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Multiple organism algorithm for finding ultraconserved elements

BACKGROUND: Ultraconserved elements are nucleotide or protein sequences with 100% identity (no mismatches, insertions, or deletions) in the same organism or between two or more organisms. Studies indicate that these conserved regions are associated with micro RNAs, mRNA processing, development and t...

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Detalles Bibliográficos
Autores principales: Christley, Scott, Lobo, Neil F, Madey, Greg
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244594/
https://www.ncbi.nlm.nih.gov/pubmed/18186941
http://dx.doi.org/10.1186/1471-2105-9-15
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author Christley, Scott
Lobo, Neil F
Madey, Greg
author_facet Christley, Scott
Lobo, Neil F
Madey, Greg
author_sort Christley, Scott
collection PubMed
description BACKGROUND: Ultraconserved elements are nucleotide or protein sequences with 100% identity (no mismatches, insertions, or deletions) in the same organism or between two or more organisms. Studies indicate that these conserved regions are associated with micro RNAs, mRNA processing, development and transcription regulation. The identification and characterization of these elements among genomes is necessary for the further understanding of their functionality. RESULTS: We describe an algorithm and provide freely available software which can find all of the ultraconserved sequences between genomes of multiple organisms. Our algorithm takes a combinatorial approach that finds all sequences without requiring the genomes to be aligned. The algorithm is significantly faster than BLAST and is designed to handle very large genomes efficiently. We ran our algorithm on several large comparative analyses to evaluate its effectiveness; one compared 17 vertebrate genomes where we find 123 ultraconserved elements longer than 40 bps shared by all of the organisms, and another compared the human body louse, Pediculus humanus humanus, against itself and select insects to find thousands of non-coding, potentially functional sequences. CONCLUSION: Whole genome comparative analysis for multiple organisms is both feasible and desirable in our search for biological knowledge. We argue that bioinformatic programs should be forward thinking by assuming analysis on multiple (and possibly large) genomes in the design and implementation of algorithms. Our algorithm shows how a compromise design with a trade-off of disk space versus memory space allows for efficient computation while only requiring modest computer resources, and at the same time providing benefits not available with other software.
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spelling pubmed-22445942008-02-15 Multiple organism algorithm for finding ultraconserved elements Christley, Scott Lobo, Neil F Madey, Greg BMC Bioinformatics Methodology Article BACKGROUND: Ultraconserved elements are nucleotide or protein sequences with 100% identity (no mismatches, insertions, or deletions) in the same organism or between two or more organisms. Studies indicate that these conserved regions are associated with micro RNAs, mRNA processing, development and transcription regulation. The identification and characterization of these elements among genomes is necessary for the further understanding of their functionality. RESULTS: We describe an algorithm and provide freely available software which can find all of the ultraconserved sequences between genomes of multiple organisms. Our algorithm takes a combinatorial approach that finds all sequences without requiring the genomes to be aligned. The algorithm is significantly faster than BLAST and is designed to handle very large genomes efficiently. We ran our algorithm on several large comparative analyses to evaluate its effectiveness; one compared 17 vertebrate genomes where we find 123 ultraconserved elements longer than 40 bps shared by all of the organisms, and another compared the human body louse, Pediculus humanus humanus, against itself and select insects to find thousands of non-coding, potentially functional sequences. CONCLUSION: Whole genome comparative analysis for multiple organisms is both feasible and desirable in our search for biological knowledge. We argue that bioinformatic programs should be forward thinking by assuming analysis on multiple (and possibly large) genomes in the design and implementation of algorithms. Our algorithm shows how a compromise design with a trade-off of disk space versus memory space allows for efficient computation while only requiring modest computer resources, and at the same time providing benefits not available with other software. BioMed Central 2008-01-11 /pmc/articles/PMC2244594/ /pubmed/18186941 http://dx.doi.org/10.1186/1471-2105-9-15 Text en Copyright © 2008 Christley et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Christley, Scott
Lobo, Neil F
Madey, Greg
Multiple organism algorithm for finding ultraconserved elements
title Multiple organism algorithm for finding ultraconserved elements
title_full Multiple organism algorithm for finding ultraconserved elements
title_fullStr Multiple organism algorithm for finding ultraconserved elements
title_full_unstemmed Multiple organism algorithm for finding ultraconserved elements
title_short Multiple organism algorithm for finding ultraconserved elements
title_sort multiple organism algorithm for finding ultraconserved elements
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244594/
https://www.ncbi.nlm.nih.gov/pubmed/18186941
http://dx.doi.org/10.1186/1471-2105-9-15
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