Orchid FASTA File Parser with Biopython

This project demonstrates how to download and parse FASTA sequence data using Biopython. The dataset used here is the ls_orchid.fasta file from the Biopython documentation examples.

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📂 Files in this Project

• ls_orchid.fasta → FASTA file containing orchid DNA sequences (downloaded from Biopython GitHub examples).
• parser.py → Python script to parse and store sequences using Biopython's SeqIO module.

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▶️ Code Explanation

Step 1: Import Biopython's SeqIO

from Bio import SeqIO

The SeqIO module allows reading and writing of sequence file formats such as FASTA, GenBank, etc.

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Step 2: Initialize a List to Store Sequences

sequences = []

We create an empty list called sequences to store the DNA sequences extracted from the FASTA file.

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Step 3: Parse the FASTA File

for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
    sequences.append(seq_record.seq)

• SeqIO.parse() reads the FASTA file one record at a time.
• Each record (seq_record) contains:
  • seq_record.id → Identifier of the sequence.
  • seq_record.seq → Actual DNA sequence.
• We append only the sequence (seq_record.seq) to our sequences list.

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Step 4: Output

After running the script, the list sequences will hold all DNA sequences from the FASTA file.

Example output (first few sequences):

[Seq('MATTYGGTTGGA...'), Seq('CTTAGGCTCCTG...'), ...]

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⚡ Usage

1. Install Biopython:

pip install biopython

2. Download the FASTA file (Python version of wget):

import urllib.request

url = "https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta"
urllib.request.urlretrieve(url, "ls_orchid.fasta")

3. Run the parser script to load sequences.

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✅ Applications

• DNA sequence analysis
• Motif finding
• Sequence alignment
• Bioinformatics pipelines

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📖 References

• Biopython Tutorial & Cookbook
• Biopython GitHub Repository