def randomize(self, nucleotideSeq):
## TESTING ONLY #### TESTING ONLY #### TESTING ONLY #### TESTING ONLY ####
#nucleotideSeq = Seq(nucleotideSeq.lower() + 'c')
## TESTING ONLY #### TESTING ONLY #### TESTING ONLY #### TESTING ONLY ####
nucleotideSeq = Seq(nucleotideSeq.lower(), generic_dna)
# Calculate the total number of permutations
permutationsCountDenom = 1
countOfACGT = 0
for n in NucleotidePermutationRandomization.Nucleotides:
countOfN = sum([1 if x == n else 0 for x in nucleotideSeq])
permutationsCountDenom *= factorial(countOfN)
countOfACGT += countOfN
permutationsCount = factorial(countOfACGT) // permutationsCountDenom
isSynonymousPosition = [
x in NucleotidePermutationRandomization.Nucleotides
for x in nucleotideSeq
]
pool = list(compress(nucleotideSeq, isSynonymousPosition))
random.shuffle(pool)
newSeq = list(nucleotideSeq)
for newNuc, pos in zip(
pool, compress(range(len(newSeq)), isSynonymousPosition)):
newSeq[pos] = newNuc
identity = sum([x == y for (x, y) in zip(nucleotideSeq, newSeq)
]) / len(newSeq)
assert (identity >= 0.0)
assert (identity <= 1.0)
return (permutationsCount, identity, ''.join(newSeq))
def ConcatenatingSeq():
protein_seq = Seq("EVRNAK", IUPAC.protein)
dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
#print(protein_seq + dna_seq) #error
protein_seq.alphabet = generic_alphabet
dna_seq.alphabet = generic_alphabet
print(protein_seq + dna_seq)
list_of_seqs = [
Seq("ACGT", generic_dna),
Seq("AACC", generic_dna),
Seq("GGTT", generic_dna)
]
concatenated = Seq("", generic_dna)
for s in list_of_seqs:
concatenated += s
print('concatenated=', concatenated)
con = sum(list_of_seqs, Seq("", generic_dna))
print('con=', con)
dna_seq = Seq("acgtACGT", generic_dna)
print('unper=', dna_seq.upper())
print('lower=', dna_seq.lower())
print("GTAC" in dna_seq)
print("GTAC" in dna_seq.upper())
def add_16db_seqs(focus_file, org, dna, rna, lower):
''' adds seqeunces to silva file as single-line fasta
'''
count = 0
with open(focus_file, "r") as inf:
for rec, seq in SimpleFastaParser(inf):
# for rec in SeqIO.parse(inf,"fasta"):
if rna:
seq = Seq(seq).transcribe()
if dna:
seq = Seq(seq).back_transcribe()
if lower:
seq = seq.lower()
sys.stdout.write(">%s\n%s\n" % (rec, seq))
count = count + 1
sys.stderr.write("focusDB seqeunces: {}\n".format(count))
# os.remove(seqs)
return (count)
def new_silvadb_for_org(count, org, silva, lower, dna, rna):
''' write new silva database with just sequences from org
'''
nlines = 0
write_next_line = False
sys.stderr.write("extracting {org} sequences\n".format(**locals()))
if os.path.splitext(silva)[-1] in ['.gz', '.gzip']:
open_fun = gzip.open
else:
open_fun = open
with open_fun(silva, "rt") as inf:
for rec, seq in SimpleFastaParser(inf):
if org in rec:
if rna:
seq = Seq(seq).transcribe()
if dna:
seq = Seq(seq).back_transcribe()
if lower:
seq = seq.lower()
sys.stdout.write(">%s\n%s\n" % (rec, seq))
nlines += 1
totalcount = count + nlines
sys.stderr.write("SILVA seqeunces: {}\n".format(nlines))
sys.stderr.write("Combined sequences: {totalcount}\n".format(**locals()))
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
from Bio.Alphabet import IUPAC
dna_seq = Seq("acgtACGT", generic_dna)
print(repr(dna_seq))
