def test_TCRcodon_single_example2():
tc = TCRcodon(organism="mouse", db_file="alphabeta_db.tsv")
r = tc.guess_reverse_translation(v_gene_name= 'TRBV29*01' , \
j_gene_name= 'TRBJ2-2*01' , cdr3_aa = 'CASSPTGQLYF')
# Only the edges are gauranteed to match the real seq shown below, as insertion codons a
# unkown and degenerate
assert r[0:10] == 'tgtgctagcagccccaccgggcagctctacttt'.upper()[0:10]
assert r[-10:-1] == 'tgtgctagcagccccaccgggcagctctacttt'.upper()[-10:-1]
assert r == 'TGTGCTAGCAGTCCTACCGGGCAGCTCTACTTT'
def test_TCRcodon_small_dataframe_beta():
tc = TCRcodon(organism="mouse", db_file="alphabeta_db.tsv")
df = clone_df_subset[[
'v_b_gene', 'j_b_gene', 'cdr3_b_aa', 'cdr3_b_nucseq'
]]
syn_nucs = df.apply(lambda r: \
tc.guess_reverse_translation(r['v_b_gene'], r['j_b_gene'], r['cdr3_b_aa'], verbose = False), axis = 1)
len_syn = [len(x) for x in syn_nucs]
len_real = [len(x) for x in df['cdr3_b_nucseq']]
assert np.all(len_syn == len_real)
def test_TCRcodon_smal_dataframe_alpha_beta_lots():
"""Bigger Example """
tc = TCRcodon(organism="mouse", db_file="alphabeta_db.tsv")
df = pd.read_csv("tcrdist/test_files_compact/dash.csv")
syn_nucs = df.apply(lambda r: \
tc.guess_reverse_translation( \
r['v_b_gene'], r['j_b_gene'], r['cdr3_b_aa'],\
verbose = False), axis = 1)
len_syn = [len(x) for x in syn_nucs]
len_real = [len(x) for x in df['cdr3_b_nucseq']]
assert np.all(len_syn == len_real)
syn_nucs = df.apply(lambda r: \
tc.guess_reverse_translation(\
r['v_a_gene'], r['j_a_gene'], r['cdr3_a_aa'],\
verbose = False), axis = 1)
# Check that synthestic and real seqs are same length
len_syn = [len(x) for x in syn_nucs]
len_real = [len(x) for x in df['cdr3_a_nucseq']]
assert np.all(len_syn == len_real)
def test_TCRcodon_smal_dataframe_delta_lots():
tc = TCRcodon(organism="human", db_file="gammadelta_db.tsv")
df = pd.read_csv("tcrdist/test_files_compact/sant.csv")
# Sant data Doesn't provide J gene so we are handicapped in that regard, for testing we just guess on
df['j_g_gene'] = 'TRGJ1*01'
syn_nucs = df.apply(lambda r: \
tc.guess_reverse_translation(\
r['v_g_gene'], r['j_g_gene'], r['cdr3_g_aa'],\
verbose = False), axis = 1)
# Check that synthestic and real seqs are same length
len_syn = [len(x) for x in syn_nucs]
len_real = [3 * len(x) for x in df['cdr3_g_aa']]
assert np.all(len_syn == len_real)
def test_TCRcodon_smal_dataframe_gama_lots():
tc = TCRcodon(organism="human", db_file="gammadelta_db.tsv")
df = pd.read_csv("tcrdist/test_files_compact/sant.csv")
df['j_d_gene'] = [
tc.get_best_j_gene(aa_seq=x, verbose=False) for x in df['cdr3_d_aa']
]
df = df[df['v_d_gene'].notna()].copy()
syn_nucs = df.apply(lambda r: \
tc.guess_reverse_translation(\
r['v_d_gene'], r['j_d_gene'], r['cdr3_d_aa'],\
verbose = False), axis = 1)
# Check that synthestic and real seqs are same length
len_syn = [len(x) for x in syn_nucs]
len_real = [3 * len(x) for x in df['cdr3_d_aa']]
assert np.all(len_syn == len_real)
assert np.all(len_syn == len_real)
def test_TCRcodon_single_example():
tc = TCRcodon(organism="mouse", db_file="alphabeta_db.tsv")
r = tc.guess_reverse_translation(v_gene_name='TRBV29*01',
j_gene_name='TRBJ1-5*01',
cdr3_aa='CASSEGEAPLF')
assert r == 'TGTGCTAGCAGTGAGGGAGAGGCTCCGCTTTTT'