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Among species within a phylogenetic group, genomic GC% values can cover a wide range that is particularly evident at third codon positions. However, among genes within a genome, genic GC% values can also cover a wide range that is, again, particularly evident at third codon positions. Individual genes and genomes each have a "homostabilizing propensity" to adopt a relatively uniform GC%. Each gene (a "microisochore") occupies a discrete GC% niche of relatively uniform base composition amongst its fellow genes, which can collectively span a wide GC% range. Homostabilization serves to recombinationally isolate both genome sectors (facilitating gene duplication and differentiation) and genomes (facilitating genome duplication and differentiation; e.g., speciation). Although they may sometimes be in conflict, the individualities of genomes, and of genes within those genomes, are separately sustained by a common mechanism, uniformity of GC%. The protection against inadvertent recombination afforded by GC% differentiation is, in the general case, a prerequisite for phenotypic differentiation.

One most notable trend during the evolution is the substantial expansion of genomes along with dramatic expansion of protein diversity. It has been discovered that, whilst in prokaryotes subunits of many proteinases are encoded by single genes, these are mostly encoded by multi-genes in eukaryotes. To understand the mechanism for the genesis of multi-isoforms of protein complex, we have analyzed amino acid sequences of Na⁺/K⁺ ATPase from various species ranging from archaea to vertebrates. Phylogenetic relationship between the selected species was considered from the perspective of important functional domains of Na⁺/K⁺ ATPase including cation ATPase N termination, E1-E2 ATPase, hydrolase, and cation ATPase C termination of the α subunit, Na⁺/K⁺ ATPase of the β subunit, and ATP1G1_PLM_MAT8 of the γ subunit. Coincident trees, obtained through comparison of aforementioned domains of the α and β subunits, were used to examine the evolutionary divergence. By conservational and phylogenetic analyses, evolution of the Na⁺/K⁺ ATPase was outlined. Evidence was also found that essential domains of the Na⁺/K⁺ ATPase have been conserved during the evolution. These investigations seem to imply that various isoforms of α and β subunits of vertebral Na⁺/K⁺ ATPases have evolved from single ancestral α and β subunit genes through duplication events. In addition, our results seem to suggest a third fate for duplicated genes, e.g. the duplicate may have the same function as their ancestor gene. The results may also provide important clues to the underlying mechanisms of genesis of Na⁺/K⁺ ATPase multi-isoforms.

Transposable elements are powerful mutagens. Along with genomic sequences, knock-out phenotypes and expression patterns are important information to elucidate the function of genes. In this review, I propose a strategy to develop tranposant lines on a large scale by combining genetic cross and tissue culture of Ac and Ds lines. Based on the facts that Ds tends to be inactive in F2 or later generation and Ds becomes reactivated via tissue culture, a large scale of transposants can be produced by tissue culture of seeds carrying Ac and inactive Ds. In this review, I describe limitations and considerations in operating transposon tagging systems in rice. Also, I discuss the efficiency of our gene trap system and technical procedures to clone Ds flanking DNA.

The next step in the understanding of the genome organization, after the determination of complete sequences, involves proteomics. The proteome includes the whole set of protein-protein interactions, and two recent independent studies have shown that its topology displays a number of surprising features shared by other complex networks, both natural and artificial. In order to understand the origins of this topology and its evolutionary implications, we present a simple model of proteome evolution that is able to reproduce many of the observed statistical regularities reported from the analysis of the yeast proteome. Our results suggest that the observed patterns can be explained by a process of gene duplication and diversification that would evolve proteome networks under a selection pressure, favoring robustness against failure of its individual components.

Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting signed genomic data. Their algorithm determines the minimum number of reversals required for rearranging a genome to another — but only in the absence of gene duplicates. However, duplicates often account for 40% of a genome. In this paper, we show how to extend Hannenhalli and Pevzner's approach to deal with genomes with multi-gene families. We propose a new heuristic algorithm to compute the nearest reversal distance between two genomes with multi-gene families via binary integer programming. The experimental results on both synthetic and real biological data demonstrate that the proposed algorithm is able to find the reversal distance with high accuracy.

Phylogenetic analysis has to overcome the grant challenge of inferring accurate species trees from evolutionary histories of gene families (gene trees) that are discordant with the species tree along whose branches they have evolved. Two well studied approaches to cope with this challenge are to solve either biologically informed gene tree parsimony (GTP) problems under gene duplication, gene loss, and deep coalescence, or the classic RF supertree problem that does not rely on any biological model. Despite the potential of these problems to infer credible species trees, they are NP-hard. Therefore, these problems are addressed by heuristics that typically lack any provable accuracy and precision. We describe fast dynamic programming algorithms that solve the GTP problems and the RF supertree problem exactly, and demonstrate that our algorithms can solve instances with data sets consisting of as many as 22 taxa. Extensions of our algorithms can also report the number of all optimal species trees, as well as the trees themselves. To better asses the quality of the resulting species trees that best fit the given gene trees, we also compute the worst case species trees, their numbers, and optimization score for each of the computational problems. Finally, we demonstrate the performance of our exact algorithms using empirical and simulated data sets, and analyze the quality of heuristic solutions for the studied problems by contrasting them with our exact solutions.

Solving the gene duplication problem is a classical approach for species tree inference from gene trees that are confounded by gene duplications. This problem takes a collection of gene trees and seeks a species tree that implies the minimum number of gene duplications. Wilkinson et al. posed the conjecture that the gene duplication problem satisfies the desirable Pareto property for clusters. That is, for every instance of the problem, all clusters that are commonly present in the input gene trees of this instance, called strict consensus, will also be found in every solution to this instance. We prove that this conjecture does not generally hold. Despite this negative result we show that the gene duplication problem satisfies a weaker version of the Pareto property where the strict consensus is found in at least one solution (rather than all solutions). This weaker property contributes to our design of an efficient scalable algorithm for the gene duplication problem. We demonstrate the performance of our algorithm in analyzing large-scale empirical datasets. Finally, we utilize the algorithm to evaluate the accuracy of standard heuristics for the gene duplication problem using simulated datasets.

Rapidly increasing numbers of organisms have been completely sequenced and most of their genes identified; homologies among these genes are also getting established. It thus has become possible to represent whole genomes as ordered lists of gene identifiers and to study the evolution of these entities through computational means, in systematics as well as in comparative genomics. While dealing with rearrangements is nontrivial, the biggest stumbling block remains gene duplication and losses, leading to considerable difficulties in determining orthologs among gene families—all the more since orthology determination has a direct impact on the selection of rearrangements. None of the existing phylogenetic reconstruction methods that use gene orders is able to exploit the information present in complete gene families—most assume singleton families and equal gene content, limiting the evolutionary operations to rearrangements, while others make it so by eliminating nonshared genes and selecting one exemplar from each gene family. In this work, we leverage our past work on genomic distances, on tight bounding of parsimony scores through linear programming, and on divide-and-conquer methods for large-scale reconstruction to build the first computational approach to phylogenetic reconstruction from complete gene order data, taking into account not only rearrangements, but also duplication and loss of genes. Our approach can handle mulitchromosomal data and gene families of arbitrary sizes and scale up to hundreds of genomes through the use of disk-covering methods. We present experimental results on simulated unichromosomal genomes in a range of sizes consistent with prokaryotes. Our results confirm that equalizing gene content, as done in existing phylogenetic tools, discards important phylogenetic information; in particular, our approach easily outperforms that most commonly referenced tool, MGR, often returning trees with less that one quarter of the errors found in the MGR trees.

Reconciliation is the process of resolving disagreement between gene and species trees, by invoking gene duplications and losses to explain topological incongruence. The resulting inferred duplication histories are a valuable source of information for a broad range of biological applications, including ortholog identification, estimating gene duplication times, and rooting and correcting gene trees. Reconciliation for binary trees is a tractable and well studied problem. However, a striking proportion of species trees are non-binary. For example, 64% of branch points in the NCBI taxonomy have three or more children. When applied to non-binary species trees, current algorithms overestimate the number of duplications because they cannot distinguish between duplication and deep coalescence. We present the first formal algorithm for reconciling binary gene trees with non-binary species trees under a duplication-loss parsimony model. Using a space efficient mapping from gene to species tree, our algorithm infers the minimum number of duplications and losses in O(|V_G| · (k_S + h_S)) time, where V_G is the number of nodes in the gene tree, h_S is the height of the species tree and k_S is the width of its largest multifurcation. We also present a dynamic programming algorithm for a combined loss model, in which losses in sibling species may be represented as a single loss in the common ancestor. Our algorithms have been implemented in NOTUNG, a robust, production quality tree-fitting program, which provides a graphical user interface for exploratory analysis and also supports automated, high-throughput analysis of large data sets.

The draft genome of Trichomonas vaginalis was recently published, but not much is known on why it has such a large genome. In part this size is due to many gene family expansions. For example we found over 100 members in the adenylyl cyclase family. About half are complete full length genes, and nearly half are initially confirmed to be pseudogenes, the remaining are either incomplete or the apparent result of assembly or sequencing problems. The family can be divided into two subgroups by sequence similarity. These can then be divided into functional and pseudo genes. Among all four of these sets the cyclase domain is very well conserved. We gave three possible hypotheses for that observation: a) Sequencing error or stop-codon read-through; b) Recency of duplication and mutation; c) The likelihood of functional pseudogene.

The rapid accumulation of whole-genome data has renewed interest in the study of the evolution of genomic architecture, under such events as rearrangements, duplications, losses. Comparative genomics, evolutionary biology, and cancer research all require tools to elucidate the mechanisms, history, and consequences of those evolutionary events, while phylogenetics could use whole-genome data to enhance its picture of the Tree of Life. Current approaches in the area of phylogenetic analysis are limited to very small collections of closely related genomes using low-resolution data (typically a few hundred syntenic blocks); moreover, these approaches typically do not include duplication and loss events. We describe a maximum likelihood (ML) approach for phylogenetic analysis that takes into account genome rearrangements as well as duplications, insertions, and losses. Our approach can handle high-resolution genomes (with 40,000 or more markers) and can use in the same analysis genomes with very different numbers of markers. Because our approach uses a standard ML reconstruction program (RAxML), it scales up to large trees. We present the results of extensive testing on both simulated and real data showing that our approach returns very accurate results very quickly. In particular, we analyze a dataset of 68 high-resolution eukaryotic genomes, with from 3,000 to 42,000 genes, from the eGOB database; the analysis, including bootstrapping, takes just 3 hours on a desktop system and returns a tree in agreement with all well supported branches, while also suggesting resolutions for some disputed placements.