Orthology inference: How are genes related across Schistocerca species?
Maeva Techer
2024-11-08
Last updated: 2024-11-08
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Knit directory:
locust-comparative-genomics/
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Comparative genomics and ortholog genes with OrthoFinder
We wanted to compare the six genomes of Schistocerca to get insights on gene evolution and relationships regarding their numbers, content, function and location. In order to achieve this, we need to identify groups of orthologous genes among our species of interest, considering at least one outgroup.
Orthologs are genes from different species that originated from a single ancestral gene and evolved through speciation events. However since genes can be lost or duplicated during evolution, some genes may not have exactly one orthologue in the genome of another species. Here we will separate the 1:1 orthologs to the the concept of orthogroups. Orthogroups can contain 1:1 orthologs but also several several orthologs from different species, including paralogs and one-to-many orthologs. Paralogs are genes within the same species that have originated from a shared ancestral genes but have diverged over time following gene duplication events.
IMPORTANT
We used OrthoFinder to identify the orthogroups
using amino acid sequences from the longest isoform of each gene. For
this part, refers to the AMAZINGLY WELL CURATED
pipeline FormicidaeMolecularEvolution
by Megan Barkdull
(PhD Student at Cornell University). We describe below the modifications
made and mostly copied the workflow from her Github.
1. Downloading data
We created the file “input-XXX.txt” as described to automatically download the coding sequence, protein sequence, GFF annotation data for each of our six Schistocerca species and annotated outgroups. The outgroups were choosen as close phylogenetic species with a RefSeq genome showing 1) a chromosome length, 2) associated with an annotation, 3) large size and 4) hybrid techniques used for assembly (last NCBI search: 28 April 2024).
Our target species for this study:
* The desert locust Schistocerca
gregaria
* The South American locust Schistocerca
cancellata
* The Central American locust Schistocerca
piceifrons
* The American grasshopper Schistocerca
americana
* The bird grasshopper Schistocerca
serialis cubense
* The vagrant locust Schistocerca
nitens
Our outgroup species:
* The two-spotted cricket Gryllus
bimaculatus. Reason: Orthoptera close relative available
with chromosome length.
* The Long Cercus Field Cricket Gryllus
longicercus. Reason: Orthoptera close relative available
with chromosome length.
* The Pacific beetle cockroach Diploptera
punctata. Reason: Orthoptera close relative available
with chromosome length.
* The walking stick Timema
podura. Reason: Polyneoptera close relative available
with chromosome length. * The European stick insect Bacillus
rossius redtenbacheri. Reason: Polyneoptera close
relative available with chromosome length.
* The Lord Howe Island stick insect Dryococelus
australis. Reason: Polyneoptera close relative available
with chromosome length.
* The American cockroach Periplaneta
americana. Reason: Polyneoptera close relative available
with chromosome length.
* The drywood termite Cryptotermes
secundus. Reason: Eusocial insect with caste
determination phenotypic plasticity.
* The Western honey bee Apis
mellifera. Reason: Model organsim and eusocial insect
with caste determination phenotypic plasticity.
* The red fire ant Solenopsis
invicta. Reason: Eusocial insect with caste
determination phenotypic plasticity.
* The pea aphid Acyrthosiphon
pisum. Reason: Insect with wing phenotypic plasticity in
response to density and environment.
* The fruit fly Drosophila
melanogaster. Reason: Model organism.
Status of Genome Data Viewer on NCBI for selecting our
outgroups
NB: During our search, we identified very valuable genomes but for which no annotation was available (e.g., CAU_Lmig_1.0 for Locusta migratoria , iqMecThal1.2 Meconema thalassinum). However upon request to NCBI team, we were informed that the Locusta genome has not been annotated because of the presence of large bacterial contamination and Meconema does not possess RNA SRAs to help with the annotation.
Using the FTP NCBI link associated with each RefSeq, we created the input file.
./scripts/DataDownload ./scripts/inputurls_18polyneoptera_Nov2024.txt On the TAMU Grace cluster, when you download do not run it on a node
as it does not work. Run it from the login node or transfer the files
already preloaded. This will creates a folder ./1_RawData
with 3 files for each species.
