Abstract

Although there are several proteomic studies testing brain responses to glucocorticoids, there were no attempts to integrate these data and compare them with responses at the level of mRNAs. Furthermore, the utility of available data is compromised by changes in nomenclature and usage of different types of identifiers. Therefore, the aim of this study was to identify the most consistent changes in protein expression in standardized mouse, rat, and human datasets and compare them with transcriptomic responses to glucocorticoids. The analysis showed that the two most frequently and consistently detected proteins were ATP synthase F1 subunit beta (Atp5f1b) and aldolase, fructose‐bisphosphate C (Aldoc), while the most consistent proteomic and transcriptomic findings included Aldoc, Plin4, Aqp4, Endod1, Glul, Anln, Aldh1l1, Parp1, Trf, Fermt2, Tmem63a, and Trim2. The study also revealed limitations of available proteomic data indicating significant gaps in knowledge. Finally, the study provides an integrated dataset with updated protein nomenclature and a complete set of major identifiers to facilitate usage of proteomic data.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Grzegorz-R-Juszczak/Scripts-for-Brain-proteomic-responses-to-GCs

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d0716ce3a2d71869c3ddbfc795d5c56d1c1be446, 26 September 2025
Languages: R (8)
Size: 10 files, 8 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
10 files
At the source: github.com/Grzegorz-R-Juszczak/Scripts-for-Brain-proteomic-responses-to-GCs

Grzegorz-R-Juszczak/Protein-coding-gene-IDs-human-mouse-rat-pig

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 9b68000aafda749833b43ef561b1715e653db3b0, 23 July 2025
Languages: R (23)
Size: 25 files, 23 scripts
Software Heritage: not archived
Found in: the text, “Updating Data Containing Only Gene Symbols”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (23 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
25 files
At the source: github.com/Grzegorz-R-Juszczak/Protein-coding-gene-IDs-human-mouse-rat-pig

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 31 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

Data are available at https://data.mendeley.com/datasets/tw95jjrpxr/3 while R scripts were deposited at https://github.com/Grzegorz‐R‐Juszczak/Scripts‐for‐Brain‐proteomic‐responses‐to‐GCs (https://github.com/Grzegorz-R-Juszczak/Scripts-for-Brain-proteomic-responses-to-GCs).

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 4 keywords, 9 MeSH terms, 2 funders, 65 references, 1 integrity notice.

Cite

This paper

Juszczak, G. R. (2026). Brain Proteomic Responses to Glucocorticoids and Their Relationship With Transcriptome: A Systematic Meta-Analysis. FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 40(8), e71772. https://doi.org/10.1096/fj.202504113r

BibTeX

@article{juszczak2026brain,
author = {Juszczak, Grzegorz R},
title = {{Brain Proteomic Responses to Glucocorticoids and Their Relationship With Transcriptome: A Systematic Meta-Analysis}},
journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology},
year = {2026},
month = apr,
volume = {40},
number = {8},
pages = {e71772},
publisher = {Wiley},
issn = {0892-6638},
doi = {10.1096/fj.202504113r},
url = {https://doi.org/10.1096/fj.202504113r},
pmid = {41996197},
pmcid = {PMC13089601}
}

RIS

TY - JOUR
AU - Juszczak, Grzegorz R
TI - Brain Proteomic Responses to Glucocorticoids and Their Relationship With Transcriptome: A Systematic Meta-Analysis
T2 - FASEB journal : official publication of the Federation of American Societies for Experimental Biology
J2 - FASEB J
PY - 2026
DA - 2026/04/01
VL - 40
IS - 8
SP - e71772
SN - 0892-6638
PB - Wiley
DO - 10.1096/fj.202504113r
UR - https://doi.org/10.1096/fj.202504113r
LA - en
ER -

CSL-JSON

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