BACKGROUND: Most current DNA diagnostic tests for identifying organisms use specific oligonucleotide probes that are complementary in sequence to, and hence only hybridize with the DNA of one target species. By contrast, in traditional taxonomy, specimens are usually identified by 'dichotomous keys' that use combinations of characters shared by different members of the target set. Using one specific character for each target is the least efficient strategy for identification. Using combinations of shared bisectionally-distributed characters is much more efficient, and this strategy is most efficient when they separate the targets in a progressively binary way. RESULTS: We have developed a practical method for finding minimal sets of sub-sequences that identify individual sequences, and could be targeted by combinations of probes, so that the efficient strategy of traditional taxonomic identification could be used in DNA diagnosis. The sizes of minimal sub-sequence sets depended mostly on sequence diversity and sub-sequence length and interactions between these parameters. We found that 201 distinct cytochrome oxidase subunit-1 (CO1) genes from moths (Lepidoptera) were distinguished using only 15 sub-sequences 20 nucleotides long, whereas only 8-10 sub-sequences 6-10 nucleotides long were required to distinguish the CO1 genes of 92 species from the 9 largest orders of insects. CONCLUSION: The presence/absence of sub-sequences in a set of gene sequences can be used like the questions in a traditional dichotomous taxonomic key; hybridisation probes complementary to such sub-sequences should provide a very efficient means for identifying individual species, subtypes or genotypes. Sequence diversity and sub-sequence length are the major factors that determine the numbers of distinguishing sub-sequences in any set of sequences.

Abstract Motivation: To devise a method that, unlike available methods, directly measures variations in phylogenetic signals in gene sequences that result from recombination, tests the significance of the signal variations and distinguishes misleading signals. Results: We have developed a method, that we call ‘sister-scanning’, for assessing phylogenetic and compositional signals in the various patterns of identity that occur between four nucleotide sequences. A Monte Carlo randomization is done for all columns (positions) within a window and \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(Z\) \end{document}-scores are obtained for four real sequences or three real sequences with an outlier that is also randomized. The usefulness of the approach is demonstrated using tobamovirus and luteovirus sequences. Contradictory phylogenetic signals were distinguished in both datasets, as were regions of sequence that contained no clear signal or potentially misleading signals related to compositional similarities. In the tobamovirus dataset, contradictory phylogenetic signals were separated by coding sequences up to a kilobase long that contained no clear signal. Our re-analysis of this dataset using sister-scanning also yielded the first evidence known to us of an inter-species recombination site within a viral RNA-dependent RNA polymerase gene together with evidence of an unusual pattern of conservation in the three codon positions. Availability: A program package, SiScan, for use under MS-DOS can be downloaded from http://life.anu.edu.au/with test data and instructions. Contact: [email protected]; [email protected]; [email protected] * To whom correspondence should be addressed.

A package of computer programs called DIPLOMO (DIstance PLOt MOnitor) has been developed for making pairwise comparisons of different estimates of the distances between a set of taxa by plotting them against each other in a simple scatter plot. Taxa with similar relative distance characteristics are thereby grouped graphically. Groupings of different taxa may be directly identified, and the distance characteristics of chosen groups visualised and compared using devices to give them different colours or symbols. The program is particularly useful for detecting and analysing subtle trends in gene sequence evolution. This is done by comparing different components of change, for example synonymous versus non-synonymous nucleotide changes, transversions versus transitions and changes in different genes of the same set of taxa, etc. The program has a wide range of other uses, for example comparing different methods of sequence analysis, assessing which components of genetic change correlate best with phenotypic change or with geographical separation. This paper describes the DIPLOMO package, and illustrates typical DIPLOMO analyses using lentivirus gene sequence data.