Biological Evaluation of d², an Algorithm for High-Performance Sequence Comparison

Abstract

A number of algorithms exist for searching sequence databases for biologically significant similarities based on the primary sequence similarity of aligned sequences. We have determined the biological sensitivity and selectivity of d2, a high-performance comparison algorithm that rapidly determines the relative dissimilarity of large datasets of genetic sequences. d2 uses sequence-word multiplicity as a simple measure of dissimilarity. It is not constrained by the comparison of direct sequence alignments and so can use word contexts to yield new information on relationships. It is extremely efficient, comparing a query of length 884 bases (INS1ECLAC) with 19,540,603 bases of the bacterial division of GenBank (release 76.0) in 51.77 CPU seconds on a Cray Y/MP-48 supercomputer. It is unique in that subsequences (words) of biological interest can be weighted to improve the sensitivity and selectivity of a search over existing methods. We have determined the ability of d2 to detect biologically significant matches between a query and large datasets of DNA sequences while varying parameters such as word-length and window size. We have also determined the distribution of dissimilarity scores within eukaryotic and prokaryotic divisions of GenBank. We have optimized parameters of the d2 program using Cray hardware and present an analysis of the sensitivity and selectivity of the algorithm. A theoretical analysis of the expectation for scores is presented. This work demonstrates that d2 is a unique, sensitive, and selective method of rapid sequence comparison that can detect novel sequence relationships which remain undetected by alternate methodologies.

Identification of Genes with Potential Roles in Apple Fruit Development and Biochemistry through Large-Scale Statistical Analysis of Expressed Sequence Tags

“…Vector artifacts and repetitive sequences were masked using CrossMatch (http://www.phrap.org). Masked sequences were clustered based on their relative similarity (.96% identity over a window of 150 nucleotides) using a word-based greedy clustering algorithm (Hide et al, 1994). Loose but related clusters were further aligned and assembled using PHRAP (http://www.…”

d2_cluster: A Validated Method for Clustering EST and Full-Length cDNA Sequences

“…The only criterion for clustering is sequence overlap and source or annotation information is not used. To detect the overlap criterion, we use the d2 algorithm and set parameters and threshold values as described in previous work (Torney et al 1990;Hide et al 1994;Wu et al 1997). The initial and final state of the algorithm is a partition of the input sequences in which each sequence is in a cluster and no sequence appears in more than one cluster.…”

“…The notation d2(A,B) is conveniently used, but, of course, d2(,) is not a function of only A and B but also of various parameters (specified in Torney et al 1990;Hide et al 1994;Wu et al 1997). The MERGE operation can be expressed in terms of convention 4 above: For all sequences, Sr, such that Cr = j, Cr is reset to be Cr = i.…”

Alternative Gene Form Discovery and Candidate Gene Selection from Gene Indexing Projects

“…1). Each UniGene cluster was subjected to a further clustering by d2 cluster (Hide et al 1994;J. Burke, D. Davison, and W. Hide, in prep.).…”

“…The sequence annotations were used to extract the 3Ј UTRs, and known repetitive elements were filtered out of the data set of UTRs. For this analysis the UniGene clustering was not used; we reclustered the data set using d2 cluster (Hide et al 1994;J. Burke, D. Davison, and W. Hide, in prep.…”