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Knit directory: fitnessGWAS/
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library(dplyr)
library(stringr)
library(future.apply)
library(org.Dm.eg.db) # install via source("https://bioconductor.org/biocLite.R"); biocLite("org.Dm.eg.db")
library(GO.db)
options(future.globals.maxSize = 2000 * 1024 ^ 2,
stringsAsFactors = FALSE)
# Helper function to split a vector into chunks
chunker <- function(x, max_chunk_size) split(x, ceiling(seq_along(x) / max_chunk_size))
# database of D. mel annotations from bioconductor
con <- dbconn(org.Dm.eg.db)The following function temporarily loads the >1GB annotation file provided on the DGRP website at http://dgrp2.gnets.ncsu.edu/data/website/dgrp.fb557.annot.txt. We then extract the following variables for each variant, and save them in a SQLite database for memory-efficient searching inside R:
get_variant_annotations <- function(){
# Load up the big annotation file, get pertinent info. It's stored in some sort of text string format
annot <- read.table("data/input/dgrp.fb557.annot.txt", header = FALSE, stringsAsFactors = FALSE)
get.info <- function(rows){
lapply(rows, function(row){
site.class.field <- strsplit(annot$V3[row], split = "]")[[1]][1]
num.genes <- str_count(site.class.field, ";") + 1
output <- cbind(rep(annot$V1[row], num.genes),
do.call("rbind", lapply(strsplit(site.class.field, split = ";")[[1]],
function(x) strsplit(x, split = "[|]")[[1]])))
if(ncol(output) == 5) return(output[,c(1,2,4,5)]) # only return SNPs that have some annotation. Don't get the gene symbol
else return(NULL)
}) %>% do.call("rbind", .)
}
plan("multisession")
variant.details <- future_lapply(chunker(1:nrow(annot), max_chunk_size = 10000), get.info) %>%
do.call("rbind", .) %>% as.data.frame()
names(variant.details) <- c("SNP", "FBID", "site.class", "distance.to.gene")
variant.details$FBID <- unlist(str_extract_all(variant.details$FBID, "FBgn[:digit:]+")) # clean up text strings for Flybase ID
variant.details %>%
dplyr::filter(site.class != "FBgn0003638") %>% # NB this is a bug in the DGRP's annotation file
mutate(chr = str_remove_all(substr(SNP, 1, 2), "_")) # get chromosome now for faster sorting later
}The following function gets the annotations for the all the genes covered by DGRP variants, from the org.Dm.eg.db database object from Bioconductor. I don’t like the select interface to those objects (it messes with any R code that uses dplyr), so here I save the info into the SQLite database for later access.
get_gene_annotations <- function(){
tbl(con, "genes") %>%
left_join(tbl(con, "flybase"), by = "_id") %>%
left_join(tbl(con, "gene_info"), by = "_id") %>%
dplyr::select(flybase_id, gene_name, symbol, gene_id) %>%
dplyr::rename(FBID = flybase_id, gene_symbol = symbol, entrez_id = gene_id) %>%
collect(n = Inf)
}get_KEGG <- function(){
tbl(dbconn(org.Dm.eg.db), "kegg") %>%
left_join(tbl(con, "flybase"), by = "_id") %>%
dplyr::select(flybase_id, path_id) %>%
dplyr::rename(FBID = flybase_id, kegg_id = path_id) %>%
collect(n = Inf)
}if(file.exists("data/derived/annotations.sqlite3")) unlink("data/derived/annotations.sqlite3")
db <- DBI::dbConnect(RSQLite::SQLite(), "data/derived/annotations.sqlite3", create = TRUE)
db %>% copy_to(get_variant_annotations(),
"variants", temporary = FALSE,
indexes = list("SNP", "FBID", "chr", "site.class"))
db %>% copy_to(get_gene_annotations(),
"genes", temporary = FALSE)
db %>% copy_to(GO, "GO", temporary = FALSE)
db %>% copy_to(get_KEGG(),
"KEGG", temporary = FALSE)
db %>% copy_to(go_meanings,
"go_meanings", temporary = FALSE)The variants table is expanded upon in the script perform_gwas.Rmd, which also adds the minor allele frequencies, the alleles that were treated as the reference and alternate, etc.
