set.seed(123) # for reproducibility
# Load required packages
library(doubletrouble)
library(here)
library(tidyverse)
library(patchwork)
source(here("code", "utils.R"))
# Load sample metadata for Ensembl instances
load(here("products", "result_files", "metadata_all.rda"))5 Runtime benchmark
Here, we will perform a runtime benchmark for functions related to duplicate classification and substitution rates calculation using model organisms.
To start, let’s load the required data and packages.
5.1 Benchmark 1: classify_gene_pairs()
Here, we will benchmark the performance of classify_gene_pairs() with model organisms.
First, let’s get the genome and annotation data.
# Create a data frame with names of model species and their Ensembl instances
model_species <- data.frame(
species = c(
"arabidopsis_thaliana", "caenorhabditis_elegans",
"homo_sapiens", "saccharomyces_cerevisiae",
"drosophila_melanogaster", "danio_rerio"
),
instance = c(
"plants", "metazoa", "ensembl", "fungi", "metazoa", "ensembl"
)
)
# For each organism, download data, and identify and classify duplicates
model_duplicates <- lapply(seq_len(nrow(model_species)), function(x) {
species <- model_species$species[x]
instance <- model_species$instance[x]
# Get annotation
annot <- get_annotation(model_species[x, ], instance)
# Get proteome and keep only primary transcripts
seq <- get_proteomes(model_species[x, ], instance)
seq <- filter_sequences(seq, annot)
# Process data
pdata <- syntenet::process_input(seq, annot, filter_annotation = TRUE)
# Perform DIAMOND search
outdir <- file.path(tempdir(), paste0(species, "_intra"))
diamond <- syntenet::run_diamond(
seq = pdata$seq,
compare = "intraspecies",
outdir = outdir,
threads = 4,
... = "--sensitive"
)
fs::dir_delete(outdir)
# Classify duplicates - standard mode
start <- Sys.time()
duplicate_pairs <- classify_gene_pairs(
blast_list = diamond,
annotation = pdata$annotation,
scheme = "standard"
)[[1]]
end <- Sys.time()
runtime <- end - start
return(runtime)
})
names(model_duplicates) <- gsub("_", " ", str_to_title(model_species$species))
# Summarize results in a table
benchmark_classification <- data.frame(
species = names(model_duplicates),
time_seconds = as.numeric(unlist(model_duplicates))
)
# Save results
save(
benchmark_classification, compress = "xz",
file = here("products", "result_files", "benchmark_classification.rda")
)5.2 Benchmark 2: pairs2kaks()
Next, we will benchmark the performance of pairs2kaks() for duplicate pairs in the Saccharomyces cerevisiae genome. We will do it using a single thread, and using parallelization (with 4 and 8 threads).
First of all, let’s get the required data for pairs2kaks().
# Load duplicate pairs for S. cerevisiae
load(here("products", "result_files", "fungi_duplicates.rda"))
scerevisiae_pairs <- fungi_duplicates["saccharomyces_cerevisiae"]
# Get CDS for S. cerevisiae
scerevisiae_cds <- get_cds_ensembl("saccharomyces_cerevisiae", "fungi")Now, we can do the benchmark.
# 1 thread
start <- Sys.time()
kaks <- pairs2kaks(
scerevisiae_pairs,
scerevisiae_cds,
threads = 1
)
end <- Sys.time()
runtime1 <- end - start
# 4 threads
start <- Sys.time()
kaks <- pairs2kaks(
scerevisiae_pairs,
scerevisiae_cds,
threads = 4
)
end <- Sys.time()
runtime4 <- end - start
# 8 threads
start <- Sys.time()
kaks <- pairs2kaks(
scerevisiae_pairs,
scerevisiae_cds,
threads = 8
)
end <- Sys.time()
runtime8 <- end - start
# Summarize results in a table
benchmark_kaks <- data.frame(
Threads = factor(c(1, 4, 8)),
Time_minutes = as.numeric(c(runtime1, runtime4, runtime8))
) |>
dplyr::mutate(
Pairs_per_minute = nrow(scerevisiae_pairs[[1]]) / Time_minutes,
Pairs_per_second = nrow(scerevisiae_pairs[[1]]) / (Time_minutes * 60)
)
save(
benchmark_kaks, compress = "xz",
file = here("products", "result_files", "benchmark_kaks.rda")
)5.3 Benchmark 3: doubletrouble vs DupGen_finder
Here, we will classify duplicate pairs in the A. thaliana genome using doubletrouble and DupGen_finder to assess if they produce the same results, and compare their runtimes.
First, let’s get all data we need (proteomes, annotation, and DIAMOND tables).
# Get annotation
smeta <- data.frame(
species = c("arabidopsis_thaliana", "amborella_trichopoda"),
instance = "plants"
)
annot <- get_annotation(smeta, "plants")
# Get proteome and keep only primary transcripts
seq <- get_proteomes(smeta, "plants")
seq <- filter_sequences(seq, annot)
# Process data
pdata <- syntenet::process_input(seq, annot, filter_annotation = TRUE)
# Run intraspecies DIAMOND search
outdir <- file.path(tempdir(), "benchmark3_intra")
diamond_intra <- syntenet::run_diamond(
seq = pdata$seq,
compare = "intraspecies",
outdir = outdir,
threads = 4,
... = "--sensitive"
)
fs::dir_delete(outdir)
# Run interspecies DIAMOND search
outdir2 <- file.path(tempdir(), "benchmark3_inter")
compare_df <- data.frame(
query = "arabidopsis.thaliana", target = "amborella.trichopoda"
)
diamond_inter <- syntenet::run_diamond(
seq = pdata$seq,
compare = compare_df,
outdir = outdir2,
threads = 4,
... = "--sensitive"
)
fs::dir_delete(outdir2)Now, let’s classify duplicates with doubletrouble.
# Classify duplicates with doubletrouble
start1 <- Sys.time()
dups1 <- classify_gene_pairs(
blast_list = diamond_intra[2],
annotation = pdata$annotation,
blast_inter = diamond_inter,
scheme = "extended",
collinearity_dir = here("products")
)
end1 <- Sys.time()
runtime1 <- end1 - start1Next, we will classify duplicates with DupGen_finder. For that, we will first export input data in the required format.
# Export data
## .blast files
b1 <- diamond_intra$arabidopsis.thaliana_arabidopsis.thaliana |>
mutate(
query = str_replace_all(query, "^ara_", ""),
db = str_replace_all(db, "^ara_", "")
)
b2 <- diamond_inter$arabidopsis.thaliana_amborella.trichopoda |>
mutate(
query = str_replace_all(query, "^ara_", ""),
db = str_replace_all(db, "^amb_", "")
)
write_tsv(b1, file = file.path(tempdir(), "Ath.blast"), col_names = FALSE)
write_tsv(b2, file = file.path(tempdir(), "Ath_Atr.blast"), col_names = FALSE)
## .gff files
gff1 <- pdata$annotation$arabidopsis.thaliana |>
as.data.frame() |>
mutate(gene = str_replace_all(gene, "^ara_", "")) |>
mutate(seqnames = str_replace_all(seqnames, "ara_", "ara-")) |>
dplyr::select(seqnames, gene, start, end)
gff2 <- pdata$annotation$amborella.trichopoda |>
as.data.frame() |>
mutate(gene = str_replace_all(gene, "^amb_", "")) |>
mutate(seqnames = str_replace_all(seqnames, "^amb_", "amb-")) |>
dplyr::select(seqnames, gene, start, end)
gff2 <- bind_rows(gff1, gff2)
write_tsv(gff1, file = file.path(tempdir(), "Ath.gff"), col_names = FALSE)
write_tsv(gff2, file = file.path(tempdir(), "Ath_Atr.gff"), col_names = FALSE)
# Classify duplicates with DupGen_finder
args = c(
"-i", tempdir(), "-t Ath -c Atr",
"-o", file.path(tempdir(), "results"),
"-e 1e-10"
)
start2 <- Sys.time()
system2("DupGen_finder.pl", args = args)
end2 <- Sys.time()
runtime2 <- end2 - start2Now, let’s compare both algorithms in terms of runtime and results.
# Load `DupGen_finder.pl` results
files <- c(
"Ath.wgd.pairs", "Ath.tandem.pairs", "Ath.proximal.pairs",
"Ath.transposed.pairs", "Ath.dispersed.pairs"
)
files <- file.path(tempdir(), "results", files)
names(files) <- c("SD", "TD", "PD", "TRD", "DD")
dups2 <- Reduce(rbind, lapply(seq_along(files), function(x) {
d <- read_tsv(files[x], show_col_types = FALSE) |>
mutate(type = names(files)[x]) |>
select(1, 3, type) |>
as.data.frame()
names(d)[c(1,2)] <- c("dup1", "dup2")
return(d)
}))
# Compare runtime
comp_runtime <- data.frame(doubletrouble = runtime1, DupGen_finder = runtime2)
# Compare number of gene pairs per category
comp_results <- inner_join(
count(dups1$arabidopsis.thaliana, type) |>
dplyr::rename(n_doubletrouble = n),
count(dups2, type) |>
dplyr::rename(n_DupGen_finder = n),
by = "type"
)
comp_results <- bind_rows(
comp_results,
data.frame(
type = "Total",
n_doubletrouble = sum(comp_results$n_doubletrouble),
n_DupGen_finder = sum(comp_results$n_DupGen_finder)
)
)list(runtime = comp_runtime, results = comp_results)$runtime
doubletrouble DupGen_finder
1 3.71474 secs 3.373682 secs
$results
type n_doubletrouble n_DupGen_finder
1 SD 4329 4355
2 TD 2075 2131
3 PD 2789 896
4 TRD 6703 5941
5 DD 31589 17834
6 Total 47485 31157Overall, results are similar, but there are important differences. In terms of runtime, there is no significant difference (this is the runtime of a single run, so there’s some stochasticity). In terms of results, the numbers of SD, TD, and TRD pairs are similar, but there are more pronounced differences for PD and DD pairs. In particular, the total number of paralogous pairs differs between algorithms. When we remove rows from the DIAMOND output based on the E-value threshold and remove self (e.g., “gene1-gene1”) and redundant hits (e.g., “gene1-gene2” and “gene2-gene1”), we get the following total number of pairs:
# Get total number of paralog pairs from DIAMOND output
evalue <- 1e-10
dmd <- diamond_intra$arabidopsis.thaliana_arabidopsis.thaliana
all_paralogs <- lapply(list(dmd), function(x) {
fpair <- x[x$evalue <= evalue, c(1, 2)]
fpair <- fpair[fpair[, 1] != fpair[, 2], ]
fpair <- fpair[!duplicated(t(apply(fpair, 1, sort))), ]
names(fpair) <- c("dup1", "dup2")
return(fpair)
})[[1]]nrow(all_paralogs)[1] 47485The total number of paralogous pairs in the DIAMOND output is the same as sum of classes for doubletrouble, but greater than the sum of classes for DupGen_finder, indicating that the latter probably does some additional (undocumented) filtering before classifying gene pairs, or it could be a bug.
