Defining alignment targets¶
Example of how to define alignment targets.
Set up for analysis¶
Import necessary Python modules:
In [1]:
import tempfile
import Bio.SeqIO
from alignparse.targets import Target, Targets
A single target¶
First we show how to define a single Target, using an example an amplicon for PacBio sequencing of RecA for a deep mutational scanning experiment.
The amplicon is defined in Genbank Flat File format.
First, let's just look at that file:
In [2]:
recA_targetfile = "../notebooks/input_files/recA_amplicon.gb"
with open(recA_targetfile) as f:
print(f.read())
LOCUS RecA_PacBio_amplicon 1342 bp ds-DNA linear 06-AUG-2018
DEFINITION PacBio amplicon for deep mutational scanning of E. coli RecA.
ACCESSION None
VERSION
SOURCE Danny Lawrence
ORGANISM .
COMMENT PacBio amplicon for RecA libraries.
COMMENT There are single nucleotide tags in the 5' and 3' termini to measure strand exchange.
FEATURES Location/Qualifiers
termini5 1..147
/label="termini 5' of gene"
gene 148..1206
/label="RecA gene"
spacer 1207..1285
/label="spacer between gene & barcode"
barcode 1286..1303
/label="18 nucleotide barcode"
termini3 1304..1342
/label="termini 3' of barcode"
variant_tag5 33..33
/label="5' variant tag"
variant_tag3 1311..1311
/label="3' variant tag"
ORIGIN
1 gcacggcgtc acactttgct atgccatagc atRtttatcc ataagattag cggatcctac
61 ctgacgcttt ttatcgcaac tctctactgt ttctccataa cagaacatat tgactatccg
121 gtattacccg gcatgacagg agtaaaaATG GCTATCGACG AAAACAAACA GAAAGCGTTG
181 GCGGCAGCAC TGGGCCAGAT TGAGAAACAA TTTGGTAAAG GCTCCATCAT GCGCCTGGGT
241 GAAGACCGTT CCATGGATGT GGAAACCATC TCTACCGGTT CGCTTTCACT GGATATCGCG
301 CTTGGGGCAG GTGGTCTGCC GATGGGCCGT ATCGTCGAAA TCTACGGACC GGAATCTTCC
361 GGTAAAACCA CGCTGACGCT GCAGGTGATC GCCGCAGCGC AGCGTGAAGG TAAAACCTGT
421 GCGTTTATCG ATGCTGAACA CGCGCTGGAC CCAATCTACG CACGTAAACT GGGCGTCGAT
481 ATCGACAACC TGCTGTGCTC CCAGCCGGAC ACCGGCGAGC AGGCACTGGA AATCTGTGAC
541 GCCCTGGCGC GTTCTGGCGC AGTAGACGTT ATCGTCGTTG ACTCCGTGGC GGCACTGACG
601 CCGAAAGCGG AAATCGAAGG CGAAATCGGC GACTCTCATA TGGGCCTTGC GGCACGTATG
661 ATGAGCCAGG CGATGCGTAA GCTGGCGGGT AACCTGAAGC AGTCCAACAC GCTGCTGATC
721 TTCATCAACC AGATCCGTAT GAAAATTGGT GTGATGTTCG GCAACCCGGA AACCACTACC
781 GGTGGTAACG CGCTGAAATT CTACGCCTCT GTTCGTCTCG ACATCCGTCG TATCGGCGCG
841 GTGAAAGAGG GCGAAAACGT GGTGGGTAGC GAAACCCGCG TGAAAGTGGT GAAGAACAAA
901 ATCGCTGCGC CGTTTAAACA GGCTGAATTC CAGATCCTCT ACGGCGAAGG TATCAACTTC
961 TACGGCGAAC TGGTTGACCT GGGCGTAAAA GAGAAGCTGA TCGAGAAAGC AGGCGCGTGG
1021 TACAGCTACA AAGGTGAGAA GATCGGTCAG GGTAAAGCGA ATGCGACTGC CTGGCTGAAA
1081 GATAACCCGG AAACCGCGAA AGAGATCGAG AAGAAAGTAC GTGAGTTGCT GCTGAGCAAC
1141 CCGAACTCAA CGCCGGATTT CTCTGTAGAT GATAGCGAAG GCGTAGCAGA AACTAACGAA
1201 GATTTTTAAt cgtcttgttt gatacacaag ggtcgcatct gcggcccttt tgctttttta
1261 agttgtaagg atatgccatt ctagannnnn nnnnnnnnnn nnnagatcgg Yagagcgtcg
1321 tgtagggaaa gagtgtggta cc
//
Read the Genbank file for the target into a BioPython SeqRecord:
In [3]:
recA_seqrecord = Bio.SeqIO.read(recA_targetfile, format="genbank")
Create a Target object:
In [4]:
target = Target(
seqrecord=recA_seqrecord,
req_features=[
"termini5",
"gene",
"spacer",
"barcode",
"termini3",
"variant_tag5",
"variant_tag3",
],
)
We can get specific features out of the Target object.
Below we look for two features and print the one that exists:
In [5]:
for feature in ["non-existent", "termini5"]:
if target.has_feature(feature):
print(f"Here is feature {feature}:\n{target.get_feature(feature)}")
else:
print(f"target lacks feature {feature}\n")
target lacks feature non-existent Here is feature termini5: Feature(name=termini5, seq=GCACGGCGTCACACTTTGCTATGCCATAGCATRTTTATCCATAAGATTAGCGGATCCTACCTGACGCTTTTTATCGCAACTCTCTACTGTTTCTCCATAACAGAACATATTGACTATCCGGTATTACCCGGCATGACAGGAGTAAAA, start=0, end=147)
We can get a dna_features_viewer GraphicRecord with Target.image, and then plot this using its .plot method, which returns a matplotlib.Axes instance.
(Note that Target.image also provides options for setting colors, labels):
In [6]:
image = target.image()
ax, _ = image.plot()
_ = ax.set_title(target.name)
Multiple targets¶
We can read multiple targets into a Targets object. Below is an example with the two LASV GP constructs - wildtype and codon optimized - from the Josiah strain.
First, let's look at these files:
In [7]:
target_file_names = ["LASV_Josiah_WT", "LASV_Josiah_OPT"]
targetfiles = [
f"../notebooks/input_files/{target_file_name}.gb"
for target_file_name in target_file_names
]
for targetfile in targetfiles:
with open(targetfile) as f:
print(f.read())
LOCUS LASV_Josiah_WT 1730 bp ds-DNA linear 14-JUN-2019
DEFINITION .
ACCESSION
VERSION
SOURCE Kate Crawford
ORGANISM .
COMMENT PacBio amplicon for LASV Josiah WT sequence
FEATURES Location/Qualifiers
T2A 85..147
/label="T2A"
WPRE 1639..1730
/label="WPRE"
ZsGreen 15..84
/label="ZsGreen"
termini3 1639..1730
/label="3'Termini"
index 9..14
/label="index"
leader5 1..8
/label="5' leader"
termini5 1..147
/label="5'Termini"
variant_tag5 34..34
/variant_1=T
/variant_2=C
/label="5'VariantTag"
variant_tag3 1702..1702
/variant_1=G
/variant_2=A
/label="3'VariantTag"
spacer 1624..1638
/label="3'Spacer"
gene 148..1623
/label="LASV_Josiah_WT"
ORIGIN
1 GACTGATANN NNNNcagcga cgccaagaac cagYagtggc acctgaccga gcacgccatc
61 gcctccggcT CCGCCTTGCC CGCTGGATCC GGCGAGGGCA GAGGAAGTCT GCTAACATGC
121 GGTGACGTCG AGGAGAATCC TGGCCCAATG GGACAAATAG TGACATTCTT CCAGGAAGTG
181 CCTCATGTAA TAGAAGAGGT GATGAACATT GTTCTCATTG CACTGTCTGT ACTAGCAGTG
241 CTGAAAGGTC TGTACAATTT TGCAACGTGT GGCCTTGTTG GTTTGGTCAC TTTCCTCCTG
301 TTGTGTGGTA GGTCTTGCAC AACCAGTCTT TATAAAGGGG TTTATGAGCT TCAGACTCTG
