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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jf2bmhtlzjh4zjrkh3rivxd2sq" style="color: black;">Genome Informatics Series</a>
Because of the increase in the size of genome sequence databases, the importance of indexing the sequences for fast queries grows. Suffix trees and suffix arrays are used for simple queries. However these are not suitable for complicated queries from huge amount of sequences because the indices are stored in disk which has slow access speed. We propose storing the indices in memory in a compressed form. We use the compressed suffix array. It compactly stores the suffix array at the cost of<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/11791236">pmid:11791236</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5vhtru6hnvhtjlfuytzxwt34ta">fatcat:5vhtru6hnvhtjlfuytzxwt34ta</a> </span>
more »... etically a small slowdown in access speed. We experimentally show that the overhead of using the compressed suffix array is reasonable in practice. We also propose an approximate string matching algorithm which is suitable for the compressed suffix array. Furthermore, we have constructed the compressed suffix array of the whole human genome. Because its size is about 2G bytes, a workstation can handle the search index for the whole data in main memory, which will accelerate the speed of solving various problems in genome informatics.
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