In summary, below are the details of each RefSeq annotation that we will be using as input for OrthoFinder. It will be important to check that after steps 2-4, the number of protein coding genes OrthoFinder is similar to the initial input.
| Species | Order | Status | Genome_Size | Annotated_Genes | Protein_Coding |
|---|---|---|---|---|---|
| Schistocerca gregaria | Orthoptera | Locust | 8.7 Gb | 99467 | 19799 |
| Schistocerca cancellata | Orthoptera | Locust | 8.5 Gb | 103533 | 16907 |
| Schistocerca piceifrons | Orthoptera | Locust | 8.7 Gb | 96806 | 17490 |
| Schistocerca americana | Orthoptera | Grasshopper | 9.0 Gb | 81274 | 17662 |
| Schistocerca serialis cubense | Orthoptera | Grasshopper | 9.1 Gb | 75810 | 17237 |
| Schistocerca nitens | Orthoptera | Grasshopper | 8.8 Gb | 72560 | 17500 |
| Gryllus bimaculatus | Orthoptera | Outgroup | 1.7 Gb | 17871 | NA |
| Gryllus longicercus | Orthoptera | Outgroup | 1.9 Gb | 14831 | NA |
| Diploptera punctata | Blattodea | Outgroup | 1.0 Gb | 15413 | 13170 |
| Timema podura | Phasmatodea | Outgroup | 1.1 Gb | 16529 | NA |
| Bacillus rossius redtenbacheri | Phasmatodea | Outgroup | 1.6 Gb | 19298 | 14448 |
| Dryococelus australis | Phasmatodea | Outgroup | 3.4 Gb | 33793 | NA |
| Periplaneta americana | Blattodea | Outgroup | 3.1 Gb | 28416 | 28414 |
| Cryptotermes secundus | Blattodea | Outgroup | 3.1 Gb | 27047 | NA |
| Apis mellifera | Hymenoptera | Outgroup | 225.2 Mb | 12398 | 9935 |
| Solenopsis invicta | Hymenoptera | Outgroup | 378.1 Mb | 16996 | 14790 |
| Acyrthosiphon pisum | Hemiptera | Outgroup | 533.6 Mb | 20307 | 17681 |
| Drosophila melanogaster | Diptera | Outgroup | 143.7 Mb | 17872 | 13962 |
2. Selecting longest isoforms
Here again, we will follow the pipeline except that to run it on Grace cluster we will make small modifications. The idea is that we will use only the single longest isoform of each gene to ease the orthology analysis. While this might not always be the principal isoform of said gene, we will apply the same bias to all genes the same way.
To run R without bothering other users, we will claim one interactive node to make sure we can proactively update the package if there are some issues:
srun --ntasks 1 --cpus-per-task 4 --mem 10G --time 01:00:00 --pty bashThen we will use any package preloaded on the cluster before needed to install our own on our user library if needed. We will need Pandoc for loading orthologr
ml GCC/12.2.0 OpenMPI/4.1.4 R_tamu/4.3.1
export R_LIBS=$SCRATCH/R_LIBS_USER/
ml Pandoc/2.13We now simply run the script as indicated on the pipeline page:
./scripts/GeneRetrieval.R ./scripts/inputurls_18polyneoptera_Nov2024.txt If the annotated genome is not done
by RefSeq but submitted by users:
When downloading genomes from NCBI, we found a few interesting ones that
were but annotated by users with a different format than NCBI Gnomon
pipeline. For example on the image below (when you zoom in), we can see
that the gene= field is present in RefSeq but is not in
users submitted but could be created using ID= field.
Example of difference in the presence of “gene” field between
S. piceifrons and Dryococelus australis
(3.4Gb).
To correct, I ran the following parsing code which append a new gene
column based on locus_tag:
# we need to modify the GFF, proteins and genome/transcript fasta file
sed -i 's/locus_tag=/gene=/g' {SPECIES}_GFF.gff
sed -i 's/locus_tag=/gene=/g' {SPECIES}_proteins.faa
sed -i 's/locus_tag=/gene=/g' {SPECIES}_transcripts.fasta
# then we check
grep 'gene=' {SPECIES}_GFF.gff | head -n 10We did it for Daus_GFF.gff, Dpunc_GFF.gff,
Gbima_GFF.gff, Glong_GFF.gff,
Pamer_GFF.gff and Tpodu_GFF.gff. Then instead
of running the GeneRetrieval.R, we computed manually (to check if error)
using the following R script:
library(orthologr)
library(tidyverse)
library(biomartr)
library(phylotools)
library(data.table)
# Define the species parameter
species <- "Gbima"
#species <- "Glong"
#species <- "Daus"
#species <- "Dpunc"
#species <- "Pamer"
#species <- "Tpodu"
# Define file paths based on the species parameter
proteomeFile <- paste0("./1_RawData/", species, "_proteins.faa")
annotationFile <- paste0("./1_RawData/", species, "_GFF.gff")
longestIsoformsFile <- paste0("./2_LongestIsoforms/", species, "_longestIsoforms.fasta")
transcriptsFile <- paste0("./1_RawData/", species, "_transcripts.fasta")
filteredTranscriptsOutput <- paste0("./2_LongestIsoforms/", species, "_filteredTranscripts.fasta")
# Step 1: Retrieve the longest isoforms for Glong
retrieve_longest_isoforms(
proteome_file = proteomeFile,
annotation_file = annotationFile,
new_file = longestIsoformsFile,
annotation_format = "gff"
)
# Step 2: Load longest isoforms and transcript files
isoforms <- phylotools::read.fasta(longestIsoformsFile)
isoform_ids <- isoforms$seq.name # Get IDs of longest isoforms
isoform_ids
transcripts <- phylotools::read.fasta(transcriptsFile)
head(transcripts$seq.name)
# Step 3: Adjust headers in transcripts to match core identifiers
transcripts$seq.name <- str_extract(transcripts$seq.name, "(?<=cds_)[^ ]+") # Extract main identifier
transcripts$seq.name <- str_extract(transcripts$seq.name, "^[^_]+") # Keep only core ID without suffix
transcripts$seq.name
# Step 4: Filter transcripts to keep only those matching isoform IDs
filtered_transcripts <- transcripts %>%
filter(seq.name %in% isoform_ids)
# Step 5: Write the filtered transcripts to a new FASTA file with simplified headers
phylotools::dat2fasta(filtered_transcripts, outfile = filteredTranscriptsOutput)
message("Filtered transcripts for ", species, " saved to: ", filteredTranscriptsOutput)
Status of Gene Retrieval script when successful
| Species | Total_Peptides | Number_Kept_Isoforms |
|---|---|---|
| Schistocerca gregaria | 37988 | 19799 |
| Schistocerca cancellata | 26362 | 16907 |
| Schistocerca piceifrons | 25717 | 17490 |
| Schistocerca americana | 26125 | 17662 |
| Schistocerca serialis cubense | 27654 | 17237 |
| Schistocerca nitens | 28445 | 17500 |
| Gryllus bimaculatus | 25032 | 17871 |
| Gryllus longicercus | 19656 | 14730 |
| Diploptera punctata | 28414 | 13170 |
| Timema podura | 16656 | 16493 |
| Bacillus rossius redtenbacheri | 29758 | 14448 |
| Dryococelus australis | 33111 | 33111 |
| Periplaneta americana | 27047 | 27047 |
| Cryptotermes secundus | 29285 | 13170 |
| Apis mellifera | 23471 | 9934 |
| Solenopsis invicta | 30910 | 14790 |
| Acyrthosiphon pisum | 27907 | 17678 |
| Drosophila melanogaster | 30802 | 13986 |
3. Cleaning the raw data
For the cleaning step of the mbarkdull’s pipeline we simply followed the command line with no modifications.
./scripts/DataCleaning ./scripts/inputurls_18polyneoptera_Nov2024.txt 
Status of Data Cleaning script when successful
4. Translating nucleotide sequences to amino acid sequences
This step does not seems to be really
necessary because Orthofinder can immediately take the
XXX_longestIsoforms.fasta files. We just run the
scriptDataCleaningIsoforms_modif on it to rename and clean
it.:
As mentioned in the pipeline page, we will mostly need to use amino
acid sequences rather than protein and we need to translate the data we
downloaded. We will be using the Python script from mbarkdull
./scripts/TranscriptFilesTranslateScript.py but before that
we will modify our input file to only include only the 12 essential
species.
We changed the script in head lines and lines 25-27, to call the module directly from the cluster as follow:
#!/bin/bash
##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=Transdecoder #Set the job name to "JobExample4"
#SBATCH --time=02:00:00 #Set the wall clock limit to 1hr and 30min
#SBATCH --ntasks=2 #Request 2 task
#SBATCH --cpus-per-task=4 #Request 8 task
#SBATCH --mem=20G #Request 50GB per node
ml GCC/10.2.0 OpenMPI/4.0.5 TransDecoder/5.5.0 Biopython/1.78 # Now we can run Transdecoder on the cleaned file:
echo "First, attempting TransDecoder run on $cleanName"
ml GCC/10.2.0 OpenMPI/4.0.5 TransDecoder/5.5.0
TransDecoder.LongOrfs -t $cleanName
TransDecoder.Predict -t $cleanName --single_best_onlyand then we launched the script by changing the top lines to put in sbatch:
sbatch ./scripts/DataTranslating_modif ./scripts/inputurls_18polyneoptera_Nov2024.txt 5. Running Orthofinder
Now we will finally run Orthofinder to identify groups of orthologous genones in our translated amino acid sequences. We will also use MAFFT to produce multiple sequence alignment across our six species of Schistocerca and the outgroups.