print(dna_seq.upper())
print(dna_seq.lower())
print("TAC" in dna_seq)
print("TAC" in dna_seq.upper())
strict_dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
print(repr(strict_dna_seq))
print(repr(strict_dna_seq.lower()))
print(my_seq[::-1]) #reverse
fasta_format_string = ">Name\n%s\n" % my_seq
print(fasta_format_string)
##连接
list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
concatenated = Seq("", generic_dna)
for s in list_of_seqs:
concatenated += s
print(concatenated)
print(sum(list_of_seqs, Seq("", generic_dna)))
##changing case
dna_seq = Seq("acgtACGT", generic_dna)
print(dna_seq.upper())
print(dna_seq.lower())
print("GTAC" in dna_seq)
print("GTAC" in dna_seq.upper())
##互补序列
my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
print(my_seq)
print(my_seq.complement())
print(my_seq.reverse_complement())
##转录
coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
messenger_rna = coding_dna.transcribe()
print(messenger_rna)
##反转录
print(messenger_rna.back_transcribe())
except:
print("字母表不兼容，连接失败！")
#连接后字母表的变化
from Bio.Alphabet import generic_nucleotide
nuc_seq = Seq("GATCGATGC", generic_nucleotide)
dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
print(nuc_seq.alphabet, "+", dna_seq.alphabet, "=", (nuc_seq + dna_seq).alphabet)
#大小写更改
from Bio.Alphabet import generic_dna
dna_seq = Seq("acgtACGT", generic_dna)
print("原始序列:", dna_seq)
print("大写序列:", dna_seq.upper())
print("小写序列:", dna_seq.lower())
#转录
print("\n###############\n5. 转录/逆转录\n---------------")
coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
print("编码链DNA:", coding_dna)
template_dna = coding_dna.reverse_complement()
print("模板链DNA:", template_dna)
messenger_rna = coding_dna.transcribe()
print(" mRNA:", messenger_rna, messenger_rna.alphabet)
#从模板链去做一个真正的生物学上的转录，需要两步：
print(" 真实步骤:", template_dna.reverse_complement().transcribe())
#mRNA 逆向转录为 DNA 编码链
print(" 逆转录:", messenger_rna.back_transcribe())
from Bio.Seq import Seq
from Bio.Alphabet import single_letter_alphabet
seq=Seq("ACGT")
print("Sequence: %s"%seq)
print("Alphabet: %s"%seq.alphabet)
print(seq)
test_seq=Seq('AGTATCGAATCGA',single_letter_alphabet)
print(test_seq)
print(test_seq.alphabet)
seq=Seq('ACGTTCGCA')
print(seq[0])
print(seq)
for i in range(0,len(seq)):
print(seq[i])
seq2=Seq('AAATTT')
seq=seq+seq2
print(seq.lower())
print(seq.upper())
seq3 = Seq(' ACGT ')
print(seq3)
print(seq3.strip())
print(my_seq)
print(len(my_seq))
print(my_seq[0])
print(my_seq[1])
print(my_seq[0:3])
my_seq.count("G")
my_seq.count("C")
my_seq.count("A")
my_seq.count("T")
my_seq.lower()
my_seq.upper()
my_seq.complement()
my_seq.reverse_complement()
my_seq.transcribe()
my_seq.translate()
from Bio.SeqUtils import GC
#get GC content
print(GC(my_seq))
#get AT content
print(100-GC(my_seq))
from Bio.Alphabet import IUPAC
from Bio.Alphabet import generic_dna
from Bio import SeqIO
#x = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
#y = print(str(x))
#print(repr(y))
"""BIOPYTHON TUTORIAL"""
#for letter, index in enumerate(x):
# print("%i %s" % (letter, index))
#print(len(x))
# print(repr(x[0:12])) # repr for print string + seq type
#fasta_format_string = ">Name\n%s\n" % x
#print(fasta_format_string)
# prot = Seq("EVRNAK" , IUPAC.protein)
# dna = Seq("ATGCCC" , IUPAC.unambiguous_dna)
# print(str(prot) + str(dna))
# for e in SeqIO.parse("hans.fasta", "fasta"): #Import Fasta sequences (SeqIO required)
# print(e)
dnas = Seq("ACGT", IUPAC.unambiguous_dna)
krass = (dnas.lower())
print(repr(krass))
oki = krass.complement()
print(repr(oki))
from Bio.Seq import Seq
tatabox_seq = Seq("tataaaggcAATATGCAGTAG")
print(tatabox_seq.upper())
print(tatabox_seq.lower())
# 4.4.3.case.py
from Bio.Seq import Seq
tatabox_seq = Seq("tataaaggcAATATGCAGTAG")
print(tatabox_seq.upper()) # TATAAAGGCAATATGCAGTAG
print(tatabox_seq.lower()) # tataaaggcaatatgcagtag
# Drug način za štetje nukleotidov je, da si pripravimo slovar, na primer:
# In[2]:
freq = {}
for x in my_seq:
freq[x] = my_seq.count(x)
print(freq) # izpiše slovar
print('A:', freq['A']) # izpiše, koliko A je v zaporedju
# Pogosto želimo, so vsi nukleotidi napisani bodisi z velikimi ali malimi črkami. Za ta namen lahko uporabimo `upper` ali `lower`:
# In[3]:
print(my_seq.lower())
# Zaporedje, ki ga analiziramo, lahko definiramo direktno, lahko pa si nastavimo, da nas računalnik sam vpraša po njem. Pri tem je pomembno, da ga pretvorimo v ustrezen objekt!
# In[4]:
# na spodnji način bo my_seq niz (string)
my_seq = input('Vpiši nukleotidno zaporedje: ')
print(my_seq)
# tako pa bo kot "sequence object" (seveda moramo prej uvoziti ustrezen modul - glej zgoraj)
my_seq = Seq(input('Vpiši nukleotidno zaporedje: '))
print(my_seq)
# ---
# ## Naloga
#
def Kaminoan(frag_list, Dtemp = In_temp, OL = In_length, ty = Format, n = Name):
"""Takes a list of strings ATGC, an overlap length (integer between 9-25bp), and a name to call the outputted product. Then calculates appropriate primers and returns them in a panda table, as well as a fasta file of the final plasmid"""
output = pd.DataFrame(columns = ["Seq_Length", "Fragments", "FW_Primer", "FW_length", "FW_inital_temp", "FW_final_temp", "RV_Primer", "RV_length", "RV_inital_temp", "RV_final_temp"])
noncon = []
delete = []
for k in frag_list:
for i in range(len(k)):
if k[i] != "A" and k[i] != "C" and k[i] != "T" and k[i] != "G" and k[i] != "a" and k[i] != "c" and k[i] != "t" and k[i] != "g"
delete.append(k)
frag_list = [x for x in frag_list if x not in delete]
for i in range(len(frag_list)):
if len(frag_list[i]) < 60:
print("fragment ", i, " is ", len(frag_list[i]), " bp, attempting PCR of fragments smaller than 60bp is not recommended")
if len(frag_list[i]) < 100:
print("fragment ", i, " is ", len(frag_list[i]), " bp long, consider just annealing oligos rather than PCR for short fragments such as these")
for j in range(len(frag_list[i])):
if frag_list[i][j] != "A" and frag_list[i][j] != "C" and frag_list[i][j] != "T" and frag_list[i][j] != "G":
noncon.append((i, j, frag_list[i][j]))
for i in range(len(frag_list)):
for j in range(len(frag_list[i])):
if frag_list[i][j] != "A" and frag_list[i][j] != "C" and frag_list[i][j] != "T" and frag_list[i][j] != "G":
print("Fragment ", i, " contains non-conventional (non-ACTG) characters and cannot be processed. Violating fragments, positions and characters are:", noncon)
return
#frag_list[i] = Seq(frag_list[i], IUPAC.unambiguous_dna)
for i in range(len(frag_list)):
if i == 0:
#FW code starts here, remember to reuse L_RVnow at the end for the final RV_TOT
L_FWnow = 7
L_FWpast = 8
C_FWnow = Seq(frag_list[i][0:L_FWnow])
C_FWpast = Seq(frag_list[-1][len(frag_list[-1])-L_FWpast:len(frag_list[-1])])
Overlap = C_FWpast + C_FWnow
while mt.Tm_NN(Overlap) < 47:
L_FWnow += 1
C_FWnow = Seq(frag_list[i][0:L_FWnow])
Overlap = C_FWpast + C_FWnow
if mt.Tm_NN(Overlap) < 47:
L_FWpast += 1
C_FWpast = Seq(frag_list[-1][len(frag_list[-1])-L_FWpast:len(frag_list[-1])])
Overlap = C_FWpast + C_FWnow
#now generate the forward primer
j = OL
FW_INI = Seq(frag_list[i][0:j])
mtFW_INI = mt.Tm_NN(FW_INI)
while mtFW_INI < Dtemp:
j += 1
FW_INI = Seq(frag_list[i][0:j])
mtFW_INI = mt.Tm_NN(FW_INI)
FW_TOT = C_FWpast.lower() + FW_INI
mtFW_TOT = mt.Tm_NN(FW_TOT)
##RV code starts here with the overlap section
L_RVnow = 7
L_RVnext = 8
C_RVnow = Seq(frag_list[i][len(frag_list[i])-L_RVnow:len(frag_list[i])])
C_RVnext = Seq(frag_list[i+1][0:L_RVnext])
Overlap = C_RVnow + C_RVnext
while mt.Tm_NN(Overlap) < 47:
L_RVnow += 1
C_RVnow = Seq(frag_list[i][len(frag_list[i])-L_RVnow:len(frag_list[i])])
Overlap = C_RVnow + C_RVnext
if mt.Tm_NN(Overlap) < 47:
L_RVnext += 1
C_RVnext = Seq(frag_list[i+1][0:L_RVnext])
Overlap = C_RVnow + C_RVnext
#now generate the reverse primer
j = OL
RV_INI = Seq(frag_list[i][len(frag_list[i])-j:len(frag_list[i])])
mtRV_INI = mt.Tm_NN(RV_INI)
while mtRV_INI < Dtemp:
j += 1
RV_INI = Seq(frag_list[i][len(frag_list[i])-j:len(frag_list[i])])
mtRV_INI = mt.Tm_NN(RV_INI)
RV_TOT = C_RVnext.reverse_complement().lower() + RV_INI.reverse_complement()
mtRV_TOT = mt.Tm_NN(RV_TOT)
#say where its going:
fragments = "Inserts %0.0f between %0.0f and %0.0f" % (i, i-1, i+1)
output.loc[i] = [len(frag_list[i]), fragments, str(FW_TOT), len(FW_TOT), mtFW_INI, mtFW_TOT, str(RV_TOT), len(RV_TOT), mtRV_INI, mtRV_TOT]
if 0 < i < len(frag_list) -1:
#generate the forward primer
j = OL
FW_INI = Seq(frag_list[i][0:j])
mtFW_INI = mt.Tm_NN(FW_INI)
while mtFW_INI < Dtemp:
j += 1
FW_INI = Seq(frag_list[i][0:j])
mtFW_INI = mt.Tm_NN(FW_INI)
FW_TOT = C_RVnow.lower() + FW_INI
mtFW_TOT = mt.Tm_NN(FW_TOT)
##RV code starts here with the overlap section
L_RVnow = 7
L_RVnext = 8
C_RVnow = Seq(frag_list[i][len(frag_list[i])-L_RVnow:len(frag_list[i])])
C_RVnext = Seq(frag_list[i+1][0:L_RVnext])
Overlap = C_RVnow + C_RVnext
while mt.Tm_NN(Overlap) < 47:
L_RVnow += 1
C_RVnow = Seq(frag_list[i][len(frag_list[i])-L_RVnow:len(frag_list[i])])
Overlap = C_RVnow + C_RVnext
if mt.Tm_NN(Overlap) < 47:
L_RVnext += 1
C_RVnext = Seq(frag_list[i+1][0:L_RVnext])
Overlap = C_RVnow + C_RVnext
#now generate the reverse primer
j = OL
RV_INI = Seq(frag_list[i][len(frag_list[i])-j:len(frag_list[i])])
mtRV_INI = mt.Tm_NN(RV_INI)
while mtRV_INI < Dtemp:
j += 1
RV_INI = Seq(frag_list[i][len(frag_list[i])-j:len(frag_list[i])])
mtRV_INI = mt.Tm_NN(RV_INI)
RV_TOT = C_RVnext.reverse_complement().lower() + RV_INI.reverse_complement()
mtRV_TOT = mt.Tm_NN(RV_TOT)
if i >= 1:
fragments = "Inserts %0.0f between %0.0f and %0.0f" % (i, i-1, i+1)
elif i == 0:
fragments = "Inserts %0.0f between %0.0f and %0.0f" % (0, len(frag_list), 1)
else:
fragments = "FRAGMENT ID ERROR!!!"
output.loc[i] = [len(frag_list[i]), fragments, str(FW_TOT), len(FW_TOT), mtFW_INI, mtFW_TOT, str(RV_TOT), len(RV_TOT), mtRV_INI, mtRV_TOT]
if i == len(frag_list) - 1:
#generate the forward primer
j = OL
FW_INI = Seq(frag_list[i][0:j])
mtFW_INI = mt.Tm_NN(FW_INI)
while mtFW_INI < Dtemp:
j += 1
FW_INI = Seq(frag_list[i][0:j])
mtFW_INI = mt.Tm_NN(FW_INI)
FW_TOT = C_RVnow.lower() + FW_INI
mtFW_TOT = mt.Tm_NN(FW_TOT)
#now generate the reverse primer
j = OL
RV_INI = Seq(frag_list[i][len(frag_list[i])-j:len(frag_list[i])])
mtRV_INI = mt.Tm_NN(RV_INI)
while mtRV_INI < Dtemp:
j += 1
RV_INI = Seq(frag_list[i][len(frag_list[i])-j:len(frag_list[i])])
mtRV_INI = mt.Tm_NN(RV_INI)
RV_TOT = C_FWpast.reverse_complement().lower() + RV_INI.reverse_complement()
mtRV_TOT = mt.Tm_NN(RV_TOT)
if i >= 1:
fragments = "Inserts %0.0f between %0.0f and %0.0f" % (i, i-1, i+1)
elif i == 0:
fragments = "Inserts %0.0f between %0.0f and %0.0f" % (0, len(frag_list), 1)
else:
fragments = "FRAGMENT ID ERROR!!!"
output.loc[i] = [len(frag_list[i]), fragments, str(FW_TOT), len(FW_TOT), mtFW_INI, mtFW_TOT, str(RV_TOT), len(RV_TOT), mtRV_INI, mtRV_TOT]
total = ""
for f in frag_list:
total += f
if len(total) > 15000:
print("WARNING: total plasmid lengths of 15kb may result in cloning problems, consult Huang et al (2017)")
totalsequence = SeqRecord(Seq(str(total), IUPAC.unambiguous_dna), id="pKam"+date, name=n, description="plasmid cloned in silico using Kaminoan in vivo cloner on the " + date)
bp = 0
for i in range(0,len(frag_list)):
my_feature = sf.SeqFeature(sf.FeatureLocation(bp,bp+len(frag_list[i])),type="misc_feature")
totalsequence.features.append(my_feature)
bp += len(frag_list[i])
#generate output map:
if ty == "genbank":
SeqIO.write(totalsequence, n, "genbank")
elif ty == "fasta":
SeqIO.write(totalsequence, n, "fasta")
else:
print("variable ty must be specified as either genbank (default) or fasta in string format. " + ty + " is not an acceptable input for this variable")
return output, totalsequence
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
from Bio.SeqUtils import GC
from Bio.Alphabet import generic_dna
my_seq = Seq("CGATGCATGCTAGTC",IUPAC.ambiguous_dna) #后面的IUPAC是对前面的序列进行格式定义，可以是dna或者protein
print (my_seq)
print (my_seq.alphabet)
for index,letter in enumerate(my_seq):
print("%i %s" %(index,letter)) #替代方式
print (len(my_seq)) #sequence length
print (my_seq[2]) #speific position nucleaicd
print (my_seq.count("TG")) #motif counts
print (GC(my_seq)) #GC count
print (my_seq[2:7]) #slicing a sequence
print (my_seq[0::3]) #从第0位开始，直到最后，每间隔2个字符，取第三个字符来显示
print (my_seq[::-1]) #反向读数
print (my_seq.reverse_complement().complement()) #同上
print (str(my_seq))
print (my_seq) #same as above
fasta_format_string = ">Name\n%s\n" %my_seq
print (fasta_format_string) #将序列转换位string来进行编辑，同时按照fasta格式排列
print (my_seq.lower()) #小写
print (my_seq.lower().upper()) #大写
from Bio.Seq import Seq
from Bio.Alphabet import generic_nucleotide
from Bio.Alphabet import generic_dna
from Bio.Alphabet import IUPAC
nuc_seq = Seq("GATCGATGC", generic_nucleotide)
dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
print(nuc_seq)
print(dna_seq)
print((nuc_seq + dna_seq).alphabet)
list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
concatenated = Seq("", generic_dna)
for s in list_of_seqs:
concatenated += s
print(concatenated, ' ', concatenated.alphabet)
print(sum(list_of_seqs, Seq("", generic_dna)))
print(concatenated.lower())
dna_seq.alphabet = generic_alphabet
protein_seq + dna_seq
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
sum(list_of_seqs, Seq("", generic_dna))
# Changing case
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
dna_seq = Seq("acgtACGT", generic_dna)
dna_seq
dna_seq.upper()
dna_seq.lower()
# Transcription
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
coding_dna
messenger_rna = coding_dna.transcribe()
messenger_rna
# Translation
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
##连接
list_of_seqs = [
Seq("ACGT", generic_dna),
Seq("AACC", generic_dna),
Seq("GGTT", generic_dna)
]
concatenated = Seq("", generic_dna)
for s in list_of_seqs:
concatenated += s
print(concatenated)
print(sum(list_of_seqs, Seq("", generic_dna)))
##changing case
dna_seq = Seq("acgtACGT", generic_dna)
print(dna_seq.upper())
print(dna_seq.lower())
print("GTAC" in dna_seq)
print("GTAC" in dna_seq.upper())
##互补序列
my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
print(my_seq)
print(my_seq.complement())
print(my_seq.reverse_complement())
##转录
coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG",
IUPAC.unambiguous_dna)
messenger_rna = coding_dna.transcribe()
print(messenger_rna)
##反转录
from Bio.Seq import Seq
string = "AGTACACTGGT2"
my_seq = Seq("AGTACACTGGT")
print("Sequence:", my_seq)
print("Lower:", my_seq.lower())
print("Complement:", my_seq.complement())
print("Reverse complement:", my_seq.reverse_complement())
print(string.isdigit())
from Bio.Alphabet import generic_nucleotide
nucseq = Seq('GATCGATGC',generic_nucleotide)
dnaseq = Seq('ACGT',IUPAC.unambiguous_dna)
print nucseq.alphabet, dnaseq.alphabet
print nucseq + dnaseq, (nucseq + dnaseq).alphabet # parent + child = parent type
from Bio.Alphabet import generic_dna
list_seqs = [Seq('ACGT', generic_dna), Seq('CCGG', generic_dna), Seq('TTATT', generic_dna)]
concatenated = Seq('',generic_dna)
for seq in list_seqs:
concatenated += seq
print concatenated
print sum(list_seqs, Seq('', generic_dna)) # function sum, the same as previous
# Seq shares many methods from string, except join method
print myseq.upper(), myseq.lower()
print 'AA' in myseq, 'AA' in myseq.upper()
myseq = myseq.upper()
print myseq.complement()
print myseq.reverse_complement()
#print myprot.complement()
#Transcription
myrna = myseq.transcribe() # replace T for U
print myrna, myrna.alphabet
#real biological transcription
mytemplate = myseq.reverse_complement()
myrna = mytemplate.reverse_complement().transcribe()
print myrna
print myrna.back_transcribe() # replace U for T (RNA->DNA)
