title: Subfamily specific conservation profiles for proteins based on n-gram patterns.
abstract: BACKGROUND
  A new algorithm has been developed for generating conservation profiles that reflect the evolutionary history of the subfamily associated with a query sequence. It is based on n-gram patterns (NP{n,m}) which are sets of n residues and m wildcards in windows of size n+m. The generation of conservation profiles is treated as a signal-to-noise problem where the signal is the count of n-gram patterns in target sequences that are similar to the query sequence and the noise is the count over all target sequences. The signal is differentiated from the noise by applying singular value decomposition to sets of target sequences rank ordered by similarity with respect to the query.
  
  
  RESULTS
  The new algorithm was used to construct 4,248 profiles from 120 randomly selected Pfam-A families. These were compared to profiles generated from multiple alignments using the consensus approach. The two profiles were similar whenever the subfamily associated with the query sequence was well represented in the multiple alignment. It was possible to construct subfamily specific conservation profiles using the new algorithm for subfamilies with as few as five members. The speed of the new algorithm was comparable to the multiple alignment approach.
  
  
  CONCLUSIONS
  Subfamily specific conservation profiles can be generated by the new algorithm without aprioi knowledge of family relationships or domain architecture. This is useful when the subfamily contains multiple domains with different levels of representation in protein databases. It may also be applicable when the subfamily sample size is too small for the multiple alignment approach.
authors.affiliation: Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. vries@ccbb.pitt.edu
authors.forename: John K
authors.initials: JK
authors.lastname: Vries
authors.forename: Xiong
authors.initials: X
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D017124:Conserved Sequence; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins