def Visualiser_sekvens(gen):
import warnings
from Bio import BiopythonParserWarning
warnings.simplefilter('ignore', BiopythonParserWarning)
if gen == 'alle':
fil = 'Artemisia%20annua.gb'
graphic_record = ChangeFeatures().translate_record(fil)
ax, _ = graphic_record.plot(figure_width=20)
ax.figure.tight_layout()
elif gen == "aldh1":
sequence = "CTGTGTCTAGATTTACGGTTTTGTTGAGTATGGAGTATTTATCCCTGTGTCTAGATTTACGGTTTGAAGACTCAGGAAACTCTCATTAAGCGATCAACGTAGCATGATCATCAAAAGCATGGTTTTGTAAACTCGACATGTCAATGTACCAGCCGATCCAAGTATCCAAGCAATTGGTTCACCACACCAAAAGAGTTTTACACTTAAAAACAACAATTAATTCTAAATAGTCTATGTAATGAAATATGTTTTGTGTGGGTTAGTTTAGTTCATAGTTGCGCCATAAGTATTTACAGCAA"
record = GraphicRecord(sequence=sequence,
features=[
GraphicFeature(start=0,
end=28,
strand=+1,
color='#ffd700',
label="Promotor"),
GraphicFeature(start=29,
end=299,
strand=+1,
color="#ffcccc",
label="aldh1")
])
ax, _ = record.plot(figure_width=50)
record.plot_sequence(ax)
record.plot_translation(ax, (29, 299), fontdict={'weight': 'bold'})
elif gen == 'CYP71AV1':
sequence = "ATTTTTGGGGGCCCCCCCCCATTTTTTGGGGGGCGCGCGATGAAGTTGGTCATTCGAAATATACTTCCAAAATATGAAGTTGGTCATTCGAAATATACTTCCAAACAACCGAGCTGGTCAGGTAGATTTTGTTTCAGATGAAGATGCAATCCACCGTTGGGGGAGTTTCATGAATAACAATCGCAAATAAGATATATTGTTGATTCTTGATGATGTTTGGTCTGATACCATCATCACCGACCTCCAATTCAGGTCACGTGGATACAAGATCCTCGTGACCTCTGAAACAACCTTTAAGAGATTCGATACATATAAAGTGAGACCTCTCAGTGTTCAAGATGCCATCAATCTGTTATGCTATTCAACACTTTCGGAGCGTGCAAGTCAAGCCACAAATGACATACAGACCTTGTTGACAAGGTGAAATTTCAAATTATTCCAAGATTCATGTTTCATACCTTTATAAGAAAGTAATATCTAAACCATATTAACAAATACTAACAATTAACTTTCAAATGTTTTTGTAGTTAACCAAATGTTGCAAGAAGAATCCGCTCGCCTTAAGTGTCATTGGTGGTCGCCTAAAGGGGACACAAATGGAAAGTTGGCATCATACACTGAAAAAGCTATCTCAAGCCACACACCCTCTTATCGACCTTCCTTTGGATGAGGCAAACAGATTTCATCTCGCAAGAGCTCTCGGTTTACTCAAAGATGATGAACGCAACAGCCCCAGAAGTTCAACCTCGAAATTGACCCGATCTTACCAAGTCA"
record = GraphicRecord(sequence=sequence,
features=[
GraphicFeature(start=1,
end=38,
strand=+1,
color='#cffccc',
label="Promotor"),
GraphicFeature(start=39,
end=774,
strand=+1,
color="#cff77d",
label="CYP71AV1")
])
ax, _ = record.plot(figure_width=100)
record.plot_sequence(ax)
record.plot_translation(ax, (39, 774), fontdict={'weight': 'bold'})
return
def test_cropping_on_the_edge():
repeated_sequence = "ATGCATGCAT"
graphic_record = GraphicRecord(
sequence_length=1000, sequence=100 * repeated_sequence
)
small_gr = graphic_record.crop((990, 1000))
assert small_gr.sequence == repeated_sequence
def test_cropping():
features = [
GraphicFeature(start=5,
end=20,
strand=+1,
color="#ffd700",
label="Small feature"),
GraphicFeature(
start=20,
end=500,
strand=+1,
color="#ffcccc",
label="Gene 1 with a very long name",
),
GraphicFeature(start=400,
end=700,
strand=-1,
color="#cffccc",
label="Gene 2"),
GraphicFeature(start=600,
end=900,
strand=+1,
color="#ccccff",
label="Gene 3"),
]
# PLOT AND EXPORT A LINEAR VIEW OF THE CONSTRUCT
record = GraphicRecord(sequence_length=1000, features=features)
cropped_record = record.crop((425, 650))
assert len(cropped_record.features) == 3
def plot(self, ax, gr: GenomeRange, **kwargs):
self.ax = ax
df = self.fetch_plot_data(gr)
if self.has_prop("row_filter"):
filters = self.properties["row_filter"]
for filter_ in filters.split(";"):
try:
op_idx = list(re.finditer("[=><!]", filter_))[0].start()
l_ = filter_[:op_idx].strip()
r_ = filter_[op_idx:]
df = eval(f'df[df["{l_}"]{r_}]')
except IndexError:
log.warning(f"row filter {filter_} is not valid.")
region_length = gr.end - gr.start
len_ratio_th = self.properties["length_ratio_thresh"]
df = df[(df["end"] - df["start"]) > region_length * len_ratio_th]
features = []
for _, row in df.iterrows():
gf = GraphicFeature(
start=row['start'],
end=row['end'],
strand=(1 if row['strand'] == '+' else -1),
label=row['gene_name'],
color=random.choice(self.colors),
)
features.append(gf)
record = GraphicRecord(sequence_length=gr.end - gr.start,
features=features,
first_index=gr.start)
record.plot(ax=ax, with_ruler=False, draw_line=False)
self.plot_label()
def contig_visualization_onefile(contig_array, genome, drug, rgi):
save_path = "contigend_visualizations_single_genome/"
Features = []
temp_array_totrack_length = []
contig_array.reset_index(drop=True, inplace=True)
for i in range(len(contig_array)):
#print(contig_array["GeneStart"][i])
a = GraphicFeature(start=contig_array["GeneStart"][i],
end=contig_array["GeneEnd"][i],
strand=contig_array["Strand"][i],
color=contig_array["Genecolor"][i],
label=str(contig_array["GeneName"][i]))
Features.append(a)
temp_array_totrack_length.append(
(contig_array["GeneStart"][i], contig_array["GeneEnd"][i]))
length = temp_array_totrack_length[-1][1] - temp_array_totrack_length[0][0]
record = GraphicRecord(first_index=temp_array_totrack_length[0][0],
sequence_length=length,
features=Features)
ax, _ = record.plot(figure_width=20, strand_in_label_threshold=7)
temp_name = drug + "_" + str(rgi) + ".png"
title = drug + "_" + str(rgi)
ax.set_title(title)
name = os.path.join(save_path, temp_name)
ax.figure.savefig(name)
def contigend_visualization(contig_array, end_direction, genome, reverse_term,
drug, rgi_gene):
Features = []
temp_array_totrack_length = []
contig_array.reset_index(drop=True, inplace=True)
if end_direction == "upward":
b = GraphicFeature(start=contig_array["GeneStart"][0] - 1500,
end=contig_array["GeneStart"][0],
strand=+1,
color="#0A090A",
label="Contig_Ends")
Features.append(b)
temp_array_totrack_length.append((contig_array["GeneStart"][0] - 1500,
contig_array["GeneStart"][0]))
for i in range(len(contig_array)):
#print(contig_array["GeneStart"][i])
a = GraphicFeature(start=contig_array["GeneStart"][i],
end=contig_array["GeneEnd"][i],
strand=contig_array["Strand"][i],
color=contig_array["Genecolor"][i],
label=str(contig_array["GeneName"][i]))
Features.append(a)
temp_array_totrack_length.append(
(contig_array["GeneStart"][i], contig_array["GeneEnd"][i]))
if end_direction == "downward":
b = GraphicFeature(start=contig_array["GeneEnd"].iloc[-1] + 1500,
end=contig_array["GeneEnd"].iloc[-1] + 1500,
strand=+1,
color="#0A090A",
label="Contig_Ends")
Features.append(b)
temp_array_totrack_length.append(
(contig_array["GeneEnd"].iloc[-1] + 1500,
contig_array["GeneEnd"].iloc[-1] + 1500))
length = temp_array_totrack_length[-1][1] - temp_array_totrack_length[0][0]
record = GraphicRecord(first_index=temp_array_totrack_length[0][0],
sequence_length=length,
features=Features)
ax, _ = record.plot(figure_width=20, strand_in_label_threshold=7)
if (reverse_term == "-1"):
ax.invert_xaxis()
title = drug + "_" + genome
ax.set_title(title)
temp_name = str(genome) + ".png"
save_path = "contigend_visualizations/"
name = os.path.join(save_path, temp_name)
ax.figure.savefig(name)
def vis_pegRNA2(df,genome_fasta=None,**kwargs):
"""Given one instance of easy-prime prediction (rawX format), generate DNA visualization
Input
--------
the data frame contains 4 rows: RTT, PBS, sgRNA, ngRNA
"""
pegRNA_id = df.index.tolist()[0]
variant_id = pegRNA_id.split("_")[0]
chr = df['CHROM'][0]
start = df['start'].min()
start -= start%10
start -= 1
end = df['end'].max()
end -= end%10
end += 10
variant_pos = df.POS.min()
ref = df.REF[0]
alt = df.ALT[0]
predicted_efficiency = df.predicted_efficiency[0]*100
pos = variant_pos-start
sequence = get_fasta_single(chr,start,end,genome_fasta).upper()
fig,ax = plt.subplots()
feature_list = []
for s,r in df.iterrows():
r_start = r.start-start
r_end = r_start+(r.end-r.start)
r_strand = get_strand(r.strand)
gf = GraphicFeature(start=r_start, end=r_end, strand=r_strand,
color=my_colors[r.type],label=r.type)
feature_list.append(gf)
record = GraphicRecord(sequence=sequence, features=feature_list)
# ax, _ = record.plot(figure_width=int(len(sequence)/5))
record.plot(ax=ax,figure_width=int(len(sequence)/5))
return 0
record.plot_sequence(ax)
ax.fill_between((pos-1.5, pos-0.5), +1000, -1000, alpha=0.5,color=my_colors['variant'])
locs, labels = plt.xticks()
new_labels = []
flag = True
for i in locs:
if flag:
new_labels.append("%s %s"%(chr,int(start+i+1)))
flag=False
else:
new_labels.append(int(start+i+1))
plt.xticks(locs,new_labels)
plt.title("ID: %s, CHR: %s, POS: %s, REF: %s, ALT: %s \n Predicted efficiency: %.1f"%(variant_id,chr,variant_pos,ref,alt,predicted_efficiency)+"%")
my_stringIObytes = io.BytesIO()
ax.figure.savefig(my_stringIObytes, format='png',bbox_inches='tight')
my_stringIObytes.seek(0)
img_string = base64.b64encode(my_stringIObytes.read())
return "data:image/png;base64,%s"%(img_string.decode("utf-8"))
def show_feature(self, figure_width=8, xlabel=""):
if len(self._features) < 1:
print("No feautres to show")
return
record = GraphicRecord(sequence_length=self._max_length,
features=self._features)
ax, _ = record.plot(figure_width=figure_width)
ax.set_xlabel(xlabel, fontweight="bold", fontsize=16)
return ax
def Protein_structure(ID,exons,domains,path,trID,exons_in_interface):
#save Image of protein Structure
features1,features2,fend=Visualize_transciript(exons,domains,exons_in_interface)
fig, (ax1, ax2) = plt.subplots(
2, 1,figsize=(16, 3.5))
record = GraphicRecord(sequence_length=fend, features=features1,)
record.plot(ax=ax1,figure_width=23,with_ruler=False)
record = GraphicRecord(sequence_length=fend, features=features2,)
record.plot(ax=ax2,figure_width=23,with_ruler=True,annotate_inline=True)
ax1.title.set_text('Coding Exons')
ax1.title.set_position([.5, -0.4])
ax2.title.set_text('Pfam Domains')
ax2.title.set_position([.5, -0.5])
fig.savefig(path+trID, bbox_inches='tight')
return
def test_to_biopython_record():
record = GraphicRecord(
sequence_length=50,
features=[
GraphicFeature(start=5, end=20, strand=+1, label="a"),
GraphicFeature(start=20, end=500, strand=+1, label="b"),
GraphicFeature(start=400, end=700, strand=-1, label="c"),
],
)
biopython_record = record.to_biopython_record(sequence=50 * "A")
features = sorted([(f.location.start, f.location.end,
f.qualifiers["label"])
for f in biopython_record.features])
assert features == [(5, 20, "a"), (20, 500, "b"), (400, 700, "c")]
def vis_sccmec(faa_file_sccmec, annotation_file, length_sccmec, core_proteins,
blastp):
# use faa file from prokka annotation on sccmec
faa_dict_sccmec = fasta2dict(faa_file_sccmec)
# update annotation based on core proteins in cluster
datafile = annotation_data(annotation_file)
update_datafile = update_annotation(datafile, blastp, faa_dict_sccmec,
core_proteins)
# create features object to visualisation using dna_features_viewer
features = []
for line in update_datafile:
id_, sense, start, end, size, length, gene = line
if gene == 'core-proteins':
color = '#ff8848'
label = None
else:
try:
color = colors[gene]
except KeyError:
color = 'grey'
try:
label = labels[gene]
except KeyError:
label = None
if '-' in sense:
features.append(
GraphicFeature(start=int(start),
end=int(end),
strand=-1,
color=color,
label=label))
if '+' in sense:
features.append(
GraphicFeature(start=int(start),
end=int(end),
strand=+1,
color=color,
label=label))
record = GraphicRecord(sequence_length=length_sccmec, features=features)
ax, _ = record.plot(figure_width=20)
id_ = annotation_file.split('_')[-1].split('.')[0]
filename = 'SCCmec_{}.png'.format(id_)
ax.figure.savefig(filename, dpi=300)
def contigend_visualization(contig_array, end_direction, genome):
Features = []
temp_array_totrack_length = []
contig_array.reset_index(drop=True, inplace=True)
if end_direction == "upward":
b = GraphicFeature(start=contig_array["GeneStart"][0] - 1500,
end=contig_array["GeneStart"][0],
strand=+1,
color="#0A090A",
label="Contig_Ends")
Features.append(b)
temp_array_totrack_length.append((contig_array["GeneStart"][0] - 1500,
contig_array["GeneStart"][0]))
for i in range(len(contig_array)):
#print(contig_array["GeneStart"][i])
a = GraphicFeature(start=contig_array["GeneStart"][i],
end=contig_array["GeneEnd"][i],
strand=contig_array["Strand"][i],
color=contig_array["Genecolor"][i],
label=str(contig_array["GeneName"][i]))
Features.append(a)
temp_array_totrack_length.append(
(contig_array["GeneStart"][i], contig_array["GeneEnd"][i]))
if end_direction == "downward":
b = GraphicFeature(start=contig_array["GeneEnd"].iloc[-1] + 1500,
end=contig_array["GeneEnd"].iloc[-1] + 2500,
strand=+1,
color="#0A090A",
label="Contig_Ends")
Features.append(b)
temp_array_totrack_length.append(
(contig_array["GeneEnd"].iloc[-1] + 1500,
contig_array["GeneEnd"].iloc[-1] + 2500))
length = temp_array_totrack_length[-1][1] - temp_array_totrack_length[0][0]
record = GraphicRecord(first_index=temp_array_totrack_length[0][0],
sequence_length=length,
features=Features)
ax, _ = record.plot(figure_width=20, strand_in_label_threshold=7)
name = str(genome) + ".png"
ax.figure.savefig(name)
def create_dna_structure(file_name):
results = request.get_json()
features = []
for i, spacerRepeat in enumerate(results['spacerRepeats']):
features.append(GraphicFeature(start=spacerRepeat['position'], end=spacerRepeat['position']+len(spacerRepeat['repeat']),
strand=+1, color="#cffccc", label="Repeat_"+str(i+1)))
if 'spacer' in spacerRepeat:
features.append(GraphicFeature(start=spacerRepeat['position']+len(spacerRepeat['repeat'])+1,
end=spacerRepeat['position']+len(spacerRepeat['repeat'])+spacerRepeat['lengths'][1], strand=+1, color="#ccccff",
label="Spacer_"+str(i+1)))
record = GraphicRecord(sequence_length=results['length'], features=features)
record = record.crop((results['spacerRepeats'][0]['position']-50,
results['spacerRepeats'][len(results['spacerRepeats'])-1]['position']+
len(results['spacerRepeats'][len(results['spacerRepeats'])-1]['repeat'])+50))
ax, _ = record.plot(figure_width=10)
ax.figure.savefig('static/logos/'+str(file_name)+'.png', bbox_inches='tight')
return jsonify('{"success":1}')
def haplotype_blocks_fig(model, ref_seq):
s1, s2 = model.align_alleles()
record = GraphicRecord(sequence=ref_seq,
sequence_length=len(ref_seq),
features=[
GraphicFeature(start=0,
end=len(s1),
strand=+1,
color='#ffcccc'),
GraphicFeature(start=0,
end=len(s2),
strand=+1,
color='#cffccc')
])
ax, _ = record.plot(figure_width=5)
record.plot_sequence(ax)
record.plot_translation(ax, (8, 23), fontdict={'weight': 'bold'})
ax.figure.savefig('haplotypes.png', bbox_inches='tight')
def visualize_mrna_strand(self, dpi=120, cmap='viridis'):
features = [
GraphicFeature(start=0,
end=self.tag_length,
color=self._colors[0],
label='Tag'),
GraphicFeature(start=self.tag_length,
end=self.total_length,
color=self._colors[1],
label='Protein'),
]
probe = self.probe_loc
cmap = cm.get_cmap(cmap)
color = np.where(probe == 1)[0]
location = np.where(probe == 1)[1]
ncolors = probe.shape[0]
colors = cmap(np.linspace(.01, .95, ncolors))
colorlabels = ['Color %d' % i for i in range(ncolors)]
for c, loc in zip(color, location):
features = features + [
GraphicFeature(start=loc,
end=loc + 2,
color=colors[c],
linecolor=colors[c]),
]
record = GraphicRecord(sequence_length=self.total_length,
features=features)
fig, ax = plt.subplots(1, dpi=dpi)
for c in range(ncolors):
ax.plot([0, 0], [0, 0], color=colors[c]) #fix the legend colors
colorlabels = ['Color %d' % i for i in range(ncolors)]
ax, _ = record.plot(figure_width=6, ax=ax)
ax.axes.legend(colorlabels, loc=7)
ax.text(0, 5, 'Transcript Name: %s' % self.name)
ax.text(0, 4, 'Total Length: %d codons' % self.total_length)
ax.text(0, 3, 'Seq: %s ...' % self.aa_seq[:10])
fig.show()
def test_split_overflowing_features():
features = [
GraphicFeature(start=10, end=20, strand=+1, label="a"),
GraphicFeature(start=40, end=55, strand=+1, label="b"),
GraphicFeature(start=-20, end=2, strand=+1, label="c"),
]
# PLOT AND EXPORT A LINEAR VIEW OF THE CONSTRUCT
record = GraphicRecord(sequence_length=50, features=features)
record.split_overflowing_features_circularly()
new_features_locations_and_labels = sorted([(f.start, f.end, f.label)
for f in record.features])
assert new_features_locations_and_labels == [
(0, 2, "c"),
(0, 5, "b"),
(10, 20, "a"),
(30, 49, "c"),
(40, 49, "b"),
]
def test_sequence_and_translation_plotting():
from dna_features_viewer import (
GraphicFeature,
GraphicRecord,
CircularGraphicRecord,
)
features = [
GraphicFeature(
start=5, end=10, strand=+1, color="#ffd700", label="bbS-1"
),
GraphicFeature(
start=8, end=15, strand=+1, color="#ffcccc", label="CrC"
),
]
record = GraphicRecord(sequence=7 * "ATGC", features=features)
ax, _ = record.plot(figure_width=5)
record.plot_sequence(ax)
record.plot_translation(ax, (8, 23), fontdict={"weight": "bold"})
def test_by_hand(tmpdir):
"""Test building a GraphicRecord "by hand" """
features = [
GraphicFeature(start=5,
end=20,
strand=+1,
color="#ffd700",
label="Small feature"),
GraphicFeature(
start=20,
end=500,
strand=+1,
color="#ffcccc",
label="Gene 1 with a very long name",
),
GraphicFeature(start=400,
end=700,
strand=-1,
color="#cffccc",
label="Gene 2"),
GraphicFeature(start=600,
end=900,
strand=+1,
color="#ccccff",
label="Gene 3"),
]
# PLOT AND EXPORT A LINEAR VIEW OF THE CONSTRUCT
record = GraphicRecord(sequence_length=1000, features=features)
record.plot(figure_width=5, with_ruler=False) # lazy, just for coverage
ax, _ = record.plot(figure_width=5)
target_file = os.path.join(str(tmpdir), "by_hand.png")
ax.figure.savefig(target_file)
# PLOT AND EXPORT A CIRCULAR VIEW OF THE CONSTRUCT
circular_rec = CircularGraphicRecord(sequence_length=1000,
features=features)
ax2, _ = circular_rec.plot(figure_width=4)
ax2.figure.tight_layout()
target_file = os.path.join(str(tmpdir), "by_hand_circular.png")
ax2.figure.savefig(target_file, bbox_inches="tight")
def visualize_markup(self, index=1):
sequences = []
for seq in self.seq:
gff_record = next(
(x for x in self.gff if str(x.id) == str(seq.id)), None)
if gff_record:
# Filter 'gene' features
genes = [x for x in gff_record.features if x.type == 'gene']
features = []
for gene in genes:
start, end, strand = gene.location.start, gene.location.end, gene.location.strand
features.append(
GraphicFeature(start,
end,
strand,
label=gene.qualifiers['Name'],
color="#cffccc"))
sequences.append(features)
output_file("test.html")
record = GraphicRecord(sequence_length=1000, features=sequences[index])
show(record.plot_with_bokeh(figure_width=5))
def plot_align(self, ax, genome_range):
gr = genome_range
df = self.fetch_intervals(gr)
df_ = df[np.bitwise_and(df['flag'], 0b100) == 0]
len_thresh = self.properties.get("length_ratio_thresh", 0.005)
df_ = df_[df_['seq'].str.len() > (gr.length * len_thresh)]
if df_.shape[0] <= 0:
return
rev_flag = np.bitwise_and(df['flag'], 0b10000) != 0
features = []
for idx, row in df_.iterrows():
start = row['pos'] - gr.start
end = row['pos'] + len(row['seq']) - gr.start
strand = -1 if rev_flag.iloc[idx] else 1
gf = GraphicFeature(
start=start,
end=end,
strand=strand,
color=self.properties['color'],
)
features.append(gf)
record = GraphicRecord(sequence_length=gr.length, features=features)
record.plot(ax=ax, with_ruler=False, draw_line=False)
def plot_align(self, ax, gr: GenomeRange):
assert isinstance(
gr, GenomeRange), "The input gr should be type GenomeRange"
df = self.fetch_plot_data(gr)
df_ = df[np.bitwise_and(df['flag'], 0b100) == 0]
len_thresh = self.properties["length_ratio_thresh"]
df_ = df_[df_['seq'].str.len() > (gr.length * len_thresh)]
if df_.shape[0] <= 0:
return
rev_flag = np.bitwise_and(df['flag'], 0b10000) != 0
features = []
for idx, row in df_.iterrows():
start = row['pos'] - gr.start
end = row['pos'] + len(row['seq']) - gr.start
strand = -1 if rev_flag.iloc[idx] else 1
gf = GraphicFeature(
start=start,
end=end,
strand=strand,
color=self.properties['color'],
)
features.append(gf)
record = GraphicRecord(sequence_length=gr.length, features=features)
record.plot(ax=ax, with_ruler=False, draw_line=False)
def probe_graph(file_name, path):
#file_name = "results_riftl_test.txt"
#file_name = sys.argv[1]
f = open(file_name, "r")
probes = []
for line in f:
print(line)
line_info = line.split(':')
#print(line)
#print(line_info)
if (line_info[0] == "Sequence"):
sequence = line_info[1]
elif (line_info[0] == "Initiator"):
initiator1 = line_info[1].upper()
initiator2 = line_info[2].upper()
elif (line_info[0] == "Probes"):
probes.append(line_info[1].upper())
probes.append(line_info[3].upper())
probes.append(line_info[2])
probes.append(line_info[4])
elif (line_info[0] == "Name"):
name = line_info[1].rstrip()
file_write = path + "/" + name + "_Probe_Set" ".txt"
w = open(file_write, "w+")
"""print(sequence)
print(initiator1)
print(initiator2)
print(probe1)
print(probe2)
print(start)"""
x = 1
print(probes)
print(name)
for a in range(0, len(probes), 4):
gstart = 28
gend = 3
probe1 = probes[a]
probe2 = probes[a + 1]
probe1 = Seq(probe1)
probe1 = probe1.complement()
probe2 = Seq(probe2)
probe2 = probe2.complement()
probe1 = str(probe1)
probe2 = str(probe2)
start = probes[a + 2]
end = probes[a + 3]
start = int(start)
start -= 4
if start <= 0:
gend = 0 - start - 1
gstart = gend + 25
start = 0
end = int(end)
subseq = sequence[start:end]
subseq = subseq.upper()
record = GraphicRecord(
sequence=subseq,
features=[
GraphicFeature(start=gstart,
end=gend,
strand=+1,
color='#ffcccc',
label=probe1),
GraphicFeature(start=gstart + 28,
end=gstart + 2,
strand=+1,
color='#ccccff',
label=probe2),
GraphicFeature(start=gstart,
end=gstart,
strand=-1,
color='m',
label="space"),
GraphicFeature(start=gstart + 1,
end=gstart + 1,
strand=-1,
color='m',
label="space"),
GraphicFeature(start=gstart,
end=(gstart - len(initiator1)),
strand=-1,
color='y',
label=initiator1),
GraphicFeature(start=gstart + 2,
end=(gstart + 2 + len(initiator1)),
strand=+1,
color='y',
label=initiator2)
])
ax, _ = record.plot(figure_width=10)
record.plot_sequence(ax)
total1 = initiator1 + "TT" + probe1[::-1]
total2 = probe2[::-1] + "TT" + initiator2
w.write("PROBE SET" + str(x) + "\n")
w.write("Probe1:" + total1 + "\n")
w.write("Probe2:" + total2 + "\n")
#print(total2)
tosave = path + "/" + name + "Plots for Probes" + str(x)
x += 1
ax.figure.savefig(tosave, bbox_inches='tight')
#break
w.close()
from dna_features_viewer import GraphicFeature, GraphicRecord
import matplotlib.pyplot as plt
record = GraphicRecord(sequence=250 * "ATGC",
features=[
GraphicFeature(start=5,
end=20,
strand=+1,
color="#ffd700",
label="Small feature"),
GraphicFeature(
start=20,
end=500,
strand=+1,
color="#ffcccc",
label="Gene 1 with a very long name"),
GraphicFeature(start=400,
end=700,
strand=-1,
color="#cffccc",
label="Gene 2"),
GraphicFeature(start=600,
end=900,
strand=+1,
color="#ccccff",
label="Gene 3")
])
zoom_start, zoom_end = 398, 428 # coordinates of the "detail"
cropped_record = record.crop((zoom_start, zoom_end))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 3))
import matplotlib.pyplot as plt
from dna_features_viewer import (GraphicFeature, GraphicRecord,
CircularGraphicRecord)
features = [
GraphicFeature(start=20,
end=500,
strand=+1,
color="#ffcccc",
label="Gene 1 with a name"),
GraphicFeature(start=400,
end=700,
strand=-1,
color="#cffccc",
label="Gene 2"),
GraphicFeature(start=600,
end=900,
strand=+1,
color="#0000ff",
label="Gene 3"),
]
record = GraphicRecord(sequence_length=1000, features=features)
record.default_box_color = None
record.default_font_family = 'Walter Turncoat'
with plt.xkcd():
plt.rcParams["font.family"] = 'Permanent Marker' # ruler font
plt.rcParams["xtick.labelsize"] = 'small'
ax, _ = record.plot(figure_width=5, annotate_inline=False)
ax.figure.tight_layout()
ax.figure.savefig("cartoon_style.png", dpi=200)
def plot(self,
ax=None,
plot_coverage=True,
plot_reference=False,
reference_ax=None,
figsize="auto",
features_filters=(),
features_properties=None,
reference_reads_shares="auto"):
"""Plot the sequencing matches.
Useful to get a general overview of the sequencing (coverage, mutations
etc.)
Parameters
----------
ax
Matplotlib ax on which to plot the alignments. If None, one will be
automatically created.
plot_coverage
If True, the plots will display in the background a filled blue line
indicating how many times each nucleotide of the sequence is covered
by the succesfull alignments.
plot_reference
If True, a schema of the reference record will be plotted, by default
above the reads plot.
reference_ax
If provided and plot_reference is True, the reference record will be
plotted on this ax.
figsize
Size of the final figure. Leave it to 'auto' for a figure of width
12 and automatically chosen height. Or e.g. (16, 'auto') for a figure
of width 12 and automatically chosen height
features_filters
List of functions (feature=>True/False). Features for which at least
one test is False will not appear in the reference record plot.
features_properties
DNA Features Viewer property functions that can be used to change
the appearance of the reference record.
reference_reads_shares
Relative shares of the pictures that should be occupied by the
reference and by the reads. It is an experimental parameter so
leave it to 'auto' for now.
"""
class AnnotationsGraphicTranslator(BiopythonTranslator):
def compute_feature_color(self, f):
return "#f9d277"
def compute_feature_label(self, f):
return BiopythonTranslator.compute_feature_label(f)[:20]
def compute_filtered_features(self, features):
def is_not_parameter(f):
label = "".join(f.qualifiers.get('label', ''))
return label not in ('cover', 'no_primer')
return [f for f in features if is_not_parameter(f)]
if plot_reference:
translator = AnnotationsGraphicTranslator(
features_filters=features_filters,
features_properties=features_properties)
grecord = translator.translate_record(self.reference)
if not self.linear:
grecord.split_overflowing_features_circularly()
if figsize == "auto":
figsize = (12, "auto")
if figsize[1] == "auto":
sequencing_ax_height = 2 + 0.35 * len(self.read_reference_matches)
if not plot_reference:
figure_height = sequencing_ax_height
else:
ref_ax, _ = grecord.plot(with_ruler=False,
figure_width=figsize[0])
ref_fig_height = ref_ax.figure.get_size_inches()[1]
figure_height = sequencing_ax_height + ref_fig_height
if reference_reads_shares == "auto":
reference_reads_shares = (int(100 * ref_fig_height),
int(100 * sequencing_ax_height))
plt.close(ref_ax.figure)
figsize = (figsize[0], figure_height)
elif reference_reads_shares == "auto":
reference_reads_shares = (1, 2)
if plot_reference:
if reference_ax is None:
gs = gridspec.GridSpec(sum(reference_reads_shares), 1)
fig = plt.figure(figsize=figsize, facecolor="w")
reference_ax = fig.add_subplot(gs[:reference_reads_shares[0]])
ax = fig.add_subplot(gs[reference_reads_shares[0]:])
grecord.plot(reference_ax, with_ruler=False, annotate_inline=True)
self.plot(ax=ax, plot_coverage=plot_coverage, plot_reference=False)
ax.set_xlim(reference_ax.get_xlim())
return ax
# so the first read in the list gets displayed on top
read_reference_matches = OrderedDict(
[item for item in list(self.read_reference_matches.items())[::-1]])
L = len(self.reference)
if ax is None:
fig, ax = plt.subplots(1, figsize=figsize)
ax.set_xlim(-2, L)
ax.set_ylim(0, len(read_reference_matches) + 2)
ax.set_yticks(range(1, len(read_reference_matches) + 1))
ax.set_yticklabels([name for name in read_reference_matches])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
gr_record = GraphicRecord(sequence_length=L, features=[])
for i, (read_name,
matches) in enumerate(read_reference_matches.items()):
y = i + 1
ax.axhline(y, ls=":", lw=0.5, color="#aaaaaa", zorder=-1000)
if matches.primer.metadata.get('available', False):
color = '#f7a3f6'
else:
color = "#a3c3f7"
for match in matches.read_matches:
gr_record.features = [
GraphicFeature(start=match.start,
end=match.end,
strand=match.strand,
color=color)
]
gr_record.split_overflowing_features_circularly()
for feature in gr_record.features:
gr_record.plot_feature(ax, feature, y, linewidth=0.2)
for match in matches.primer_matches:
feature = GraphicFeature(start=match.start,
end=match.end,
strand=match.strand,
color="#e85558")
gr_record.plot_feature(ax, feature, y, linewidth=0.2)
if plot_coverage:
ax.fill_between(range(len(self.coverage)),
self.coverage,
zorder=-2000,
alpha=0.2,
facecolor="#a3c3f7")
return ax
from dna_features_viewer import GraphicFeature, GraphicRecord
record = GraphicRecord(sequence="ATGCATGCATGCATGCATGCATGCATGC", features=[
GraphicFeature(start=5, end=10, strand=+1, color='#ffcccc'),
GraphicFeature(start=8, end=15, strand=+1, color='#ccccff')
])
ax, _ = record.plot(figure_width=5)
record.plot_sequence(ax)
record.plot_translation(ax, (8, 23), fontdict={'weight': 'bold'})
ax.figure.savefig('sequence_and_translation.png', bbox_inches='tight')
GraphicFeature(start=14920, end=14947, strand=+1, color="purple"),
GraphicFeature(start=14956, end=14969, strand=+1, color="purple"),
GraphicFeature(start=15305, end=15442, strand=+1, color="purple"),
GraphicFeature(start=15804, end=15836, strand=+1, color="purple"),
GraphicFeature(start=15903, end=16204, strand=+1, color="purple"),
GraphicFeature(start=16222, end=16665, strand=+1, color="purple"),
GraphicFeature(start=16666, end=16712, strand=+1, color="purple"),
GraphicFeature(start=16823, end=17181, strand=+1, color="purple"),
GraphicFeature(start=17192, end=17448, strand=+1, color="purple"),
GraphicFeature(start=18106, end=18148, strand=+1, color="purple"),
GraphicFeature(start=18851, end=19485, strand=+1, color="purple"),
GraphicFeature(start=19501, end=19522, strand=+1, color="purple"),
GraphicFeature(start=19535, end=19630, strand=+1, color="purple"),
GraphicFeature(start=19906, end=20212, strand=+1, color="purple"),
GraphicFeature(start=20265, end=20324, strand=+1, color="purple"),
GraphicFeature(start=20347, end=20364, strand=+1, color="purple"),
GraphicFeature(start=20526, end=20656, strand=+1, color="purple"),
GraphicFeature(start=23548, end=23571, strand=+1, color="purple"),
