# Primer design for HT expression of CAZymes

<a target="_blank" href="https://colab.research.google.com/github/pydna-group/pydna/blob/master/docs/notebooks/Example_HT_cazyme_primer_design.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>


```python
%%capture
# Install pydna (only when running on Colab)
import sys
if 'google.colab' in sys.modules:
    %pip install pydna[clipboard,download,express,gel] teemi
    !curl -LO "https://github.com/pydna-group/pydna/raw/master/docs/notebooks/GeneByLocusTag_CAZymes.fasta"
    !curl -LO "https://github.com/pydna-group/pydna/raw/master/docs/notebooks/GeneByLocusTag_prot_CAZymes.fasta"
    !curl -LO "https://github.com/pydna-group/pydna/raw/master/docs/notebooks/SignalP_output_CAZymes.gff3"
```

> ⚠️ This notebook uses the extra dependency `teemi`. Run it in google Colab, or in an environment where you install `teemi` as well as `pydna`.

Shamelessly adapeted from: https://github.com/hiyama341/cazyme_primer_design/blob/main/notebooks/00-cazyme_primer_design.ipynb

In this notebook, we design a high-throughput CAZyme library by automating key steps in the primer design workflow. Specifically, we:

1. Read nucleotide sequences from a text file

2. Translate sequences to protein

3. Import SignalP output to identify signal peptides

4. Remove signal peptides from the native sequences

5. Generate primers for downstream applications

This approach enables efficient processing of large sequence datasets and systematic design of primers for CAZymes without Signal peptides. In this example we have 11 targets but in principle there is not a limit. Happy bioengineering. 



Functions for the notebook


```python
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import SeqIO
import pandas as pd
from Bio.Seq import Seq
from typing import List, Dict, Union
import requests
import json
from teemi.design.combinatorial_design import simple_amplicon_maker
from pydna.design import primer_design

def open_gff3_files(path: str = "") -> List[List[str]]:
    """
    Opens and reads a GFF3 file and returns its contents as a list of lists.

    Parameters:
    -----------
    path: str
        The path to the GFF3 file.

    Returns:
    --------
    List[List[str]]
        A list of lists containing the contents of the GFF3 file.
    """
    with open(path, "r") as infile:
        LINES = []
        for line in infile:
            LINES.append(line[:].split("\t"))
        LINES = LINES[1:]

    return LINES


def tidy_up_gff(lst_of_gff: list) -> list:
    """
    This function takes a list of GFF lines and returns a list of dictionaries,
    with each dictionary containing information on the signal peptides in the GFF file.

    Parameters:
    lst_of_gff (list): A list of GFF lines.

    Returns:
    list_of_peptides (list): A list of dictionaries, with each dictionary containing information on the signal peptides in the GFF file.
    """
    signal_peptides = {}
    list_of_peptides = []

    for peptide in lst_of_gff:
        signal_peptides["gene"] = peptide[0][:19]
        signal_peptides["start_pos"] = int(peptide[3]) - 1
        signal_peptides["end_pos"] = int(peptide[4]) + 1
        signal_peptides["signal_peptide_likelyhood"] = peptide[5]
        list_of_peptides.append(signal_peptides)
        signal_peptides = {
            "gene": "",
            "start_pos": "",
            "end_pos": "",
            "signal_peptide_likelyhood": "",
        }

    return list_of_peptides


def dict_of_signal_peptides(path: str = "") -> List[Dict[str, Union[str, int]]]:
    """
    Given a path to a GFF3 file, returns a list of dictionaries with information on signal peptides.

    Args:
        path (str): Path to the GFF3 file. Default is an empty string.

    Returns:
        list: A list of dictionaries where each dictionary contains the following keys:
            - 'gene' (str): Gene name of the signal peptide.
            - 'start_pos' (int): Start position of the signal peptide in the protein sequence.
            - 'end_pos' (int): End position of the signal peptide in the protein sequence.
            - 'signal_peptide_likelyhood' (str): The likelihood of the sequence being a signal peptide.
    """
    gff = open_gff3_files(path)
    dict_of_signal_peptides = tidy_up_gff(gff)
    return dict_of_signal_peptides


def read_gff_to_pd(path: str = "") -> pd.DataFrame:
    """
    Reads a GFF3 file and returns a pandas DataFrame with columns 'gene', 'start_pos', 'end_pos',
    and 'signal_peptide_likelyhood'.

    Parameters:
    -----------
    path : str
        The path to the GFF3 file.

    Returns:
    --------
    df : pandas.DataFrame
        A DataFrame with columns 'gene', 'start_pos', 'end_pos', and 'signal_peptide_likelyhood'.
    """

    gff = open_gff3_files(path)
    dict_of_signal_peptides = tidy_up_gff(gff)
    df = pd.DataFrame.from_records(dict_of_signal_peptides)

    return df




def primer_ta_neb(primer1, primer2, conc=0.4, prodcode="phusion-0"):
    """Calculates primer pair melting temp TA,  from NEB.

    Parameters
    ----------
    primer1 : str
        first primer to be used for finding the optimal ta
    primer2 : str
        second primer to be used for finding the optimal ta
    conc : float
    prodcode : str
        find product codes on nebswebsite: https://tmapi.neb.com/docs/productcodes

    Returns
    -------
    ta : int
        primer pair annealing temp

    """

    url = "https://tmapi.neb.com/tm/batch"
    seqpairs = [[primer1, primer2]]

    input = {"seqpairs": seqpairs, "conc": conc, "prodcode": prodcode}
    headers = {"content-type": "application/json"}
    res = requests.post(url, data=json.dumps(input), headers=headers)

    r = json.loads(res.content)

    if r["success"]:
        for row in r["data"]:
            return row["ta"]

    else:
        print("request failed")
        print(r["error"][0])


def primer_tm_neb1(primer, conc=0.4, prodcode="phusion-0"):
    """Calculates a single primers melting temp from NEB.

    Parameters
    ----------
    primer1 : str
    conc : float
    prodcode : str
        find product codes on nebswebsite: https://tmapi.neb.com/docs/productcodes

    Returns
    -------
    tm : int
        primer melting temperature

    """

    url = "https://tmapi.neb.com/tm/batch"
    seqpairs = [[primer]]

    input = {"seqpairs": seqpairs, "conc": conc, "prodcode": prodcode}
    headers = {"content-type": "application/json"}
    res = requests.post(url, data=json.dumps(input), headers=headers)

    r = json.loads(res.content)

    if r["success"]:
        for row in r["data"]:
            return row["tm1"]
    else:
        print("request failed")
        print(r["error"][0])
        
        
def make_amplicons(
    list_of_amplicons: list, target_tm=58, limit=10, tm_function=primer_tm_neb1):
    """Generates pydna.amplicons which contains primers with a target temperature.

    Parameters
    ----------
    list_of_amplicons : list
        list of pydna.Dseqrecords
    target_tm : int
        representing the target melting temperature for the primers (default=55)
    limit: int
        representing the minimum primer size (default=5)
    tm_function : function
        for calculating primer melting temperature (default=primer_tm_neb)

    Returns:
    amplicons: list
        list of amplicon objects with designed primer sequences
    """
    amplicons = []
    for i in range(len(list_of_amplicons)):
        amplicon = primer_design(
            list_of_amplicons[i],
            target_tm=target_tm,
            limit=limit,
            tm_function=tm_function,
        )

        amplicons.append(amplicon)

    return amplicons

```

# Steps

I would like:
1) Read sequences from a text file
2) Translate sequences
3) Read in SignalP output
4) Remove signal peptides from native sequences
5) Generate primers

# 1 - Read sequences


```python
from Bio import SeqIO
```


```python
sequences = list()
for sequence in SeqIO.parse('GeneByLocusTag_CAZymes.fasta',format='fasta'):
    print(sequence)
    sequences.append(sequence)
```

    ID: AO090005001389
    Name: AO090005001389
    Description: AO090005001389
    Number of features: 0
    Seq('ATGGGAGCTCCCCGGCTTCGGATCAAAGGCGCAACCTTCAAAGACCCAAATAAT...TAG')
    ID: AO090038000444
    Name: AO090038000444
    Description: AO090038000444
    Number of features: 0
    Seq('ATGCGTTCCTTGTCGTCTATCGCACTCCTGTCTGTGGTGGGAGCTGCGTCTGCC...TAA')
    ID: AO090012000917
    Name: AO090012000917
    Description: AO090012000917
    Number of features: 0
    Seq('ATGCGGGCGTCACTACCGTTTCTTACTGCCTTGGGATGTATTCCAGCCGCTTTG...TGA')
    ID: AO090005001553
    Name: AO090005001553
    Description: AO090005001553
    Number of features: 0
    Seq('ATGAAATTCCGTAACCTTTTCTTTGCTGCCGTAGCTGGCTCTGCGGTTGCTGCT...TAG')
    ID: AO090011000715
    Name: AO090011000715
    Description: AO090011000715
    Number of features: 0
    Seq('ATGAGAATCGGCAACTTGATCGTGGCTGCAAGTGCTGCAAGCCTGGTGCATGCG...TAA')
    ID: AO090011000757
    Name: AO090011000757
    Description: AO090011000757
    Number of features: 0
    Seq('ATGAAAGTCACCAGGCTAGCGGTTCTGAATACCCTGGCAACCCTAACTGTTGCC...TAG')
    ID: AO090003000990
    Name: AO090003000990
    Description: AO090003000990
    Number of features: 0
    Seq('ATGTTTATTAAACTTTTAAACAAAGCGCTTCTAGTGCTTGGTCTGCTGTCAGCT...TAA')
    ID: AO090009000373
    Name: AO090009000373
    Description: AO090009000373
    Number of features: 0
    Seq('ATGACTGACCCAAACTATACACCACCAACCGTGGAGGATATATTTCGCTACCGA...TGA')
    ID: AO090012000006
    Name: AO090012000006
    Description: AO090012000006
    Number of features: 0
    Seq('ATGGCAATGCTGTTCACTCCCATTGCGGCTGCATCACTGCTAGCGGTGGTGGGC...TAA')
    ID: AO090001000604
    Name: AO090001000604
    Description: AO090001000604
    Number of features: 0
    Seq('ATGCCGAGTCACTCGCGCAGTCGGGACCGCTATCGCAGCGAGCGGGATCCGAGC...TAA')
    ID: AO090010000122
    Name: AO090010000122
    Description: AO090010000122
    Number of features: 0
    Seq('ATGGAGCTCTCTTCTGGATCAAGAGCGGAGCATCGCCCGATCCACGCGTGGGCC...TGA')
    ID: AO090003001590
    Name: AO090003001590
    Description: AO090003001590
    Number of features: 0
    Seq('ATGCAACCATTACGGCTTTTGGCTCTCACAGCCATTCTTAAGGGCGCCTGGGCC...TGA')



```python
print(sequences[0])
```

    ID: AO090005001389
    Name: AO090005001389
    Description: AO090005001389
    Number of features: 0
    Seq('ATGGGAGCTCCCCGGCTTCGGATCAAAGGCGCAACCTTCAAAGACCCAAATAAT...TAG')


# 2 - Translate sequences


```python
proteins = list()
for sequence in SeqIO.parse('GeneByLocusTag_prot_CAZymes.fasta',format='fasta'):
    print(sequence)
    proteins.append(sequence)
```

    ID: AO090005001389
    Name: AO090005001389
    Description: AO090005001389
    Number of features: 0
    Seq('MGAPRLRIKGATFKDPNNREITLRGINVAGESKYPKSPDTPSYVPDKFFETDDV...TIM')
    ID: AO090038000444
    Name: AO090038000444
    Description: AO090038000444
    Number of features: 0
    Seq('MRSLSSIALLSVVGAASAQAGPWAQCGGKSFSGSSECASGWKCQELNEWFSQCV...NKN')
    ID: AO090012000917
    Name: AO090012000917
    Description: AO090012000917
    Number of features: 0
    Seq('MRASLPFLTALGCIPAALAAPHPRVQSPEYVNWTTFKANGVNLGGWLVQESTID...DTK')
    ID: AO090005001553
    Name: AO090005001553
    Description: AO090005001553
    Number of features: 0
    Seq('MKFRNLFFAAVAGSAVAAPLAKEQKKRDSVFQWIGANESGAEFGENNLPGVWGT...YLG')
    ID: AO090011000715
    Name: AO090011000715
    Description: AO090011000715
    Number of features: 0
    Seq('MRIGNLIVAASAASLVHAYPTRDIKKRGSGFTWVGVSESGAEFGSSIPGTLGTD...QCL')
    ID: AO090011000757
    Name: AO090011000757
    Description: AO090011000757
    Number of features: 0
    Seq('MKVTRLAVLNTLATLTVAWLPTTDKTITSSNGTDLFKASHGKIRGVNLGSQFVF...RSS')
    ID: AO090003000990
    Name: AO090003000990
    Description: AO090003000990
    Number of features: 0
    Seq('MFIKLLNKALLVLGLLSAGTQAATIRLDPRASSFDYNGEKVRGVNLGGWLVLEP...KQC')
    ID: AO090009000373
    Name: AO090009000373
    Description: AO090009000373
    Number of features: 0
    Seq('MTDPNYTPPTVEDIFRYRYQHGTNLGSMFVHGPWLSDGASSSDSGGSRELEEVK...SVH')
    ID: AO090012000006
    Name: AO090012000006
    Description: AO090012000006
    Number of features: 0
    Seq('MAMLFTPIAAASLLAVVGTQPTGVFAQDAEGWYKAHPGMSRIKDVNQDTHQIVD...TRK')
    ID: AO090001000604
    Name: AO090001000604
    Description: AO090001000604
    Number of features: 0
    Seq('MPSHSRSRDRYRSERDPSRRYREVYDDDDDDDFDYHPRERRRYRRDDYQHDIRS...EYY')
    ID: AO090010000122
    Name: AO090010000122
    Description: AO090010000122
    Number of features: 0
    Seq('MELSSGSRAEHRPIHAWAQNVYQGRISPESRFYKNSRILKSELSFRNRQRTANN...SRK')
    ID: AO090003001590
    Name: AO090003001590
    Description: AO090003001590
    Number of features: 0
    Seq('MQPLRLLALTAILKGAWALSANCTGSFDAISASDFVANINPGWNLGNSLDATPN...LTV')


# 3 - Read in SignalP output


```python
signal_pep = read_gff_to_pd('SignalP_output_CAZymes.gff3')
signal_pep
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>gene</th>
      <th>start_pos</th>
      <th>end_pos</th>
      <th>signal_peptide_likelyhood</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AO090038000444</td>
      <td>0</td>
      <td>19</td>
      <td>0.9994235</td>
    </tr>
    <tr>
      <th>1</th>
      <td>AO090012000917</td>
      <td>0</td>
      <td>20</td>
      <td>0.99983215</td>
    </tr>
    <tr>
      <th>2</th>
      <td>AO090005001553</td>
      <td>0</td>
      <td>28</td>
      <td>0.999823</td>
    </tr>
    <tr>
      <th>3</th>
      <td>AO090011000715</td>
      <td>0</td>
      <td>28</td>
      <td>0.9998376</td>
    </tr>
    <tr>
      <th>4</th>
      <td>AO090011000757</td>
      <td>0</td>
      <td>19</td>
      <td>0.9998413</td>
    </tr>
    <tr>
      <th>5</th>
      <td>AO090003000990</td>
      <td>0</td>
      <td>23</td>
      <td>0.9998205</td>
    </tr>
    <tr>
      <th>6</th>
      <td>AO090012000006</td>
      <td>0</td>
      <td>27</td>
      <td>0.99984413</td>
    </tr>
    <tr>
      <th>7</th>
      <td>AO090003001590</td>
      <td>0</td>
      <td>19</td>
      <td>0.99982584</td>
    </tr>
  </tbody>
</table>
</div>




