BYSY project Notebook

10_Master Table

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Building a master table with the ARS, OEM, dist to CEN, dist to JUNC, dist to TER, signifRFD/MH, ∆ICgn, signif∆IC, ∆nanoT, signif∆nanoT, CTF19, RIF1, FKH1FKH2, dist toRFDsig.

suppressMessages(library(GenomicRanges))
suppressMessages(library(tidyverse))
suppressMessages(library(rtracklayer))
`%+%`<-paste0
seqinfSY <- readRDS("Data/seqinfSY.rds")

Import Ori table

ars2keep <- import("Data/ars2keep_lim10k_cl1500_2_dtac1500.bed") %>% sort
ars2keep$score <- NULL
ars2 <- mcols(ars2keep) %>% as_tibble

add OEM data

Data were compiled from OEM bigwig

oemBY1_bw <- import("BigFiles/BigWig/BY1_oem_10k.bw")
oemBY2_bw <- import("BigFiles/BigWig/BY2_oem_10k.bw")
oemBY3_bw <- import("BigFiles/BigWig/BY3_oem_10k.bw")
oemSY1_bw <- import("BigFiles/BigWig/SY1_oem_10k.bw")
oemSY2_bw <- import("BigFiles/BigWig/SY2_oem_10k.bw")
oemSY3_bw <- import("BigFiles/BigWig/SY3_oem_10k.bw")
oemBY1 <- coverage(oemBY1_bw,weight=oemBY1_bw$score)
oemBY2 <- coverage(oemBY2_bw,weight=oemBY2_bw$score)
oemBY3 <- coverage(oemBY3_bw,weight=oemBY3_bw$score)
oemSY1 <- coverage(oemSY1_bw,weight=oemSY1_bw$score)
oemSY2 <- coverage(oemSY2_bw,weight=oemSY2_bw$score)
oemSY3 <- coverage(oemSY3_bw,weight=oemSY3_bw$score)

ars2$OEMBY1 <- sapply(seq_along(ars2keep), function(i) {
    mean(oemBY1[ars2keep[i]],na.rm=T)
    })
ars2$OEMBY2 <- sapply(seq_along(ars2keep), function(i) {
    mean(oemBY2[ars2keep[i]],na.rm=T)
    })
ars2$OEMBY3 <- sapply(seq_along(ars2keep), function(i) {
    mean(oemBY3[ars2keep[i]],na.rm=T)
    })
ars2$OEMSY1 <- sapply(seq_along(ars2keep), function(i) {
    mean(oemSY1[ars2keep[i]],na.rm=T)
    })
ars2$OEMSY2 <- sapply(seq_along(ars2keep), function(i) {
    mean(oemSY2[ars2keep[i]],na.rm=T)
    })
ars2$OEMSY3 <- sapply(seq_along(ars2keep), function(i) {
    mean(oemSY3[ars2keep[i]],na.rm=T)
    })

ars2$dOEM1 <- ars2$OEMSY1-ars2$OEMBY1
ars2$dOEM2 <- ars2$OEMSY2-ars2$OEMBY2
ars2$dOEM3 <- ars2$OEMSY3-ars2$OEMBY3
ars2 <- ars2 %>%
    mutate(OEMm_BY=pmap_dbl(.,function(OEMBY1,OEMBY2,OEMBY3,...) mean(c(OEMBY1,OEMBY2,OEMBY3),na.rm=T))) %>%
    mutate(OEMm_SY=pmap_dbl(.,function(OEMSY1,OEMSY2,OEMSY3,...) mean(c(OEMSY1,OEMSY2,OEMSY3),na.rm=T))) %>%
    mutate(d_OEMm=pmap_dbl(., function(dOEM1,dOEM2,dOEM3,...) mean(c(dOEM1,dOEM2,dOEM3),na.rm=T)))

