How do you solve Repeated DNA Sequences on LeetCode?

Repeated DNA Sequences (LeetCode #187) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using a set allows for O(1) average time complexity for insertions and lookups.

What are the interview tips for Repeated DNA Sequences?

Always clarify the constraints and edge cases before diving into coding. Think about the efficiency of your solution; discuss trade-offs between time and space complexity. Practice explaining your thought process as you code, as communication is key in interviews.

What are common mistakes when solving Repeated DNA Sequences?

Not considering edge cases where the string is shorter than 10 characters. Confusing the use of a set versus a list for tracking seen sequences.

Repeated DNA Sequences — LeetCode #187 (Medium)

Tags: Hash Table, String, Bit Manipulation, Sliding Window, Rolling Hash, Hash Function

Related patterns: Hash Map, Sliding Window

Brute Force approach

Time complexity: O(n²). Space complexity: O(n).

The brute force approach involves generating all possible 10-letter-long substrings from the DNA sequence and checking for duplicates. This is straightforward but inefficient for large strings.

The time complexity is O(n²) because we potentially check every substring against all previously seen substrings. The space complexity is O(n) for storing seen sequences.

Step 1: Initialize an empty list to store repeated sequences.
Step 2: Use a nested loop to generate all possible 10-letter-long substrings.
Step 3: Use a set to track seen substrings and add duplicates to the list.

1. Input: s = 'AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT' 2. Initialize seen = {}, output = {} 3. i = 0, seq = 'AAAAACCCCC', seen = {'AAAAACCCCC'} 4. i = 1, seq = 'AAAACCCCCA', seen = {'AAAAACCCCC', 'AAAACCCCCA'} 5. i = 2, seq = 'AAACCCCCAA', seen = {'AAAAACCCCC', 'AAAACCCCCA', 'AAACCCCCAA'} 6. i = 5, seq = 'CCCCCAAAAA', seen = {'AAAAACCCCC', 'AAAACCCCCA', 'AAACCCCCAA', 'CCCCCAAAAA'} (output now contains 'AAAAACCCCC', 'CCCCCAAAAA')

Optimal Solution approach

Time complexity: O(n). Space complexity: O(n).

The optimal solution uses a sliding window approach with a hash set to track seen sequences efficiently. This reduces the time complexity significantly.

The time complexity is O(n) because we only traverse the string once. The space complexity is O(n) for storing the sequences in the sets.

Step 1: Initialize two sets: one for seen sequences and one for output.
Step 2: Loop through the string, extracting each 10-letter-long substring.
Step 3: If the substring is already in the seen set, add it to the output set; otherwise, add it to the seen set.

1. Input: s = 'AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT' 2. Initialize seen = {}, output = {} 3. i = 0, seq = 'AAAAACCCCC', seen = {'AAAAACCCCC'} 4. i = 1, seq = 'AAAACCCCCA', seen = {'AAAAACCCCC', 'AAAACCCCCA'} 5. i = 5, seq = 'CCCCCAAAAA', seen = {'AAAAACCCCC', 'AAAACCCCCA', 'CCCCCAAAAA'} (output now contains 'AAAAACCCCC', 'CCCCCAAAAA') 6. Final output: ['AAAAACCCCC', 'CCCCCAAAAA']

Key Insights

Using a set allows for O(1) average time complexity for insertions and lookups.
The problem can be visualized as a sliding window of fixed size (10) moving through the string.

Common Mistakes

Not considering edge cases where the string is shorter than 10 characters.
Confusing the use of a set versus a list for tracking seen sequences.

Interview Tips

Always clarify the constraints and edge cases before diving into coding.
Think about the efficiency of your solution; discuss trade-offs between time and space complexity.
Practice explaining your thought process as you code, as communication is key in interviews.

#187

Repeated DNA Sequences

Medium

Hash Table↗String↗Bit Manipulation↗Sliding Window↗Rolling Hash↗Hash Function↗Hash Map↗Sliding Window↗

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(n)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal solution uses a sliding window approach with a hash set to track seen sequences efficiently. This reduces the time complexity significantly.

⚙️

Algorithm

3 steps

1Step 1: Initialize two sets: one for seen sequences and one for output.
2Step 2: Loop through the string, extracting each 10-letter-long substring.
3Step 3: If the substring is already in the seen set, add it to the output set; otherwise, add it to the seen set.

solution.py9 lines

1def findRepeatedDnaSequences(s):
2    seen, output = set(), set()
3    for i in range(len(s) - 9):
4        seq = s[i:i + 10]
5        if seq in seen:
6            output.add(seq)
7        else:
8            seen.add(seq)
9    return list(output)

ℹ

Complexity note: The time complexity is O(n) because we only traverse the string once. The space complexity is O(n) for storing the sequences in the sets.

1Using a set allows for O(1) average time complexity for insertions and lookups.
2The problem can be visualized as a sliding window of fixed size (10) moving through the string.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.