361 GAACTAAACA TGGAGACACT CAATATGACC ATGCCTCTCT CCTGCACAAA GAACAACAGT
421 CATCATTATA TAATGGTGGG CAATGAGACA GGACTAGAAC TGACCTTGAC CAACACGAGC
481 ATTATTAATC ACAAATTTTG CAATCTGTCT GATGCCCACA AAAAGAACCT CTATGACCAC
541 GCTCTTATGA GCATAATCTC AACTTtccac ttgtccatcc ccaacTTCAA TCAGTATGAG
601 GCAATGAGCT GCGATTTTAA TGGGGGAAAG ATTAGTGTGC AGTACAACCT GAGTCACAGC
661 TATGCTGGGG ATGCAGCCAA CCATTGTGGT ACTGTTGCAA ATGGTGTGTT ACAGACTTTT
721 ATGAGGATGG CTTGGGGTGG GAGCTACATT GCTCTTGACT CAGGCCGTGG CAACTGGGAC
781 TGTATTATGA CTAGTTATCA ATATCTGATA ATCCAAAATA CAACCTGGGA AGATCACTGC
841 CAATTCTCGA GACCATCTCC CATCGGTTAT CTCGGGCTCC TCTCACAAAG GACTAGAGAT
901 ATTTATATTA GTAGAAGATT GCTAGGCACA TTCACATGGA CACTGTCAGA TTCTGAAGGT
961 AAAGACACAC CAGGGGGATA TTGTCTGACC AGGTGGATGC TAATTGAGGC TGAACTAAAA
1021 TGCTTCGGGA ACACAGCTGT GGCAAAATGT AATGAGAAGC ATGATGAgga attttgtgac
1081 atgctgaggc TGTTTGACTT CAACAAACAA GCCATTCAAA GGTTGAAAGC TGAAGCACAA
1141 ATGAGCATTC AGTTGATCAA CAAAGCAGTA AATGCTTTGA TAAATGACCA ACTTATAATG
1201 AAGAACCATC TACGGGACAT CATGGGAATT CCATACTGTA ATTACAGCAA GTATTGGTAC
1261 CTCAACCACA CAACTACTGG GAGAACATCA CTGCCCAAAT GTTGGCTTGT ATCAAATGGT
1321 TCATACTTGA ACGAGACCCA CTTTTCTGAT GATATTGAAC AACAAGCTGA CAATATGATC
1381 ACTGAGATGT TACAGAAGGA GTATATGGAG AGGCAGGGGA AGACACCATT GGGTCTAGTT
1441 GACCTCTTTG TGTTCAGCAC AAGTTTCTAT CTTATTAGCA TCTTCCTTCA CCTAGTCAAA
1501 ATACCAACTC ATAGGCATAT TGTAGGCAAG TCGTGTCCCA AACCTCACAG ATTGAATCAT
1561 ATGGGCATTT GTTCCTGTGG ACTCTACAAA CAGCCTGGTG TGCCTGTGAA ATGGAAGAGA
1621 TGAGCTAGCT AAACGCGTTG ATCCtaatca acctctggat tacaaaattt gtgaaagatt
1681 gactggtatt cttaactatg tRgctccttt tacgctatgt ggatacgctg
//
LOCUS LASV_Josiah_OPT 1730 bp ds-DNA linear 14-JUN-2019
DEFINITION .
ACCESSION
VERSION
SOURCE Kate Crawford
ORGANISM .
COMMENT PacBio amplicon for LASV Josiah OPT sequence
FEATURES Location/Qualifiers
T2A 85..147
/label="T2A"
WPRE 1639..1730
/label="WPRE"
ZsGreen 15..84
/label="ZsGreen"
termini3 1639..1730
/label="3'Termini"
index 9..14
/label="index"
leader5 1..8
/label="5' leader"
termini5 1..147
/label="5'Termini"
variant_tag5 34..34
/variant_1=T
/variant_2=C
/label="5'VariantTag"
variant_tag3 1702..1702
/variant_1=G
/variant_2=A
/label="3'VariantTag"
spacer 1624..1638
/label="3'Spacer"
gene 148..1623
/label="LASV_Josiah_OPT"
ORIGIN
