PSALR : Parallel Sequence Alignment for long Sequence Read with Hash Model

Sequence alignment and genome mapping pose significant challenges, primarily focusing on speed and storage space requirements for mapped sequences. With the ever-increasing volume of DNA sequence data, it becomes imperative to develop efficient alignment methods that not only reduce storage demands but also offer rapid alignment. This study introduces the Parallel Sequence Alignment with a Hash-Based Model (PSALR) algorithm, specifically designed to enhance alignment speed and optimize storage space while maintaining utmost accuracy. In contrast to other algorithms like BLAST, PSALR efficiently indexes data using a hash table, resulting in reduced computational load and processing time. This algorithm utilizes data compression and packetization with conventional bandwidth sizes, distributing data among different nodes to reduce memory and transfer time. Upon receiving compressed data, nodes can seamlessly perform searching and mapping, eliminating the need for unpacking and decoding at the destination. As an additional innovation, PSALR not only divides sequences among processors but also breaks down large sequences into sub-sequences, forwarding them to nodes. This approach eliminates any restrictions on query length sent to nodes, and evaluation results are returned directly to the user without central node involvement. Another notable feature of PSALR is its utilization of overlapping sub-sequences within both query and reference sequences. This ensures that the search and mapping process includes all possible sub-sequences of the target sequence, rather than being limited to a subset. Performance tests indicate that the PSALR algorithm outperforms its counterparts, positioning it as a promising solution for efficient sequence alignment and genome mapping.