Instead of using the ./scripts/DataOrthofinder from the
pipeline, we will be running our own command line here. Before doing so,
we did manually the creating of folders and moving of fasta files as
follow:
mkdir LocustsGenomeEvolution/tmp
mkdir -p ./5_OrthoFinder/fasta
# cp ./4_1_TranslatedData/OutputFiles/translated* ./5_OrthoFinder/fasta
cp ./3_CleanedData/*longestIsoforms.fasta ./5_OrthoFinder/fasta
cd ./5_OrthoFinder/fasta
# rename translated '' translated*
# Loop through files and rename them
for file in cleaned*_longestIsoforms.fasta; do
# Remove "cleaned" and replace "_longestIsoforms" with "_filteredproteome"
new_name=$(echo "$file" | sed 's/^cleaned//; s/_longestIsoforms/_filteredproteome/')
mv "$file" "$new_name"
done
cd ../You can also rename them with a simple name like “S_americana.fasta”, this will help ease the visualization later on.
To run orthofinder onto our Grace cluster, I checked the
compatibility of the modules required and these are the versions
acceptable to run in sbatchorthofinder.sh:
#!/bin/bash
##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=orthofinder-blast #Set the job name to "JobExample4"
#SBATCH --time=2-00:00:00 #Set the wall clock limit to 1hr and 30min
#SBATCH --ntasks=1 #Request 1 task
#SBATCH --cpus-per-task=48 #Request 1 task
#SBATCH --mem=50G #Request 100GB per node
module purge
ml iccifort/2019.5.281 impi/2018.5.288 OrthoFinder/2.3.11-Python-3.7.4
ml IQ-TREE/1.6.12 FastTree/2.1.11
ml MAFFT/7.453-with-extensions
proteome_dir="/scratch/group/songlab/maeva/LocustsGenomeEvolution/Version3/5_OrthoFinder/fasta"
temporary_dir="/scratch/group/songlab/maeva/LocustsGenomeEvolution/tmp"
# Check if directories exist
if [[ ! -d $proteome_dir ]]; then
echo "Proteome directory $proteome_dir does not exist. Exiting."
exit 1
fi
if [[ ! -d $temporary_dir ]]; then
echo "Temporary directory $temporary_dir does not exist. Creating it now."
mkdir -p $temporary_dir
fi
# Run OrthoFinder
orthofinder -S diamond \
-T iqtree \
-A mafft \
-a 24 \
-I 1.5 \
-t 24 \
-M msa \
-f "$proteome_dir" \
-p "$temporary_dir"Here we use:
-S diamond DIAMOND as a sequence search program
-T iqtree IQTREE as a tree inference program
-A mafft MAFFT as the multiple sequence alignment (MSA)
program
-I 1.5 MCL inflation parameter (default from the
pipeline)
-t 32 number of threads
If you expect tightly conserved orthogroups (e.g., highly conserved core genes), consider a higher inflation value (e.g., -I 2.0 or even -I 3.0). This will favor clusters with tighter connections, reducing the possibility of grouping genes that diverge functionally.
If you’re studying functionally diverse or rapidly evolving gene families (e.g., gene families with species-specific expansions), a lower inflation value (e.g., -I 1.2 to -I 1.5) may help retain related genes in the same orthogroup, even if they have evolved to some degree.
We will run this analysis with also the -I 3 since we
expect close relation among Orthoptera genes.
If we run orthofinder using BLAST as it can give -2% accuracy increase over DIAMOND but will be computationally more heavy. For this we will only change:
ml BLAST+/2.9.0
orthofinder -S blastand we can run the script with the blast, iqtree and mafft options:
sbatch scripts/orthofinder_blast.shNow we are all done, we can explore the results and go on for the next steps.
