Research article summary (published 9 Oct 2007):

XSTREAM: a practical algorithm for identification and architecture modeling of tandem repeats in protein sequences.

Full Abstract

BACKGROUND: Biological sequence repeats arranged in tandem patterns are widespread in DNA and proteins. While many software tools have been designed to detect DNA tandem repeats (TRs), useful algorithms for identifying protein TRs with varied levels of degeneracy are still needed. RESULTS: To address limitations of current repeat identification methods, and to provide an efficient and flexible algorithm for the detection and analysis of TRs in protein sequences, we designed and implemented a new computational method called XSTREAM. Running time tests confirm the practicality of XSTREAM for analyses of multi-genome datasets. Each of the key capabilities of XSTREAM (e.g., merging, nesting, long-period detection, and TR architecture modeling) are demonstrated using anecdotal examples, and the utility of XSTREAM for identifying TR proteins was validated using data from a recently published paper. CONCLUSION: We show that XSTREAM is a practical and valuable tool for TR detection in protein and nucleotide sequences at the multi-genome scale, and an effective tool for modeling TR domains with diverse architectures and varied levels of degeneracy. Because of these useful features, XSTREAM has significant potential for the discovery of naturally-evolved modular proteins with applications for engineering novel biostructural and biomimetic materials, and identifying new vaccine and diagnostic targets.

Author information

Author/s: Newman, Aaron M (AM); Cooper, James B (JB);

Affiliation: Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA. a_newman(-atsign-)lifesci.ucsb.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: BMC bioinformatics (BMC Bioinformatics), published in England. (Language: eng)

Reference: 2007-; vol 8 (issue ) : pp 382

Dates: Created 2008/02/07; Completed 2008/03/27; Revised 2008/11/20;

PMID: 17931424, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Amino Acid Sequence - genetics Artificial Intelligence Databases, Protein Genome, Protozoan Molecular Sequence Data Pattern Recognition, Automated - methods

Pattern Recognition, Automated - methods Sequence Analysis, DNA Sequence Analysis, Protein - methods Sequence Homology, Amino Acid Sequence Homology, Nucleic Acid Tandem Repeat Sequences - genetics

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