Substring searching

A crucial step in read mapping is determining where each sequencing read originates within a reference genome. Given the vast size of genomic sequences and the sheer volume of reads generated by high-throughput sequencing technologies, this task must be performed both rapidly and accurately. At its core, this challenge is known in computer science as a substring search problem, where we aim to locate a given query (the sequencing read) within a much larger text (the reference genome).

Performing substring searches efficiently requires specialized data structures and algorithms that avoid the inefficiencies of naïve search methods. Instead of scanning the entire genome for every read, modern approaches build indices that enable rapid lookup of substrings. These indices allow for fast matching of sequencing reads to the reference, significantly reducing computational time and resources. The choice of an appropriate substring searching strategy is critical, as it directly influences the speed and accuracy of read mapping, impacting downstream analyses such as variant calling, transcript quantification, and genome annotation.

Two major classes of algorithms are widely used for substring searching in bioinformatics.

Hash-based methods use hash tables or hash functions to create an index for rapid substring lookups. Hash-based approaches preprocess the reference genome by breaking it into small k-mers (subsequences of length k) and storing them in a hash table, allowing for quick retrieval.

Suffix-based methods rely on suffix trees and suffix arrays, which provide a structured representation of the reference sequence to facilitate efficient searching. A suffix tree is a compact data structure that allows for rapid substring matching by organizing all suffixes of a given sequence in a hierarchical manner. Similarly, suffix arrays store sorted suffixes of a sequence, enabling binary search-based lookup for substring queries.

In the following sections, we will explore these approaches in detail, discussing their underlying principles, implementations, and trade-offs in the context of read mapping and bioinformatics applications.