```python
N_pos = signal_pep['end_pos'].to_list()
genes_with_signal_peptides = signal_pep['gene'].to_list()

my_dict = {'name':genes_with_signal_peptides, 'n_pos':N_pos }
my_dict


```




    {'name': ['AO090038000444',
      'AO090012000917',
      'AO090005001553',
      'AO090011000715',
      'AO090011000757',
      'AO090003000990',
      'AO090012000006',
      'AO090003001590'],
     'n_pos': [19, 20, 28, 28, 19, 23, 27, 19]}



# 4 - Remove signal peptides


```python

clean_seq = list()
for i in range(len(genes_with_signal_peptides)):
    for seq in sequences: 
        if genes_with_signal_peptides[i] == seq.id:
            clean_seq.append(seq[N_pos[i]*3:-3])
            
for seq in sequences:
    if seq.id not in genes_with_signal_peptides:
        clean_seq.append(seq[3:-3])
            
len(clean_seq)
```




    12




```python
clean_seq
```




    [SeqRecord(seq=Seq('GCTGGTCCTTGGGCTCAGTGTGGAGGCAAGTCCTTCTCCGGCTCATCCGAGTGT...AAC'), id='AO090038000444', name='AO090038000444', description='AO090038000444', dbxrefs=[]),
     SeqRecord(seq=Seq('CCCCATCCCCGAGTGCAGAGTCCGGAGTATGTCAACTGGACAACTTTCAAAGCC...AAG'), id='AO090012000917', name='AO090012000917', description='AO090012000917', dbxrefs=[]),
     SeqRecord(seq=Seq('TCAGTTTTCCAATGTTAGTCTGCCCTTCTTCTTCTTCTTCTTCTTCTTCTTTCT...GGG'), id='AO090005001553', name='AO090005001553', description='AO090005001553', dbxrefs=[]),
     SeqRecord(seq=Seq('TCGGGATTCACCTGTACGTCAAGTATTGGCTCTGAAGTTACTGGATCTAACGAA...CTG'), id='AO090011000715', name='AO090011000715', description='AO090011000715', dbxrefs=[]),
     SeqRecord(seq=Seq('CTTCCAACAACCGACAAGACAATCACCTCTAGTAATGGAACCGATCTCTTCAAA...AGC'), id='AO090011000757', name='AO090011000757', description='AO090011000757', dbxrefs=[]),
     SeqRecord(seq=Seq('ACTATGTCAGTATAACCGTGATGTTACCCCTTTTGCTCTGCATAGTCCCTTACT...TGC'), id='AO090003000990', name='AO090003000990', description='AO090003000990', dbxrefs=[]),
     SeqRecord(seq=Seq('GATGCCGAGGGTTGGTACAAGGCTCATCCCGGAATGTCGCGCATCAAGGATGTG...AAA'), id='AO090012000006', name='AO090012000006', description='AO090012000006', dbxrefs=[]),
     SeqRecord(seq=Seq('AGTGCCAATTGTACAGGGTCCTTTGACGCCATCTCGGCCTCTGATTTCGTCGCA...GTT'), id='AO090003001590', name='AO090003001590', description='AO090003001590', dbxrefs=[]),
     SeqRecord(seq=Seq('GGAGCTCCCCGGCTTCGGATCAAAGGCGCAACCTTCAAAGACCCAAATAATCGA...ATG'), id='AO090005001389', name='AO090005001389', description='AO090005001389', dbxrefs=[]),
     SeqRecord(seq=Seq('ACTGACCCAAACTATACACCACCAACCGTGGAGGATATATTTCGCTACCGATAT...CAT'), id='AO090009000373', name='AO090009000373', description='AO090009000373', dbxrefs=[]),
     SeqRecord(seq=Seq('CCGAGTCACTCGCGCAGTCGGGACCGCTATCGCAGCGAGCGGGATCCGAGCAGA...TAT'), id='AO090001000604', name='AO090001000604', description='AO090001000604', dbxrefs=[]),
     SeqRecord(seq=Seq('GAGCTCTCTTCTGGATCAAGAGCGGAGCATCGCCCGATCCACGCGTGGGCCCAG...AAG'), id='AO090010000122', name='AO090010000122', description='AO090010000122', dbxrefs=[])]




```python
from pydna.dseqrecord import Dseqrecord
```


```python
clean_seq = [Dseqrecord(seq) for seq in clean_seq]
```


```python
clean_seq[0].seq
```




    Dseq(-1510)
    GCTG..GAAC
    CGAC..CTTG



# 5 - Generate primers 


```python
amplicons = make_amplicons(clean_seq,
                           target_tm=60, # target melting temperature
                           limit=15,  # minimum primer length
                           tm_function = primer_tm_neb1 #calculating melting temperature 
                           )
```


```python
amplicons[0].forward_primer.seq
```




    Seq('GCTGGTCCTTGGGCT')




```python
forward_primer = [str(f.forward_primer.seq) for f in amplicons]
r_primer = [str(r.reverse_primer.seq) for r in amplicons]
name = [str(r.name) for r in amplicons]
gene = [str(r.id) for r in clean_seq]
aneal_f = [primer_tm_neb1(str(r)) for r in forward_primer]
aneal_r = [primer_tm_neb1(str(r)) for r in r_primer]
```


```python
ta= [primer_ta_neb(str(f.forward_primer.seq),str(f.reverse_primer.seq))  for f in amplicons]
```


```python
ta
```




    [61, 61, 62, 61, 61, 63, 61, 62, 63, 63, 62, 62]




```python
df = pd.DataFrame({'gene_name (locus_tag)': gene, 
                   'f_seq':forward_primer, 
                   'r_seq':r_primer,
                    'f_tm': aneal_f, 
                    'r_tm': aneal_r, 
                    'ta':ta})
```


```python
df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>gene_name (locus_tag)</th>
      <th>f_seq</th>
      <th>r_seq</th>
      <th>f_tm</th>
      <th>r_tm</th>
      <th>ta</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AO090038000444</td>
      <td>GCTGGTCCTTGGGCT</td>
      <td>GTTCTTGTTGACATTTTCCATATGGT</td>
      <td>57</td>
      <td>60</td>
      <td>61</td>
    </tr>
    <tr>
      <th>1</th>
      <td>AO090012000917</td>
      <td>CCCCATCCCCGAGTG</td>
      <td>CTTCGTATCATGGAATCTCGTAAG</td>
      <td>57</td>
      <td>58</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>AO090005001553</td>
      <td>TCAGTTTTCCAATGTTAGTCTGC</td>
      <td>CCCCAGGTATGCCTGC</td>
      <td>59</td>
      <td>58</td>
      <td>62</td>
    </tr>
    <tr>
      <th>3</th>
      <td>AO090011000715</td>
      <td>TCGGGATTCACCTGTACG</td>
      <td>CAGGCACTGGCTGTAGT</td>
      <td>58</td>
      <td>58</td>
      <td>61</td>
    </tr>
    <tr>
      <th>4</th>
      <td>AO090011000757</td>
      <td>CTTCCAACAACCGACAAGAC</td>
      <td>GCTGCTTCGACCTTTACAG</td>
      <td>58</td>
      <td>58</td>
      <td>61</td>
    </tr>
    <tr>
      <th>5</th>
      <td>AO090003000990</td>
      <td>ACTATGTCAGTATAACCGTGATGTTAC</td>
      <td>GCACTGCTTAGGGAACTCC</td>
      <td>60</td>
      <td>59</td>
      <td>63</td>
    </tr>
    <tr>
      <th>6</th>
      <td>AO090012000006</td>
      <td>GATGCCGAGGGTTGGT</td>
      <td>TTTACGAGTGATTGTGATAGTGATGT</td>
      <td>58</td>
      <td>60</td>
      <td>61</td>
    </tr>
    <tr>
      <th>7</th>
      <td>AO090003001590</td>
      <td>AGTGCCAATTGTACAGGGT</td>
      <td>AACAGTCAAAGTAAAGTTGACCG</td>
      <td>58</td>
      <td>59</td>
      <td>62</td>
    </tr>
    <tr>
      <th>8</th>
      <td>AO090005001389</td>
      <td>GGAGCTCCCCGGCTT</td>
      <td>CATGATAGTACAGGCTCCACGTT</td>
      <td>60</td>
      <td>61</td>
      <td>63</td>
    </tr>
    <tr>
      <th>9</th>
      <td>AO090009000373</td>
      <td>ACTGACCCAAACTATACACCAC</td>
      <td>ATGAACACTGTAAGTGTTGATACTAACC</td>
      <td>59</td>
      <td>61</td>
      <td>63</td>
    </tr>
    <tr>
      <th>10</th>
      <td>AO090001000604</td>
      <td>CCGAGTCACTCGCGC</td>
      <td>ATAATATTCCGGTAAATCCCCGAAAC</td>
      <td>59</td>
      <td>61</td>
      <td>62</td>
    </tr>
    <tr>
      <th>11</th>
      <td>AO090010000122</td>
      <td>GAGCTCTCTTCTGGATCAAGAG</td>
      <td>CTTACGACTGTTGATGGCCG</td>
      <td>59</td>
      <td>60</td>
      <td>62</td>
    </tr>
  </tbody>
</table>
</div>




```python
amplicon_names = [f"#{i}_{name[i]}_{list(df['gene_name (locus_tag)'])[i]}" for i in range(len(name))]

for i in range(len(amplicon_names)):
    amplicons[i].name = amplicon_names[i]
    
```


```python
for amplicon in amplicons:
    print(amplicon.name)
    print(amplicon.figure())
    print()
```

    #0_1510bp_PCR_prod_AO090038000444
    5GCTGGTCCTTGGGCT...ACCATATGGAAAATGTCAACAAGAAC3
                       ||||||||||||||||||||||||||
                      3TGGTATACCTTTTACAGTTGTTCTTG5
    5GCTGGTCCTTGGGCT3
     |||||||||||||||
    3CGACCAGGAACCCGA...TGGTATACCTTTTACAGTTGTTCTTG5
    
    #1_1116bp_PCR_prod_AO090012000917
    5CCCCATCCCCGAGTG...CTTACGAGATTCCATGATACGAAG3
                       ||||||||||||||||||||||||
                      3GAATGCTCTAAGGTACTATGCTTC5
    5CCCCATCCCCGAGTG3
     |||||||||||||||
    3GGGGTAGGGGCTCAC...GAATGCTCTAAGGTACTATGCTTC5
    
    #2_1279bp_PCR_prod_AO090005001553
    5TCAGTTTTCCAATGTTAGTCTGC...GCAGGCATACCTGGGG3
                               ||||||||||||||||
                              3CGTCCGTATGGACCCC5
    5TCAGTTTTCCAATGTTAGTCTGC3
     |||||||||||||||||||||||
    3AGTCAAAAGGTTACAATCAGACG...CGTCCGTATGGACCCC5
    
    #3_1600bp_PCR_prod_AO090011000715
    5TCGGGATTCACCTGTACG...ACTACAGCCAGTGCCTG3
                          |||||||||||||||||
                         3TGATGTCGGTCACGGAC5
    5TCGGGATTCACCTGTACG3
     ||||||||||||||||||
    3AGCCCTAAGTGGACATGC...TGATGTCGGTCACGGAC5
    
    #4_1266bp_PCR_prod_AO090011000757
    5CTTCCAACAACCGACAAGAC...CTGTAAAGGTCGAAGCAGC3
                            |||||||||||||||||||
                           3GACATTTCCAGCTTCGTCG5
    5CTTCCAACAACCGACAAGAC3
     ||||||||||||||||||||
    3GAAGGTTGTTGGCTGTTCTG...GACATTTCCAGCTTCGTCG5
    
    #5_1299bp_PCR_prod_AO090003000990
    5ACTATGTCAGTATAACCGTGATGTTAC...GGAGTTCCCTAAGCAGTGC3
                                   |||||||||||||||||||
                                  3CCTCAAGGGATTCGTCACG5
    5ACTATGTCAGTATAACCGTGATGTTAC3
     |||||||||||||||||||||||||||
    3TGATACAGTCATATTGGCACTACAATG...CCTCAAGGGATTCGTCACG5
    
    #6_1481bp_PCR_prod_AO090012000006
    5GATGCCGAGGGTTGGT...ACATCACTATCACAATCACTCGTAAA3
                        ||||||||||||||||||||||||||
                       3TGTAGTGATAGTGTTAGTGAGCATTT5
    5GATGCCGAGGGTTGGT3
     ||||||||||||||||
    3CTACGGCTCCCAACCA...TGTAGTGATAGTGTTAGTGAGCATTT5
    
    #7_1950bp_PCR_prod_AO090003001590
    5AGTGCCAATTGTACAGGGT...CGGTCAACTTTACTTTGACTGTT3
                           |||||||||||||||||||||||
                          3GCCAGTTGAAATGAAACTGACAA5
    5AGTGCCAATTGTACAGGGT3
     |||||||||||||||||||
    3TCACGGTTAACATGTCCCA...GCCAGTTGAAATGAAACTGACAA5
    
    #8_2465bp_PCR_prod_AO090005001389
    5GGAGCTCCCCGGCTT...AACGTGGAGCCTGTACTATCATG3
                       |||||||||||||||||||||||
                      3TTGCACCTCGGACATGATAGTAC5
    5GGAGCTCCCCGGCTT3
     |||||||||||||||
    3CCTCGAGGGGCCGAA...TTGCACCTCGGACATGATAGTAC5
    
    #9_1584bp_PCR_prod_AO090009000373
    5ACTGACCCAAACTATACACCAC...GGTTAGTATCAACACTTACAGTGTTCAT3
                              ||||||||||||||||||||||||||||
                             3CCAATCATAGTTGTGAATGTCACAAGTA5
    5ACTGACCCAAACTATACACCAC3
     ||||||||||||||||||||||
    3TGACTGGGTTTGATATGTGGTG...CCAATCATAGTTGTGAATGTCACAAGTA5
    
    #10_2616bp_PCR_prod_AO090001000604
    5CCGAGTCACTCGCGC...GTTTCGGGGATTTACCGGAATATTAT3
                       ||||||||||||||||||||||||||
                      3CAAAGCCCCTAAATGGCCTTATAATA5
    5CCGAGTCACTCGCGC3
     |||||||||||||||
    3GGCTCAGTGAGCGCG...CAAAGCCCCTAAATGGCCTTATAATA5
    
    #11_1442bp_PCR_prod_AO090010000122
    5GAGCTCTCTTCTGGATCAAGAG...CGGCCATCAACAGTCGTAAG3
                              ||||||||||||||||||||
                             3GCCGGTAGTTGTCAGCATTC5
    5GAGCTCTCTTCTGGATCAAGAG3
     ||||||||||||||||||||||
    3CTCGAGAGAAGACCTAGTTCTC...GCCGGTAGTTGTCAGCATTC5