Automating the translation of DNA sequences to protein sequences is a foundational task in gene annotation and comparative genomics. When faced with a large dataset of gene sequences, you need to efficiently convert each DNA string into its corresponding protein sequence, following the standard genetic code. This process involves reading each DNA sequence in triplets (`codons`), translating each `codon` into an amino acid, and stopping translation at stop codons or when an incomplete codon is encountered at the end of a sequence. Handling multiple sequences at once allows you to scale up your analyses and prepare data for downstream bioinformatics tasks.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_single_sequence_translation(self):
        import user_code
        importlib.reload(user_code)
        # Sequence with stop codon in the middle
        seqs = ["ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG"]
        result = user_code.batch_translate_dna_to_protein(seqs)
        expected = ["MAIVMGR"]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            f"Correctly translates single DNA sequence with stop codon",
            f"Expected {expected}, got {result}"
        )

    def test_multiple_sequences(self):
        import user_code
        importlib.reload(user_code)
        seqs = ["ATGTACTAA", "ATGCGT"]
        result = user_code.batch_translate_dna_to_protein(seqs)
        expected = ["MY", "MR"]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            f"Correctly translates multiple DNA sequences",
            f"Expected {expected}, got {result}"
        )

    def test_incomplete_codon_ignored(self):
        import user_code
        importlib.reload(user_code)
        seqs = ["ATGAAATGA", "ATGTTTGGGCCC"]
        result = user_code.batch_translate_dna_to_protein(seqs)
        expected = ["MK", "MFGP"]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            f"Ignores incomplete codons at the end",
            f"Expected {expected}, got {result}"
        )

    def test_stop_codon_at_start(self):
        import user_code
        importlib.reload(user_code)
        seqs = ["TAAATGCGT"]
        result = user_code.batch_translate_dna_to_protein(seqs)
        expected = [""]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            f"Returns empty string for sequence that starts with stop codon",
            f"Expected {expected}, got {result}"
        )

    def test_empty_sequence(self):
        import user_code
        importlib.reload(user_code)
        seqs = [""]
        result = user_code.batch_translate_dna_to_protein(seqs)
        expected = [""]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            f"Returns empty string for empty DNA sequence",
            f"Expected {expected}, got {result}"
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

Learn how Python is used in biology for analyzing DNA sequences, processing biological data, and visualizing research results. Includes hands-on examples with bioinformatics libraries.

Explore how Python can be used to analyze DNA and other biological sequences, including searching for motifs, calculating GC content, and basic sequence manipulations.

Delve into protein sequences, amino acid composition, and basic protein analysis using Python.

Learn how to visualize biological data using Python, including plotting sequence statistics and creating informative charts for research.

Challenge: Batch DNA to Protein Translation

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Challenge: Batch DNA to Protein Translation

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