db %>% tbl("variants")# Source: table<variants> [?? x 5]
# Database: sqlite 3.30.1 [data/derived/annotations.sqlite3]
SNP FBID site.class distance.to.gene chr
<chr> <chr> <chr> <chr> <chr>
1 2L_10000016_SNP FBgn0051755 NON_SYNONYMOUS_CODING 0 2L
2 2L_10000016_SNP FBgn0051875 INTRON 0 2L
3 2L_10000023_SNP FBgn0051755 NON_SYNONYMOUS_CODING 0 2L
4 2L_10000023_SNP FBgn0051875 INTRON 0 2L
5 2L_10000029_SNP FBgn0051755 NON_SYNONYMOUS_CODING 0 2L
6 2L_10000029_SNP FBgn0051875 INTRON 0 2L
7 2L_10000033_SNP FBgn0051755 SYNONYMOUS_CODING 0 2L
8 2L_10000033_SNP FBgn0051875 INTRON 0 2L
9 2L_10000089_SNP FBgn0051875 INTRON 0 2L
10 2L_10000089_SNP FBgn0051755 NON_SYNONYMOUS_CODING 0 2L
# … with more rowsdb %>% tbl("genes")# Source: table<genes> [?? x 4]
# Database: sqlite 3.30.1 [data/derived/annotations.sqlite3]
FBID gene_name gene_symbol entrez_id
<chr> <chr> <chr> <chr>
1 FBgn0040373 uncharacterized protein CG3038 30970
2 FBgn0040372 G9a G9a 30971
3 FBgn0261446 uncharacterized protein CG13377 30972
4 FBgn0000316 cinnamon cin 30973
5 FBgn0005427 erect wing ewg 30975
6 FBgn0040370 uncharacterized protein CG13375 30976
7 FBgn0040371 uncharacterized protein CG12470 30977
8 FBgn0029521 Odorant receptor 1a Or1a 30978
9 FBgn0024989 uncharacterized protein CG3777 30979
10 FBgn0004034 yellow y 30980
# … with more rows
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] GO.db_3.11.4 org.Dm.eg.db_3.11.4 AnnotationDbi_1.50.0
[4] IRanges_2.22.2 S4Vectors_0.26.1 Biobase_2.48.0
[7] BiocGenerics_0.34.0 future.apply_1.5.0 future_1.17.0
[10] stringr_1.4.0 dplyr_1.0.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 dbplyr_1.4.4 compiler_4.0.3 pillar_1.4.4
[5] later_1.0.0 git2r_0.27.1 workflowr_1.6.2 tools_4.0.3
[9] bit_1.1-15.2 digest_0.6.25 memoise_1.1.0 RSQLite_2.2.0
[13] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.1 pkgconfig_2.0.3
[17] rlang_0.4.6 cli_2.0.2 DBI_1.1.0 yaml_2.2.1
[21] xfun_0.19 knitr_1.30 generics_0.0.2 fs_1.4.1
[25] vctrs_0.3.0 globals_0.12.5 bit64_0.9-7 rprojroot_1.3-2
[29] tidyselect_1.1.0 glue_1.4.2 listenv_0.8.0 R6_2.4.1
[33] fansi_0.4.1 rmarkdown_2.5 blob_1.2.1 purrr_0.3.4
[37] magrittr_2.0.1 whisker_0.4 backports_1.1.7 promises_1.1.0
[41] codetools_0.2-16 htmltools_0.5.0 ellipsis_0.3.1 assertthat_0.2.1
[45] httpuv_1.5.3.1 utf8_1.1.4 stringi_1.5.3 crayon_1.3.4