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Investigating the dog genome we found 178965 introns with a moderate length of 200-1000 bp. A screening of these sequences against 23 different repeat libraries to find insertions of short interspersed elements (SINEs) detected 45276 SINEs. Virtually all of these SINEs (98%) belong to the tRNA-derived Can-SINE family. Can-SINEs arose about 55 million years ago before Carnivora split into two basal groups, the Caniformia (doglike carnivores) and the Feliformia (cat-like carnivores). Genome comparisons of dog and cat recovered 506 putatively informative SINE loci for caniformian phylogeny. In this study we show how to use such genome information of model organisms to research the phylogeny of related non-model species of interest. Investigating a dataset including representatives of all major caniformian lineages, we analysed 24 randomly chosen loci for 22 taxa. All loci were amplifiable and revealed 17 parsimony- informative SINE insertions. The screening for informative SINE insertions yields a large amount of sequence information, in particular of introns, which contain reliable phylogenetic information as well. A phylogenetic analysis of intron- and SINE sequence data provided a statistically robust phylogeny which is congruent with the absence/presence pattern of our SINE markers. This phylogeny strongly supports a sistergroup relationship of Musteloidea and Pinnipedia. Within Pinnipedia, we see strong support from bootstrapping and the presence of a SINE insertion for a sistergroup relationship of the walrus with the Otariidae.
Introductory Bioinformatics
(2009)
Motivation: Full-length DNA and protein sequences that span the entire length of a gene are ideally used for multiple sequence alignments (MSAs) and the subsequent inference of their relationships. Frequently, however, MSAs contain a substantial amount of missing data. For example, expressed sequence tags (ESTs), which are partial sequences of expressed genes, are the predominant source of sequence data for many organisms. The patterns of missing data typical for EST-derived alignments greatly compromise the accuracy of estimated phylogenies. Results: We present a statistical method for inferring phylogenetic trees from EST-based incomplete MSA data. We propose a class of hierarchical models for modeling pairwise distances between the sequences, and develop a fully Bayesian approach for estimation of the model parameters. Once the distance matrix is estimated, the phylogenetic tree may be constructed by applying neighbor-joining (or any other algorithm of choice). We also show that maximizing the marginal likelihood from the Bayesian approach yields similar results to a pro. le likelihood estimation. The proposed methods are illustrated using simulated protein families, for which the true phylogeny is known, and one real protein family.