GraphicFeature(start=23588, end=23595, strand=+1, color="purple"),
GraphicFeature(start=23624, end=23650, strand=+1, color="purple"),
GraphicFeature(start=23671, end=23688, strand=+1, color="purple"),
GraphicFeature(start=23707, end=23736, strand=+1, color="purple"),
GraphicFeature(start=24257, end=24624, strand=+1, color="purple"),
GraphicFeature(start=24633, end=24653, strand=+1, color="purple"),
GraphicFeature(start=24676, end=25173, strand=+1, color="purple"),
]
record = GraphicRecord(sequence_length=25525, features=features)
ax, _ = record.plot(figure_width=20)
ax.figure.savefig("images.png")
def upload(request):
posted = False
sequences = []
ids = []
organisms = []
gene_sequence = ''
locations = []
all_locations = []
matched_sequences = []
matched_organisms = []
database = []
matched_database = []
matched_ids = []
features = []
zipped = {}
figure_name = ''
sequence_nos = []
i = 0
if request.method == 'POST':
posted = True
uploaded_file = request.FILES['document']
position = int(request.POST.get('position'))
#position = int(position)*3
with open('myapp/Jaspar.txt', "r") as file:
for line in file:
line = line.split(';')
ids.append(line[0])
sequences.append(line[1])
organisms.append(line[2])
database.append(line[3].rstrip('\n'))
for line in uploaded_file:
line = line.decode('utf-8')
gene_sequence += line
gene_sequence = gene_sequence[0:position]
for sequence in sequences:
locations = [
m.start() for m in re.finditer(sequence, gene_sequence)
]
if locations != []:
matched_sequences.append(sequence)
all_locations.append(locations)
matched_organisms.append(organisms[i])
matched_database.append(database[i])
matched_ids.append(ids[i])
for location in locations:
features.append(
GraphicFeature(start=location,
end=location + len(sequence),
strand=+1,
color="#ffd700",
label=sequence))
i += 1
record = GraphicRecord(sequence_length=len(gene_sequence),
features=features)
ax, _ = record.plot(figure_width=30)
figure_name = uploaded_file.name + str(position) + '.png'
ax.figure.savefig('myapp/static/' + uploaded_file.name +
str(position) + '.png',
bbox_inches='tight')
print(matched_sequences)
print(matched_organisms)
print(all_locations)
print(matched_database)
sequence_nos = list(range(len(matched_sequences)))
zipped = tuple(
zip(matched_ids, matched_sequences, matched_organisms,
matched_database, all_locations, sequence_nos))
return render(
request, 'upload.html', {
'posted': posted,
'zipped': zipped,
'figure_name': figure_name,
'gene_sequence': gene_sequence
})
def get_map(phage_id, UPLOAD_FOLDER):
"""Creates and returns a map of the genome.
Args:
UPLOAD_FOLDER:
The folder containing all of the uploaded files.
Returns:
A dictionary containing an image of the genome map.
"""
features = []
for cds in db.session.query(Annotations).filter_by(
phage_id=phage_id).order_by(Annotations.left):
if cds.function != '@DELETED' and cds.status != 'trnaDELETED':
if cds.strand == '+':
if cds.status == "tRNA":
features.append(
GraphicFeature(start=cds.left,
end=cds.right,
strand=+1,
color="#7570b3",
label=cds.id))
else:
features.append(
GraphicFeature(start=cds.left,
end=cds.right,
strand=+1,
color="#1b9e77",
label=cds.id))
else:
if cds.status == "tRNA":
features.append(
GraphicFeature(start=cds.left,
end=cds.right,
strand=-1,
color="#7570b3",
label=cds.id))
else:
features.append(
GraphicFeature(start=cds.left,
end=cds.right,
strand=-1,
color="#d95f02",
label=cds.id))
fasta_file = helper.get_file_path("fasta", UPLOAD_FOLDER)
genome = SeqIO.read(fasta_file, "fasta").seq
sequence = str(genome)
record = GraphicRecord(sequence_length=len(sequence), features=features)
ax, _ = record.plot(figure_width=len(sequence) / 1000)
ax.figure.savefig(os.path.join(UPLOAD_FOLDER,
'sequence_and_translation.png'),
bbox_inches='tight')
image_byte_string = ""
with open(os.path.join(UPLOAD_FOLDER, 'sequence_and_translation.png'),
"rb") as image_file:
image_byte_string = base64.b64encode(image_file.read())
response_object = {}
response_object['status'] = "success"
response_object['image'] = str(image_byte_string)
return response_object
output_file_utr
]),
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
p.wait()
features = []
with open(output_file_utr) as fp:
next(fp)
for line in fp:
print(line)
content = line.split("\t")
features.append(
GraphicFeature(start=int(content[3]),
end=int(content[4]),
strand=+1,
color=get_color(content[8]),
label=re.sub(r'mmu-', '', content[1])))
record = GraphicRecord(sequence_length=len(str(utr.seq)),
features=features) # Circular
record.plot(figure_width=12)
plt.title(' '.join([utr.id, 'sequence']))
patch1 = mpatches.Patch(color="#00ff99", label='6mer')
patch2 = mpatches.Patch(color="#9999ff", label='7mer-1a')
patch3 = mpatches.Patch(color="#ff66cc", label='7mer-m8')
patch4 = mpatches.Patch(color="#ff0000", label='8mer-1a')
plt.legend(handles=[patch1, patch2, patch3, patch4])
plt.show()