How to use ChatGPT for Bioinformatics Scientist Job Role

python

import numpy as np

# Define scoring parameters
MATCH_SCORE = 1
MISMATCH_SCORE = -1
GAP_PENALTY = -2

def create_score_matrix(seq1, seq2):
    # Initialize the score matrix
    score_matrix = np.zeros((len(seq1) + 1, len(seq2) + 1))
    for i in range(len(seq1) + 1):
        score_matrix[i][0] = i * GAP_PENALTY
    for j in range(len(seq2) + 1):
        score_matrix[0][j] = j * GAP_PENALTY
    return score_matrix


def fill_score_matrix(score_matrix, seq1, seq2):
    for i in range(1, len(seq1) + 1):
        for j in range(1, len(seq2) + 1):
            match = score_matrix[i-1][j-1] + (MATCH_SCORE if seq1[i-1] == seq2[j-1] else MISMATCH_SCORE)
            delete = score_matrix[i-1][j] + GAP_PENALTY
            insert = score_matrix[i][j-1] + GAP_PENALTY
            score_matrix[i][j] = max(match, delete, insert)
    return score_matrix


def traceback(score_matrix, seq1, seq2):
    aligned_seq1 = ''
    aligned_seq2 = ''
    i, j = len(seq1), len(seq2)
    while i > 0 and j > 0:
        if score_matrix[i][j] == score_matrix[i-1][j-1] + (MATCH_SCORE if seq1[i-1] == seq2[j-1] else MISMATCH_SCORE):
            aligned_seq1 += seq1[i-1]
            aligned_seq2 += seq2[j-1]
            i -= 1
            j -= 1
        elif score_matrix[i][j] == score_matrix[i-1][j] + GAP_PENALTY:
            aligned_seq1 += seq1[i-1]
            aligned_seq2 += '-'
            i -= 1
        else:
            aligned_seq1 += '-'
            aligned_seq2 += seq2[j-1]
            j -= 1
    while i > 0:
        aligned_seq1 += seq1[i-1]
        aligned_seq2 += '-'
        i -= 1
    while j > 0:
        aligned_seq1 += '-'
        aligned_seq2 += seq2[j-1]
        j -= 1
    return aligned_seq1[::-1], aligned_seq2[::-1]


def needleman_wunsch(seq1, seq2):
    score_matrix = create_score_matrix(seq1, seq2)
    score_matrix = fill_score_matrix(score_matrix, seq1, seq2)
    aligned_seq1, aligned_seq2 = traceback(score_matrix, seq1, seq2)
    return aligned_seq1, aligned_seq2

# Example usage
seq1 = 'AGCT'
seq2 = 'AGT'
aligned_seq1, aligned_seq2 = needleman_wunsch(seq1, seq2)
print('Aligned Sequences:')
print(aligned_seq1)
print(aligned_seq2)

python

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the gene expression data
# Replace 'gene_expression_data.csv' with the path to your uploaded file
file_path = 'gene_expression_data.csv'
gene_expression_data = pd.read_csv(file_path, index_col=0)

# Display the first few rows of the data
print(gene_expression_data.head())

# Create a heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(gene_expression_data, cmap='viridis', annot=True, fmt='.2f', linewidths=.5)
plt.title('Gene Expression Heatmap')
plt.xlabel('Samples')
plt.ylabel('Genes')
plt.tight_layout()

# Save the heatmap as an image
plt.savefig('gene_expression_heatmap.png')
plt.show()

bash

pip install pandas seaborn matplotlib