title: Similarity landscapes: a way to detect many structural and sequence motifs in both introns and exons.
abstract: When investigators undertake searches of DNA databases, they normally discard large numbers of alignments that demonstrate very weak resemblances to each other, retaining only those that show statistically significant levels of resemblance. We show here that a great deal of information can be extracted from these weak alignments by examining them en masse. This is done by building three-dimensional similarity landscapes from the alignments, landscapes that reveal whether an unusual number of individually nonsignificant alignments tend to match up to a particular region of the query sequence being searched. The power of the search is increased by the use of libraries consisting entirely of introns or of exons. We show that (1) similarity landscapes with a variety of features can be generated from both intron and exon libraries, using introns or exons as query sequences; (2) the landscape features are real and not a statistical artifact; (3) well-known protein motifs used as query sequences can generate various landscape features; and (4) there is some evidence for resemblances between short regions of sequence carried by introns and exons. One possible interpretation of these results is that both introns and exons may have been built up during their evolution from short regions of sequence that as a result are now widely distributed throughout eukaryotic genomes. Such an interpretation would imply that these short regions have common ancestry. Alternatively, the wide sharing of short pieces of DNA may reflect regions with particular structural properties that have arisen through convergent evolution. The similarity-landscape approach can be used to detect such widespread structural motifs and sequence motifs in the genome that might be missed by less-global searches. It can also be used in conjunction with algorithms developed for detecting significant multiple alignments by isolating promising subsets of the databases that can be examined in more detail.
authors.affiliation: Department of Biology, University of California, San Diego, La Jolla 92093.
authors.forename: M
authors.initials: M
authors.lastname: Hultner
authors.forename: D W
authors.initials: DW
authors.lastname: Smith
authors.forename: C
authors.initials: C
authors.lastname: Wills
mesh_terms: D000818:Animals; D001482:Base Composition; D016208:Databases, Factual; D005091:Exons; D006801:Humans; D007438:Introns; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid