Blast two sequences from a python script

When working with Python, it is common to encounter situations where you need to blast two sequences from a script. In this article, we will explore three different ways to solve this problem, each with its own advantages and disadvantages.

Option 1: Using Biopython

Biopython is a powerful library that provides tools for biological computation. It includes a module called SeqIO that allows us to read and write sequences in various formats, including FASTA. To blast two sequences using Biopython, we can follow these steps:

from Bio.Blast import NCBIWWW

# Read the sequences from a file or define them as strings
sequence1 = "ATCGATCGATCG"
sequence2 = "GATCGATCGATC"

# Perform the blast
result_handle = NCBIWWW.qblast("blastn", "nt", sequence1 + sequence2)

# Parse and print the result
print(result_handle.read())

This code snippet uses the qblast function from the NCBIWWW module to perform a nucleotide blast. The first argument specifies the blast program to use (in this case, “blastn”), and the second argument specifies the database to search against (in this case, “nt” for the nucleotide database). The sequences are concatenated and passed as the third argument.

Option 2: Using subprocess

If you prefer a more lightweight solution without external dependencies, you can use the subprocess module to execute the blast command-line tool directly. Here’s an example:

import subprocess

# Read the sequences from a file or define them as strings
sequence1 = "ATCGATCGATCG"
sequence2 = "GATCGATCGATC"

# Execute the blast command
result = subprocess.run(["blastn", "-query", "input.fasta", "-subject", "subject.fasta"], capture_output=True, text=True)

# Print the result
print(result.stdout)

In this code snippet, we use the subprocess.run function to execute the blast command-line tool. The command and its arguments are passed as a list of strings. The capture_output=True argument captures the command’s output, and text=True ensures that the output is returned as a string.

Option 3: Using an API

If you prefer a more flexible and scalable solution, you can use an API provided by a blast service. This option requires an internet connection and an API key. Here’s an example using the NCBI BLAST API:

import requests

# Read the sequences from a file or define them as strings
sequence1 = "ATCGATCGATCG"
sequence2 = "GATCGATCGATC"

# Set up the API request
url = "https://blast.ncbi.nlm.nih.gov/Blast.cgi"
params = {
    "CMD": "Put",
    "PROGRAM": "blastn",
    "DATABASE": "nt",
    "QUERY": sequence1 + sequence2,
}

# Send the request
response = requests.post(url, data=params)

# Print the result
print(response.text)

In this code snippet, we use the requests.post function to send a POST request to the NCBI BLAST API. The request parameters are specified in the params dictionary. The response is returned as a string, which can be parsed or processed further as needed.

After exploring these three options, it is clear that the best choice depends on your specific requirements. If you are already using Biopython or need advanced sequence manipulation capabilities, Option 1 is a great choice. If you prefer a lightweight solution without external dependencies, Option 2 is a good fit. Finally, if you need a flexible and scalable solution, Option 3 using an API is the way to go. Consider your project’s needs and constraints to determine the most suitable approach.

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11 Responses

    1. Biopython may seem convenient to you, but its not for everyone. Option 2 might require a little more effort, but it offers flexibility and customization that Biopython lacks. Its all about personal preference and the specific needs of the programmer. #DifferentStrokesForDifferentFolks

    1. Sorry, but I have to disagree. Option 2 is where its at! R is the king of statistical analysis and data visualization. Its versatile, powerful, and widely used in the scientific community. #RStats #DataGeeksUnite 📊👑

  1. Option 1: Biopython is the way to go! Its like having a trusty Python sidekick for your blast sequences. #BiopythonRocking

    Option 2: Why complicate things with subprocess when you can blast away with Biopython? #SimplicityWins

    Option 3: APIs are cool and all, but nothing beats the power and control of Biopython in blasting sequences! #BiopythonFTW

  2. Option 2 seems like a hassle. Why not stick to the simplicity of Option 1 or try something futuristic with Option 3? 🤔

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