How do you solve DNA Pattern Recognition on LeetCode?

DNA Pattern Recognition (LeetCode #3475) 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: Patterns can be checked in a single pass for efficiency.

What is the brute force solution for DNA Pattern Recognition ?

The brute force approach for DNA Pattern Recognition is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach checks each DNA sequence for the required patterns by scanning through the string multiple times. This is straightforward but inefficient, especially for long sequences. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve DNA Pattern Recognition ?

DNA Pattern Recognition uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for DNA Pattern Recognition ?

Explain your thought process clearly. Discuss edge cases and how you'd handle them. Practice writing clean, efficient code.

What are common mistakes when solving DNA Pattern Recognition ?

Not considering all stop codons. Overlooking edge cases like empty sequences.

#3475

DNA Pattern Recognition

Medium

DatabaseHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal solution processes each DNA sequence only once, checking for all patterns in a single pass. This reduces the time complexity significantly and is more efficient for larger datasets.

⚙️

Algorithm

4 steps

1Step 1: Initialize an empty result list to store the output.
2Step 2: For each DNA sequence, check for all patterns in a single iteration.
3Step 3: Use string methods to check for the start codon, stop codons, motif, and consecutive G's.
4Step 4: Append the results to the result list and return it ordered by sample_id.

solution.py10 lines

1def identify_patterns(samples):
2    result = []
3    for sample in samples:
4        sample_id, dna_sequence, species = sample
5        has_start = dna_sequence.startswith('ATG')
6        has_stop = any(dna_sequence.endswith(stop) for stop in ['TAA', 'TAG', 'TGA'])
7        has_atat = 'ATAT' in dna_sequence
8        has_consecutive_g = 'GGG' in dna_sequence
9        result.append((sample_id, dna_sequence, species, has_start, has_stop, has_atat, has_consecutive_g))
10    return sorted(result, key=lambda x: x[0])

ℹ

Complexity note: The time complexity is O(n) because we only traverse each DNA sequence once, checking all patterns in a single pass. The space complexity is O(n) due to the storage of results.

1Patterns can be checked in a single pass for efficiency.
2Using built-in string methods can simplify the logic.

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