Optimal neighborhood indexing for protein similarity search

Background: Similarity inference, one of the main bioinformatics tasks, has to face an exponential growth of the biological data. A classical approach used to cope with this data flow involves heuristics with large seed indexes. In order to speed up this technique, the index can be enhanced by storing additional information to limit the number of random memory accesses. However, this improvement leads to a larger index that may become a bottleneck. In the case of protein similarity search, we propose to decrease the index size by reducing the amino acid alphabet. Results: The paper presents two main contributions. First, we show that an optimal neighborhood indexing combining an alphabet reduction and a longer neighborhood leads to a reduction of 35% of memory involved into the process, without sacrificing the quality of results nor the computational time. Second, our approach led us to develop a new kind of substitution score matrices and their associated e-value parameters. In contr...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Pierre Peterlongo, Laurent Noé, Dominique Lavenier, Van Hoa Nguyen, Gregory Kucherov, Mathieu Giraud
• Keywords: Amino Acid, BMCBI 2008, Proteins, Substitution Score Matrices