inst/script/data_acquisition.md
In almeidasilvaf/syntenet: Inference And Analysis Of Synteny Networks
Data acquisition
Data in data/
annotation.rda
Here, we will get the annotation for the Ostreococcus sp. algae
species from Pico-PLAZA 3.0 and filter them to include only chromosomes
1, 2, and 3 for these species for package size issues.
library(GenomicRanges)
links <- c(
Olucimarinus = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/GFF/olu/annotation.selected_transcript.all_features.olu.gff3.gz",
OspRCC809 = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/GFF/orcc809/annotation.selected_transcript.all_features.orcc809.gff3.gz"
)
annotation <- lapply(links, rtracklayer::import)
annotation <- lapply(annotation, function(x) {
a <- x[x$type == "gene", ]
a$Parent <- NULL
a$name <- NULL
a$phase <- NULL
a$score <- NULL
a$source <- NULL
a$pid <- NULL
if("chr_1" %in% seqnames(a)) {
a <- keepSeqlevels(a, c("chr_1", "chr_2", "chr_3"),
pruning.mode = "coarse")
} else {
a <- keepSeqlevels(a, c("Chr_1", "Chr_2", "Chr_3"),
pruning.mode = "coarse")
}
return(a)
})
annotation <- GenomicRanges::GRangesList(annotation)
usethis::use_data(
annotation, compress = "xz", overwrite = TRUE
)
proteomes.rda
The protein sequences of the Ostreococcus sp. algae species (from
primary transcripts only) were obtained from Pico-PLAZA 3.0 and stored
in a list of AAStringSet objects. As above, only chromosomes 1, 2, and 3
were kept.
links <- c(
Olucimarinus = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/Fasta/proteome.selected_transcript.olu.fasta.gz",
OspRCC809 = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/Fasta/proteome.selected_transcript.orcc809.fasta.gz"
)
filt_genes <- unlist(lapply(annotation, function(x) return(x$gene_id)))
proteomes <- lapply(links, function(x) {
seq <- Biostrings::readAAStringSet(x)
names(seq) <- gsub("\\.[0-9] .*", "", names(seq))
seq <- seq[names(seq) %in% filt_genes]
return(seq)
})
usethis::use_data(
proteomes, compress = "xz", overwrite = TRUE
)
# For some reason, the dramatic reduction in gene number does not affect
# much the size of the proteomes.rda file. Let's solve it by exporting
# each sequence to a file, reading them, and saving again.
library(Biostrings)
data("proteomes")
dir <- tempdir()
seq1 <- proteomes$Olucimarinus
seq2 <- proteomes$OspRCC809
# Save .fa.gz files to tempdir
writeXStringSet(seq1, filepath = file.path(dir, "seq1.fa"), compress = "gzip")
writeXStringSet(seq2, filepath = file.path(dir, "seq2.fa"), compress = "gzip")
# Read files as AAStringSet objetcs and save them to a list
aa1 <- readAAStringSet(file.path(dir, "seq1.fa"))
aa2 <- readAAStringSet(file.path(dir, "seq2.fa"))
proteomes <- list(Olucimarinus = aa1, OspRCC809 = aa2)
usethis::use_data(
proteomes, compress = "xz", overwrite = TRUE
)
blast_list.rda
This object contains the blast list inferred from the example data, and
it is used in the vignette.
# Load data
data(proteomes)
data(annotation)
# Process data
pdata <- process_input(proteomes, annotation)
# Get blast list
blast_list <- run_diamond(seq = pdata$seq)
blast_list <- lapply(blast_list, function(x) return(x[x$perc_identity >= 50, ]))
# Save object
usethis::use_data(blast_list, compress = "xz", overwrite = TRUE)
network.rda
The synteny network for BUSCO genes was downloaded from this Dataverse
repository,
which is associated with the paper Network-based microsynteny analysis
identifies major differences and genomic outliers in mammalian and
angiosperm genomes. The
dataset was filtered to only include the following species:
- Vigna radiata
- Vigna angularis
- Phaseolus vulgaris
- Glycine max
- Cajanus cajan
- Trifolium pratense
- Medicago truncatula
- Arachis duranensis
- Lotus japonicus
- Lupinus angustifolius
- Cicer arietinum
- Prunus mume
- Prunus persica
- Pyrus x bretschneideri
- Malus domestica
- Rubus occidentalis
- Fragaris vesca
- Morus notabilis
- Ziziphus jujuba
- Jatropha curcas
- Manihot esculenta
- Ricinus communis
- Linum usitatissimum
- Populus trichocarpa
#----Download file--------------------------------------------------------------
net_file <- file.path(tempdir(), "busco_network.txt.gz")
download.file("https://dataverse.harvard.edu/api/access/datafile/:persistentId?persistentId=doi:10.7910/DVN/BDMA7A/7JWAFI", destfile = net_file)
#----Read file and filter it----------------------------------------------------
busco_network <- readr::read_delim(net_file, show_col_types = FALSE,
col_names = FALSE, delim = " ")
species <- unique(substr(c(busco_network$X1, busco_network$X2), 1, 5))
species <- unique(gsub("_.*", "", species))
species <- sort(unique(gsub("[0-9]*$", "", species)))
selected_species <- c(
"vra", "van", "pvu", "gma", "cca", "tpr", "mtr", "adu", "lja",
"Lang", "car", # Fabaceae
"pmu", "ppe", "pbr", "mdo", "roc", "fve",
"Mnot", "Zjuj", "hlu",
"jcu", "mes", "rco", "lus", "ptr"
)
s_patt <- paste0("^", selected_species)
s_patt <- paste0(s_patt, collapse = "|")
# Filtered network
busco_network <- busco_network[grepl(s_patt, busco_network$X1), ]
busco_network <- busco_network[grepl(s_patt, busco_network$X2), ]
# Clean names (i.e., separate species IDs by _)
not_underscore <- unique(substr(c(busco_network$X1, busco_network$X2), 1, 5))
not_underscore <- not_underscore[!grepl("_", not_underscore)]
busco_network$X1 <- gsub("Lang", "Lang_", busco_network$X1)
busco_network$X2 <- gsub("Lang", "Lang_", busco_network$X2)
busco_network$X1 <- gsub("Mnot", "Mnot_", busco_network$X1)
busco_network$X2 <- gsub("Mnot", "Mnot_", busco_network$X2)
busco_network$X1 <- gsub("Zjuj", "Zjuj_", busco_network$X1)
busco_network$X2 <- gsub("Zjuj", "Zjuj_", busco_network$X2)
network <- busco_network
network <- as.data.frame(network)
names(network) <- c("node1", "node2")
usethis::use_data(network, compress = "xz", overwrite = TRUE)
edges.rda
# Load data
data(proteomes)
data(annotation)
data(blast_list)
# Process input data
pdata <- process_input(proteomes, annotation)
# Infer network
edges <- infer_syntenet(blast_list, pdata$annotation)
usethis::use_data(edges, compress = "xz")
clusters.rda
The synteny network clusters for BUSCO genes were downloaded from this
Dataverse
repository,
which is associated with the paper Network-based microsynteny analysis
identifies major differences and genomic outliers in mammalian and
angiosperm genomes. The
dataset was filtered to only include the species mentioned above.
#----Download file--------------------------------------------------------------
cfile <- file.path(tempdir(), "clusters.txt.gz")
download.file("https://dataverse.harvard.edu/api/access/datafile/:persistentId?persistentId=doi:10.7910/DVN/BDMA7A/THEQD7", destfile = cfile)
#----Read file and filter it----------------------------------------------------
clusters <- readr::read_tsv(cfile, show_col_types = FALSE)
clusters <- clusters[grepl(s_patt, clusters$names), ]
names(clusters) <- c("Gene", "Cluster")
clusters$Gene <- gsub("Lang", "Lang_", clusters$Gene)
clusters$Gene <- gsub("Mnot", "Mnot_", clusters$Gene)
clusters$Gene <- gsub("Zjuj", "Zjuj_", clusters$Gene)
clusters <- as.data.frame(clusters)
usethis::use_data(clusters, compress = "xz", overwrite = TRUE)
angiosperm_phylogeny.rda
Phylogenomic profiles for the whole genome of 123 angiosperm species
were downloaded from this Dataverse
repository,
which is associated with the publication Whole-genome
microsynteny-based phylogeny of
angiosperms.
download.file(
url = "https://dataverse.harvard.edu/api/access/datafile/:persistentId?persistentId=doi:10.7910/DVN/7ZZWIH/HFZBGE",
destfile = file.path(tempdir(), "profiles.tar.gz"))
system2("tar", args = c("-zxvf", file))
#----Load file------------------------------------------------------------------
prof <- read.table("123genome_profiled_allsize", header = TRUE, sep = " ",
row.names = 1)
unlink("123genome_profiled_allsize")
#----Binarize and transpose profiles--------------------------------------------
transposed_profiles <- binarize_and_transpose(prof)
#----Infer microsynteny-based phylogeny-----------------------------------------
# Using Amborella trichopoda as an outgroup
phylo <- infer_microsynteny_phylogeny(transposed_profiles, outgroup = "atr")
angiosperm_phylogeny <- treeio::read.tree(phylo[10])
# Replace abbreviations with species names
angiosperm_phylogeny$tip.label
angiosperm_phylogeny$tip.label <- stringr::str_replace_all(
angiosperm_phylogeny$tip.label,
c("^pmu" = "Prunus mume",
"^ppe" = "Prunus persica",
"^pbr" = "Pyrus x bretschneideri",
"^Mald" = "Malus domestica",
"^Rchi" = "Rosa chinensis",
"^fve" = "Fragaria vesca",
"^roc" = "Rubus occidentalis",
"^Dryd" = "Dryas drummondii",
"^Pand" = "Parasponia andersonii",
"^Tori" = "Trema orientale",
"^Mnot" = "Morus notabilis",
"^Zjuj" = "Ziziphus jujuba",
"^cme" = "Cucumis melo",
"^csa" = "Cucumis sativus",
"^cla" = "Citrullus lanatus",
"^Cuma" = "Cucurbita maxima",
"^Begf" = "Begonia fuchsioides",
"^Datg" = "Datisca glomerata",
"^van" = "Vigna angularis",
"^vra" = "Vigna radiata",
"^pvu" = "Phaseolus vulgaris",
"^gma" = "Glycine max",
"^cca" = "Cajanus cajan",
"^mtr" = "Medicago truncatula",
"^tpr" = "Trifolium pratense",
"^car" = "Cicer arietinum",
"^lja" = "Lotus japonicus",
"^Lang" = "Lupinus angustifolius",
"^Anan" = "Ammopiptanthus nanus",
"^adu" = "Arachis duranensis",
"^Cgla" = "Casuarina pendula",
"^Bpen" = "Betula pendula",
"^Cill" = "Carya illinoinensis",
"^Qrob" = "Quercus robur",
"^bnp" = "Brassica napus",
"^bol" = "Brassica oleracea",
"^bra" = "Brassica rapa",
"^spa" = "Schrenkiella parvula",
"^tsa" = "Thellungiella salsuginea",
"^thh" = "Thellungiella halophila",
"^ath" = "Arabidopsis thaliana",
"^Alyr" = "Arabidopsis lyrata",
"^Csat" = "Camelina sativa",
"^cru" = "Capsella rubella",
"^Bost" = "Boechera stricta",
"^Lmey" = "Lepidium meyenii",
"^Alp" = "Arabis alpina",
"^aar" = "Aethionema arabicum",
"^tha" = "Tarenaya hassleriana",
"^cgy" = "Cleome gynandra",
"^cpa" = "Carica papaya",
"^Ghir" = "Gossypium hirsutum",
"^Goba" = "Gossypium barbadense",
"^gra" = "Gossypium raimondii",
"^Dzib" = "Durio zibethinus",
"^tca" = "Theobroma cacao",
"^csi" = "Citrus sinensis",
"^Cmax" = "Citrus maxima",
"^Xsor" = "Xanthoceras sorbifolium",
"^rco" = "Ricinus communis",
"^mes" = "Manihot esculenta",
"^ptr" = "Populus trichocarpa",
"^lus" = "Linum usitatissimum",
"^Pgra" = "Punica granatum",
"^egr" = "Eucalyptus grandis",
"^Cach" = "Capsicum chinense",
"^Caba" = "Capsicum baccatum",
"^can" = "Capsicum annuum",
"^sly" = "Solanum lycopersicum",
"^spe" = "Solanum pennellii",
"^stu" = "Solanum tuberosum",
"^pax" = "Petunia axillaris",
"^Cuca" = "Cuscuta campestris",
"^Inil" = "Ipomoea nil",
"^coc" = "Coffea canephora",
"^mgu" = "Mimulus guttatus",
"^sin" = "Sesamum indicum",
"^Oeur" = "Olea europaea",
"^HanX" = "Helianthus annuus",
"^Lsat" = "Lactuca sativa",
"^dca" = "Daucus carota",
"^Aeri" = "Actinidia eriantha",
"^ach" = "Actinidia chinensis",
"^bvu" = "Beta vulgaris",
"^Cqui" = "Chenopodium quinoa",
"^Ahyp" = "Amaranthus hypochondriacus",
"^Kalf" = "Kalanchoe fedtschenkoi",
"^Mole" = "Malania oleifera",
"^vvi" = "Vitis vinifera",
"^nnu" = "Nelumbo nucifera",
"^Mcor" = "Macleaya cordata",
"^Psom" = "Papaver somniferum",
"^Aqco" = "Aquilegia coerulea",
"^Sevi" = "Setaria viridis",
"^sit" = "Setaria italica",
"^Ecru" = "Echinochloa crus-galli",
"^Sacc" = "Saccharum officinarum",
"^sbi" = "Sorghum bicolor",
"^Zmay" = "Zea mays",
"^oth" = "Oropetium thomaeum",
"^osa" = "Oryza sativa",
"^ogl" = "Oryza glaberrima",
"^oru" = "Oryza rufipogon",
"^Opun" = "Oryza punctata",
"^lpe" = "Leersia perrieri",
"^HORV" = "Hordeum vulgare",
"^Trdc" = "Triticum turgidum",
"^bdi" = "Brachypodium distachyon",
"^aco" = "Ananas comosus",
"^mac" = "Musa acuminata",
"^Pdac" = "Phoenix dactylifera",
"^egu" = "Elaeis guineensis",
"^Ashe" = "Apostasia shenzhenica",
"^peq" = "Phalaenopsis equestris",
"^Aoff" = "Asparagus officinalis",
"^Xvis" = "Xerophyta viscosa",
"^zom" = "Zostera marina",
"^spo" = "Spirodela polyrhiza",
"^CKAN" = "Cinnamomum kanehirae",
"^Peam" = "Persea americana",
"^Lchi" = "Liriodendron chinense",
"^Nymp" = "Nymphaea colorata",
"^atr" = "Amborella trichopoda"
)
)
usethis::use_data(angiosperm_phylogeny, compress = "xz", overwrite = TRUE)
scerevisiae_annot.rda + scerevisiae_diamond.rda
These files contain a processed annotation list (as returned by
process_input()) and a list of intragenome DIAMOND comparisons.
library(doubletrouble)
# Load data from {doubletrouble}
data("yeast_annot")
data("yeast_seq")
data("diamond_intra")
pdata <- process_input(yeast_seq, yeast_annot)
# Create objects
scerevisiae_annot <- pdata$annotation[1]
scerevisiae_diamond <- list(
Scerevisiae_Scerevisiae = diamond_intra$Scerevisiae_Scerevisiae |>
dplyr::filter(evalue <= 1e-10)
)
# Save objects
usethis::use_data(scerevisiae_annot, compress = "xz")
usethis::use_data(scerevisiae_diamond, compress = "xz", overwrite = TRUE)
Data in inst/extdata
Scerevisiae.collinearity
This file contains the intragenome synteny blocks for S. cerevisiae.
# Load data
data(scerevisiae_annot)
data(scerevisiae_diamond)
# Detect intragenome synteny
intra_syn <- intraspecies_synteny(
scerevisiae_diamond, scerevisiae_annot
)
# Move file to inst/extdata
fs::file_move(
path = intra_syn,
new_path = here::here("inst", "extdata")
)
data(proteomes)
data(annotation)
processed <- process_input(proteomes, annotation)
if(diamond_is_installed()) {
blast_list <- run_diamond(processed$seq, ... = "--sensitive")
}
net <- infer_syntenet(blast_list, processed$annotation)
# Move file
olu_col <- file.path(tempdir(), "intraspecies_synteny", "Olu.collinearity")
out <- here::here("inst", "extdata")
fs::file_move(path = olu_col, new_path = out)
sequences/
This directory contains FASTA files that correspond to a subset of the
sequences in the example data set proteomes.
data(proteomes)
# Create a list of vector with IDs of genes to subset - here, first 100 genes
genes_subset <- lapply(proteomes, function(x) names(x)[1:100])
# Path to sequences/ directory
seq_dir <- here::here("inst", "extdata", "sequences")
# Export sequences as FASTA files
Biostrings::writeXStringSet(
proteomes$Olucimarinus[genes_subset$Olucimarinus],
filepath = file.path(seq_dir, "Olucimarinus.fa.gz"),
compress = TRUE
)
Biostrings::writeXStringSet(
proteomes$OspRCC809[genes_subset$OspRCC809],
filepath = file.path(seq_dir, "OspRCC809.fa.gz"),
compress = TRUE
)
annotation/
This directory contains GFF3 files that correspond to a subset of the
ranges in the example data set annotation.
data(annotation)
# Path to annotation/ directory
annot_dir <- here::here("inst", "extdata", "annotation")
# Export ranges as GFF3 files
rtracklayer::export(
annotation$Olucimarinus[annotation$Olucimarinus$gene_id %in%
genes_subset$Olucimarinus, ],
con = file.path(annot_dir, "Olucimarinus.gff3.gz")
)
rtracklayer::export(
annotation$OspRCC809[annotation$OspRCC809$gene_id %in%
genes_subset$OspRCC809, ],
con = file.path(annot_dir, "OspRCC809.gff3.gz")
)
RefSeq_parsing_example/
This directory contains a subset of 10 genes from the Alosa alosa
genome (FASTA and GFF3 files) to demonstrate how to parse RefSeq data.
Gene annotation (GFF3) and whole-genome protein sequences (FASTA) were
downloaded from NCBI’s Data
Hub and
processed using the code below.
- Aalosa.gff3.gz
# Bash
head -n 474 genomic.gff | awk '$3 == "gene" || $3 == "CDS" || $1 ~ /^#/' > Aalosa.gff3
gzip Aalosa.gff3
- Aalosa.fa
# Read files
ranges <- rtracklayer::import(here::here(
"inst", "extdata", "RefSeq_parsing_example", "Aalosa.gff3.gz"
))
sequence <- Biostrings::readAAStringSet(
"~/Downloads/protein.faa"
)
# Get proteins to extract from FASTA file
proteins <- unique(ranges[ranges$type == "CDS"]$Name)
# Subset sequences
pmatch <- paste0(proteins, collapse = "|")
final_seqs <- sequence[grep(pmatch, names(sequence))]
# Write to file
Biostrings::writeXStringSet(
final_seqs,
file = here::here(
"inst", "extdata", "RefSeq_parsing_example", "Aalosa.fa.gz"
),
compress = TRUE
)
almeidasilvaf/syntenet documentation built on Dec. 23, 2024, 6:26 a.m.
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Data acquisition
Data in data/
annotation.rda
Here, we will get the annotation for the Ostreococcus sp. algae species from Pico-PLAZA 3.0 and filter them to include only chromosomes 1, 2, and 3 for these species for package size issues.
library(GenomicRanges)
links <- c(
Olucimarinus = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/GFF/olu/annotation.selected_transcript.all_features.olu.gff3.gz",
OspRCC809 = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/GFF/orcc809/annotation.selected_transcript.all_features.orcc809.gff3.gz"
)
annotation <- lapply(links, rtracklayer::import)
annotation <- lapply(annotation, function(x) {
a <- x[x$type == "gene", ]
a$Parent <- NULL
a$name <- NULL
a$phase <- NULL
a$score <- NULL
a$source <- NULL
a$pid <- NULL
if("chr_1" %in% seqnames(a)) {
a <- keepSeqlevels(a, c("chr_1", "chr_2", "chr_3"),
pruning.mode = "coarse")
} else {
a <- keepSeqlevels(a, c("Chr_1", "Chr_2", "Chr_3"),
pruning.mode = "coarse")
}
return(a)
})
annotation <- GenomicRanges::GRangesList(annotation)
usethis::use_data(
annotation, compress = "xz", overwrite = TRUE
)
proteomes.rda
The protein sequences of the Ostreococcus sp. algae species (from primary transcripts only) were obtained from Pico-PLAZA 3.0 and stored in a list of AAStringSet objects. As above, only chromosomes 1, 2, and 3 were kept.
links <- c(
Olucimarinus = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/Fasta/proteome.selected_transcript.olu.fasta.gz",
OspRCC809 = "ftp://ftp.psb.ugent.be/pub/plaza/plaza_pico_03/Fasta/proteome.selected_transcript.orcc809.fasta.gz"
)
filt_genes <- unlist(lapply(annotation, function(x) return(x$gene_id)))
proteomes <- lapply(links, function(x) {
seq <- Biostrings::readAAStringSet(x)
names(seq) <- gsub("\\.[0-9] .*", "", names(seq))
seq <- seq[names(seq) %in% filt_genes]
return(seq)
})
usethis::use_data(
proteomes, compress = "xz", overwrite = TRUE
)
# For some reason, the dramatic reduction in gene number does not affect
# much the size of the proteomes.rda file. Let's solve it by exporting
# each sequence to a file, reading them, and saving again.
library(Biostrings)
data("proteomes")
dir <- tempdir()
seq1 <- proteomes$Olucimarinus
seq2 <- proteomes$OspRCC809
# Save .fa.gz files to tempdir
writeXStringSet(seq1, filepath = file.path(dir, "seq1.fa"), compress = "gzip")
writeXStringSet(seq2, filepath = file.path(dir, "seq2.fa"), compress = "gzip")
# Read files as AAStringSet objetcs and save them to a list
aa1 <- readAAStringSet(file.path(dir, "seq1.fa"))
aa2 <- readAAStringSet(file.path(dir, "seq2.fa"))
proteomes <- list(Olucimarinus = aa1, OspRCC809 = aa2)
usethis::use_data(
proteomes, compress = "xz", overwrite = TRUE
)
blast_list.rda
This object contains the blast list inferred from the example data, and it is used in the vignette.
# Load data
data(proteomes)
data(annotation)
# Process data
pdata <- process_input(proteomes, annotation)
# Get blast list
blast_list <- run_diamond(seq = pdata$seq)
blast_list <- lapply(blast_list, function(x) return(x[x$perc_identity >= 50, ]))
# Save object
usethis::use_data(blast_list, compress = "xz", overwrite = TRUE)
network.rda
The synteny network for BUSCO genes was downloaded from this Dataverse repository, which is associated with the paper Network-based microsynteny analysis identifies major differences and genomic outliers in mammalian and angiosperm genomes. The dataset was filtered to only include the following species:
- Vigna radiata
- Vigna angularis
- Phaseolus vulgaris
- Glycine max
- Cajanus cajan
- Trifolium pratense
- Medicago truncatula
- Arachis duranensis
- Lotus japonicus