Importing RFD test results

bgsigMH2 <- import("Data/bgsigMH.bed")
distArs2RFD <- distanceToNearest(ars2keep,bgsigMH2)
ars2$dt_signif_RFD <- mcols(distArs2RFD)[,1]
#ars2 <- ars2 %>%
#   mutate(signif_RFDMH=dt_signif_RFD<5000)

Add distance to CEN, TEL and Junction

CEN <- import("Genome_annotations/SY14_CEN.gff3") 
chromSY <- import("Genome_annotations/chromBYonSYgr.bed") %>% sort
seqinfo(chromSY) <- seqinfSY

chromJunction <- gaps(chromSY-10)
chromTrueJunction <- chromJunction[4:18]
TEL_SY <- chromJunction[c(3,19)]

ars2$dt_CEN <- data.frame(distanceToNearest(resize(ars2keep,fix="center",width=1),CEN))[,3]
ars2$dt_JUNC <- data.frame(distanceToNearest(resize(ars2keep,fix="center",width=1),chromTrueJunction))[,3]
ars2$dt_TEL <- data.frame(distanceToNearest(resize(ars2keep,fix="center",width=1),TEL_SY))[,3]

Add results on Init

ars.init <- readRDS("Data/ARSinitMAnorm2.rds") %>%
    mutate(dInit_SY_BY=Mval) %>%    select(c(name,dInit_SY_BY,dInitSY_BY_padj=padj))
ars3 <- left_join(ars2,ars.init,by="name")

To affect the variation of the initiations between SY and BY (dInit_SY_BY), we used Mval from the MAnorm2 analysis which correspond to difference between the normalised initiation signal. If this value is positive, it means that there are more initiation in the SY compared to the BY.

Add results on nanoT

ars.RT <- readRDS("Data/ARS_nanotiming_MApadj.rds") %>%
    # mutate(RT_BY=pmap_dbl(., function(BY_rep1, BY_rep2,BY_rep3,...) mean(c(BY_rep1,BY_rep2,BY_rep3)))) %>%
    # mutate(RT_SY=pmap_dbl(., function(SY_rep1,SY_rep2,SY_rep3,...) mean(c(SY_rep1, SY_rep2,SY_rep3)))) %>%
    mutate(dRT_SY_BY=pmap_dbl(., function(SY_rep1,SY_rep2,SY_rep3,BY_rep1, BY_rep2,BY_rep3,...) mean(c(SY_rep1, SY_rep2,SY_rep3))-mean(c(BY_rep1,BY_rep2,BY_rep3)))) %>%
#   select(c(name,RT_BY,RT_SY,dRT_SY_BY,dRT_SY_BY_padj=padj))
    select(c(name,dRT_SY_BY,dRT_SY_BY_padj=padj))
ars4 <- left_join(ars3,ars.RT,by="name")

dRT_SY_BY is the difference of the mean RT values between BY and SY. If it is negative, it indicates that this Ori replicated later in SY compared to BY

Add biblio data

arsbiblio <- read_tsv("Data/ARS_withBiblioData_namesBYSY.tsv",show_col_types = FALSE) %>%
    select(c(6,22:25))

ars5 <- left_join(ars4,arsbiblio,by="name")

write_tsv(ars5,"Data/BYSY_MasterTable.tsv")

ars6 <- ars5 %>% select(-c(4:12))

write_tsv(ars6,"Data/BYSY_MasterTable_simple.tsv")

Add Table 1,2 and 3 annotations

AnnotTable <- read_tsv("Data_raw/Annot_Table.tsv",show_col_types = FALSE,na = character()) %>% mutate(Table123=paste0(Table1,Table2,Table3)) %>% mutate(Table123=ifelse(Table123=="AA","A",Table123))

ars7 <- left_join(ars6,AnnotTable,by="name")

write_tsv(ars7,"Data/BYSY_MasterTable_simple_Table123.tsv")
write_tsv(ars7,"Data/TableS2.tsv")