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Open Access Highly Accessed Research article

The word landscape of the non-coding segments of the Arabidopsis thaliana genome

Jens Lichtenberg1*, Alper Yilmaz2, Joshua D Welch1, Kyle Kurz1, Xiaoyu Liang1, Frank Drews1, Klaus Ecker1, Stephen S Lee3, Matt Geisler4, Erich Grotewold2 and Lonnie R Welch156

Author Affiliations

1 Bioinformatics Laboratory, School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, USA

2 Department of Plant Cellular and Molecular Biology, Plant Biotechnology Center, The Ohio State University, Columbus, Ohio, USA

3 Department of Statistics, University of Idaho, Moscow, Idaho, USA

4 Department of Plant Biology, Southern Illinois University, Carbondale, Illinois, USA

5 Biomedical Engineering Program, Ohio University, Athens, Ohio, USA

6 Molecular and Cellular Biology Program, Ohio University, Athens, Ohio, USA

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BMC Genomics 2009, 10:463  doi:10.1186/1471-2164-10-463

Published: 8 October 2009

Abstract

Background

Genome sequences can be conceptualized as arrangements of motifs or words. The frequencies and positional distributions of these words within particular non-coding genomic segments provide important insights into how the words function in processes such as mRNA stability and regulation of gene expression.

Results

Using an enumerative word discovery approach, we investigated the frequencies and positional distributions of all 65,536 different 8-letter words in the genome of Arabidopsis thaliana. Focusing on promoter regions, introns, and 3' and 5' untranslated regions (3'UTRs and 5'UTRs), we compared word frequencies in these segments to genome-wide frequencies. The statistically interesting words in each segment were clustered with similar words to generate motif logos. We investigated whether words were clustered at particular locations or were distributed randomly within each genomic segment, and we classified the words using gene expression information from public repositories. Finally, we investigated whether particular sets of words appeared together more frequently than others.

Conclusion

Our studies provide a detailed view of the word composition of several segments of the non-coding portion of the Arabidopsis genome. Each segment contains a unique word-based signature. The respective signatures consist of the sets of enriched words, 'unwords', and word pairs within a segment, as well as the preferential locations and functional classifications for the signature words. Additionally, the positional distributions of enriched words within the segments highlight possible functional elements, and the co-associations of words in promoter regions likely represent the formation of higher order regulatory modules. This work is an important step toward fully cataloguing the functional elements of the Arabidopsis genome.