- Lupinus angustifolius
- Cicer arietinum
- Prunus mume
- Prunus persica
- Pyrus x bretschneideri
- Malus domestica
- Rubus occidentalis
- Fragaris vesca
- Morus notabilis
- Ziziphus jujuba
- Jatropha curcas
- Manihot esculenta
- Ricinus communis
- Linum usitatissimum
- Populus trichocarpa
#----Download file--------------------------------------------------------------
net_file <- file.path(tempdir(), "busco_network.txt.gz")
download.file("https://dataverse.harvard.edu/api/access/datafile/:persistentId?persistentId=doi:10.7910/DVN/BDMA7A/7JWAFI", destfile = net_file)
#----Read file and filter it----------------------------------------------------
busco_network <- readr::read_delim(net_file, show_col_types = FALSE,
col_names = FALSE, delim = " ")
species <- unique(substr(c(busco_network$X1, busco_network$X2), 1, 5))
species <- unique(gsub("_.*", "", species))
species <- sort(unique(gsub("[0-9]*$", "", species)))
selected_species <- c(
"vra", "van", "pvu", "gma", "cca", "tpr", "mtr", "adu", "lja",
"Lang", "car", # Fabaceae
"pmu", "ppe", "pbr", "mdo", "roc", "fve",
"Mnot", "Zjuj", "hlu",
"jcu", "mes", "rco", "lus", "ptr"
)
s_patt <- paste0("^", selected_species)
s_patt <- paste0(s_patt, collapse = "|")
# Filtered network
busco_network <- busco_network[grepl(s_patt, busco_network$X1), ]
busco_network <- busco_network[grepl(s_patt, busco_network$X2), ]
# Clean names (i.e., separate species IDs by _)
not_underscore <- unique(substr(c(busco_network$X1, busco_network$X2), 1, 5))
not_underscore <- not_underscore[!grepl("_", not_underscore)]
busco_network$X1 <- gsub("Lang", "Lang_", busco_network$X1)
busco_network$X2 <- gsub("Lang", "Lang_", busco_network$X2)
busco_network$X1 <- gsub("Mnot", "Mnot_", busco_network$X1)
busco_network$X2 <- gsub("Mnot", "Mnot_", busco_network$X2)
busco_network$X1 <- gsub("Zjuj", "Zjuj_", busco_network$X1)
busco_network$X2 <- gsub("Zjuj", "Zjuj_", busco_network$X2)
network <- busco_network
network <- as.data.frame(network)
names(network) <- c("node1", "node2")
usethis::use_data(network, compress = "xz", overwrite = TRUE)
edges.rda
# Load data
data(proteomes)
data(annotation)
data(blast_list)
# Process input data
pdata <- process_input(proteomes, annotation)
# Infer network
edges <- infer_syntenet(blast_list, pdata$annotation)
usethis::use_data(edges, compress = "xz")
clusters.rda
The synteny network clusters for BUSCO genes were downloaded from this Dataverse repository, which is associated with the paper Network-based microsynteny analysis identifies major differences and genomic outliers in mammalian and angiosperm genomes. The dataset was filtered to only include the species mentioned above.
#----Download file--------------------------------------------------------------
cfile <- file.path(tempdir(), "clusters.txt.gz")
download.file("https://dataverse.harvard.edu/api/access/datafile/:persistentId?persistentId=doi:10.7910/DVN/BDMA7A/THEQD7", destfile = cfile)
#----Read file and filter it----------------------------------------------------
clusters <- readr::read_tsv(cfile, show_col_types = FALSE)
clusters <- clusters[grepl(s_patt, clusters$names), ]
names(clusters) <- c("Gene", "Cluster")
clusters$Gene <- gsub("Lang", "Lang_", clusters$Gene)
clusters$Gene <- gsub("Mnot", "Mnot_", clusters$Gene)
clusters$Gene <- gsub("Zjuj", "Zjuj_", clusters$Gene)
clusters <- as.data.frame(clusters)
usethis::use_data(clusters, compress = "xz", overwrite = TRUE)
angiosperm_phylogeny.rda
Phylogenomic profiles for the whole genome of 123 angiosperm species were downloaded from this Dataverse repository, which is associated with the publication Whole-genome microsynteny-based phylogeny of angiosperms.
download.file(
url = "https://dataverse.harvard.edu/api/access/datafile/:persistentId?persistentId=doi:10.7910/DVN/7ZZWIH/HFZBGE",
destfile = file.path(tempdir(), "profiles.tar.gz"))
system2("tar", args = c("-zxvf", file))
#----Load file------------------------------------------------------------------
prof <- read.table("123genome_profiled_allsize", header = TRUE, sep = " ",
row.names = 1)
unlink("123genome_profiled_allsize")
#----Binarize and transpose profiles--------------------------------------------
transposed_profiles <- binarize_and_transpose(prof)
#----Infer microsynteny-based phylogeny-----------------------------------------
# Using Amborella trichopoda as an outgroup
phylo <- infer_microsynteny_phylogeny(transposed_profiles, outgroup = "atr")
angiosperm_phylogeny <- treeio::read.tree(phylo[10])
# Replace abbreviations with species names
angiosperm_phylogeny$tip.label
angiosperm_phylogeny$tip.label <- stringr::str_replace_all(
angiosperm_phylogeny$tip.label,
c("^pmu" = "Prunus mume",
"^ppe" = "Prunus persica",
"^pbr" = "Pyrus x bretschneideri",
"^Mald" = "Malus domestica",
"^Rchi" = "Rosa chinensis",
"^fve" = "Fragaria vesca",
"^roc" = "Rubus occidentalis",
"^Dryd" = "Dryas drummondii",
"^Pand" = "Parasponia andersonii",
"^Tori" = "Trema orientale",
"^Mnot" = "Morus notabilis",
"^Zjuj" = "Ziziphus jujuba",
"^cme" = "Cucumis melo",
"^csa" = "Cucumis sativus",
"^cla" = "Citrullus lanatus",
"^Cuma" = "Cucurbita maxima",
"^Begf" = "Begonia fuchsioides",
"^Datg" = "Datisca glomerata",
"^van" = "Vigna angularis",
"^vra" = "Vigna radiata",
"^pvu" = "Phaseolus vulgaris",
"^gma" = "Glycine max",
"^cca" = "Cajanus cajan",
"^mtr" = "Medicago truncatula",
"^tpr" = "Trifolium pratense",
"^car" = "Cicer arietinum",
"^lja" = "Lotus japonicus",
"^Lang" = "Lupinus angustifolius",
"^Anan" = "Ammopiptanthus nanus",
"^adu" = "Arachis duranensis",
"^Cgla" = "Casuarina pendula",
"^Bpen" = "Betula pendula",
"^Cill" = "Carya illinoinensis",
"^Qrob" = "Quercus robur",
"^bnp" = "Brassica napus",
"^bol" = "Brassica oleracea",
"^bra" = "Brassica rapa",
"^spa" = "Schrenkiella parvula",
"^tsa" = "Thellungiella salsuginea",
"^thh" = "Thellungiella halophila",
"^ath" = "Arabidopsis thaliana",
"^Alyr" = "Arabidopsis lyrata",
"^Csat" = "Camelina sativa",
"^cru" = "Capsella rubella",
"^Bost" = "Boechera stricta",
"^Lmey" = "Lepidium meyenii",
"^Alp" = "Arabis alpina",
"^aar" = "Aethionema arabicum",
"^tha" = "Tarenaya hassleriana",
"^cgy" = "Cleome gynandra",
"^cpa" = "Carica papaya",
"^Ghir" = "Gossypium hirsutum",
"^Goba" = "Gossypium barbadense",
"^gra" = "Gossypium raimondii",
"^Dzib" = "Durio zibethinus",
"^tca" = "Theobroma cacao",
"^csi" = "Citrus sinensis",
"^Cmax" = "Citrus maxima",
"^Xsor" = "Xanthoceras sorbifolium",
"^rco" = "Ricinus communis",
"^mes" = "Manihot esculenta",
"^ptr" = "Populus trichocarpa",
"^lus" = "Linum usitatissimum",
"^Pgra" = "Punica granatum",
"^egr" = "Eucalyptus grandis",
"^Cach" = "Capsicum chinense",
"^Caba" = "Capsicum baccatum",
"^can" = "Capsicum annuum",
"^sly" = "Solanum lycopersicum",
"^spe" = "Solanum pennellii",
"^stu" = "Solanum tuberosum",
"^pax" = "Petunia axillaris",
"^Cuca" = "Cuscuta campestris",
"^Inil" = "Ipomoea nil",
"^coc" = "Coffea canephora",
"^mgu" = "Mimulus guttatus",
"^sin" = "Sesamum indicum",
"^Oeur" = "Olea europaea",
"^HanX" = "Helianthus annuus",
"^Lsat" = "Lactuca sativa",
"^dca" = "Daucus carota",
"^Aeri" = "Actinidia eriantha",
"^ach" = "Actinidia chinensis",
"^bvu" = "Beta vulgaris",
"^Cqui" = "Chenopodium quinoa",
"^Ahyp" = "Amaranthus hypochondriacus",
"^Kalf" = "Kalanchoe fedtschenkoi",
"^Mole" = "Malania oleifera",
"^vvi" = "Vitis vinifera",
"^nnu" = "Nelumbo nucifera",
"^Mcor" = "Macleaya cordata",
"^Psom" = "Papaver somniferum",
"^Aqco" = "Aquilegia coerulea",
"^Sevi" = "Setaria viridis",
"^sit" = "Setaria italica",
"^Ecru" = "Echinochloa crus-galli",
"^Sacc" = "Saccharum officinarum",
"^sbi" = "Sorghum bicolor",
"^Zmay" = "Zea mays",
"^oth" = "Oropetium thomaeum",
"^osa" = "Oryza sativa",
"^ogl" = "Oryza glaberrima",
"^oru" = "Oryza rufipogon",
"^Opun" = "Oryza punctata",
"^lpe" = "Leersia perrieri",
"^HORV" = "Hordeum vulgare",
"^Trdc" = "Triticum turgidum",
"^bdi" = "Brachypodium distachyon",
"^aco" = "Ananas comosus",
"^mac" = "Musa acuminata",
"^Pdac" = "Phoenix dactylifera",
"^egu" = "Elaeis guineensis",
"^Ashe" = "Apostasia shenzhenica",
"^peq" = "Phalaenopsis equestris",
"^Aoff" = "Asparagus officinalis",
"^Xvis" = "Xerophyta viscosa",
"^zom" = "Zostera marina",
"^spo" = "Spirodela polyrhiza",
"^CKAN" = "Cinnamomum kanehirae",
"^Peam" = "Persea americana",
"^Lchi" = "Liriodendron chinense",
"^Nymp" = "Nymphaea colorata",
"^atr" = "Amborella trichopoda"
)
)
usethis::use_data(angiosperm_phylogeny, compress = "xz", overwrite = TRUE)
scerevisiae_annot.rda + scerevisiae_diamond.rda
These files contain a processed annotation list (as returned by
process_input()) and a list of intragenome DIAMOND comparisons.
library(doubletrouble)
# Load data from {doubletrouble}
data("yeast_annot")
data("yeast_seq")
data("diamond_intra")
pdata <- process_input(yeast_seq, yeast_annot)
# Create objects
scerevisiae_annot <- pdata$annotation[1]
scerevisiae_diamond <- list(
Scerevisiae_Scerevisiae = diamond_intra$Scerevisiae_Scerevisiae |>
dplyr::filter(evalue <= 1e-10)
)
# Save objects
usethis::use_data(scerevisiae_annot, compress = "xz")
usethis::use_data(scerevisiae_diamond, compress = "xz", overwrite = TRUE)
Data in inst/extdata
Scerevisiae.collinearity
This file contains the intragenome synteny blocks for S. cerevisiae.
# Load data
data(scerevisiae_annot)
data(scerevisiae_diamond)
# Detect intragenome synteny
intra_syn <- intraspecies_synteny(
scerevisiae_diamond, scerevisiae_annot
)
# Move file to inst/extdata
fs::file_move(
path = intra_syn,
new_path = here::here("inst", "extdata")
)
data(proteomes)
data(annotation)
processed <- process_input(proteomes, annotation)
if(diamond_is_installed()) {
blast_list <- run_diamond(processed$seq, ... = "--sensitive")
}
net <- infer_syntenet(blast_list, processed$annotation)
# Move file
olu_col <- file.path(tempdir(), "intraspecies_synteny", "Olu.collinearity")
out <- here::here("inst", "extdata")
fs::file_move(path = olu_col, new_path = out)
sequences/
This directory contains FASTA files that correspond to a subset of the
sequences in the example data set proteomes.
data(proteomes)
# Create a list of vector with IDs of genes to subset - here, first 100 genes
genes_subset <- lapply(proteomes, function(x) names(x)[1:100])
# Path to sequences/ directory
seq_dir <- here::here("inst", "extdata", "sequences")
# Export sequences as FASTA files
Biostrings::writeXStringSet(
proteomes$Olucimarinus[genes_subset$Olucimarinus],
filepath = file.path(seq_dir, "Olucimarinus.fa.gz"),
compress = TRUE
)
Biostrings::writeXStringSet(
proteomes$OspRCC809[genes_subset$OspRCC809],
filepath = file.path(seq_dir, "OspRCC809.fa.gz"),
compress = TRUE
)
annotation/
This directory contains GFF3 files that correspond to a subset of the
ranges in the example data set annotation.
data(annotation)
# Path to annotation/ directory
annot_dir <- here::here("inst", "extdata", "annotation")
# Export ranges as GFF3 files
rtracklayer::export(
annotation$Olucimarinus[annotation$Olucimarinus$gene_id %in%
genes_subset$Olucimarinus, ],
con = file.path(annot_dir, "Olucimarinus.gff3.gz")
)
rtracklayer::export(
annotation$OspRCC809[annotation$OspRCC809$gene_id %in%
genes_subset$OspRCC809, ],
con = file.path(annot_dir, "OspRCC809.gff3.gz")
)
RefSeq_parsing_example/
This directory contains a subset of 10 genes from the Alosa alosa genome (FASTA and GFF3 files) to demonstrate how to parse RefSeq data.
Gene annotation (GFF3) and whole-genome protein sequences (FASTA) were downloaded from NCBI’s Data Hub and processed using the code below.
- Aalosa.gff3.gz
# Bash
head -n 474 genomic.gff | awk '$3 == "gene" || $3 == "CDS" || $1 ~ /^#/' > Aalosa.gff3
gzip Aalosa.gff3
- Aalosa.fa
# Read files
ranges <- rtracklayer::import(here::here(
"inst", "extdata", "RefSeq_parsing_example", "Aalosa.gff3.gz"
))
sequence <- Biostrings::readAAStringSet(
"~/Downloads/protein.faa"
)
# Get proteins to extract from FASTA file
proteins <- unique(ranges[ranges$type == "CDS"]$Name)
# Subset sequences
pmatch <- paste0(proteins, collapse = "|")
final_seqs <- sequence[grep(pmatch, names(sequence))]
# Write to file
Biostrings::writeXStringSet(
final_seqs,
file = here::here(
"inst", "extdata", "RefSeq_parsing_example", "Aalosa.fa.gz"
),
compress = TRUE
)
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