SPLASH: structural pattern localization analysis by sequential histograms
A Califano - Bioinformatics, 2000 - academic.oup.com
Bioinformatics, 2000•academic.oup.com
Motivation: The discovery of sparse amino acid patterns that match repeatedly in a set of
protein sequences is an important problem in computational biology. Statistically significant
patterns, that is patterns that occur more frequently than expected, may identify regions that
have been preserved by evolution and which may therefore play a key functional or
structural role. Sparseness can be important because a handful of non-contiguous residues
may play a key role, while others, in between, may be changed without significant loss of …
protein sequences is an important problem in computational biology. Statistically significant
patterns, that is patterns that occur more frequently than expected, may identify regions that
have been preserved by evolution and which may therefore play a key functional or
structural role. Sparseness can be important because a handful of non-contiguous residues
may play a key role, while others, in between, may be changed without significant loss of …
Abstract
Motivation: The discovery of sparse amino acid patterns that match repeatedly in a set of protein sequences is an important problem in computational biology. Statistically significant patterns, that is patterns that occur more frequently than expected, may identify regions that have been preserved by evolution and which may therefore play a key functional or structural role. Sparseness can be important because a handful of non-contiguous residues may play a key role, while others, in between, may be changed without significant loss of function or structure. Similar arguments may be applied to conserved DNA patterns. Available sparse pattern discovery algorithms are either inefficient or impose limitations on the type of patterns that can be discovered.
Results: This paper introduces a deterministic pattern discovery algorithm, called Splash, which can find sparse amino or nucleic acid patterns matching identically or similarly in a set of protein or DNA sequences. Sparse patterns of any length, up to the size of the input sequence, can be discovered without significant loss in performances.
Splash is extremely efficient and embarrassingly parallel by nature. Large databases, such as a complete genome or the non-redundant SWISS-PROT database can be processed in a few hours on a typical workstation. Alternatively, a protein family or superfamily, with low overall homology, can be analyzed to discover common functional or structural signatures. Some examples of biologically interesting motifs discovered by Splash are reported for the histone I and for the G-Protein Coupled Receptor families. Due to its efficiency, Splash can be used to systematically and exhaustively identify conserved regions in protein family sets. These can then be used to build accurate and sensitive PSSM or HMM models for sequence analysis.
Availability: Splash is available to non-commercial research centers upon request, conditional on the signing of a test field agreement.
Contact: acal@us.ibm.com, Splash main page http://www.research.ibm.com/splash
Oxford University Press