Local Sequence Alignment With A Parallel Hash Based Model

The two main alignment problems in genetic sequencing mapping are mapping speed and storage space. in recent decades, effective methods are required to speed up the alignment process and reduce storage space due to increasing dna sequence data. this study is conducted to provide a hash-based search algorithm to reduce data storage space, which is one of the major issues in hash bases, in addition to increased accuracy and speed. like hash algorithms, the proposed method uses a hash table to store data, but changes the basis of its work to them, especially the blast family, and uses a method other than seed-and-extend. this increases the speed several times. this method uses the compression technique to reduce the storage space so that the data is stored compressed in the hash table and used during the alignment process, there is no need to decrypt the data in the search and mapping steps, and memory saved is preserved until the end of the process. besides, unlike most hash methods, this method does not include all possible subsequences of the required length in the hash table when creating the table, and the table is created by passing and recording the position of the existing subsequences of the reference sequence. another feature of the algorithm is the use of the multiprocessor technique to increase the algorithm execution speed so that the two main and time-consuming steps of the alignment process, one is creating a hash table and the other mapping, are done concurrently.