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Computational Approaches and Tools Used in Identification of Dispersed Repetitive DNA Sequences

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Abstract

It has become clear that dispersed repeat sequences have played multiple roles in eukaryotic genome evolution including increasing genetic diversity through mutation, inducing changes in gene expression, and facilitating generation of novel genes. Growing recognition of the importance of dispersed repeats has fueled development of computational tools designed to expedite discovery and classification of repeats. Here we review major existing repeat exploration tools and discuss the algorithms utilized by these tools. Special attention is devoted to ab initio programs, i.e., those tools that do not rely upon previously identified repeats to find new repeat elements. We conclude by discussing the strengths and weaknesses of current tools and highlighting additional approaches that may advance repeat discovery/characterization.

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Notes

  1. Haas and Salzberg [33] have recently reviewed a subset of the repeat finders that we discuss. The focus of much of their review is mechanisms for handling the complications presented by repeats during genome assembly. The focus of our review is the use of these tools for identification of novel dispersed repeats in genomes.

  2. BLAST is an acronym for the “Basic Local Alignment and Search Tool” developed by Altschul et al. [3]. There are currently several different BLAST modules specially designed for comparisons between different data types (see http://www.ncbi.nlm.nih.gov/blast/Blast.cgi). WU-BLAST is a powerful alternative implementation of BLAST available from Washington University (http://blast.wustl.edu/). Crossmatch is a similarity search tool traditionally packaged with Phrap (www.phrap.org).

Abbreviations

BLAST:

Basic Local Alignment and Search Tool

bp:

base pair

Mb:

megabase

Gb:

gigabase

MITE:

miniature inverted-repeat transposable element

PALS:

Pairwise Alignment of Long Sequences

SSR:

simple sequence repeat

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Acknowledgements

This research was supported, in part, by the National Science Foundation (DBI-0421717 to D.G.P. and EPS-0556308 to S.M.B.), the United States Department of Agriculture (CSREES-2006-34506-17290 and ARS-58-6402-7-241 to D.G.P.), and the Mississippi Corn Promotion Board (to D.G.P.).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, 39762, USA

    Surya Saha & Susan Bridges

  2. Mississippi Genome Exploration Laboratory, Mississippi State University, Mississippi State, MS, 39762, USA

    Surya Saha, Zenaida V. Magbanua & Daniel G. Peterson

  3. Institute for Digital Biology, Mississippi State University, Mississippi State, MS, 39762, USA

    Surya Saha, Susan Bridges, Zenaida V. Magbanua & Daniel G. Peterson

  4. Department of Plant & Soil Sciences, Mississippi State University, Mississippi State, MS, 39762, USA

    Zenaida V. Magbanua & Daniel G. Peterson

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  1. Surya Saha
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  2. Susan Bridges
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  3. Zenaida V. Magbanua
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  4. Daniel G. Peterson
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Correspondence to Daniel G. Peterson.

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Communicated by Dr. Ray Ming

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Saha, S., Bridges, S., Magbanua, Z.V. et al. Computational Approaches and Tools Used in Identification of Dispersed Repetitive DNA Sequences. Tropical Plant Biol. 1, 85–96 (2008). https://doi.org/10.1007/s12042-007-9007-5

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