Comparison of Boolean analysis and standard phylogenetic methods using artificially evolved and natural mt-tRNA sequences from great apes.

Boolean analysis (or BOOL-AN; Jakó et al., 2009. BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction. Mol. Phylogenet. Evol. 52, 887-97.), a recently developed method for sequence comparison uses the Iterative Canonical Form of Boolean functions. It considers sequence information in a way entirely different from standard phylogenetic methods (i.e. Maximum Parsimony, Maximum-Likelihood, Neighbor-Joining, and Bayesian analysis). The performance and reliability of Boolean analysis were tested and compared with the standard phylogenetic methods, using artificially evolved - simulated - nucleotide sequences and the 22 mitochondrial tRNA genes of the great apes. At the outset, we assumed that the phylogeny of Hominidae is generally well established, and the guide tree of artificial sequence evolution can also be used as a benchmark. These offer a possibility to compare and test the performance of different phylogenetic methods. Trees were reconstructed by each method from 2500 simulated sequences and 22 mitochondrial tRNA sequences. We also introduced a special re-sampling method for Boolean analysis on permuted sequence sites, the P-BOOL-AN procedure. Considering the reliability values (branch support values of consensus trees and Robinson-Foulds distances) we used for simulated sequence trees produced by different phylogenetic methods, BOOL-AN appeared as the most reliable method. Although the mitochondrial tRNA sequences of great apes are relatively short (59-75 bases long) and the ratio of their constant characters is about 75%, BOOL-AN, P-BOOL-AN and the Bayesian approach produced the same tree-topology as the established phylogeny, while the outcomes of Maximum Parsimony, Maximum-Likelihood and Neighbor-Joining methods were equivocal. We conclude that Boolean analysis is a promising alternative to existing methods of sequence comparison for phylogenetic reconstruction and congruence analysis.

Identifying novel genes involved in both deer physiological and human pathological osteoporosis.

Osteoporosis attacks 10% of the population worldwide. Humans or even the model animals of the disease cannot recover from porous bone. Regeneration in skeletal elements is the unique feature of our newly investigated osteoporosis model, the red deer (Cervus elaphus) stag. Cyclic physiological osteoporosis is a consequence of the annual antler cycle. This phenomenon raises the possibility to identify genes involved in the regulation of bone mineral density on the basis of comparative genomics between deer and human. We compare gene expression activity of osteoporotic and regenerating rib bone samples versus autumn dwell control in red deer by microarray hybridization. Identified genes were tested on human femoral bone tissue from non-osteoporotic controls and patients affected with age-related osteoporosis. Expression data were evaluated by Principal Components Analysis and Canonical Variates Analysis. Separation of patients into a normal and an affected group based on ten formerly known osteoporosis reference genes was significantly improved by expanding the data with newly identified genes. These genes include IGSF4, FABP3, FABP4, FKBP2, TIMP2, TMSB4X, TRIB, and members of the Wnt signaling. This study supports that extensive comparative genomic analyses, here deer and human, provide a novel approach to identify new targets for human diagnostics and therapy.

BOOL-AN: a method for comparative sequence analysis and phylogenetic reconstruction.

A novel discrete mathematical approach is proposed as an additional tool for molecular systematics which does not require prior statistical assumptions concerning the evolutionary process. The method is based on algorithms generating mathematical representations directly from DNA/RNA or protein sequences, followed by the output of numerical (scalar or vector) and visual characteristics (graphs). The binary encoded sequence information is transformed into a compact analytical form, called the Iterative Canonical Form (or ICF) of Boolean functions, which can then be used as a generalized molecular descriptor. The method provides raw vector data for calculating different distance matrices, which in turn can be analyzed by neighbor-joining or UPGMA to derive a tree, or by principal coordinates analysis to get an ordination scattergram. The new method and the associated software for inferring phylogenetic trees are called the Boolean analysis or BOOL-AN.

In silico detection of tRNA sequence features characteristic to aminoacyl-tRNA synthetase class membership.

Aminoacyl tRNA synthetases (aaRS) are grouped into Class I and II based on primary and tertiary structure and enzyme properties suggesting two independent phylogenetic lineages. Analogously, tRNA molecules can also form two respective classes, based on the class membership of their corresponding aaRS. Although some aaRS-tRNA interactions are not extremely specific and require editing mechanisms to avoid misaminoacylation, most aaRS-tRNA interactions are rather stereospecific. Thus, class-specific aaRS features could be mirrored by class-specific tRNA features. However, previous investigations failed to detect conserved class-specific nucleotides. Here we introduce a discrete mathematical approach that evaluates not only class-specific 'strictly present', but also 'strictly absent' nucleotides. The disjoint subsets of these elements compose a unique partition, named extended consensus partition (ECP). By analyzing the ECP for both Class I and II tDNA sets from 50 (13 archaeal, 30 bacterial and 7 eukaryotic) species, we could demonstrate that class-specific tRNA sequence features do exist, although not in terms of strictly conserved nucleotides as it had previously been anticipated. This finding demonstrates that important information was hidden in tRNA sequences inaccessible for traditional statistical methods. The ECP analysis might contribute to the understanding of tRNA evolution and could enrich the sequence analysis tool repertoire.