biopythonn 0.1.2

A Python library for biological data operations

Biopythonn

This is a Python library named biopythonn for biological data operations.

Installation

from Bio import PDB import os

Step 1: Define PDB ID and fetch the structure from RCSB PDB

pdb_id = "1TUP" # Example: Human p53 DNA-binding domain pdb_filename = f"{pdb_id}.pdb"

Use PDBList to download the structure if not already present

pdbl = PDB.PDBList() pdbl.retrieve_pdb_file(pdb_id, pdir=".", file_format="pdb")

Step 2: Parse the downloaded PDB file

parser = PDB.PDBParser(QUIET=True) structure = parser.get_structure("protein", f"pdb{pdb_id.lower()}.ent") # Bio.PDB saves as 'pdbXXXX.ent'

Step 3: Access model and chain

model = structure[0] # First model chain = model['A'] # Select chain A

Step 4: Print info about the selected chain

print(f"\nResidues in Chain {chain.id}:") for residue in chain: print(residue)

Step 5: Save to a new PDB file for visualization in PyMOL, Chimera, etc.

io = PDB.PDBIO() io.set_structure(chain) # You can save full structure or just a chain io.save("selected_chain_A.pdb")

print("\nSaved chain A to 'selected_chain_A.pdb' for 3D visualization.")

from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio import SeqIO

Step 1: Create a DNA sequence

dna_sequence = Seq("ATGCGTACGTAGCTAGCTAG")

Step 2: Create a SeqRecord object with metadata and annotations

record = SeqRecord( dna_sequence, id="SEQ001", # Must be <= 10 characters for GenBank name="ExampleGene", # Gene name description="Example gene sequence", annotations={ "molecule_type": "DNA" # Required for GenBank format } )

Optional: add features or custom annotations as comments

record.annotations["comment"] = "Function: Hypothetical protein"

Step 3: Write the record to a GenBank file

output_file_path = "C:/Users/Admin/Downloads/q4_genbank.gb" with open(output_file_path, "w") as output_file: SeqIO.write(record, output_file, "genbank") print("GenBank file written successfully.")

Step 4: Read and print the contents of the GenBank file

with open(output_file_path, "r") as input_file: record_read = SeqIO.read(input_file, "genbank") print("\nContents of the GenBank file:") print(record_read)

#Output is a q4_genbank.gb file

from Bio import SeqIO from Bio.SeqRecord import SeqRecord

Function to convert a FASTA file to GenBank format

def convert_fasta_to_genbank(fasta_file, genbank_file): # List to hold GenBank records records = []

# Parse the FASTA file
for record in SeqIO.parse(fasta_file, "fasta"):
    # Create a new SeqRecord with GenBank-required annotations
    genbank_record = SeqRecord(
        record.seq,
        id=record.id[:10],  # GenBank requires ID to be ≤10 characters
        name="ExampleGene",
        description=record.description,
        annotations={
            "molecule_type": "DNA"  # Required field
        }
    )
    # Optional: add a comment field for gene/function info
    genbank_record.annotations["comment"] = "Gene: ExampleGene | Function: Hypothetical protein"
    
    # Append to the list of records
    records.append(genbank_record)

# Write all records to GenBank format
with open(genbank_file, "w") as output_handle:
    SeqIO.write(records, output_handle, "genbank")

print(f"All FASTA sequences converted to GenBank format and saved as '{genbank_file}'")

File paths (replace with actual ones if needed)

fasta_file = "C:/Users/Admin/Downloads/fasta_1.fasta" genbank_file = "C:/Users/Admin/Downloads/example_output.gb"

Call the function

convert_fasta_to_genbank(fasta_file, genbank_file)

from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio.SeqFeature import SeqFeature, FeatureLocation

Step 1: Create a DNA sequence

dna_sequence = Seq("ATGCGTACGTAGCTAGCTAG")

Step 2: Create a SeqRecord object with basic info

record = SeqRecord( dna_sequence, id="seq1", name="Example_Gene", description="An example DNA sequence for gene annotation.", )

Step 3: Add annotations

record.annotations["gene"] = "ExampleGene" record.annotations["function"] = "Hypothetical protein" record.annotations["organism"] = "Synthetic organism"

Step 4: Add a feature for the gene (with start and end positions)

gene_feature = SeqFeature( FeatureLocation(0, len(dna_sequence)), # 0-based indexing, end is exclusive type="gene", qualifiers={"gene": "ExampleGene", "note": "Example gene feature"} ) record.features.append(gene_feature)

Step 5: Modify one of the annotations

record.annotations["function"] = "Hypothetical protein with modified function"

Step 6: Print the updated SeqRecord

print(f"ID: {record.id}") print(f"Name: {record.name}") print(f"Description: {record.description}") print("\nAnnotations:") for key, value in record.annotations.items(): print(f" {key}: {value}")

print("\nFeatures:") for feature in record.features: print(f" Type: {feature.type}") print(f" Location: {feature.location}") print(f" Qualifiers: {feature.qualifiers}")

from Bio import Entrez, SeqIO

Step 1: Provide your email (required by NCBI)

Entrez.email = "[e-mail]" # Replace with your actual email

Step 2: Accession number of the sequence

accession_number = "NM_001301717" # Example: Human gene

Step 3: Fetch GenBank record using Entrez

with Entrez.efetch(db="nucleotide", id=accession_number, rettype="gb", retmode="text") as handle: seq_record = SeqIO.read(handle, "genbank")

Step 4: Print sequence and metadata

print(f"Accession Number: {seq_record.id}") print(f"Description: {seq_record.description}") print(f"Organism: {seq_record.annotations.get('organism', 'Not available')}") print(f"Sequence (first 100 bases): {seq_record.seq[:100]}...") # Print only first 100 bases print(f"Length of Sequence: {len(seq_record.seq)}") print(f"Number of Features: {len(seq_record.features)}")

Optional: Print top-level features (like CDS, gene, etc.)

print("\nTop Features:") for feature in seq_record.features[:5]: # Show only first 5 features print(f" Type: {feature.type}, Location: {feature.location}")

from Bio import pairwise2 from Bio.pairwise2 import format_alignment

Define two DNA sequences

seq1 = "AGTACACTGGT" seq2 = "AGTACGCTGGT"

Perform global alignment

alignments = pairwise2.align.globalxx(seq1, seq2) # 'xx' = match = 1, mismatch = 0

Print the best alignment and score

print("Aligned Sequences:") print(format_alignment(*alignments[0]))

✅ Steps to Perform MSA with MUSCLE in Biopython

Install MUSCLE separately:

Download from: https://www.drive5.com/muscle/

Add it to your system PATH or provide the full path in the script.

Save input sequences to a FASTA file.

Call MUSCLE using MuscleCommandline from Biopython.

Read the aligned output using AlignIO.

from Bio import AlignIO from Bio.Align.Applications import MuscleCommandline

Step 1: Define three DNA sequences in FASTA format

seq1 = """>seq1 ATGCGTACGTA """ seq2 = """>seq2 ATGCGTACGTC """ seq3 = """>seq3 ATGCGTACGAG """

Step 2: Write sequences to input FASTA file

with open("input_sequences.fasta", "w") as f: f.write(seq1) f.write(seq2) f.write(seq3)

Step 3: Set path to MUSCLE executable

If MUSCLE is in PATH, just use "muscle"

Otherwise, provide full path e.g. "C:/Users/Anurag/muscle.exe"

muscle_exe = "muscle"

Step 4: Set up MUSCLE command line

muscle_cline = MuscleCommandline( cmd=muscle_exe, input="input_sequences.fasta", out="aligned_sequences.fasta" )

Step 5: Run MUSCLE

stdout, stderr = muscle_cline()

Step 6: Read and print the alignment

alignment = AlignIO.read("aligned_sequences.fasta", "fasta") print("\nAligned Sequences:") print(alignment)

from Bio import AlignIO, Phylo from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor

Step 1: Load the aligned sequences (in CLUSTAL or FASTA format)

alignment = AlignIO.read("aligned_sequences.fasta", "fasta") # or "clustal" if using .aln

Step 2: Calculate the distance matrix

calculator = DistanceCalculator("identity") # You can also use "blastn", "trans", etc. distance_matrix = calculator.get_distance(alignment)

Step 3: Construct the phylogenetic tree using UPGMA or NJ

constructor = DistanceTreeConstructor() tree = constructor.upgma(distance_matrix)

tree = constructor.nj(distance_matrix) # Alternatively use neighbor-joining

Step 4: Visualize the tree

Phylo.draw(tree)

from Bio import PDB import os

Step 1: Define PDB ID and fetch the structure from RCSB PDB

pdb_id = "1TUP" # Example: Human p53 DNA-binding domain pdb_filename = f"{pdb_id}.pdb"

Use PDBList to download the structure if not already present

pdbl = PDB.PDBList() pdbl.retrieve_pdb_file(pdb_id, pdir=".", file_format="pdb")

Step 2: Parse the downloaded PDB file

parser = PDB.PDBParser(QUIET=True) structure = parser.get_structure("protein", f"pdb{pdb_id.lower()}.ent") # Bio.PDB saves as 'pdbXXXX.ent'

Step 3: Access model and chain

model = structure[0] # First model chain = model['A'] # Select chain A

Step 4: Print info about the selected chain

print(f"\nResidues in Chain {chain.id}:") for residue in chain: print(residue)

Step 5: Save to a new PDB file for visualization in PyMOL, Chimera, etc.

io = PDB.PDBIO() io.set_structure(chain) # You can save full structure or just a chain io.save("selected_chain_A.pdb")

print("\nSaved chain A to 'selected_chain_A.pdb' for 3D visualization.")