Finally, since the number of PD pairs identified by doubletrouble is much greater than the number of PD pairs identified by DupGen_finder, we will explore a few of these PD pairs so check whether doubletrouble misclassified them.
pd1 <- dups1$arabidopsis.thaliana |>
filter(type == "PD")
pd2 <- dups2 |>
filter(type == "PD")
# Get all pairs in `pd1` and `pd2`
p1 <- t(apply(pd1[, 1:2], 1, sort)) |>
as.data.frame() |>
mutate(V1 = str_replace_all(V1, "^ara_", "")) |>
mutate(V2 = str_replace_all(V2, "^ara_", "")) |>
mutate(pair_string = str_c(V1, V2, sep = "-")) |>
pull(pair_string)
p2 <- t(apply(pd2[, 1:2], 1, sort)) |>
as.data.frame() |>
mutate(pair_string = str_c(V1, V2, sep = "-")) |>
pull(pair_string)
# Sample PD pairs from doubletrouble that are not PD pairs in DupGen_finder
examples <- p1[!p1 %in% p2] |> head(n = 5)
# Check whether they are PD pairs or not
ath_annot <- pdata$annotation$arabidopsis.thaliana
range_examples <- lapply(examples, function(x) {
g1 <- paste0("ara_", strsplit(x, "-")[[1]][1])
g2 <- paste0("ara_", strsplit(x, "-")[[1]][2])
ranges <- ath_annot[ath_annot$gene %in% c(g1, g2)]
return(ranges)
})range_examples[[1]]
GRanges object with 2 ranges and 1 metadata column:
seqnames ranges strand | gene
<Rle> <IRanges> <Rle> | <character>
128 ara_1 428650-430720 - | ara_AT1G02220
130 ara_1 437860-439559 - | ara_AT1G02250
-------
seqinfo: 7 sequences from an unspecified genome; no seqlengths
[[2]]
GRanges object with 2 ranges and 1 metadata column:
seqnames ranges strand | gene
<Rle> <IRanges> <Rle> | <character>
127 ara_1 427548-427811 - | ara_AT1G02210
130 ara_1 437860-439559 - | ara_AT1G02250
-------
seqinfo: 7 sequences from an unspecified genome; no seqlengths
[[3]]
GRanges object with 2 ranges and 1 metadata column:
seqnames ranges strand | gene
<Rle> <IRanges> <Rle> | <character>
17117 ara_4 656407-659178 - | ara_AT4G01520
17120 ara_4 673862-676445 - | ara_AT4G01550
-------
seqinfo: 7 sequences from an unspecified genome; no seqlengths
[[4]]
GRanges object with 2 ranges and 1 metadata column:
seqnames ranges strand | gene
<Rle> <IRanges> <Rle> | <character>
15329 ara_3 17882465-17884515 + | ara_AT3G48290
15332 ara_3 17887996-17889942 + | ara_AT3G48310
-------
seqinfo: 7 sequences from an unspecified genome; no seqlengths
[[5]]
GRanges object with 2 ranges and 1 metadata column:
seqnames ranges strand | gene
<Rle> <IRanges> <Rle> | <character>
25845 ara_5 21378702-21379744 + | ara_AT5G52720
25847 ara_5 21382482-21383432 + | ara_AT5G52740
-------
seqinfo: 7 sequences from an unspecified genome; no seqlengthsIn these first five examples of pairs that are classified as PD pairs by doubletrouble, but not by DupGen_finder, we can see based on the numbers in row names that they are indeed very close, separated by only a few genes (<10). Thus, they are true PD pairs that DupGen_finder failed to classify as PD pairs, or removed in their undocumented filtering (described above).
Session info
This document was created under the following conditions:
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14)
os Ubuntu 22.04.4 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
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tz Europe/Brussels
date 2024-07-29
pandoc 3.1.1 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
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[1] /home/faalm/R/x86_64-pc-linux-gnu-library/4.4
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library
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