diff --git a/src/.docker_modules/r-bolero/1.0/HBV_RNAs_count.R b/src/.docker_modules/r-bolero/1.0/HBV_RNAs_count.R
index ea15c73677d5bd5d9a21cd23eee4d02a6d4c0fad..16d67f3f8dd8a9161ff7006c410bda1291d45e63 100644
--- a/src/.docker_modules/r-bolero/1.0/HBV_RNAs_count.R
+++ b/src/.docker_modules/r-bolero/1.0/HBV_RNAs_count.R
@@ -74,7 +74,7 @@ countSP <- dplyr::inner_join(palette_complete,
 #print(names(countSP))
 
 countSP$nom <- factor(countSP$nom, levels = all_species_name)
-countSP <- mutate(countSP,
+countSP <- dplyr::mutate(countSP,
                   proportion = (as.numeric(n)/sum(as.numeric(n))*100))
 #print(countSP)
 ggplot(countSP, aes(x = "percent", 
@@ -112,7 +112,7 @@ classified_reads <- read.table(file = opt$classification,
                                header = TRUE)
 
 not_spliced <- classified_reads[!(classified_reads$read_ID %in% clean_SP$id),]
-not_spliced <- mutate(not_spliced,
+not_spliced <- dplyr::mutate(not_spliced,
                       species = not_spliced$promoter)
 #print(not_spliced)
 not_spliced <- not_spliced %>% select(read_ID, species)
@@ -124,7 +124,7 @@ colnames(clean_SP_type) <- c("id", "species")
 df_species <- rbind.data.frame(not_spliced, clean_SP_type, 
                                stringsAsFactors = FALSE)
 count_species <- df_species %>% count(species)
-count_species <- mutate(count_species,
+count_species <- dplyr::mutate(count_species,
                         percent = (as.numeric(n)/sum(as.numeric(n))*100))
 #print(count_species)
 write.table(df_species, file = "All_reads_identified.csv", 
@@ -161,7 +161,7 @@ count_species_SPxx <- rbind.data.frame(count_species_SPxx,
                                        stringsAsFactors = FALSE)
 count_species_SPxx <- count_species_SPxx[count_species_SPxx$species %in% 
                                            all_species_name[c(1:3,5,35)],]
-count_species_SPxx <- mutate(count_species_SPxx,
+count_species_SPxx <- dplyr::mutate(count_species_SPxx,
                              percent=(as.numeric(n)/sum(as.numeric(n))*100))
 #print(count_species_SPxx)
 # save the tab:
@@ -229,7 +229,7 @@ ggsave(file = "Count_RNAs_species_clear.png",
 
 # SP composition clear:
 count_clear <- clean_SP[clean_SP$SP_name %in% SPvariants,] %>% count(SP_name)
-count_clear <- mutate(count_clear,
+count_clear <- dplyr::mutate(count_clear,
                       proportion=(as.numeric(n)/sum(as.numeric(n))*100))
 #print(count_clear)
 count_clear <- dplyr::inner_join(palette_complete,
diff --git a/src/.docker_modules/r-bolero/1.0/Junctions_NanoSplicer.R b/src/.docker_modules/r-bolero/1.0/Junctions_NanoSplicer.R
index bce38457601babac31813c62f3362a8736683671..5567a378b0c61e716092c77a7702f2360b2bf2b2 100644
--- a/src/.docker_modules/r-bolero/1.0/Junctions_NanoSplicer.R
+++ b/src/.docker_modules/r-bolero/1.0/Junctions_NanoSplicer.R
@@ -36,7 +36,7 @@ df <- df %>%
 df$donor <- str_replace(df$donor, '[(]', '')
 df$acceptor <- str_replace(df$acceptor, '[)]', '')
 
-df <- mutate(df, 
+df <- dplyr::mutate(df, 
              pg_donor = as.numeric(donor)-122,
              pg_acceptor = as.numeric(acceptor)-122)
 
@@ -110,7 +110,7 @@ assignation_acceptor <- function(pg_acceptor) {
 
 df$donor_site <- sapply(df$pg_donor, assignation_donor)
 df$acceptor_site <- sapply(df$pg_acceptor, assignation_acceptor)
-df <- mutate(df,
+df <- dplyr::mutate(df,
              junction = paste0(donor_site, acceptor_site))
 
 write.table(df, file = "JWR_check_parsed.csv", row.names = FALSE, sep = "\t")
diff --git a/src/.docker_modules/r-bolero/1.0/Start_positions.R b/src/.docker_modules/r-bolero/1.0/Start_positions.R
index 683336e0aadc3fa2dd71374f3e4f3a2b4a07c6f5..f31afaae99dbcfd8686576f070d979db06277800 100644
--- a/src/.docker_modules/r-bolero/1.0/Start_positions.R
+++ b/src/.docker_modules/r-bolero/1.0/Start_positions.R
@@ -47,9 +47,9 @@ parsingData <- function(df) {
   tmp$Start <- as.numeric(tmp$Start)
   
   df2 <- as_tibble(tmp) %>% 
-    mutate(bin = round(Start/binsize)*binsize) %>% 
+    dplyr::mutate(bin = round(Start/binsize)*binsize) %>% 
     group_by(bin) %>% 
-    summarize(nb_reads = sum(Freq, na.rm = T))
+    dplyr::summarize(nb_reads = sum(Freq, na.rm = T))
   df2[is.na(df2)] <- 0
   df2[3] <- rep(df[1,3], length(df2$bin))
   colnames(df2) <- c("Start_position", "nb_reads", "Barcode")