def Tm_feature(data, pam_audit=True, learn_options=None):
if learn_options is None or "Tm segments" not in learn_options:
segments = [(19, 24), (11, 19), (6, 11), (4, 24)]
else:
segments = learn_options["Tm segments"]
sequence = data["30mer"].values
featarray = np.ones((sequence.shape[0], 5))
rna = True
for i, seq in enumerate(sequence):
if pam_audit and seq[25:27] != "GG":
continue
raise Exception("excepted GG but found %s" % seq[25:27])
featarray[i, 0] = Tm.Tm_staluc(seq, rna=rna) #30mer
featarray[i, 1] = Tm.Tm_staluc(
seq[segments[0][0]:segments[0][1]],
rna=rna) #5nts immediately proximal of the NGG PAM
featarray[i, 2] = Tm.Tm_staluc(seq[segments[1][0]:segments[1][1]],
rna=rna) #8-mer
featarray[i, 3] = Tm.Tm_staluc(seq[segments[2][0]:segments[2][1]],
rna=rna) #5-mer
featarray[i, 4] = Tm.Tm_staluc(seq[segments[3][0]:segments[3][1]],
rna=rna) #20-spacer
feat = pd.DataFrame(featarray,
index=data.index,
columns=[
"Tm global_30mer%s" % rna,
"5mer_end_%s" % rna,
"8mer_middle_%s" % rna,
"5mer_start_%s" % rna,
"Tm global_spacer_%s" % rna
])
return feat
def get_primer(seq, direction, name):
# Tm_NN: Calculation based on nearest neighbor thermodynamics. Several
# tables for DNA/DNA, DNA/RNA and RNA/RNA hybridizations are included.
# Correction for mismatches, dangling ends, salt concentration and other
# additives are available.
# Tm_staluc is the 'old' NN calculation and is kept for compatibility.
# It is, however, recommended to use Tm_NN instead, since Tm_staluc may be
# depreceated in the future. Also, Tm_NN has much more options. Using
# Tm_staluc and Tm_NN with default parameters gives (essentially) the same results.
global PRIMER_NUM
global PRIMER_TM
PRIMER_LENGTH = 15 # min primer lenght
if direction == "fwd":
while mt.Tm_staluc(seq[0:PRIMER_LENGTH]) <= PRIMER_TM and PRIMER_LENGTH <= 65:
PRIMER_LENGTH += 1
primer_seq = seq[0:PRIMER_LENGTH]
primer_tm = mt.Tm_staluc(primer_seq)
elif direction == "rev":
while mt.Tm_staluc(seq[-PRIMER_LENGTH:]) <= PRIMER_TM and PRIMER_LENGTH <= 65:
PRIMER_LENGTH += 1
primer_seq = revcomplement(seq[-PRIMER_LENGTH:]).lower()
primer_tm = mt.Tm_staluc(primer_seq)
primer_seq = str(primer_seq)
primer_name = "{}_{}_{}".format(PRIMER_NUM, name, direction)
primer = list([primer_name, primer_seq, primer_tm, PRIMER_LENGTH])
PRIMER_NUM += 1
return primer
def Temper(sequence):
seq=sequence
seq_7=seq[:7]
seq_8=seq[7:15]
seq_5=seq[15:20]
TDic={}
TDic['T20']=Tm.Tm_staluc(seq)
TDic['T7']=Tm.Tm_staluc(seq_7)
TDic['T8']=Tm.Tm_staluc(seq_8)
TDic['T5']=Tm.Tm_staluc(seq_5)
return TDic
def gene_feature(Y):
"""
Things like the sequence of the gene, the DNA Tm of the gene, etc.
"""
gene_names = Y["Target gene"]
gene_length = np.zeros((gene_names.values.shape[0], 1))
gc_content = np.zeros((gene_names.shape[0], 1))
temperature = np.zeros((gene_names.shape[0], 1))
molecular_weight = np.zeros((gene_names.shape[0], 1))
for gene in gene_names.unique():
seq = util.get_gene_sequence(gene)
gene_length[gene_names.values == gene] = len(seq)
gc_content[gene_names.values == gene] = SeqUtil.GC(seq)
temperature[gene_names.values == gene] = Tm.Tm_staluc(seq, rna=False)
molecular_weight[gene_names.values == gene] = SeqUtil.molecular_weight(
seq, "DNA")
everything = np.concatenate(
(gene_length, gc_content, temperature, molecular_weight), axis=1)
df = pd.DataFrame(
data=everything,
index=gene_names.index,
columns=[
"gene length",
"gene GC content",
"gene temperature",
"gene molecular weight",
],
)
return df
def Tm_feature(data, pam_audit=True, learn_options=None):
"""
assuming '30-mer'is a key
get melting temperature features from:
0-the 30-mer ("global Tm")
1-the Tm (melting temperature) of the DNA:RNA hybrid from positions 16 - 20 of the sgRNA,
i.e. the 5nts immediately proximal of the NGG PAM
2-the Tm of the DNA:RNA hybrid from position 8 - 15 (i.e. 8 nt)
3-the Tm of the DNA:RNA hybrid from position 3 - 7 (i.e. 5 nt)
"""
if learn_options is None or "Tm segments" not in learn_options:
segments = [(19, 24), (11, 19), (6, 11)]
else:
segments = learn_options["Tm segments"]
sequence = data["30mer"].values
featarray = np.ones((sequence.shape[0], 4))
for i, seq in enumerate(sequence):
if pam_audit and seq[25:27] != "GG":
raise Exception(f"expected GG but found {seq[25:27]}")
rna = False
featarray[i, 0] = Tm.Tm_staluc(seq, rna=rna) # 30mer Tm
featarray[i, 1] = Tm.Tm_staluc(
seq[segments[0][0]:segments[0][1]],
rna=rna) # 5nts immediately proximal of the NGG PAM
featarray[i, 2] = Tm.Tm_staluc(seq[segments[1][0]:segments[1][1]],
rna=rna) # 8-mer
featarray[i, 3] = Tm.Tm_staluc(seq[segments[2][0]:segments[2][1]],
rna=rna) # 5-mer
feat = pd.DataFrame(
featarray,
index=data.index,
columns=[
f"Tm global_{rna}",
f"5mer_end_{rna}",
f"8mer_middle_{rna}",
f"5mer_start_{rna}",
],
)
return feat
def tm_nn(self, dnaconc=500, saltconc=50):
"""
dnaconc - float, [DNA] nM
saltconc - float, [salt] mM
@return float, nearest neighbore dna/dna melting temperature
"""
if not self.sequence:
return 0
r = TM.Tm_staluc(self.sequence, dnac=dnaconc, saltc=saltconc)
return round(r,1)
from Bio.SeqUtils import MeltingTemp as MT
import xlwt
PRIMER_FILE = '../../samples/primers.txt'
# w is the name of a newly created workbook.
w = xlwt.Workbook()
# ws is the name of a new sheet in this workbook.
ws = w.add_sheet('Result')
# These two lines writes the titles of the columns.
ws.write(0, 0, 'Primer Sequence')
ws.write(0, 1, 'Tm')
for index, line in enumerate(open(PRIMER_FILE)):
# For each line in the input file, write the primer
# sequence and the Tm
prm = line[3:len(line) - 4].replace(' ', '')
ws.write(index + 1, 0, prm)
ws.write(index + 1, 1, '{0:.2f}'.format(MT.Tm_staluc(prm)))
# Save the spreadsheel into a file.
w.save('primerout.xls')
def melting_temp(s):
return np.array([Tm.Tm_staluc(x, rna=False) for x in s[1:]]) / 1e3
print IUPACData.extended_protein_letters
print IUPACData.ambiguous_dna_letters
print IUPACData.unambiguous_dna_letters
print IUPACData.ambiguous_rna_letters
print IUPACData.unambiguous_rna_letters
print IUPACData.ambiguous_dna_complement #dictionary of complements
#and a lot more
from Bio.Data import CodonTable
print CodonTable.generic_by_id[2]
#SeqUtils. Several functions to deal with DNA and protein sequences.
#DNA utils
import Bio.SeqUtils as SeqUtils
print SeqUtils.GC('gacgatcggtattcgtag') #GC content
from Bio.SeqUtils import MeltingTemp
print MeltingTemp.Tm_staluc('tgcagtacgtatcgt') #DNA/RNA melting temperature
#checksum functions: short alphanumeric string signature of a file or sequence
#usually written in description of sequence
#cgc is a easy, weak, very used checksum (better crc32, crc64)
from Bio.SeqUtils import CheckSum
myseq='acaagatgccattgtcccccggcctcctgctgctgct'
print CheckSum.gcg(myseq)
print CheckSum.crc32(myseq)
print CheckSum.crc64(myseq)
print CheckSum.seguid(myseq)
#Protein utils
from Bio.SeqUtils import ProtParam
myprot=ProtParam.ProteinAnalysis('MLTNK')
print myprot.count_amino_acids()
print myprot.get_amino_acids_percent()
print myprot.molecular_weight()
