title: Optimal neighborhood indexing for protein similarity search.
abstract: 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 contrast to usual matrices, these matrices are rectangular since they compare amino acid groups from different alphabets. We describe the method used for computing those matrices and we provide some typical examples that can be used in such comparisons. Supplementary data can be found on the website http://bioinfo.lifl.fr/reblosum.
  
  
  CONCLUSIONS
  We propose a practical index size reduction of the neighborhood data, that does not negatively affect the performance of large-scale search in protein sequences. Such an index can be used in any study involving large protein data. Moreover, rectangular substitution score matrices and their associated statistical parameters can have applications in any study involving an alphabet reduction.
authors.affiliation: Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr
authors.forename: Pierre
authors.initials: P
authors.lastname: Peterlongo
authors.forename: Laurent
authors.initials: L
authors.lastname: Noé
authors.forename: Dominique
authors.initials: D
authors.lastname: Lavenier
authors.forename: Van Hoa
authors.initials: VH
authors.lastname: Nguyen
authors.forename: Gregory
authors.initials: G
authors.lastname: Kucherov
authors.forename: Mathieu
authors.initials: M
authors.lastname: Giraud
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016247:Information Storage and Retrieval; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins