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To meet this requirement, we preprocess the database by sorting the subject sequences by the length in the ascending order.
Two widely used software packages that do preprocess the database for biological sequence comparison are SSAHA (Ning et al., 2001) and BLAT (Kent, 2002).
However, production usage of sequence comparison methods that preprocess the database has been limited to programs such as BLAT and SSAHA that are designed to find matches when query and database subsequences are highly similar.
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Numerous research studies have suggested that preprocessing the database instead would give better performance.
We also developed a program makembindex that preprocesses the database into a data structure for rapid seed searching.
Undertaking this project was positively influenced by several research papers suggesting that preprocessing the database for initial lookup could give faster search times than preprocessing the query.
Numerous research studies, recently reviewed in Jiang et al. (2007), suggest that preprocessing the database and searching a database-derived data structure can yield much faster search times.
Williams and Zobel (2002) implemented a research prototype for nucleotide and protein searching, called CAFE, in which they preprocessed the database into an inverted index.
Therefore, the results obtained are promising for the application in GWAS, because most of the works in this area apply standard filters for preprocessing the database.
We presented a new implementation of the seed search phase of MegaBLAST (Zhang et al., 2000) in which seeds are found by searching an index structure of k-mers derived from preprocessing the database.
The microarray gene expression data were normalized and preprocessed in the databases.
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