Cheatography
https://cheatography.com
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This is a draft cheat sheet. It is a work in progress and is not finished yet.
Installing Biopython
pip install biopython
pip install --upgrade biopython
# import the library
import Bio
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Creating Sequences
from Bio.Seq import Seq
my_seq = Seq("AATGCACGTTG")
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To create a sequence we use the Seq
function from Bio
library
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Filling Sequences
# filling sequences
fragments = [Seq("GTAT"), Seq("TACT")]
filler = Seq("A"*3)
print(filler.join(fragments))
#output :
>>> GTATAAATACT
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Slicing Sequences
# defining sequences
my_seq = Seq("AAGTCCAGTGT")
my_seq_2 = Seq("AAAA")
# slicing sequences
print(my_seq[1:6])
print(my_seq[0::2])
# output :
>>> AGTCC
>>> AGCATT
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Slicing Sequences is the same as that of a python list ( we use []
)
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Appending Sequences
my_seq = Seq("AAGTCCAGTGT")
my_seq_2 = Seq("AAAA")
#appending sequences
print(my_seq + my_seq_2)
# output
>>> AAGTCCAGTGTAAAA
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Appending sequences is the same as appending strings in python
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Sequence Counting
from Bio.Seq import Seq
# creating a sequence
seq_example = Seq("AGTACACTGGT")
seq_length = len(seq_example)
occ = seq_example.count("C")
print("The length of the sequence is", len(seq_example))
print("The number of occurrences for nucleotide C is ", occ )
#output :
The length of the sequence is 11
The number of occurrences for nucleotide C is 2
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This provides how to get the length of a sequence and the number of occurrences of a specific nucleotide
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Finding Sub-sequence Index
my_seq = Seq("AAGTCCAGTGT")
index = my_seq.find("GTC")
print(f"GTC index is {index}")
# output :
GTC index is 2
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This returns the start index of the selected sub-sequence
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Reading Sequence Files
from Bio import SeqIO
records = SeqIO.parse("sequence_file.fasta", "fasta")
for record in records :
print(record.seq)
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We can also access other attributes from the records :
- record.seq
: returns one sequence from list of records
- record.id
: returns the identifier of the sequence
- record.description
: returns the sequence description
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Writing Sequences into a file
from Bio import SeqIO
# Define your sequence as a string
sequence = "ATCGATCGATCGATCGATC"
# Defining file name and format
filename = "my_sequence.fasta"
format = "fasta"
# defining the sequence
seq = SeqIO.SeqRecord(SeqIO.Seq(sequence1),
id="my_id", description="My sequence description")
# Open the file for writing in text mode
with open(filename, "w") as file:
# Create a SeqRecord object
record = SeqIO.SeqRecord(seq)
# Write the record to the file using the specified format
SeqIO.write(record, file, format)
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Converting Files
# syntax
SeqIO.convert(inp_file, inp_format, outp_file, outp_format, alphabet=None)
#example
SeqIO.convert("sequence.gbk", "genbank", "sequence_converted.fasta", "fasta")
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inp_file : path to input file
inp_format : input file format/extention
outp_file : path to output file
outp_format : output file format/extention
alphabet : specify the correct alphabet (DNA,RNA or Protein) to avoid conversion confusion
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Sequence Molecular Weight
from Bio.SeqUtils import molecular_weight
from Bio.Seq import Seq
seq_example = Seq("TGTACCCTGGT")
mw = molecular_weight(seq_example)
print(mw)
#output :
>>> 3403.1577
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Molecular weight is a way to guess how heavy a tiny building block of life (like a protein or piece of DNA) is compared to a single carbon atom where the bigger the building blocks , the higher the molecular weight
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GC-Content
from Bio.SeqUtils import gc_fraction
from Bio.Seq import Seq
# creating a sequence
seq_example = Seq("AGATTCACTGGT")
gc_content = gc_fraction(seq_example)
print(gc_content)
# output :
>>> 0.41
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G-C content refers to the percentage of guanine (G) and cytosine (C) molecules out of all the building blocks (called nucleotides) in a strand of DNA or RNA.
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Reverse Complement
from Bio.Seq import Seq
#creating a sequence
seq_example = Seq("AGTACACTGGT")
print("Sequence is :",seq_example)
# getting the reverse compliment
rev_comp = seq_example.reverse_complement()
print("Reverse complement:", rev_comp)
#output :
>>> Sequence is : AGTACACTGGT
>>> The reverse complement : ACCAGTGTACT
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Reverse complement of a DNA sequence is like a mirror image on the opposite strand.
- Reverse: Flips the order of the DNA letters (A, C, G, T) from left to right to right to left.
- Complement: Swaps each letter according to its pair: A pairs with T, and C pairs with G.
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Transcription & Translation
seq_example = Seq("ATGAAGTTTTAG")
transc = seq_example.transcribe()
print("Transcription:", transc)
transl = seq_example.translate()
print("Translation:", transl)
#output :
>>> Transcription: AUGAAGUUUUAG
>>> Translation: MKF*
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Transcription and translation are the two main steps that turn the instructions in our genes (DNA) into the building blocks of life (proteins).
- Transcription : is going from DNA to RNA ( creating a copy )
- Translation : is going from RNA to Protein
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Accessing NCBI Database using esearch()
from Bio import Entrez
handle = Entrez.esearch(db="nucleotide", term="BRCA1 gene", retmax=20)
record = Entrez.read(handle)
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- db
: The name of the Entrez database to search ("nucleotide", "protein"....)
- term
: The search term (e.g., gene name, protein ID ....)
- retmode (str, optional)
: The format (return mode) to return results in (default: "xml").
- retmax (int, optional)
: Maximum number of IDs to return (default: 10).
- sort (str, optional)
: Sorting criteria for results (default: "relevance")
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Accessing NCBI Database using efetch()
from Bio import Entrez
id_list = ["NM_007294.3", "NM_000546.5"]
handle = Entrez.efetch(db="nucleotide", id=id_list, rettype="gb")
records = Entrez.read(handle)
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db
: The name of the Entrez database to search ("nucleotide", "protein"....)
id
(list or str): A single ID or a list of IDs to retrieve
rettype
(str, optional): The type of information to return (default: "gb" for GenBank format)
retmode
(str, optional): The format to return results in (default: "xml").
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