Create a Dendogram from Genome
I wanted to play around with genomic data:
Species_A = ctnngtggaccgacaagaacagtttcgaatcggaagcttgcttaacgtag
Species_B = ctaagtggactgacaggaactgtttcgaatcggaagcttgcttaacgtag
Species_C = ctacgtggaccgacaagaacagtttcgactcggaagcttgcttaacgtag
Species_D = ctacgtggaccgacaagaacagtttcgactcggaagcttgcttaacgccg
Species_E = ctgtgtggancgacaaggacagttccaaatcggaagcttgcttaacacag
I wanted to create a dendrogram based on how close these organisms are related to each other given the genome sequence above. What I did first was to count the number of a's, c's, t's and g's of each species then I created an array, then plotted a dendrogram:
gen_size1 = len(Species_A)
a1 = float(Species_A.count('a'))/float(gen_size1)
c1 = float(Species_A.count('c'))/float(gen_size1)
g1 = float(Species_A.count('g'))/float(gen_size1)
t1 = float(Species_A.count('t'))/float(gen_size1)
.
.
.
gen_size5 = len(Species_E)
a5 = float(Species_E.count('a'))/float(gen_size5)
c5 = float(Species_E.count('c'))/float(gen_size5)
g5 = float(Species_E.count('g'))/float(gen_size5)
t5 = float(Species_E.count('t'))/float(gen_size5)
my_genes = np.array([[a1,c1,g1,t1],[a2,c2,g2,t2],[a3,c3,g3,t3],[a4,c4,g4,t4],[a5,c5,g5,t5]])
plt.subplot(1,2,1)
plt.title("Mononucleotide")
linkage_matrix = linkage(my_genes, "single")
print linkage_matrix
dendrogram(linkage_matrix,truncate_mode='lastp', color_threshold=1, labels=[Species_A, Species_B, Species_C, Species_D, Species_E], show_leaf_counts=True)
plt.show()
Species A and B are variants of the same organism and I am expecting that both should diverge from a common clade form the root, same goes with Species C and D which should diverge from another common clade from the root then with Species E diverging from the main root because it is not related to Species A to D. Unfortunately the dendrogram result was mixed up with Species A and E diverging from a common clade, then Species C, D and B in another clade (pretty messed up).
I have read about hierarchical clustering for genome sequence but I have observed that it only accommodates 2 dimensional system, unfortunately I have 4 dimensions which are a,c,t and g. Any other strategy for this? thanks for the help!
This is a fairly common problem in bioinformatics, so you should use a bioinformatics library like BioPython that has this functionality builtin.
First you create a multi FASTA file with your sequences:
import os
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import generic_dna
sequences = ['ctnngtggaccgacaagaacagtttcgaatcggaagcttgcttaacgtag',
'ctaagtggactgacaggaactgtttcgaatcggaagcttgcttaacgtag',
'ctacgtggaccgacaagaacagtttcgactcggaagcttgcttaacgtag',
'ctacgtggaccgacaagaacagtttcgactcggaagcttgcttaacgccg',
'ctgtgtggancgacaaggacagttccaaatcggaagcttgcttaacacag']
my_records = [SeqRecord(Seq(sequence, generic_dna),
id='Species_{}'.format(letter), description='Species_{}'.format(letter))
for sequence, letter in zip(sequences, 'ABCDE')]
root_dir = r"C:\Users\BioGeek\Documents\temp"
filename = 'my_sequences'
fasta_path = os.path.join(root_dir, '{}.fasta'.format(filename))
SeqIO.write(my_records, fasta_path, "fasta")
This creates the file C:\Users\BioGeek\Documents\temp\my_sequences.fasta that looks like this:
>Species_A
ctnngtggaccgacaagaacagtttcgaatcggaagcttgcttaacgtag
>Species_B
ctaagtggactgacaggaactgtttcgaatcggaagcttgcttaacgtag
>Species_C
ctacgtggaccgacaagaacagtttcgactcggaagcttgcttaacgtag
>Species_D
ctacgtggaccgacaagaacagtttcgactcggaagcttgcttaacgccg
>Species_E
ctgtgtggancgacaaggacagttccaaatcggaagcttgcttaacacag
Next, use the command line tool ClustalW to do a multiple sequence alignment:
from Bio.Align.Applications import ClustalwCommandline
clustalw_exe = r"C:\path\to\clustalw-2.1\clustalw2.exe"
assert os.path.isfile(clustalw_exe), "Clustal W executable missing"
clustalw_cline = ClustalwCommandline(clustalw_exe, infile=fasta_path)
stdout, stderr = clustalw_cline()
print stdout
This prints:
CLUSTAL 2.1 Multiple Sequence Alignments
Sequence format is Pearson
Sequence 1: Species_A 50 bp
Sequence 2: Species_B 50 bp
Sequence 3: Species_C 50 bp
Sequence 4: Species_D 50 bp
Sequence 5: Species_E 50 bp
Start of Pairwise alignments
Aligning...
Sequences (1:2) Aligned. Score: 90
Sequences (1:3) Aligned. Score: 94
Sequences (1:4) Aligned. Score: 88
Sequences (1:5) Aligned. Score: 84
Sequences (2:3) Aligned. Score: 90
Sequences (2:4) Aligned. Score: 84
Sequences (2:5) Aligned. Score: 78
Sequences (3:4) Aligned. Score: 94
Sequences (3:5) Aligned. Score: 82
Sequences (4:5) Aligned. Score: 82
Guide tree file created: [C:\Users\BioGeek\Documents\temp\my_sequences.dnd]
There are 4 groups
Start of Multiple Alignment
Aligning...
Group 1: Sequences: 2 Score:912
Group 2: Sequences: 2 Score:921
Group 3: Sequences: 4 Score:865
Group 4: Sequences: 5 Score:855
Alignment Score 2975
CLUSTAL-Alignment file created [C:\Users\BioGeek\Documents\temp\my_sequences.aln]
The my_sequences.dnd file ClustalW creates, is a standard Newick tree file and Bio.Phylo can parse these:
from Bio import Phylo
newick_path = os.path.join(root_dir, '{}.dnd'.format(filename))
tree = Phylo.read(newick_path, "newick")
Phylo.draw_ascii(tree)
Which prints:
____________ Species_A
____|
| |_____________________________________ Species_B
|
_| ____ Species_C
|_________|
| |_________________________ Species_D
|
|__________________________________________________________________ Species_E
Or, if you have matplotlib or pylab installed, you can create a graphic using the draw function:
tree.rooted = True
Phylo.draw(tree, branch_labels=lambda c: c.branch_length)
which produces:
This dendrogram clearly illustrates what you observed: that species A and B are variants of the same organism and both diverge from a common clade from the root. Same goes with Species C and D, both diverge from another common clade from the root. Finally, Species E diverges from the main root because it is not related to Species A to D.