Plot phylogenetic tree_heatmap and MSA on yopBDJTEMKOH[NR]

gene_x 0 like s 704 view s

Tags: plot, python, R, processing, scripts

| x | y | | ------------ | ------------ | | x | y | | x | y |

|Isolate|yopJ|yopB|yopT|yopE|yopD|yopM|yopK|yopO|yopH| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |Yersinia_enterocolitica_1055Rr|1|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_2516-87|0|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_8081|1|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_8081_bis|1|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_KNG22703|1|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_WA|1|1|1|1|1|0|1|1|1| |Yersinia_enterocolitica_Y1|1|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_Y11|1|1|1|1|1|1|1|1|1| |Yersinia_enterocolitica_YE1|1|0|1|1|1|1|1|1|1| |Yersinia_enterocolitica_YE165|1|1|1|1|0|1|1|0|1| |Yersinia_enterocolitica_YE3|1|0|1|1|1|1|1|0|1| |Yersinia_enterocolitica_YE5|1|0|1|1|1|1|1|1|1| |Yersinia_enterocolitica_YE6|1|1|1|1|1|1|1|0|1| |Yersinia_enterocolitica_YE7|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_1045|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_1412|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_1413|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_1522|1|1|0|0|1|1|1|1|1| |Yersinia_pestis_20|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_2944|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_3067|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_3770|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_790|0|1|1|1|1|1|1|0|1| |Yersinia_pestis_8787|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_91001|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_94|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Angola|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Angola_bis|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Antiqua|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Antiqua_bis|0|1|1|1|1|1|1|1|1| |Yersinia_pestis_CO92|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_CO92_pgm-_pPCP1-|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Cadman|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_D106004|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_D182038|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Dodson|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_EV76-CN|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_El_Dorado|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_FDAARGOS_601|1|1|1|1|1|1|1|0|1| |Yersinia_pestis_FDAARGOS_602|0|1|1|1|1|0|1|1|1| |Yersinia_pestis_FDAARGOS_603|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Harbin_35|1|1|1|1|1|1|1|0|1| |Yersinia_pestis_Harbin_35_bis|1|0|1|1|1|1|1|0|1| |Yersinia_pestis_Java9|1|1|1|1|1|1|1|0|1| |Yersinia_pestis_KIM5|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Nicholisk_41|1|1|1|0|1|1|1|0|1| |Yersinia_pestis_PBM19|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Pestoides_B|0|1|1|1|1|1|1|1|1| |Yersinia_pestis_Pestoides_F|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_Pestoides_F_bis|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_Pestoides_G|1|1|0|1|1|1|1|1|1| |Yersinia_pestis_R|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_S19960127|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_SCPM-O-B-5935_I-1996|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_SCPM-O-B-5942_I-2638|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_SCPM-O-B-6530|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_SCPM-O-B-6899_231|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_SCPM-O-DNA-18_I-3113|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Shasta|1|1|1|1|1|1|1|1|1| |Yersinia_pestis_Z176003|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_598|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_EP2+|0|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_FDAARGOS_579|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_FDAARGOS_580|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_FDAARGOS_581|1|1|1|0|1|1|1|1|1| |Yersinia_pseudotuberculosis_FDAARGOS_582|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_FDAARGOS_583|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_IP2666pIB1|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_IP32953|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_IP32953_bis|1|1|1|1|0|1|1|1|1| |Yersinia_pseudotuberculosis_NZYP4713|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_PA3606|1|1|1|1|1|1|1|1|1| |Yersinia_pseudotuberculosis_PB1+_bis|1|1|1|1|1|0|1|1|1|

ggtree_and_gheatmap_yopK

alignment_yopK

  1. This step uses rsync to download data from the NCBI server to a local directory, save all gff-files in the directory prokka.

    rsync --copy-links --recursive --times --verbose rsync://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/001/696/305/GCF_001696305.1_UCN72.1 Yersinia_pestis_1045

  2. The step processes GFF files containing gene annotations for a set of samples in the directory prokka. The primary goal is to modify the GFF files and create new ones with specific changes and to save them in the directory prokka_plus. The script operates on each sample one by one, and for each sample, it performs the following steps:

    • Replace all occurrences of \tCDS\t with CDS in the original GFF file.
    • Extract all lines containing CDS and save them in a new file with the suffix _CDS.gff.
    • Replace all occurrences of ID= with ID_old= in the new _CDS.gff file.
    • Cut the second field (delimited by ;) from the _CDS.gff file and save it in a new file with the suffix _CDS_f2.
    • Replace all occurrences of Parent=gene- with ID= in the _CDS_f2 file.
    • Paste the contents of the CDS.gff and _CDS_f2 files side by side, with a ; delimiter, and save the result in a new file with the suffix _CDS.gff.

    • Run the enum.py script on the CDS.gff file to add line numbers at the end, and save the result in a new file with the suffix _CDS__.gff. import sys

        if len(sys.argv) < 2:
      print("Please provide a filename as an argument.")
      sys.exit(1)

  filename = sys.argv[1]

  try:
      with open(filename) as f:
          for i, line in enumerate(f):
              print(f"{line.strip()}_{i+1}")
  except FileNotFoundError:
      print(f"File {filename} not found.")

    • Extract all lines from the original GFF file that do not contain CDS and save them in a new file with the suffix _nonCDS.gff.

    • Remove all lines containing ### from the nonCDS.gff file and save the result in a new file with the suffix _nonCDS.gff.
    • Concatenate the contents of the nonCDS.gff and _CDS__.gff files and save the result in a new file with the suffix _nonCDS_CDS.gff.
    • Replace all occurrences of CDS with \tCDS\t in the _nonCDS_CDS.gff file.
    • Append the string ##FASTA to the end of the _nonCDS_CDS.gff file.
    • Modify the FASTA file associated with the sample by replacing the first field (delimited by a space) with the corresponding sample name.
    • Concatenate the modified GFF file (_nonCDS_CDS.gff) and the modified FASTA file, and save the result in the ../prokka_plus/ directory with a new name based on the sample name.
    • After processing all samples, the script removes intermediate files generated during the process.
      for sample in Yersinia_pestis_1045 Yersinia_pestis_SCPM-O-B-6291_C-25 Yersinia_pestis_2944 Yersinia_pestis_KIM10+ Yersinia_pestis_M-1482; do
  sed -i 's/\tCDS\t/_CDS_/g' ${sample}.gff
  grep "_CDS_" ${sample}.gff > ${sample}_CDS.gff
  sed -i 's/ID=/ID_old=/g' ${sample}_CDS.gff
  cut -d';' -f2 ${sample}_CDS.gff > ${sample}_CDS_f2
  sed -i 's/Parent=gene-/ID=/g' ${sample}_CDS_f2
  paste -d';' ${sample}_CDS.gff ${sample}_CDS_f2 > ${sample}_CDS_.gff
  python enum.py ${sample}_CDS_.gff > ${sample}_CDS__.gff   # add a line number to end to avoid the sameple Gene_ID

  grep -v "_CDS_" ${sample}.gff > ${sample}_nonCDS.gff
  grep -v "###" ${sample}_nonCDS.gff > ${sample}_nonCDS_.gff

  cat ${sample}_nonCDS_.gff ${sample}_CDS__.gff > ${sample}_nonCDS_CDS.gff
  sed -i 's/_CDS_/\tCDS\t/g' ${sample}_nonCDS_CDS.gff
  echo "##FASTA" >> ${sample}_nonCDS_CDS.gff

  cut -d' ' -f1 ../assembly/${sample}.fna > ../assembly/${sample}.fasta;
  cat ${sample}_nonCDS_CDS.gff ../assembly/${sample}.fasta > ../prokka_plus/$(echo $sample | cut -d'_' -f3- | tr " " "_").gff;
done
rm *_CDS.gff *_CDS_f2 *_CDS_.gff *_CDS__.gff *_nonCDS.gff *_nonCDS_.gff

  3. This step runs Roary, a tool for pan-genome analysis. It takes annotated bacterial genomes in GFF3 format as input and clusters the genes based on sequence similarity.

    roary -p 4 -f ./roary -i 95 -cd 99 -s -e -n -v  prokka_plus/1045.gff prokka_plus/SCPM-O-B-6291_C-25.gff prokka_plus/2944.gff prokka_plus/KIM10+.gff

  4. This step extracts the coding sequences (CDS) of specific genes from multiple genome files and saves them to an output file. Start-files: roary/pan_genome_reference.fa and roary/gene_presence_absence.csv

    #grep "yopT" roary/gene_presence_absence.csv
> yopT_seq.txt
for gene_id in M486_RS20945_3996 YE105_RS20560_4012; do
  for gbff in  Yersinia_massiliensis_2011N-4075/GCF_013282765.1_ASM1328276v1/GCF_013282765.1_ASM1328276v1_genomic.gbff.gz Yersinia_pestis_EV_NIIEG/GCF_000590535.2_ASM59053v2/GCF_000590535.2_ASM59053v2_genomic.gbff.gz Yersinia_pestis_Shasta/GCF_000834335.1_ASM83433v1/GCF_000834335.1_ASM83433v1_genomic.gbff.gz Yersinia_ruckeri_NVI-492/GCF_023212565.2_ASM2321256v2/GCF_023212565.2_ASM2321256v2_genomic.gbff.gz Yersinia_pestis_Pestoides_G/GCF_000834985.1_ASM83498v1/GCF_000834985.1_ASM83498v1_genomic.gbff.gz; do
    output=$(python3 extract_CDS_of_a_locus_tag.py ${gbff} $(echo "${gene_id}" | cut -d '_' -f 1-2))
    if [[ ! -z "${output}" ]]; then
        gbff_short=$(echo "${gbff}" | cut -d '/' -f 1)
        printf "%s\t%s\n" "${gbff_short}" "${output}" >> yopT_seq.txt
    fi
  done
done

#-- code snippet of extract_CDS_of_a_locus_tag.py --
from Bio import SeqIO
import sys
import gzip

#python3 extract_CDS_of_a_locus_tag.py GCF_001188735.1_ASM118873v1_genomic.gbff.gz M486_RS20950

# Get the file name from the command-line argument
if len(sys.argv) == 3:
    filename = sys.argv[1]
    locus_tag = sys.argv[2]
else:
    print(sys.argv)
    print("Error: no file name provided")
    sys.exit(1)

# Open the compressed file and read the sequences
with gzip.open(filename, "rt") as f:

    # Open the GenBank file and read the first record
    #    record = SeqIO.read("GCF_001188735.1_ASM118873v1_genomic.gbff", "genbank")
    for record in SeqIO.parse(f, "genbank"):
        #print("%s %i" % (record.id, len(record)))

        # Define the locus_tag you want to extract
        #locus_tag = "M486_RS20950"

        # Loop through the features and extract the CDS with the specified locus_tag
        for feature in record.features:
            if feature.type == "CDS" and "locus_tag" in feature.qualifiers and feature.qualifiers["locus_tag"][0] == locus_tag:
                # Extract the CDS location information and sequence
                location = feature.location
                seq = location.extract(record).seq
                #print(f">{locus_tag}")
                print(f"{seq}")
                # Translate the nucleotide sequence to protein sequence
                #protein_seq = seq.translate()
                #print(f"Locus tag: {locus_tag}\nProtein sequence: {protein_seq}")

  5. The given code converts a DNA sequence file to a protein sequence file, aligns the protein sequences using MAFFT or MUSCLE, and constructs a phylogenetic tree using FastTree.

    #mafft --clustalout --adjustdirection yopK_seqs.fasta > yopK_seqs.output
#fasttree -gtr -gamma -nt  yopK_alignment.fasta > yopK_alignment.tree
#raxml-ng --all --model GTR+G+ASC_LEWIS --prefix core_gene_raxml --threads 6 --msa yopK_alignment_.fasta --bs-trees 1000 -slow 
#fasttree <input_file> > <output_file>

for yop in yopJ yopB yopT yopE yopD yopM yopK yopO yopH; do
    python3 ../../../plotTreeHeatmap/dna_to_protein.py ${yop}_seq.txt ${yop}_protein.fasta
    python3 ../../../plotTreeHeatmap/protein_alignment.py ${yop}_protein.fasta ${yop}_aligned_protein.fasta mafft
    awk -F '_' '/^>/ { printf(">%s", $3); for (i = 4; i <= NF; ++i) printf("_%s", $i); printf("\n"); next } { print }' ${yop}_aligned_protein.fasta > ${yop}_aligned_protein_.fasta
done

fasttree yopE_aligned_protein_.fasta > yopE_aligned_protein.tree
fasttree yopO_aligned_protein_.fasta > yopO_aligned_protein.tree
fasttree yopT_aligned_protein_.fasta > yopT_aligned_protein.tree

grep ">" yopE_aligned_protein.fasta > typing_yopE.csv
grep ">" yopO_aligned_protein.fasta > typing_yopO.csv
grep ">" yopT_aligned_protein.fasta > typing_yopT.csv

#-- construct typing_for yopE --
cut -f1 -d'_' typing_yopE.csv > f1    
cut -f2 -d'_' typing_yopE.csv > f2 
cut -f3- -d'_' typing_yopE.csv > f3_
paste f3_ typing_yopE.csv > temp1
paste f1 f2 > temp2 
paste temp1 temp2 > typing_yopE_.csv

#Isolate,Name,Genus,Species,yopE
#R,Yersinia_pestis_R,Yersinia,pestis,Yes
#20,Yersinia_pestis_20,Yersinia,pestis,Yes
#...

#-- for yopO --
cut -f1 -d'_' typing_yopO.csv > f1    
cut -f2 -d'_' typing_yopO.csv > f2 
cut -f3- -d'_' typing_yopO.csv > f3_
paste f3_ typing_yopO.csv > temp1
paste f1 f2 > temp2 
paste temp1 temp2 > typing_yopO_.csv

#-- for yopT --
cut -f1 -d'_' typing_yopT.csv > f1    
cut -f2 -d'_' typing_yopT.csv > f2 
cut -f3- -d'_' typing_yopT.csv > f3_
paste f3_ typing_yopT.csv > temp1
paste f1 f2 > temp2 
paste temp1 temp2 > typing_yopT_.csv

# code snippet of dna_to_protein.py
    from Bio import SeqIO
    from Bio.Seq import Seq
    from Bio.SeqRecord import SeqRecord
    import sys

    def translate_dna_to_protein(dna_fasta_file, protein_fasta_file):
        protein_records = []

        for record in SeqIO.parse(dna_fasta_file, "fasta"):
            protein_seq = record.seq.translate(to_stop=True)
            protein_record = SeqRecord(protein_seq, id=record.id, description=record.description)
            protein_records.append(protein_record)

        SeqIO.write(protein_records, protein_fasta_file, "fasta")

    if __name__ == "__main__":
        input_file = sys.argv[1]
        output_file = sys.argv[2]
        translate_dna_to_protein(input_file, output_file)

# code snippet of protein_alignment.py
    import sys
    from Bio.Align.Applications import MafftCommandline, MuscleCommandline
    from Bio import SeqIO
    from Bio import AlignIO

    def run_alignment(input_file, output_file, aligner):
        if aligner.lower() == "mafft":
            mafft_cline = MafftCommandline(input=input_file)
            stdout, stderr = mafft_cline()
        elif aligner.lower() == "muscle":
            muscle_cline = MuscleCommandline(input=input_file, out=output_file)
            stdout, stderr = muscle_cline()
        else:
            print("Invalid aligner. Please choose 'mafft' or 'muscle'.")
            sys.exit(1)

        with open(output_file, "w") as aligned_fasta:
            aligned_fasta.write(stdout)

    if __name__ == "__main__":
        if len(sys.argv) < 4:
            print("Usage: python protein_alignment.py input_fasta output_fasta aligner")
            print("Example: python protein_alignment.py input_protein.fasta aligned_protein.fasta mafft")
            sys.exit(1)
        else:
            input_fasta = sys.argv[1]
            output_fasta = sys.argv[2]
            aligner_choice = sys.argv[3]
            run_alignment(input_fasta, output_fasta, aligner_choice)

  6. This step generates a circular phylogenetic tree, a heatmap, and a multiple alignment sequence in a single figure. The tree is constructed from the aligned protein sequence "yopE_aligned_protein_.fasta" and the heatmap is based on the "typing_yopE_.csv" data. The multiple alignment sequence is added into the final figure by extracting specific sequences from the aligned protein sequence file.

    library(ggtree)
library(ggplot2)
library(Biostrings)
library(stringr)
library(dplyr)

library(ape)
library(ggmsa)

#install.packages("cowplot")
library(cowplot)

setwd("/home/jhuang/DATA/Data_Gunnar_Yersiniomics/plotTreeHeatmap/")

# -- edit tree --
#https://icytree.org/
#0.000780
#info <- read.csv("typing_198.csv")
info <- read.csv("typing_yopE_.csv")

info$name <- info$Isolate
#rownames(info) <- info$name
info$yopE <- factor(info$yopE)

#tree <- read.tree("core_gene_alignment_fasttree_directly_from_186isoaltes.tree")  --> NOT GOOD!
#tree <- read.tree("raxml.tree")
#tree <- read.tree("yopK_alignment_modified.tree") 
tree <- read.tree("yopE_aligned_protein.tree") 
#tree <- read.tree("core_gene_raxml.raxml.bestTree") 
cols <- c(Yes='purple2', No='skyblue2')

# -- tree --
tree_plot <- ggtree(tree, layout='circular', branch.length='none') %<+% info + scale_color_manual(values=cols) + geom_tiplab2(aes(label=name), offset=1) + geom_tippoint(aes(color=yopE)) 
#+ scale_color_manual(values=cols) + geom_tiplab2(aes(label=name), offset=1)
#, geom='text', align=TRUE,  linetype=NA, hjust=1.8,check.overlap=TRUE, size=3.3
#difference between geom_tiplab and geom_tiplab2?
#+ theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) + theme(axis.text = element_text(size = 20))  + scale_size(range = c(1, 20))
#font.size=10, 
png("ggtree2.png", width=1260, height=1260)
#png("ggtree.png", width=1000, height=1000)
#svg("ggtree.svg", width=1260, height=1260)
tree_plot
dev.off()

# -- heatmap --
#heatmapData2 <- info %>% select(Isolate, cgMLST, Lineage)
heatmapData2 <- info %>% select(Isolate, Species)
rn <- heatmapData2$Isolate
heatmapData2$Isolate <- NULL
heatmapData2 <- as.data.frame(sapply(heatmapData2, as.character))
rownames(heatmapData2) <- rn
  #"1","2","3","4","9","12","13","14","16","18",  "19","30","32","36","39","41","42","43","44","53",  "64","79","83","84","92","140","148","154","171","172", "173","194","215","217","252","277","279","282","290","312", "335","NA"
#https://bookdown.org/hneth/ds4psy/D-3-apx-colors-basics.html
#"blue","cyan", "skyblue2", "azure3","blueviolet","darkgoldenrod",  "tomato","mediumpurple4","indianred", 
#"cornflowerblue","darkgreen","seagreen3","tan","red","green","orange","pink","brown","magenta",     "cornflowerblue","darkgreen","red","tan","brown",

#heatmap.colours <- c("cornflowerblue","darkgreen","seagreen3","tan","red",  "navyblue", "gold",     "lightcyan3","green","orange","pink","purple","magenta","brown", "darksalmon","chocolate4","darkkhaki",  "maroon","lightgreen",      "darkgreen","seagreen3","tan","red",  "navyblue", "gold",     "green","orange","pink","purple","magenta","brown", "darksalmon","chocolate4","darkkhaki", "lightcyan3", "maroon","lightgreen", "darkgrey")
#names(heatmap.colours) <- c("pestis","pseudotuberculosis","similis","enterocolitica","frederiksenii","kristensenii","occitanica","intermedia","hibernica","canariae","alsatica","rohdei","massiliensis","bercovieri","aleksiciae","mollaretii","aldovae","ruckeri","entomophaga",     "1","1Aa","1B","2","2/3-9a","2/3-9b","4","5","6","8","10","11","12","13","14","16","17","29","NA")

heatmap.colours <- c("cornflowerblue","darkgreen","seagreen3","tan","red",  "navyblue", "gold",     "lightcyan3","green","orange","pink","purple","magenta","brown", "darksalmon","chocolate4","darkkhaki",  "maroon","lightgreen")
names(heatmap.colours) <- c("pestis","pseudotuberculosis","similis","enterocolitica","frederiksenii","kristensenii","occitanica","intermedia","hibernica","canariae","alsatica","rohdei","massiliensis","bercovieri","aleksiciae","mollaretii","aldovae","ruckeri","entomophaga")

#"cornflowerblue","darkgreen","seagreen3","tan","red","green","orange","pink","brown","magenta","cornflowerblue",
#"2.MED","4.ANT","2.ANT","1.ORI","1.IN","1.ANT","0.ANT3","0.PE4","0.PE3","0.PE2","1a",
#mydat$Regulation <- factor(mydat$Regulation, levels=c("up","down"))
#rochesterensis-->occitanica

png("ggtree_and_gheatmap_yopE.png", width=1000, height=800)
#png("ggtree_and_gheatmap.png", width=1290, height=1000)
#png("ggtree_and_gheatmap.png", width=1690, height=1400)
#svg("ggtree_and_gheatmap.svg", width=1290, height=1000)
#svg("ggtree_and_gheatmap.svg", width=17, height=15)
tree_heatmap_plot <- gheatmap(tree_plot, heatmapData2, width=0.2,colnames_position="top", colnames_angle=90, colnames_offset_y = 0.1, hjust=0.5, font.size=4, offset = 8) + scale_fill_manual(values=heatmap.colours) +  theme(legend.text = element_text(size = 14)) + theme(legend.title = element_text(size = 14)) + guides(fill=guide_legend(title=""), color = guide_legend(override.aes = list(size = 5)))  
tree_heatmap_plot
dev.off()

#samtools faidx yopE_aligned_protein_.fasta "1522" > yopE_aligned_protein_sorted.fasta
#samtools faidx yopE_aligned_protein_.fasta "Pestoides_F_bis" >> yopE_aligned_protein_sorted.fasta
#...
#samtools faidx yopE_aligned_protein_.fasta "8081" >> yopE_aligned_protein_sorted.fasta
#samtools faidx yopE_aligned_protein_.fasta "WA" >> yopE_aligned_protein_sorted.fasta

#https://bioconductor.org/packages/devel/bioc/vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html
#https://github.com/YuLab-SMU/supplemental-ggmsa
#https://github.com/YuLab-SMU/ggmsa

library(ggmsa)
library(ggplot2)
library(ggtree)
#library(gggenes)
library(ape)
library(Biostrings)
library(ggnewscale)
library(dplyr)
library(ggtreeExtra)
library(phangorn)
library(RColorBrewer)
library(patchwork)
library(ggplotify)
library(aplot)
library(magick)
library(treeio)

#data <- "../supplemental-ggmsa-main/data/s_RBD.fasta"
data <- "yopE_aligned_protein_.fasta"
#data <- "yopE_aligned_protein_sorted.fasta"
#dms <- read.csv("data/DMS.csv")
#del <- c("expr_lib1", "expr_lib2","expr_avg","bind_lib1","bind_lib2")
#dms <- dms[,!colnames(dms) %in% del]
tidymsa <- tidy_msa(data)
#tidymsa <- assign_dms(tidymsa, dms)
#Mapping the position to the protein-protein interaction plot
#tidymsa$position <- tidymsa$position + 330

#dms = TRUE, 
png("alignment_yopE.png", width=1100, height=5400)
msa_plot <- ggplot() +
geom_msa(data = tidymsa, char_width = 0.5, seq_name = TRUE, show.legend = TRUE) + theme_msa() + facet_msa(50)
#scale_fill_gradientn(name = "ACE2 binding", colors = c(colRD(75),colBU(25)))
msa_plot
dev.off()

# #Combine the ggtree and ggmsa plots using the cowplot package:
# combined_plot <- cowplot::plot_grid(msa_plot, tree_heatmap_plot, ncol = 1, align = "v", axis = "l", rel_heights = c(3, 2))
# ggsave("combined_plot.png", combined_plot, width = 10, height = 10, dpi = 300)

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