6. Orthofinder Results
library(cogeqc)
library(ggtree)
library(treeio)
library(dplyr)
library(ggplot2)
# Set the base directory for your Orthofinder results
ortho_dir <- "/Users/maevatecher/Library/Mobile Documents/com~apple~CloudDocs/Documents/GitHub/locust-comparative-genomics/data/orthofinder/"
# Load the orthogroup file
orthogroups <- read_orthogroups(file.path(ortho_dir, "Orthogroups/Orthogroups_reprocessed.tsv"))
head(orthogroups) Orthogroup Species Gene
1 OG0000000 Amel_filteredproteome XP_001119836.2
2 OG0000000 Amel_filteredproteome XP_006568900.1
3 OG0000000 Amel_filteredproteome XP_016769943.1
4 OG0000000 Amel_filteredproteome XP_026299104.1
5 OG0000000 Amel_filteredproteome XP_393467.3
6 OG0000000 Amel_filteredproteome XP_395948.5# Load the directory with the actual stats from the Orthofinder run
ortho_stats <- read_orthofinder_stats(file.path(ortho_dir, "Comparative_Genomics_Statistics"))
ortho_stats$stats Species N_genes N_genes_in_OGs Perc_genes_in_OGs N_ssOGs
1 Amel_filteredproteome 9934 9556 96.2 16
2 Apisu_filteredproteome 17678 16711 94.5 526
3 Brsri_filteredproteome 14448 13835 95.8 110
4 Csecu_filteredproteome 13170 12795 97.2 62
5 Daus_filteredproteome 33111 31890 96.3 474
6 Dmela_filteredproteome 13986 12109 86.6 425
7 Dpunc_filteredproteome 28414 22309 78.5 730
8 Gbima_filteredproteome 17871 13744 76.9 92
9 Glong_filteredproteome 14730 13769 93.5 113
10 Pamer_filteredproteome 27047 26507 98.0 289
11 Samer_filteredproteome 17662 17393 98.5 22
12 Scanc_filteredproteome 16907 16597 98.2 44
13 Sgreg_filteredproteome 19799 17845 90.1 96
14 Sinvi_filteredproteome 14790 14308 96.7 250
15 Snite_filteredproteome 17500 17166 98.1 15
16 Spice_filteredproteome 17490 17194 98.3 13
17 Sscub_filteredproteome 17237 16952 98.3 15
18 Tpodu_filteredproteome 16493 14167 85.9 148
N_genes_in_ssOGs Perc_genes_in_ssOGs Dups
1 74 0.7 358
2 2473 14.0 6527
3 485 3.4 2169
4 219 1.7 1591
5 2899 8.8 23496
6 1659 11.9 2613
7 3254 11.5 7739
8 254 1.4 1389
9 471 3.2 1905
10 2105 7.8 17726
11 104 0.6 981
12 178 1.1 1123
13 341 1.7 1321
14 1532 10.4 4022
15 197 1.1 1274
16 58 0.3 1060
17 40 0.2 848
18 528 3.2 3132tree <- treeio::read.tree(file.path(ortho_dir, "Species_Tree/SpeciesTree_rooted_node_labels.txt"))
#tree$tip.label
#custom plot_species
plot_species_tree <- function(tree = NULL, xlim = c(0, 2), stats_list = NULL, custom_labels = NULL) {
# Basic tree plot with customized theme
p <- ggtree(tree) +
xlim(xlim) +
theme_tree() + # Use a clean theme
ggtitle("Species Tree with Duplications") + # Set a title
theme(plot.title = element_text(hjust = 0.5)) # Center the title
# Customize tip labels if provided
if (!is.null(custom_labels)) {
# Ensure length of custom_labels matches the number of tree tips
if (length(custom_labels) == length(tree$tip.label)) {
p <- p + geom_tiplab(aes(label = custom_labels), size = 4, fontface = "bold.italic", color = "darkblue")
} else {
stop("Length of custom_labels must match the number of tip labels in the tree.")
}
} else {
# Default tip labels if no custom labels are provided
p <- p + geom_tiplab(size = 4, fontface = "bold.italic", color = "darkblue")
}
if (!is.null(stats_list)) {
# Extract duplications
dups <- stats_list$duplications
dups <- dups[dups$Node %in% tree$tip.label, ] # Filter for relevant nodes
names(dups) <- c("label", "dups")
# Check for matching nodes
if (nrow(dups) > 0) {
p$data <- merge(p$data, dups, by.x = "label", by.y = "label", all.x = TRUE)
# Add duplications to the plot with larger text
p <- p +
ggtree::geom_text2(
aes(label = .data$dups),
hjust = 1.3, vjust = -0.5,
size = 5, color = "red" # Customize size and color of duplication labels
) +
labs(subtitle = "Number of Duplications per Node") # Subtitle for clarity
} else {
message("No matching nodes found for duplications.")
}
}
# Add circles around the nodes
p <- p + geom_point(size = 2, shape = 21, color = "black", fill = "black") # Circle around nodes
return(p)
}
# Plotting the tree
labels18 <- c("Drosophila melanogaster", "Acyrthosiphon pisum", "Gryllus longicercus", "Gryllus bimaculatus",
"Timema podura", "Dryococelus australis", "Bacillus rossius redtenbacheri", "Diploptera punctata",
"Cryptotermes secundus", "Periplaneta americana", "Schistocerca gregaria", "Schistocerca piceifrons",
"Schistocerca americana", "Schistocerca serialis cubense", "Schistocerca cancellata", "Schistocerca nitens", "Apis mellifera", "Solenopsis invicta")
# Call the custom plot function with your species tree, stats, and custom labels
p<- plot_species_tree(tree, xlim = c(0, 1.5), stats_list = ortho_stats)
plot_duplications(ortho_stats)
plot_genes_in_ogs(ortho_stats)
plot_species_specific_ogs(ortho_stats)
plot_orthofinder_stats(
tree = tree,
xlim = c(-0.1, 2),
stats_list = ortho_stats
)
plot_og_overlap(ortho_stats)
plot_og_sizes(orthogroups)
plot_og_sizes(orthogroups, log = TRUE) 
7. Orthogroups to GeneID
At the end we obtain several folders, for which the content is extremely well explain by the software developer David Emms here.
Briefly, one of the important output in the folder
Orthogroups is the actual Orthogroups.txt
file. Although other files there are also important as we can use
Orthogroups.GeneCount.tsv for CAFE5 later on,
for example.
One of the issue with the pipeline we use is that, there will be some suffix in front of our protein coding, so we will remove that by using the following python code:
import re
# Define the input and output file names
input_file = 'Orthogroups.txt'
output_file = 'Orthogroups_reprocessed.txt'
# Read the input file
with open(input_file, 'r') as file:
data = file.read()
# Remove all prefixes before any occurrence of "_XP", "_NP", and "_YP"
result = re.sub(r'\b\w+_(XP|NP|YP)', r'\1', data)
# Write the result to the output file
with open(output_file, 'w') as file:
file.write(result)
print(f"Processed data has been written to {output_file}")We used the python script protein2geneid_loop.py written
by David to extract the protein_id from each gene_id and make a full
table with all species. For this we need to run the script in the folder
1_RawData.
import os
import re
# Get the current working directory
gff_directory = os.getcwd()
# Create a directory to store the output files
output_directory = os.path.join(gff_directory, "output_files")
os.makedirs(output_directory, exist_ok=True)
# List GFF files with "_GFF.gff" extension
species_list = [filename for filename in os.listdir(gff_directory) if filename.endswith("_GFF.gff")]
# Process each species' GFF file
for species_filename in species_list:
# Extract the species name from the file name
species_name = re.sub(r"_GFF\.gff$", "", species_filename)
# Construct input and output file paths
input_path = os.path.join(gff_directory, species_filename)
output_filename = f"xp{species_name}.gff" # Remove the dot before species_name
output_path = os.path.join(output_directory, output_filename)
# Use 'grep' to filter lines containing "XP" and save to the output file
grep_command = f'grep "XP" "{input_path}" > "{output_path}"'
os.system(grep_command)
# Print a message indicating the filtering process
print(f"Filtered {species_filename} to {output_filename}")
# Construct the output TSV file path
tsv_output_filename = f'gffKey{species_name}.tsv' # Remove the dot before species_name
tsv_output_path = os.path.join(output_directory, tsv_output_filename)
lol = {}
# Read and process the contents of the GFF file
with open(output_path) as gffFile:
for line in gffFile:
if re.search(r"product=[^;]+", line):
gene_match = re.search(r"gene=[^;=]+", line)
product_match = re.search(r"product=[^;]+", line)
proteinID_match = re.search(r"protein_id=(.+)", line)
if gene_match and product_match and proteinID_match:
gene = gene_match.group(0)
if gene.endswith('product'):
gene = gene[5:-7]
else:
gene = gene[5:]
product = product_match.group(0)
product = product.replace("product=", "")
product = product.rstrip()
proteinID = proteinID_match.group(1)
if proteinID not in lol.keys():
lol[proteinID] = [gene, product, species_name] # Add species name
# Write the processed data to the output TSV file
with open(tsv_output_path, 'w') as output:
for proID, lis in lol.items():
output.writelines(proID + '\t' + lis[0] + '\t' + lis[1] + '\t' + lis[2] + '\n') # Include species name
# Print a message indicating the processing of the GFF file
print(f"Processed {output_filename} to {tsv_output_filename}")
# Concatenate all final files into a single file
final_output_filename = "allspecies_protein2geneid.tsv"
final_output_path = os.path.join(output_directory, final_output_filename)
with open(final_output_path, 'w') as final_output:
for species_filename in species_list:
species_name = re.sub(r"_GFF\.gff$", "", species_filename)
tsv_output_filename = f'gffKey{species_name}.tsv' # Remove the dot before species_name
tsv_output_path = os.path.join(output_directory, tsv_output_filename)
with open(tsv_output_path, 'r') as species_output:
final_output.write(species_output.read())
print(f"Concatenated all species files into {final_output_filename}")
Launch the script as python protein2geneid_loop.py.
