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finalize-allele-and-znf-lists.md

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Finalize PRDM9 allele and znf lists

  • Add marker if znf/allele has actually been observed in the human population or if it has only been observed in sperm cells or as somatic mutations in blood cells (i.e.; only observed in Jeffreys et al. 2013)
  • Arrange alleles in reasonable order:
    • Alphabetical according to common names (Baudat and Berg A-I, L# names)
    • Then in publication order
    • Then in GenBank accession order
  • Give permanent standardized names to both znfs and alleles
  • Highlight alleles/znfs with inconsistent/repeted names
  • Create table of all GenBank accessions, znf content, and standard allele names

Step 10. Check if znfs and alleles have been observed in the human population, arrange and give final standardized names

# done in R

library(dplyr)
library(tidyr)
library(stringr)

all.znfs <- read.table("intermediate-files/standardized-znf-sequences-map-step7.tsv",
                       header=T)
all.alleles <- read.table("intermediate-files/standardized-allele-znf-content-map-step9.tsv",
                          header=T)
genbank.accessions <- read.table("genbank-records/standardized-allele-znf-content-step7.tsv",
                                 header=F, col.names=c("GenBankAccession", "StandardZnfContent"))

# get alleles observed in the population
alleles.with.pop.status <- all.alleles %>%
  mutate(InPopulation=case_when(
    !is.na(Baudat.2010) | 
      !is.na(Berg.2010) | 
      !is.na(Berg.2011) | 
      !is.na(Beyter.2021) | 
      !is.na(Borel.2012) | 
      !is.na(Hussin.2013) | 
      !is.na(Oliver.2009) | 
      !is.na(Parvanov.2010) | 
      !is.na(Ponting.2011) | 
      !is.na(GenBank.2021) ~ T,
    grepl("M", Alleva.2021) ~ T,
    TRUE ~ F), .before=2)

# get znfs from the alleles observed in the population
znfs.in.pop <- alleles.with.pop.status %>%
  filter(InPopulation==T) %>%
  select(StandardZnfContent) %>%
  separate_rows(StandardZnfContent, sep="_") %>%
  distinct() %>%
  arrange(StandardZnfContent) %>% 
  pull(StandardZnfContent)

# update znf list
znfs.with.pop.status <- all.znfs %>%
  mutate(InPopulation=case_when(
    StandardZnfName %in% znfs.in.pop ~ T,
    TRUE ~ F), .before=2) %>%
  # already in publication order, since only additions after initial list were GenBank
  # update to final standardized names
  mutate(StandardZnfName=sub("z", "Z", StandardZnfName))

# order for common allele names
baudat.berg.style.names <- c(LETTERS[c(1:6,8,9,11)], paste0("L", 1:nrow(alleles.with.pop.status %>% pivot_longer(cols=contains("20"), names_to="Publication", values_to="PubName", values_drop_na=T) %>% filter(grepl("L[0-9]{1,2}$", PubName)) %>% distinct(PubName))))

# get alleles with common names
alleles.common.names <- alleles.with.pop.status %>%
  pivot_longer(cols=contains("20"), names_to="Publication", values_to="PubName",
               values_drop_na=T) %>%
  filter(PubName %in% baudat.berg.style.names) %>%
  select(-Publication) %>%
  arrange(factor(PubName, levels=baudat.berg.style.names)) %>%
  filter(!duplicated(StandardName)) 

# publication order
pub.order <- c("Oliver.2009", "Parvanov.2010", "Baudat.2010", "Berg.2010", "Ponting.2011", "Berg.2011", "Borel.2012", "Jeffreys.2013", "Hussin.2013", "Beyter.2021", "Alleva.2021","GenBank.2021")

alleva.novel.order <- paste0("M", 1:length(alleles.with.pop.status$Alleva.2021[grepl("M", alleles.with.pop.status$Alleva.2021)]))

# get alleles with non-common names
alleles.not.common.names <- alleles.with.pop.status %>%
  pivot_longer(cols=contains("20"), names_to="Publication", values_to="PubName",
               values_drop_na=T) %>%
  filter(!(StandardName %in% alleles.common$StandardName)) %>%
  left_join(genbank.accessions) %>%
  mutate(PubName=ifelse(Publication=="GenBank.2021", GenBankAccession, PubName)) %>%
  select(-GenBankAccession) %>%
  arrange(factor(Publication, levels=not.common.pub.order), factor(PubName, levels=alleva.novel.order), PubName) %>%
  filter(!duplicated(StandardName))
  
allele.order <- c(alleles.common.names$StandardName, alleles.not.common.names$StandardName)  

# get final list
alleles.with.pop.status.final <- alleles.with.pop.status %>%
  arrange(factor(StandardName, levels=allele.order)) %>%
  # made up names for Jeffreys and Alleva describes them better, so remove
  select(-Jeffreys.2013) %>%
  mutate(StandardName=paste0("P", str_pad(row_number(), 3, pad="0")),
         StandardZnfContent=gsub("z", "Z", StandardZnfContent))

# save to file
write.table(alleles.with.pop.status.final, 
            "standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-znf-content-map.tsv",
            row.names=F, quote=F, col.names=T, sep="\t")
write.table(alleles.with.pop.status.final %>% 
              select(StandardName, StandardZnfContent), 
            "standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-znf-content.tsv",
            row.names=F, quote=F, col.names=F, sep="\t")
write.table(znfs.with.pop.status, "standardized-lists/population-and-sperm-somatic-PRDM9-standardized-znf-sequences-map.tsv",
            row.names=F, quote=F, col.names=T, sep="\t")
write.table(znfs.with.pop.status %>% 
              select(StandardZnfName, Sequence),
            "standardized-lists/population-and-sperm-somatic-PRDM9-standardized-znf-sequences.tsv",
            row.names=F, quote=F, col.names=F, sep="\t")

# just alleles/znfs found in pop
write.table(alleles.with.pop.status.final %>% 
              filter(InPopulation==T), 
            "standardized-lists/PRDM9-standardized-allele-znf-content-map.tsv",
            row.names=F, quote=F, col.names=T, sep="\t")
write.table(alleles.with.pop.status.final %>% 
              filter(InPopulation==T) %>% 
              select(StandardName, StandardZnfContent), 
            "standardized-lists/PRDM9-standardized-allele-znf-content.tsv",
            row.names=F, quote=F, col.names=F, sep="\t")
write.table(znfs.with.pop.status  %>% 
              filter(InPopulation==T), 
            "standardized-lists/PRDM9-standardized-znf-sequences-map.tsv",
            row.names=F, quote=F, col.names=T, sep="\t")
write.table(znfs.with.pop.status  %>% 
              filter(InPopulation==T) %>% 
              select(StandardZnfName, Sequence),
            "standardized-lists/PRDM9-standardized-znf-sequences.tsv",
            row.names=F, quote=F, col.names=F, sep="\t")

Step 11. Regenerate allele sequences from znf sequences

Replace standard znf names with znf sequences to get full allele sequences for newly organized alleles

cp standardized-lists/PRDM9-standardized-allele-znf-content.tsv standardized-lists/PRDM9-standardized-allele-sequences.tsv

while read ZNF SEQUENCE
do
sed -i '' "s/$ZNF/$SEQUENCE/g" standardized-lists/PRDM9-standardized-allele-sequences.tsv
done < standardized-lists/PRDM9-standardized-znf-sequences.tsv

# check if any znfs left
grep "Z" standardized-lists/PRDM9-standardized-allele-sequences.tsv
# no

# remove _ between znfs
sed -i '' -e 's/_//g' standardized-lists/PRDM9-standardized-allele-sequences.tsv

Repeat for all alleles (including those only found in sperm/somatic blood)

cp standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-znf-content.tsv standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-sequences.tsv

while read ZNF SEQUENCE
do
sed -i '' "s/$ZNF/$SEQUENCE/g" standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-sequences.tsv
done < standardized-lists/population-and-sperm-somatic-PRDM9-standardized-znf-sequences.tsv

# check if any znfs left
grep "Z" standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-sequences.tsv
# no

# remove _ between znfs
sed -i '' -e 's/_//g' standardized-lists/population-and-sperm-somatic-PRDM9-standardized-allele-sequences.tsv