Repeated DNA Sequences Leetcode Problem 187 [Python Solution]

Welcome back to another coding tutorial! Today, we will be solving the Repeated DNA Sequences problem on LeetCode.

This is a great problem to test your skills in working with strings and sets.

We’ll provide you with a Python solution that is both efficient and easy to understand, making it perfect for beginners.

So, let’s dive right in!

Problem Overview

The problem can be summarized as follows:

You are given a DNA sequence represented as a string composed of four different characters: ‘A’, ‘C’, ‘G’, and ‘T’.

Your goal is to identify all 10-letter-long sequences (substrings) that occur more than once in the DNA sequence.

You can return the answer in any order.

Example 1:

Input: “AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT”
Output: [“AAAAACCCCC”,”CCCCCAAAAA”]

Example 2:

Input: “AAAAAAAAAAAAA”
Output: [“AAAAAAAAAA”]

Constraints:

• 1 <= s.length <= 105
• s[i] is either ‘A’, ‘C’, ‘G’, or ‘T’

Now that we have a clear understanding of the problem, let’s proceed with the solution.

Efficient Python Code Solution

Here’s the Python solution to the Repeated Dna Sequences problem:

def findRepeatedDnaSequences(s: str) -> list[str]:
    result = set()
    seen = set()
    for i in range(len(s) - 9):
        current = s[i:i+10]
        if current in seen:
            result.add(current)
        seen.add(current)
    return list(result)

This Python function takes a single argument, s, which is the DNA sequence string.

It returns a list of 10-letter-long sequences that occur more than once in the input string.

Let’s break down the code:

• We initialize two sets, result and seen. result will store the unique repeated sequences, and seen will keep track of the sequences we have encountered.
• We iterate through the input string s using a sliding window of size 10. We stop nine characters before the end to ensure there are at least nine characters remaining for a 10-character window.
• In each iteration, we extract the current 10-character substring from the input string.
• We check if this substring is already in the seen set.

If it is, we add it to the result set.

• Regardless of whether it’s a new or repeated sequence, we add it to the seen set to keep track of it.

After processing the entire string, we return the result set converted to a list.

This ensures that we only return unique repeated sequences.

Time and Space Complexity

Let’s analyze the time and space complexity of our solution:

Time Complexity: The loop iterates through the input string of length n, and each iteration takes constant time O(1) due to the sliding window of size 10. So, the overall time complexity is O(n).

Space Complexity: We use two sets, result and seen, which can store a maximum of unique 10-character sequences.

In the worst case, this could be O(n) space.

Therefore, the space complexity is O(n).

Reasoning Behind Our Approach

Our approach utilizes two sets to efficiently track repeated sequences while ensuring that we don’t include duplicates in the final result.

By using a sliding window of size 10, we can process the input string with minimal overhead.

This approach is both intuitive and efficient, making it a great choice for solving this problem.

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Conclusion

In this tutorial, we tackled the Repeated Dna Sequences problem on LeetCode.

We provided a Python solution that efficiently identifies 10-letter-long sequences that occur more than once in a DNA sequence.

We discussed the problem’s constraints, presented a clean code solution, and analyzed its time and space complexity.

We hope this tutorial has been helpful to you, especially if you’re a beginner in coding.

If you have any questions, suggestions, or feedback, please feel free to leave a comment.

Don’t forget to like and engage to support the our platform and stay updated with more coding tutorials.

Happy coding!

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