Once we have obtained both files, we can join them with R and we will have a correspondence among orthogroups_id, protein_id, gene_id, gene description and species. I had to sort the processed file by hand with excel for the first 6 Orthogroups (too long line, cuting names).
NB: I noticed there are still some small issues with the file
allspecies_protein2geneid.tsv so I fixed it by hand
afterwards.
library(readr)
# rename for future merging
names(orthogroups)[names(orthogroups) == "Gene"] <- "protein_id"
# Optional if you did not rename fasta sequence before
orthogroups$Species <- gsub("_filteredproteome", "", orthogroups$Species)
# Export the table to tab-separated text file (you can change the delimiter if needed)
output_file <- file.path(ortho_dir, "Orthogroups_18species_Nov2024.txt")
# Write the transformed data to the file
write.table(orthogroups,
file = output_file,
sep = "\t",
quote = FALSE,
row.names = FALSE)
proteingene_path <- file.path(ortho_dir, "../list/allspecies_protein2geneid.tsv")
proteingeneid <- read_tsv(proteingene_path, col_names = TRUE )
head(proteingeneid)# A tibble: 6 × 4
protein_id gene_id gene_description species
<chr> <chr> <chr> <chr>
1 XP_008178278.2 LOC100569061 protein ALP1-like Acyrth…
2 XP_029341669.1 LOC115033410 uncharacterized protein LOC115033410 Acyrth…
3 XP_016656080.1 LOC107882353 major centromere autoantigen B-like Acyrth…
4 XP_016656355.1 LOC107882485 uncharacterized protein LOC107882485 isof… Acyrth…
5 XP_029341671.1 LOC107882485 uncharacterized protein LOC107882485 isof… Acyrth…
6 XP_016656356.2 LOC107882485 uncharacterized protein LOC107882485 isof… Acyrth…final_orthotable <- left_join(orthogroups, proteingeneid, by = "protein_id")
output_file <- file.path(ortho_dir, "Orthogroups_genesprotein_18species_Nov2024.csv")
write.table(final_orthotable, file = output_file, sep = ",", quote = FALSE, row.names = FALSE)There we have it, the final table with all the corresponding IDs. This table is too big and have all species in it, so we want to reduce to only single copy orthologs and remove entries with other species than Schistocerca for now.
# Filter the final_orthotable to keep only rows with species 'Schistocerca'
filtered_final_orthotable <- final_orthotable %>%
filter(species %in% c("Schistocerca gregaria", "Schistocerca piceifrons", "Schistocerca americana", "Schistocerca cancellata", "Schistocerca serialis cubense", "Schistocerca nitens"))
# Optionally, save the filtered data
output_file <- file.path(ortho_dir, "Orthogroups_genesprotein_Schisto_Nov2024.txt")
write.table(filtered_final_orthotable, file = output_file, sep = "\t", quote = FALSE, row.names = FALSE)
# Step 1: Read the single copy orthologs table
single_copy_orthologs_path <- file.path(ortho_dir, "Orthogroups/Orthogroups_SingleCopyOrthologues.txt")
single_copy_orthologs <- read.table(single_copy_orthologs_path, header = FALSE, stringsAsFactors = FALSE)
# Step 2: Ensure the column name for orthogroups matches in both data frames
# If necessary, rename the column in single_copy_orthologs to match
colnames(single_copy_orthologs) <- c("Orthogroup") # Replace with the actual name if different
# Step 3: Perform the intersection
scopy_final_orthotable <- final_orthotable[final_orthotable$Orthogroup %in% single_copy_orthologs$Orthogroup, ]
# Step 4: Optionally, save the filtered table
output_file <- file.path(ortho_dir, "SingleCopyOrthogroups_genesprotein_18species_Nov2024.txt")
write.table(filtered_final_orthotable, file = output_file, sep = "\t", quote = FALSE, row.names = FALSE)References
If you use this script, and orthologr please cite:
Drost et al. 2015. Evidence for Active Maintenance of Phylotranscriptomic Hourglass Patterns in Animal and Plant Embryogenesis. Mol. Biol. Evol. 32 (5): 1221-1231. doi:10.1093/molbev/msv012
If you use Transdecoder, please cite it as:
Haas, B., and A. Papanicolaou. “TransDecoder.” (2017).
If you use Orthofinder, please cite it:
OrthoFinder’s orthogroup and ortholog inference are described here:
Emms, D.M., Kelly, S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol 16, 157 (2015).
Emms, D.M., Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol 20, 238 (2019).
If you use the OrthoFinder species tree then also cite:
Emms D.M. & Kelly S. STRIDE: Species Tree Root Inference from Gene Duplication Events (2017), Mol Biol Evol 34(12): 3267-3278.
Emms D.M. & Kelly S. STAG: Species Tree Inference from All Genes (2018), bioRxiv https://doi.org/10.1101/267914.
Please also cite MAFFT:
K. Katoh, K. Misawa, K. Kuma, and T. Miyata. 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30(14): 3059-3066.
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readr_2.1.5 ggplot2_3.5.1 dplyr_1.1.4 treeio_1.28.0
[5] ggtree_3.12.0 cogeqc_1.8.0 kableExtra_1.4.0 knitr_1.48
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 viridisLite_0.4.2 vipor_0.4.7
[4] farver_2.1.2 Biostrings_2.72.1 fastmap_1.2.0
[7] lazyeval_0.2.2 promises_1.3.0 digest_0.6.37
[10] lifecycle_1.0.4 tidytree_0.4.6 magrittr_2.0.3
[13] compiler_4.4.1 rlang_1.1.4 sass_0.4.9
[16] tools_4.4.1 igraph_2.1.1 utf8_1.2.4
[19] yaml_2.3.10 labeling_0.4.3 bit_4.5.0
[22] plyr_1.8.9 xml2_1.3.6 aplot_0.2.3
[25] workflowr_1.7.1 withr_3.0.2 purrr_1.0.2
[28] BiocGenerics_0.50.0 grid_4.4.1 stats4_4.4.1
[31] fansi_1.0.6 git2r_0.35.0 colorspace_2.1-1
[34] scales_1.3.0 cli_3.6.3 rmarkdown_2.29
[37] crayon_1.5.3 generics_0.1.3 rstudioapi_0.17.1
[40] tzdb_0.4.0 httr_1.4.7 reshape2_1.4.4
[43] ggbeeswarm_0.7.2 ape_5.8 cachem_1.1.0
[46] stringr_1.5.1 zlibbioc_1.50.0 parallel_4.4.1
[49] ggplotify_0.1.2 XVector_0.44.0 vctrs_0.6.5
[52] yulab.utils_0.1.8 jsonlite_1.8.9 hms_1.1.3
[55] gridGraphics_0.5-1 IRanges_2.38.1 patchwork_1.3.0
[58] S4Vectors_0.42.1 bit64_4.5.2 beeswarm_0.4.0
[61] systemfonts_1.1.0 jquerylib_0.1.4 tidyr_1.3.1
[64] glue_1.8.0 stringi_1.8.4 gtable_0.3.6
[67] later_1.3.2 GenomeInfoDb_1.40.1 UCSC.utils_1.0.0
[70] munsell_0.5.1 tibble_3.2.1 pillar_1.9.0
[73] htmltools_0.5.8.1 GenomeInfoDbData_1.2.12 R6_2.5.1
[76] rprojroot_2.0.4 vroom_1.6.5 evaluate_1.0.1
[79] lattice_0.22-6 highr_0.11 httpuv_1.6.15
[82] ggfun_0.1.7 bslib_0.8.0 Rcpp_1.0.13-1
[85] svglite_2.1.3 nlme_3.1-166 whisker_0.4.1
[88] xfun_0.49 fs_1.6.5 pkgconfig_2.0.3