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Open Access Highly Accessed Methodology article

Multiple organism algorithm for finding ultraconserved elements

Scott Christley12*, Neil F Lobo3 and Greg Madey12

Author Affiliations

1 Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA

2 Interdisciplinary Center for the Study of Biocomplexity, University of Notre Dame, USA

3 Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA

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BMC Bioinformatics 2008, 9:15  doi:10.1186/1471-2105-9-15

Published: 11 January 2008

Abstract

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.