1 GACTGATANN NNNNcagcga cgccaagaac cagYagtggc acctgaccga gcacgccatc
61 gcctccggcT CCGCCTTGCC CGCTGGATCC GGCGAGGGCA GAGGAAGTCT GCTAACATGC
121 GGTGACGTCG AGGAGAATCC TGGCCCAATG GGCCAGATCG TGACCTTCTT CCAAGAAGTG
181 CCTCATGTGA TTGAGGAGGT GATGAATATC GTGCTGATCG CTTTAAGCGT GCTGGCCGTT
241 CTTAAGGGCC TCTATAACTT CGCCACTTGT GGTTTAGTCG GACTGGTGAC ATTTCTGCTG
301 CTGTGTGGCA GATCTTGTAC CACATCTTTA TACAAGGGCG TGTACGAGCT GCAGACTTTA
361 GAACTGAACA TGGAGACTTT AAACATGACC ATGCCTTTAA GCTGTACCAA GAACAATAGC
421 CACCACTACA TCATGGTGGG CAACGAGACC GGTTTAGAAC TGACACTCAC CAACACCAGC
481 ATTATCAACC ATAAGTTCTG CAACCTCTCC GACGCTCACA AGAAGAATTT ATACGACCAC
541 GCTTTAATGA GCATCATCTC CACCTTCCAT CTCTCCATTC CTAATttcaa ccagtacgag
601 gccatgAGCT GCGACTTTAA CGGCGGCAAG ATCTCCGTGC AGTACAATTT ATCCCATAGC
661 TACGCCGGCG ATGCCGCCAA TCACTGCGGA ACCGTGGCCA ACGGCGTGCT GCAGACATTC
721 ATGAGGATGG CTTGGGGCGG CTCCTATATC GCTTTAGACT CCGGCAGAGG AAACTGGGAC
781 TGTATCATGA CCAGCTACCA ATATTTAATC ATTCAGAACA CCACATGGGA GGACCACTGC
841 CAATTCTCTC GTCCCTCTCC TATCGGCTAT CTGGGACTGC TGTCCCAGAG GACCAGAGAC
901 ATCTACATCT CTCGTAGGCT GCTGGGCACA TTCACTTGGA CTTTAAGCGA CAGCGAAGGC
961 AAAGATACTC CCGGTGGCTA CTGTTTAACA AGATGGATGC TGATCGAGGC CGAGCTCAAG
1021 TGCTTCGGAA ATACCGCCGT GGCCAAATGC AACGAGAAAC ACGACGAGGA GTTCTGCGAC
1081 ATGCTGAGGC TCTTCGACTT CAacaagcaa gccattcaga ggcTGAAGGC CGAAGCCCAG
1141 ATGTCCATCC AGCTGATTAA TAAGGCCGTG AATGCCCTCA TTAACGACCA GCTGATCATG
1201 AAGAACCATT TAAGGGACAT CATGGGCATC CCTTATTGCA ACTACAGCAA ATACTGGTAT
1261 TTAAATCATA CCACCACCGG TCGTACATCC TTACCTAAGT GCTGGCTGGT CAGCAATGGC
1321 TCCTATTTAA ACGAGACACA CTTCTCCGAC GACATCGAGC AGCAAGCCGA CAACATGATC
1381 ACCGAAATGC TCCAGAAGGA GTACATGGAG AGGCAAGGTA AGACTCCTCT GGGTTTAGTG
1441 GATTTATTCG TCTTCAGCAC CTCCTTCTAT TTAATCTCCA TCTTTCTTCA TCTGGTGAAG
1501 ATTCCTACCC ACAGACACAT TGTGGGCAAG AGCTGTCCTA AGCCTCATAG ACTGAACCAC
1561 ATGGGCATCT GTAGCTGCGG TTTATATAAA CAGCCCGGTG TTCCCGTTAA GTGGAAGAGG
1621 TGAGCTAGCT AAACGCGTTG ATCCtaatca acctctggat tacaaaattt gtgaaagatt
1681 gactggtatt cttaactatg tRgctccttt tacgctatgt ggatacgctg
//
Read the sequences into a Targets:
In [8]:
lasv_parse_specs_file = "../notebooks/input_files/lasv_feature_parse_specs.yaml"
lasv_targets = Targets(
seqsfile=targetfiles,
feature_parse_specs=lasv_parse_specs_file,
allow_extra_features=True,
allow_clipped_muts_seqs=True,
)
Iterate through lasv_targets to identify features present:
In [9]:
for lasv_target in lasv_targets.targets:
print(f"\ntarget = {lasv_target.name}")
for feature in ["non-existent", "termini5"]:
if lasv_target.has_feature(feature):
print(f"Here is feature {feature}:\n{target.get_feature(feature)}")
else:
print(f"target lacks feature {feature}\n")
target = LASV_Josiah_WT target lacks feature non-existent Here is feature termini5: Feature(name=termini5, seq=GCACGGCGTCACACTTTGCTATGCCATAGCATRTTTATCCATAAGATTAGCGGATCCTACCTGACGCTTTTTATCGCAACTCTCTACTGTTTCTCCATAACAGAACATATTGACTATCCGGTATTACCCGGCATGACAGGAGTAAAA, start=0, end=147) target = LASV_Josiah_OPT target lacks feature non-existent Here is feature termini5: Feature(name=termini5, seq=GCACGGCGTCACACTTTGCTATGCCATAGCATRTTTATCCATAAGATTAGCGGATCCTACCTGACGCTTTTTATCGCAACTCTCTACTGTTTCTCCATAACAGAACATATTGACTATCCGGTATTACCCGGCATGACAGGAGTAAAA, start=0, end=147)
We can plot the Targets:
In [10]:
_ = lasv_targets.plot(ax_width=10)
We can write them to a file for alignment:
In [11]:
with tempfile.NamedTemporaryFile(mode="w") as f:
lasv_targets.write_fasta(f.name)
f.flush()
fasta_text = open(f.name).read()
print(fasta_text)
>LASV_Josiah_WT GACTGATANNNNNNCAGCGACGCCAAGAACCAGYAGTGGCACCTGACCGAGCACGCCATCGCCTCCGGCTCCGCCTTGCCCGCTGGATCCGGCGAGGGCAGAGGAAGTCTGCTAACATGCGGTGACGTCGAGGAGAATCCTGGCCCAATGGGACAAATAGTGACATTCTTCCAGGAAGTGCCTCATGTAATAGAAGAGGTGATGAACATTGTTCTCATTGCACTGTCTGTACTAGCAGTGCTGAAAGGTCTGTACAATTTTGCAACGTGTGGCCTTGTTGGTTTGGTCACTTTCCTCCTGTTGTGTGGTAGGTCTTGCACAACCAGTCTTTATAAAGGGGTTTATGAGCTTCAGACTCTGGAACTAAACATGGAGACACTCAATATGACCATGCCTCTCTCCTGCACAAAGAACAACAGTCATCATTATATAATGGTGGGCAATGAGACAGGACTAGAACTGACCTTGACCAACACGAGCATTATTAATCACAAATTTTGCAATCTGTCTGATGCCCACAAAAAGAACCTCTATGACCACGCTCTTATGAGCATAATCTCAACTTTCCACTTGTCCATCCCCAACTTCAATCAGTATGAGGCAATGAGCTGCGATTTTAATGGGGGAAAGATTAGTGTGCAGTACAACCTGAGTCACAGCTATGCTGGGGATGCAGCCAACCATTGTGGTACTGTTGCAAATGGTGTGTTACAGACTTTTATGAGGATGGCTTGGGGTGGGAGCTACATTGCTCTTGACTCAGGCCGTGGCAACTGGGACTGTATTATGACTAGTTATCAATATCTGATAATCCAAAATACAACCTGGGAAGATCACTGCCAATTCTCGAGACCATCTCCCATCGGTTATCTCGGGCTCCTCTCACAAAGGACTAGAGATATTTATATTAGTAGAAGATTGCTAGGCACATTCACATGGACACTGTCAGATTCTGAAGGTAAAGACACACCAGGGGGATATTGTCTGACCAGGTGGATGCTAATTGAGGCTGAACTAAAATGCTTCGGGAACACAGCTGTGGCAAAATGTAATGAGAAGCATGATGAGGAATTTTGTGACATGCTGAGGCTGTTTGACTTCAACAAACAAGCCATTCAAAGGTTGAAAGCTGAAGCACAAATGAGCATTCAGTTGATCAACAAAGCAGTAAATGCTTTGATAAATGACCAACTTATAATGAAGAACCATCTACGGGACATCATGGGAATTCCATACTGTAATTACAGCAAGTATTGGTACCTCAACCACACAACTACTGGGAGAACATCACTGCCCAAATGTTGGCTTGTATCAAATGGTTCATACTTGAACGAGACCCACTTTTCTGATGATATTGAACAACAAGCTGACAATATGATCACTGAGATGTTACAGAAGGAGTATATGGAGAGGCAGGGGAAGACACCATTGGGTCTAGTTGACCTCTTTGTGTTCAGCACAAGTTTCTATCTTATTAGCATCTTCCTTCACCTAGTCAAAATACCAACTCATAGGCATATTGTAGGCAAGTCGTGTCCCAAACCTCACAGATTGAATCATATGGGCATTTGTTCCTGTGGACTCTACAAACAGCCTGGTGTGCCTGTGAAATGGAAGAGATGAGCTAGCTAAACGCGTTGATCCTAATCAACCTCTGGATTACAAAATTTGTGAAAGATTGACTGGTATTCTTAACTATGTRGCTCCTTTTACGCTATGTGGATACGCTG >LASV_Josiah_OPT GACTGATANNNNNNCAGCGACGCCAAGAACCAGYAGTGGCACCTGACCGAGCACGCCATCGCCTCCGGCTCCGCCTTGCCCGCTGGATCCGGCGAGGGCAGAGGAAGTCTGCTAACATGCGGTGACGTCGAGGAGAATCCTGGCCCAATGGGCCAGATCGTGACCTTCTTCCAAGAAGTGCCTCATGTGATTGAGGAGGTGATGAATATCGTGCTGATCGCTTTAAGCGTGCTGGCCGTTCTTAAGGGCCTCTATAACTTCGCCACTTGTGGTTTAGTCGGACTGGTGACATTTCTGCTGCTGTGTGGCAGATCTTGTACCACATCTTTATACAAGGGCGTGTACGAGCTGCAGACTTTAGAACTGAACATGGAGACTTTAAACATGACCATGCCTTTAAGCTGTACCAAGAACAATAGCCACCACTACATCATGGTGGGCAACGAGACCGGTTTAGAACTGACACTCACCAACACCAGCATTATCAACCATAAGTTCTGCAACCTCTCCGACGCTCACAAGAAGAATTTATACGACCACGCTTTAATGAGCATCATCTCCACCTTCCATCTCTCCATTCCTAATTTCAACCAGTACGAGGCCATGAGCTGCGACTTTAACGGCGGCAAGATCTCCGTGCAGTACAATTTATCCCATAGCTACGCCGGCGATGCCGCCAATCACTGCGGAACCGTGGCCAACGGCGTGCTGCAGACATTCATGAGGATGGCTTGGGGCGGCTCCTATATCGCTTTAGACTCCGGCAGAGGAAACTGGGACTGTATCATGACCAGCTACCAATATTTAATCATTCAGAACACCACATGGGAGGACCACTGCCAATTCTCTCGTCCCTCTCCTATCGGCTATCTGGGACTGCTGTCCCAGAGGACCAGAGACATCTACATCTCTCGTAGGCTGCTGGGCACATTCACTTGGACTTTAAGCGACAGCGAAGGCAAAGATACTCCCGGTGGCTACTGTTTAACAAGATGGATGCTGATCGAGGCCGAGCTCAAGTGCTTCGGAAATACCGCCGTGGCCAAATGCAACGAGAAACACGACGAGGAGTTCTGCGACATGCTGAGGCTCTTCGACTTCAACAAGCAAGCCATTCAGAGGCTGAAGGCCGAAGCCCAGATGTCCATCCAGCTGATTAATAAGGCCGTGAATGCCCTCATTAACGACCAGCTGATCATGAAGAACCATTTAAGGGACATCATGGGCATCCCTTATTGCAACTACAGCAAATACTGGTATTTAAATCATACCACCACCGGTCGTACATCCTTACCTAAGTGCTGGCTGGTCAGCAATGGCTCCTATTTAAACGAGACACACTTCTCCGACGACATCGAGCAGCAAGCCGACAACATGATCACCGAAATGCTCCAGAAGGAGTACATGGAGAGGCAAGGTAAGACTCCTCTGGGTTTAGTGGATTTATTCGTCTTCAGCACCTCCTTCTATTTAATCTCCATCTTTCTTCATCTGGTGAAGATTCCTACCCACAGACACATTGTGGGCAAGAGCTGTCCTAAGCCTCATAGACTGAACCACATGGGCATCTGTAGCTGCGGTTTATATAAACAGCCCGGTGTTCCCGTTAAGTGGAAGAGGTGAGCTAGCTAAACGCGTTGATCCTAATCAACCTCTGGATTACAAAATTTGTGAAAGATTGACTGGTATTCTTAACTATGTRGCTCCTTTTACGCTATGTGGATACGCTG
In [ ]: