def test_abund_similarity():
E1 = MinHash(n=5, ksize=20, track_abundance=True)
E2 = MinHash(n=5, ksize=20, track_abundance=True)
for i in [1]:
E1.add_hash(i)
for i in [1, 2]:
E2.add_hash(i)
assert round(E1.similarity(E1)) == 1.0
assert round(E1.similarity(E2), 2) == 0.5
assert round(E1.similarity(E1, ignore_abundance=True)) == 1.0
assert round(E1.similarity(E2, ignore_abundance=True), 2) == 0.5
def test_abund_similarity():
E1 = MinHash(n=5, ksize=20, track_abundance=True)
E2 = MinHash(n=5, ksize=20, track_abundance=True)
for i in [1]:
E1.add_hash(i)
for i in [1, 2]:
E2.add_hash(i)
assert round(E1.similarity(E1)) == 1.0
assert round(E1.similarity(E2), 2) == 0.5
assert round(E1.similarity(E1, ignore_abundance=True)) == 1.0
assert round(E1.similarity(E2, ignore_abundance=True), 2) == 0.5
def test_abund_similarity_zero():
E1 = MinHash(n=5, ksize=20, track_abundance=True)
E2 = MinHash(n=5, ksize=20, track_abundance=True)
for i in [1]:
E1.add_hash(i)
assert E1.similarity(E2) == 0.0
def test_abund_similarity_zero():
E1 = MinHash(n=5, ksize=20, track_abundance=True)
E2 = MinHash(n=5, ksize=20, track_abundance=True)
for i in [1]:
E1.add_hash(i)
assert E1.similarity(E2) == 0.0
def fetchneighborhood2(index, features_upstream=0, features_downstream=0):
cluster = iaa_positive_df.iloc[index, :]
acc = cluster['accession']
assembly = re.sub('.gbff', '_proteins.fa.indexprot', cluster['filename'])
#make the genome database from the .fa.index file
assembly_index_file = 'index_files/' + assembly
print(assembly_index_file)
db = pd.read_csv(assembly_index_file,
sep="!!",
header=None,
engine='python')
#db.columns = ["filename","assembly","accession","locus_tag","old_locus_tag","name","biosample","protein_name","coordinates","protein_id"]
db.columns = [
"filename", "assembly", "accession", "locus_tag", "old_locus_tag",
"name", "biosample", "protein_name", "coordinates", "protein_id",
"pseudogene", "protein_seq"
]
db['direction'] = [
-1 if re.match('complement', c) else 1 for c in db['coordinates']
]
db['start_coord'] = [
re.search('\d+?(?=\.\.(\d|\>))', str(c)).group(0)
for c in db['coordinates']
]
db['start_coord'] = [
re.sub('complement|>|<|\)|\(', "", c) for c in db['start_coord']
]
db['start_coord'] = db['start_coord'].astype(int)
db['end_coord'] = [
re.search('(?<=\.(\.|\>))\d+', str(c)).group(0)
for c in db['coordinates']
]
db['end_coord'] = [re.sub('>|<|\)|\(', "", c) for c in db['end_coord']]
db['end_coord'] = db['end_coord'].astype(int)
hit_list = cluster['hit_list']
query_list = cluster['query_list']
cluster_number = cluster['cluster_number']
hit_dict = dict(zip(hit_list, query_list))
genome = db.loc[db['accession'] == acc].copy()
start = genome[genome['locus_tag'] == hit_list[0]].index.values.astype(
int)[0] - features_upstream
stop = genome[genome['locus_tag'] == hit_list[-1]].index.values.astype(
int)[0] + features_downstream
neighborhood = genome.loc[start:stop, ].copy()
neighborhood['query_match'] = neighborhood['locus_tag'].map(hit_dict)
coord_list = list(
zip(neighborhood['start_coord'], neighborhood['end_coord'],
neighborhood['direction'], neighborhood['query_match']))
#function to find GC content of cluster vs genome
gbff_str = str(db['filename'][0][1:])
with open("gbff_files_unzipped/" + gbff_str) as file:
gbff_file = file.read()
genome_seq = "".join(re.findall("(?<=ORIGIN)[\s+\S+]+?(?=\/\/)",
gbff_file))
genome_seq = re.sub('\s|\d|\n', '', genome_seq)
Gg = genome_seq.count("g")
Gc = genome_seq.count("c")
Ga = genome_seq.count("a")
Gt = genome_seq.count("t")
genomeGC = (Gg + Gc) / (Gg + Gc + Ga + Gt)
start = min(coord_list)[0]
end = max(coord_list)[1]
regex_str = acc + "[\s+\S+]+\/\/"
all_cluster_fasta = re.findall(regex_str, gbff_file)[0]
all_cluster_fasta = re.findall("(?<=ORIGIN)[\s+\S+]+(?=\/\/)",
all_cluster_fasta)[0]
all_cluster_fasta = re.sub(" |\d|\n", "", all_cluster_fasta)
cluster_seq = all_cluster_fasta[start - 1:end - 1]
g = cluster_seq.count("g")
c = cluster_seq.count("c")
a = cluster_seq.count("a")
t = cluster_seq.count("t")
clusterGC = (g + c) / (g + c + a + t)
diffGC = abs(clusterGC - genomeGC)
#compare minhash values between cluster and genome
kmer_size = 5
n = 0
sc = 1
cluster_minhash = MinHash(n=n, ksize=kmer_size, scaled=sc)
cluster_minhash.add_sequence(cluster_seq, force=True)
cluster_minhash.add_sequence(complement(cluster_seq), force=True)
#
genome_minhash = MinHash(n=n, ksize=kmer_size, scaled=sc)
genome_minhash.add_sequence(genome_seq, force=True)
genome_minhash.add_sequence(complement(genome_seq), force=True)
minhash_sim = cluster_minhash.similarity(genome_minhash)
# genome_minus_cluster=re.sub(cluster_seq,'',genome_seq)
# #print(len(genome_seq)-len(genome_minus_cluster))
# genome_minus_cluster_minhash=MinHash(n=n, ksize=kmer_size,scaled=sc)
# genome_minus_cluster_minhash.add_sequence(genome_minus_cluster,force=True)
# genome_minus_cluster_minhash.add_sequence(complement(genome_minus_cluster),force=True)
# minhash_sim_minus_cluster=cluster_minhash.similarity(genome_minus_cluster_minhash)
#print(minhash_sim)
#compare tetranucleotide frequency between cluster and genomes
bases = ['a', 't', 'g', 'c']
four_mers = [''.join(p) for p in itertools.product(bases, repeat=4)]
four_mer_count_genome = np.add(
[genome_seq.count(i) for i in four_mers],
[complement(genome_seq).count(i) for i in four_mers])
four_mer_freq_genome = [
i / sum(four_mer_count_genome) for i in four_mer_count_genome
]
four_mer_count_cluster = np.add(
[cluster_seq.count(i) for i in four_mers],
[complement(cluster_seq).count(i) for i in four_mers])
four_mer_freq_cluster = [
i / sum(four_mer_count_cluster) for i in four_mer_count_cluster
]
four_mer_distance = scipy.spatial.distance.cityblock(
four_mer_freq_cluster, four_mer_freq_genome)
####
if sum(neighborhood[neighborhood['query_match'].notnull()]
['direction']) < 0:
neighborhood['actual_start_tmp'] = neighborhood['start_coord']
neighborhood['start_coord'] = neighborhood['end_coord'] * -1
neighborhood['end_coord'] = neighborhood['actual_start_tmp'] * -1
neighborhood['direction'] = neighborhood['direction'] * -1
neighborhood = neighborhood.sort_values(by='start_coord')
neighborhood['query_match'] = neighborhood['query_match'].replace(
np.nan, "x")
nhbrhood_hit_list = list(neighborhood['query_match'])
nhbrhood_locus_tags = list(neighborhood['locus_tag'])
nhbrhood_old_locus_tags = list(neighborhood['old_locus_tag'])
nhbrhood_prot_ids = list(neighborhood['protein_id'])
nhbrhood_prot_name = list(neighborhood['protein_name'])
nhbrhood_prot_seq = list(neighborhood['protein_seq'])
order = [("| " + gene['query_match'] + " 〉") if gene['direction'] == 1 else
("〈 " + gene['query_match'] + " |")
for index, gene in neighborhood.iterrows()]
dist = list(
np.array(neighborhood['start_coord'][1:]) -
np.array(neighborhood['end_coord'][:-1]))
dist = ["-" + str(d) + "-" for d in dist]
adj_coord_list = list(
zip(neighborhood['start_coord'], neighborhood['end_coord'],
neighborhood['direction'], neighborhood['query_match']))
if min(neighborhood['start_coord']) < 0:
tare_value = abs(min(neighborhood['start_coord']))
tared_adj_coord_list = list(
zip([v + tare_value for v in neighborhood['start_coord']],
[v + tare_value for v in neighborhood['end_coord']],
neighborhood['direction'], neighborhood['query_match']))
else:
tare_value = min(neighborhood['start_coord'])
tared_adj_coord_list = list(
zip([v - tare_value for v in neighborhood['start_coord']],
[v - tare_value for v in neighborhood['end_coord']],
neighborhood['direction'], neighborhood['query_match']))
# making an ITOL compatible string
gene_color_dict = {
'IaaP': '#ff5969',
'IaaQ': '#2db34e',
'IaaR': '#fb77e0',
'IaaA': '#00bc7e',
'IaaB': '#8d006e',
'IaaC': '#cfdd63',
'IaaD': '#0060d0',
'IaaE': '#bb7b00',
'IaaF': '#7c2c29',
'IaaG': '#f1d17a',
'IaaH': '#37589E',
'IaaI': '#ACC92A',
'IaaJ': '#752AC9',
'IaaK': '#D4B5E6',
'IaaL': '#211E45',
'IaaM': '#BFB3E6',
'x': '#d1d1d1'
}
max_len = tared_adj_coord_list[-1][1]
itol_diagram = []
for g in tared_adj_coord_list:
gene_string = []
gene_length = g[1] - g[0]
if g[2] > 0:
gene_string.append('RE')
gene_string.append(str(g[0]))
gene_string.append(str(g[1] - (0.1 * gene_length)))
#gene_string.append('#34b4eb')
gene_string.append(gene_color_dict[g[3]])
gene_string.append(str(g[3]))
gene_string.append(',')
gene_string.append('TR')
gene_string.append(str(g[1] - (0.1 * gene_length)))
gene_string.append(str(g[1]))
#gene_string.append('#34b4eb')
gene_string.append(gene_color_dict[g[3]])
gene_string.append('')
else:
gene_string.append('TL')
gene_string.append(str(g[0]))
gene_string.append(str(g[0] + (0.1 * gene_length)))
#gene_string.append('#34b4eb')
gene_string.append(gene_color_dict[g[3]])
gene_string.append('')
gene_string.append(',')
gene_string.append('RE')
gene_string.append(str(g[0] + (0.1 * gene_length)))
gene_string.append(str(g[1]))
#gene_string.append('#34b4eb')
gene_string.append(gene_color_dict[g[3]])
gene_string.append(str(g[3]))
itol_gene = '|'.join(gene_string)
itol_diagram.append(itol_gene)
itol_diagram_joined = ",".join(map(str, itol_diagram))
itol_diagram_string = str(max_len) + ',' + itol_diagram_joined
itol_diagram_string = re.sub(',\|', ',', itol_diagram_string)
#obtains "| A 〉-23-| B 〉-23-| C 〉"
synteny_dir_dist = ''.join(sum(zip(order, dist + [0]), ())[:-1])
synteny_dir_dist = re.sub("iaa", "", synteny_dir_dist)
#obtains "| A 〉| B 〉| C 〉"
synteny_dir = ''.join(order)
synteny_dir = re.sub("iaa", "", synteny_dir)
#obtains "| A:23.23 〉| B:23.23〉| C:23.23 〉"
#synteny_dir_pident =''.join(order_pident)
#synteny_dir_pident = re.sub("iaa" ,"", synteny_dir_pident)
#obtains "A-B-C"
synteny = re.sub("\n", "-",
neighborhood['query_match'].to_string(index=False))
synteny = re.sub("Iaa| ", "", synteny)
synteny_alphabet = "".join([
gene['query_match'].replace("Iaa", "").upper() if gene['direction']
== 1 else gene['query_match'].replace("Iaa", "").lower()
for index, gene in neighborhood.iterrows()
])
cluster_len = max(neighborhood['end_coord']) - min(
neighborhood['start_coord'])
assembly = re.sub("\{|\}|\'|>", "", str(set(neighborhood['assembly'])))
accession = re.sub("\{|\}|\'", "", str(set(neighborhood['accession'])))
title = re.sub("\{|\}|\'", "", str(set(neighborhood['name'])))
print(assembly_index_file + " successfully used")
return ([
accession, assembly, title,
len(neighborhood), cluster_len, synteny, synteny_alphabet,
synteny_dir_dist, synteny_dir, cluster_number, coord_list,
adj_coord_list, tared_adj_coord_list, itol_diagram_string,
nhbrhood_hit_list, nhbrhood_locus_tags, nhbrhood_old_locus_tags,
nhbrhood_prot_ids, nhbrhood_prot_name, nhbrhood_prot_seq, clusterGC,
genomeGC, diffGC, minhash_sim, four_mer_distance,
four_mer_freq_cluster, cluster_seq
])
