def test_dotplot_raw_seq_basic():
data = dict()
data['seq-name-1'] = "xxx"
data['seq-content-1'] = "CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG"
data['seq-name-2'] = "yyy"
data['seq-content-2'] = "AATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCGTGACCCTGCTTTGTTGTTGG"
dotplot = DotPlot(data)
results = dotplot.raw_sequence()
expected_results = [
{'name': "Sequence xxx length", 'value': 70},
{'name': "Sequence yyy length", 'value': 70},
{'name': "Coverage [%]", 'value': 100},
{'name': "Average identity [%]", 'value': 61.4},
{'name': "Fragmental identity [%]", 'value': 61.4},
]
base64_img = dotplot.get_dot_plot_image()
alignment = dotplot.get_alignments()
assert isinstance(base64_img, str)
assert isinstance(alignment, str)
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_consensus_sequence_raw_seq_basic(muscle_command_line_mock,
muscle_standard_seq_return_value):
muscle_command_line_mock.return_value.return_value = muscle_standard_seq_return_value
data = dict()
data['sequences'] = """>gi|2765658
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG"""
expected_results = [
{
'name':
"Consensus sequence",
'value':
"CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTGAATCCGGAGGACCGGTGT"
"ACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG",
},
{
'name': "Sequence length",
'value': 140
},
]
con_seq = ConsensusSequence(data)
results = con_seq.raw_sequence()
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_dotplot_genebank_basic():
data = dict()
data['seq-name-1'] = "2765658"
data['seq-name-2'] = "2765657"
dotplot = DotPlot(data)
results = dotplot.genebank_seq()
expected_results = [
{'name': "Seq id. Z78533.1 C.irapeanum 5.8S rRNA gen ... length [bp]", 'value': 740},
{'name': "Seq id. Z78532.1 C.californicum 5.8S rRNA ... length [bp]", 'value': 753},
{'name': "Coverage [%]", 'value': 98.3},
{'name': "Average identity [%]", 'value': 82.4},
{'name': "Fragmental identity [%]", 'value': 83.1},
]
base64_img = dotplot.get_dot_plot_image()
alignment = dotplot.get_alignments()
assert isinstance(base64_img, str)
assert isinstance(alignment, str)
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_hw_basic():
data = dict()
data["ho"] = 4
data["he"] = 3
data["rho"] = 2
data["alfa"] = 0.05
hw = HardyWeinbergCalculation(data)
results = hw.calculate()
expected_results = [
{'name': "expected number of homozygotes", 'value': 3.36},
{'name': "expected number of heterozygotes", 'value': 4.28},
{'name': "expected number of rare homozygotes", 'value': 1.36},
{'name': "p", 'value': 0.61111},
{'name': "q", 'value': 0.38889},
{'name': "p-value", 'value': 0.66931},
{'name': "Chi-square value", 'value': 0.803},
{'name': "Yate`s chi-square value", 'value': 0.16133},
{'name': "Yate`s p-value", 'value': 0.9225},
{
'name': "status",
'value': "Distribution consistent with Hardy Weinberg's law at the level of significance: 0.05",
},
]
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_pic_codominant_basic():
data = dict()
data["count"] = 3
data["allele-0"] = 4
data["allele-1"] = 2
data["allele-2"] = 3
pic_codominant = Codominant(data)
results = pic_codominant.calculate()
expected_results = [{
'name': "H",
'value': 0.642
}, {
'name': "PIC",
'value': 0.5676
}]
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_sequences_tools_complement():
data = dict()
data['type'] = "complement"
data['sequences'] = """>2765658
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
CCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCCCGGCGCAGTTTGGGCGCCAAGCCATATGAA
AGCATCACCGGCGAATGGCATTGTCTTCCCCAAAACCCGGAGCGGCGGCGTGCTGTCGCGTGCCCAATGA
>2765657
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAGAATATATGATCGAGTG
AATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCGTGACCCTGCTTTGTTGTTGG
GCCTCCTCAAGAGCTTTCATGGCAGGTTTGAACTTTAGTACGGTGCAGTTTGCGCCAAGTCATATAAAGC
>2765656
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAGAACATACGATCGAGTG
AATCCGGAGGACCCGTGGTTACACGGCTCACCGTGGCTTTGCTCTCGTGGTGAACCCGGTTTGCGACCGG
GCCGCCTCGGGAACTTTCATGGCGGGTTTGAACGTCTAGCGCGGCGCAGTTTGCGCCAAGTCATATGGAG"""
seq_tools = SequencesTools(data)
results = seq_tools.calculate()
expected_results = [
{
'name':
">2765658",
'value':
"GCATTGTTCCAAAGGCATCCACTTGGACGCCTTCCTAGTAACTACTCTGGCACCTTATTTGCTAGCTCACTTAGGCCTCCTGGCCACATGA"
"GTCGAGTGGCCCCCGTAACGAGGGCACCACTGGGACTAAACAACAACCCGGCGGAGCCCTCGCAGGTACCGCCCAAACTTGGAGATCGGGCC"
"GCGTCAAACCCGCGGTTCGGTATACTTTCGTAGTGGCCGCTTACCGTAACAGAAGGGGTTTTGGGCCTCGCCGCCGCACGACAGCGCACGGGT"
"TACT",
},
{
'name':
">2765657",
'value':
"GCATTGTTCCAAAGGCATCCACTTGGACGCCTTCCTAGTAACAACTCTGTTGTCTTATATACTAGCTCACTTAGACCTCCTGGACACCATTGAGTC"
"GAGCAGCACCGTGACGAAAACAGCACTGGGACGAAACAACAACCCGGAGGAGTTCTCGAAAGTACCGTCCAAACTTGAAATCATGCCACGTCAAA"
"CGCGGTTCAGTATATTTCG",
},
{
'name':
">2765656",
'value':
"GCATTGTTCCAAAGGCATCCACTTGGACGCCTTCCTAGTAACAACTCTGTCGTCTTGTATGCTAGCTCACTTAGGCCTCCTGGGCACCAATGT"
"GCCGAGTGGCACCGAAACGAGAGCACCACTTGGGCCAAACGCTGGCCCGGCGGAGCCCTTGAAAGTACCGCCCAAACTTGCAGATCGCGCCGCG"
"TCAAACGCGGTTCAGTATACCTC",
},
]
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_sequences_tools_transcription():
data = dict()
data['type'] = "transcription"
data['sequences'] = """>2765658
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
CCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCCCGGCGCAGTTTGGGCGCCAAGCCATATGAA
AGCATCACCGGCGAATGGCATTGTCTTCCCCAAAACCCGGAGCGGCGGCGTGCTGTCGCGTGCCCAATGA
>2765657
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAGAATATATGATCGAGTG
AATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCGTGACCCTGCTTTGTTGTTGG
GCCTCCTCAAGAGCTTTCATGGCAGGTTTGAACTTTAGTACGGTGCAGTTTGCGCCAAGTCATATAAAGC
>2765656
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAGAACATACGATCGAGTG
AATCCGGAGGACCCGTGGTTACACGGCTCACCGTGGCTTTGCTCTCGTGGTGAACCCGGTTTGCGACCGG
GCCGCCTCGGGAACTTTCATGGCGGGTTTGAACGTCTAGCGCGGCGCAGTTTGCGCCAAGTCATATGGAG"""
seq_tools = SequencesTools(data)
results = seq_tools.calculate()
expected_results = [
{
'name':
">2765658",
'value':
"CGUAACAAGGUUUCCGUAGGUGAACCUGCGGAAGGAUCAUUGAUGAGACCGUGGAAUAAACGAUCGAGUGAAUCCGGAGGACCGGUGUACUCAGCUC"
"ACCGGGGGCAUUGCUCCCGUGGUGACCCUGAUUUGUUGUUGGGCCGCCUCGGGAGCGUCCAUGGCGGGUUUGAACCUCUAGCCCGGCGCAGUUUGGG"
"CGCCAAGCCAUAUGAAAGCAUCACCGGCGAAUGGCAUUGUCUUCCCCAAAACCCGGAGCGGCGGCGUGCUGUCGCGUGCCCAAUGA",
},
{
'name':
">2765657",
'value':
"CGUAACAAGGUUUCCGUAGGUGAACCUGCGGAAGGAUCAUUGUUGAGACAACAGAAUAUAUGAUCGAGUGAAUCUGGAGGACCUGUGGUAACUC"
"AGCUCGUCGUGGCACUGCUUUUGUCGUGACCCUGCUUUGUUGUUGGGCCUCCUCAAGAGCUUUCAUGGCAGGUUUGAACUUUAGUACGGUGCAGU"
"UUGCGCCAAGUCAUAUAAAGC",
},
{
'name':
">2765656",
'value':
"CGUAACAAGGUUUCCGUAGGUGAACCUGCGGAAGGAUCAUUGUUGAGACAGCAGAACAUACGAUCGAGUGAAUCCGGAGGACCCGUGGUUACACG"
"GCUCACCGUGGCUUUGCUCUCGUGGUGAACCCGGUUUGCGACCGGGCCGCCUCGGGAACUUUCAUGGCGGGUUUGAACGUCUAGCGCGGCGCAGUU"
"UGCGCCAAGUCAUAUGGAG",
},
]
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_chi_sqaure_basic():
data = dict()
data['row-0'] = [4.0, 4.0]
data['row-1'] = [2.0, 3.0]
data['column-0'] = [4.0, 2.0]
data['column-0'] = [4.0, 3.0]
data['width'] = 2
data['height'] = 2
data['field_sum'] = 13
expected_results = [
{
'name': "coefficient of contingency type",
'value': "Phi"
},
{
'name': "dof",
'value': 1
},
{
'name': "Chi square",
'value': 0.12381
},
{
'name': "Chi square p-value",
'value': 0.72494
},
{
'name': "Chi-square correlation",
'value': 0.09759
},
{
'name': "Yate`s Chi square",
'value': 0.04836
},
{
'name': "Yate`s Chi square p-value",
'value': 0.82594
},
{
'name': "Yate`s chi-square correlation",
'value': 0.06099
},
]
chi_square = ChiSquareCalculation(data)
results = chi_square.calculate()
for result in results:
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_sequences_tools_translation_to_amino_acid():
data = dict()
data['type'] = "translation_to_amino_acid"
data['sequences'] = """>2765658
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
CCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCCCGGCGCAGTTTGGGCGCCAAGCCATATGAA
AGCATCACCGGCGAATGGCATTGTCTTCCCCAAAACCCGGAGCGGCGGCGTGCTGTCGCGTGCCCAATGAq
>2765657
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAGAATATATGATCGAGTG
AATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCGTGACCCTGCTTTGTTGTTGG
GCCTCCTCAAGAGCTTTCATGGCAGGTTTGAACTTTAGTACGGTGCAGTTTGCGCCAAGTCATATAAAGC
>2765656
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAGAACATACGATCGAGTG
AATCCGGAGGACCCGTGGTTACACGGCTCACCGTGGCTTTGCTCTCGTGGTGAACCCGGTTTGCGACCGG
GCCGCCTCGGGAACTTTCATGGCGGGTTTGAACGTCTAGCGCGGCGCAGTTTGCGCCAAGTCATATGGAG"""
seq_tools = SequencesTools(data)
results = seq_tools.calculate()
expected_results = [
{
'name':
">2765658",
'value':
"RNKVSVGEPAEGSLMRPWNKRSSESGGPVYSAHRGHCSRGDPDLLLGRLGSVHGGFEPLARRSLGAKPYESITGEWHCLPQNPERRRAVACPM",
},
{
'name':
">2765657",
'value':
"RNKVSVGEPAEGSLLRQQNI*SSESGGPVVTQLVVALLLS*PCFVVGPPQELSWQV*TLVRCSLRQVI*S"
},
{
'name':
">2765656",
'value':
"RNKVSVGEPAEGSLLRQQNIRSSESGGPVVTRLTVALLSW*TRFATGPPRELSWRV*TSSAAQFAPSHME"
},
]
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_pic_dominant_basic():
data = dict()
data["amplified_marker"] = 2
data["absence_marker"] = 3
pic_dominant = Dominant(data)
results = pic_dominant.calculate()
expected_results = [{'name': "PIC", 'value': 0.48}]
for i, result in enumerate(results):
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value
def test_chi_sqaure_goodness_basic():
data = dict()
data['observed'] = [4.0, 3.0, 2.0]
data['expected'] = [3.0, 2.0, 4.0]
expected_results = [
{'name': "Chi square", 'value': 1.83333},
{'name': "Chi square p-value", 'value': 0.39985},
{'name': "dof", 'value': 2},
{'name': "Chi square p-value", 'value': 0.72494},
{'name': "Yate`s Chi square", 'value': 0.77083},
{'name': "Yate`s Chi square p-value", 'value': 0.68017},
]
chi_square_goodness = ChiSquareGoodness(data['observed'], data['expected'])
results = chi_square_goodness.calculate()
for result in results:
name = result.get('name')
value = result.get('value')
expected_value = find_value_by_name(expected_results, name)
assert expected_value == value