algo-suffix-arrays

name	algo-suffix-arrays
description	Build a suffix array (Manber-Myers O(n log n)) and LCP array (Kasai's O(n)) in Python; binary-search substrings, count k-mers, find longest repeated motifs. Use for text indexing, pattern search, or aligner (BWA-like) internals.
tool_type	python
primary_tool	Python

Suffix Arrays

When to Use

• Need to search a fixed text for many different patterns (build once, query O(m log n) each)
• Implementing or understanding the index behind short-read aligners (BWA, bowtie) or full-text search
• Finding the longest repeated substring/motif, counting distinct substrings, or de-duplicating k-mers in a DNA sequence
• Memory-constrained alternative to a suffix tree (an int array vs. a pointer-heavy tree)
• Comparing construction strategies (naive vs. doubling) for a coursework/algorithms exercise

Version Compatibility

Pure Python 3.8+ (uses only collections, list slicing, str comparisons). No third-party dependencies. For production-scale genomic indexing (millions of bases), use pydivsufsort or pysuffixarray (SA-IS, O(n)) instead of the O(n log n) code here.

Prerequisites

• Comfortable with Python string slicing and binary search
• Related: algo-suffix-trees (tree alternative), algo-kmp-algorithm / algo-rabin-karp (single-pattern search without an index)
• No external packages required

Always append a sentinel (e.g. '$') lexicographically smaller than every other character — guarantees no suffix is a prefix of another, so sorting is well-defined.

Construction

Goal: Turn a string into SA, an array of integers where SA[i] is the start position of the i-th lexicographically smallest suffix.

Approach: Naive sort is easiest to reason about (O(n^2 log n) — each of the O(n log n) comparisons costs O(n)). Manber-Myers replaces whole-suffix comparisons with O(1) rank-pair comparisons by doubling the compared prefix length each round, giving O(n log n).

def build_suffix_array_naive(text: str) -> list[int]:
    """Sort all suffixes directly. O(n^2 log n) time, O(n^2) space.

    >>> build_suffix_array_naive("banana$")
    [6, 5, 3, 1, 0, 4, 2]
    """
    n = len(text)
    suffixes = [(text[i:], i) for i in range(n)]
    suffixes.sort(key=lambda pair: pair[0])
    return [pos for _, pos in suffixes]


def build_suffix_array_manber_myers(text: str) -> list[int]:
    """Doubling-rank construction. O(n log n) time, O(n) space.

    Each round sorts by (rank[i], rank[i+k]) pairs -- an O(1) comparison
    key -- instead of comparing raw substrings, then re-derives ranks.

    >>> build_suffix_array_manber_myers("banana$")
    [6, 5, 3, 1, 0, 4, 2]
    """
    n = len(text)
    if n <= 1:
        return list(range(n))

    sa = list(range(n))
    rank = [ord(c) for c in text]
    tmp = [0] * n
    k = 1
    while k < n:
        def key(i, k=k):
            return (rank[i], rank[i + k] if i + k < n else -1)

        sa.sort(key=key)
        tmp[sa[0]] = 
         i  (, n):
            tmp[sa[i]] = tmp[sa[i - ]] + (  key(sa[i]) > key(sa[i - ])  )
        rank, tmp = tmp, rank
         rank[sa[-]] == n - :  
            
        k *= 
     sa

Pattern Search and LCP Array

Goal: Given SA, find every occurrence of a pattern, and find the longest repeated substring.

Approach: A pattern occurring in text must be a prefix of some suffix, and suffixes are sorted in SA, so binary search finds the contiguous range [lower, upper) of matching suffixes in O(m log n). Kasai's algorithm then builds the LCP array (LCP[i] = shared prefix length of suffixes SA[i-1] and SA[i]) in O(n) by reusing the previous match length across the pass, avoiding re-scanning from zero each time.

def search_pattern(text: str, sa: list[int], pattern: str) -> list[int]:
    """Binary search for all occurrences of pattern. O(m log n).

    >>> sa = build_suffix_array_naive("banana$")
    >>> search_pattern("banana$", sa, "ana")
    [1, 3]
    """
    n, m = len(text), len(pattern)
    if m == 0:
        return list(range(n))

    def bound(strict):
        lo, hi = 0, n
        while lo < hi:
            mid = (lo + hi) // 2
            suffix_prefix = text[sa[mid]:sa[mid] + m]
            cond = suffix_prefix <= pattern if strict else suffix_prefix < pattern
            if cond:
                lo = mid + 1
            else:
                hi = mid
        return lo

    lower, upper = bound(False), bound(True)
    return sorted(sa[i] for i in range(lower, upper))


def build_lcp_array(text: str, sa: list[int]) -> list[int]:
    """Kasai's algorithm: LCP[i] = shared prefix length of SA[i-1], SA[i]. O(n).

    >>> sa = build_suffix_array_naive("banana$")
    >>> build_lcp_array("banana$", sa)
    [0, 0, 1, 3, 0, 0, 2]
    """
    n = (text)
    rank = [] * n
     i, pos  (sa):
        rank[pos] = i

    lcp = [] * n
    k = 
     i  (n):
         rank[i] == :
            k = 
            
        j = sa[rank[i] - ]
         i + k < n  j + k < n  text[i + k] == text[j + k]:
            k += 
        lcp[rank[i]] = k
         k > :
            k -=   
     lcp


 () -> [, []]:
    
     (text) <= :
         (, [])
    sa = build_suffix_array_manber_myers(text)
    lcp = build_lcp_array(text, sa)
    max_idx = (((lcp)), key= i: lcp[i])
     lcp[max_idx] == :
         (, [])
    lrs = text[sa[max_idx]:sa[max_idx] + lcp[max_idx]]
     (lrs, search_pattern(text, sa, lrs))

K-mer Counting on a DNA Sequence

Goal: Count occurrences of every distinct k-mer in a genomic string using the suffix array instead of a hash table.

Approach: Build SA, then for each starting index derive its k-mer and use search_pattern (or count contiguous SA runs with the same k-length prefix) to get its frequency; distinct k-mers are deduplicated via a set.

def count_kmer_occurrences(sequence: str, k: int) -> list[tuple[str, int]]:
    """Count every distinct k-mer using the suffix array + binary search.

    Args:
        sequence: DNA string, should end with a '$' sentinel.
        k: k-mer length.

    Returns:
        (kmer, count) pairs sorted by descending count, then kmer.
    """
    sa = build_suffix_array_naive(sequence)
    seen = set()
    counts = []
    for pos in sa:
        kmer = sequence[pos:pos + k]
        if len(kmer) < k or kmer in seen or "$" in kmer:
            continue
        seen.add(kmer)
        counts.append((kmer, len(search_pattern(sequence, sa, kmer))))
    counts.sort(key=lambda pair: (-pair[1], pair[0]))
    return counts


if __name__ == "__main__":
    dna = "ATCGATCGATCGAATTCCGATCGATCGATCG$"
    top5 = count_kmer_occurrences(dna, k=4)[:5]
    assert top5[0][0] == "ATCG" and top5[0][1] >= 4, top5
    assert longest_repeated_substring("banana$") == ("ana", [1, 3])
    print("top 4-mers:", top5)

Complexity Comparison

Method	Construction	Space	Pattern search
Naive	O(n^2 log n)	O(n^2)	O(m log n)
Manber-Myers	O(n log n)	O(n)	O(m log n)
SA-IS / DC3	O(n)	O(n)	O(m log n)
LCP (Kasai)	O(n), needs SA first	O(n)	n/a

Pitfalls

• Forgetting the sentinel '$' causes incorrect sorting when one suffix is a true prefix of another (e.g. "ab" vs "abab").
• Naive construction materializes every suffix as a Python string slice — O(n^2) memory; switch to Manber-Myers (or SA-IS) once n exceeds a few thousand.
• search_pattern's two binary searches must use < for the lower bound and <= for the upper bound (or the run range is off by one/empty).
• Kasai's algorithm requires sa and its rank (inverse permutation) both built from the same text — rebuild both if the text changes.
• For real genomes, use a C-backed SA-IS library (pydivsufsort) — the pure-Python code here is for correctness/teaching, not multi-megabase genomes.

See Also

• algo-suffix-trees — pointer-based alternative structure with the same query capabilities
• algo-kmp-algorithm, algo-rabin-karp — single-pattern search without building a full index
• algo-aho-corasick — multi-pattern search when patterns (not the text) are fixed in advance
• bio-read-alignment-bwa-alignment — real-world aligner built on an FM-index/suffix-array-like structure