Biomedical subjects

Koichiro Tamura

Publications and source records attributed to Koichiro Tamura.

9 recordsLinked to original sources

PubMed2026

Phylogenomic subsampling and upsampling for efficient evolutionary analyses of big data.

Long runtimes, high memory demands, and reliance on high-performance computing impede phylogenomic analyses. We review a scalable phylogenomic subsampling with upsampling (PSU) framework to address this challenge, which reduces runtime and memory requirements by orders of magnitude. In PSU, small subsamples of sites from a concatenated alignment are analyzed, which are expanded by upsampling before inference, and the resulting inferences are aggregated to obtain evolutionary estimates. PSU harnesses the fact that the computational cost of maximum likelihood analysis is strongly influenced by the number of distinct site patterns in the concatenated alignment, whereas statistical power depends primarily on the amount of evolutionary information represented by the total number of sites and substitutions. By reducing the former while restoring the latter through upsampling, PSU can approximate many full-alignment analyses at substantially lower computational cost. Analysis of simulated and empirical datasets shows that PSU can accurately estimate bootstrap support values, select the optimal substitution model, test evolutionary hypotheses, and infer branch lengths, divergence times, and associated uncertainty measures. PSU also provides distributions of inferred clade support across independent subsamples, enabling detection of conflicting phylogenetic signals that may remain hidden in conventional bootstrap analysis of concatenated alignments. Automated tuning of subsample size, the number of subsamples, and the number of upsampling replicates make PSU practical. We suggest that PSU is a general approach for scalable phylogenomic inference using a broad range of statistical methods. By enabling analyses of genome-scale alignments on commodity hardware, PSU broadens research access and reduces environmental and infrastructural costs of big-data phylogenomics.

Phylogeny↗

PubMed2026

Phylogenomic subsampling and upsampling for efficient evolutionary analyses of big data.

Long runtimes, high memory demands, and reliance on high-performance computing impede phylogenomic analyses. We review a scalable phylogenomic subsampling with upsampling (PSU) framework, in which small subsamples of sites from a concatenated alignment are expanded by upsampling before inference, and the resulting analyses are then aggregated to obtain evolutionary estimates. PSU harnesses the fact that the computational cost of maximum likelihood analysis is strongly influenced by the number of distinct site patterns in the concatenated alignment, whereas statistical power depends primarily on the amount of evolutionary information represented by the total number of sites and substitutions. By reducing the former while restoring the latter through upsampling, PSU can approximate many full-data analyses at substantially lower computational cost. Analysis of simulated and empirical datasets shows that PSU can accurately estimate bootstrap support values, select the optimal substitution model, test evolutionary hypotheses, and infer branch lengths, divergence times, and associated uncertainty measures, while reducing runtime and memory requirements by orders of magnitude. PSU also provides distributions of inferred clade support across independent subsamples, enabling detection of conflicting phylogenetic signals that may remain hidden in conventional bootstrap analysis. Automated tuning of subsample size, the number of subsamples, and the number of upsampling replicates make PSU practical across diverse datasets. We suggest that PSU is a general strategy for scalable phylogenomic inference using a broad range of statistical methods. By enabling analyses of genome-scale alignments on commodity hardware, PSU broadens research access and reduces environmental and infrastructural costs of big-data phylogenomics.

confidence limits↗

PubMed2006

Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005. Unusual evolution of interspersed repeat sequences in the Drosophila ananassae subgroup.

New repeat sequences were found in the Drosophila ananassae genome sequence. They accounted for approximately 1.2% of the D. ananassae genome and were estimated to be more abundant in genomes of its closely related species belonging to the Drosophila bipectinata complex, whereas it was entirely absent in the Drosophila melanogaster genome. They were interspersed throughout euchromatic regions of the genome, usually as short tandem arrays of unit sequences, which were mostly 175-200 bp long with two distinct peaks at 180 and 189 bp in the length distribution. The nucleotide differences among unit sequences within the same array (locus) were much smaller than those between separate loci, suggesting within-locus concerted evolution. The phylogenetic tree of the repeat sequences from different loci showed that divergences between sequences from different chromosome arms occurred only at earlier stages of evolution, while those within the same chromosome arm occurred thereafter, resulting in the increase in copy number. We found RNA polymerase III promoter sequences (A box and B box), which play a critical role in retroposition of short interspersed elements. We also found conserved stem-loop structures, which are possibly associated with certain DNA rearrangements responsible for the increase in copy number within a chromosome arm. Such an atypical combination of characteristics (i.e., wide dispersal and tandem repetition) may have been generated by these different transposition mechanisms during the course of evolution.

Animals↗

PubMed2005

A novel chimeric gene, siren, with retroposed promoter sequence in the Drosophila bipectinata complex.

Retrotransposons often produce a copy of host genes by their reverse transcriptase activity operating on host gene transcripts. Since transcripts normally do not contain promoter, a retroposed gene copy usually becomes a retropseudogene. However, in Drosophila bipectinata and a closely related species we found a new chimeric gene, whose promoter was likely produced by retroposition. This chimeric gene, named siren, consists of a tandem duplicate of Adh and a retroposed fragment of CG11779 containing the promoter and a partial intron in addition to the first exon. We found that this unusual structure of a retroposed fragment was obtained by retroposition of nanos, which overlaps with CG11779 on the complementary strand. The potential of retroposition to produce a copy of promoter and intron sequences in the context of gene overlapping was demonstrated.

Alcohol Dehydrogenase↗

PubMed2005

Replication origin of mitochondrial DNA in insects.

The precise position of the replication origin (O(R)) of mtDNA was determined for insect species belonging to four different orders (four species of Drosophila, Bombyx mori, Triborium castaneum, and Locusta migratoria, which belong to Diptera, Lepidoptera, Coleoptera, and Orthoptera, respectively). Since the free 5' ends of the DNA strands of mtDNA are interpreted as the O(R), their positions were mapped at 1-nucleotide resolution within the A + T-rich region by using the ligation-mediated PCR method. In all species examined, the free 5' ends were found within a very narrow range of several nucleotides in the A + T-rich region. For four species of Drosophila, B. mori, and T. castaneum, which belong to holometabolous insects, although the O(R)'s were located at different positions, they were located immediately downstream of a series of thymine nucleotides, the so-called T-stretch. These results strongly indicate that the T-stretch is involved in the recognition of the O(R) of mtDNA at least among holometabolous insects. For L. migratoria (hemimetabolous insect), on the other hand, none of the long stretches of T's was found in the upstream portion of the O(R), suggesting that the regulatory sequences involved in the replication initiation process have changed through insect evolution.

AT Rich Sequence↗

PubMed2004

Prospects for inferring very large phylogenies by using the neighbor-joining method.

Current efforts to reconstruct the tree of life and histories of multigene families demand the inference of phylogenies consisting of thousands of gene sequences. However, for such large data sets even a moderate exploration of the tree space needed to identify the optimal tree is virtually impossible. For these cases the neighbor-joining (NJ) method is frequently used because of its demonstrated accuracy for smaller data sets and its computational speed. As data sets grow, however, the fraction of the tree space examined by the NJ algorithm becomes minuscule. Here, we report the results of our computer simulation for examining the accuracy of NJ trees for inferring very large phylogenies. First we present a likelihood method for the simultaneous estimation of all pairwise distances by using biologically realistic models of nucleotide substitution. Use of this method corrects up to 60% of NJ tree errors. Our simulation results show that the accuracy of NJ trees decline only by approximately 5% when the number of sequences used increases from 32 to 4,096 (128 times) even in the presence of extensive variation in the evolutionary rate among lineages or significant biases in the nucleotide composition and transition/transversion ratio. Our results encourage the use of complex models of nucleotide substitution for estimating evolutionary distances and hint at bright prospects for the application of the NJ and related methods in inferring large phylogenies.

Computer Simulation↗

PubMed2004

MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment.

With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.

Databases, Genetic↗

PubMed2003

Temporal patterns of fruit fly (Drosophila) evolution revealed by mutation clocks.

Drosophila melanogaster has been a canonical model organism to study genetics, development, behavior, physiology, evolution, and population genetics for nearly a century. Despite this emphasis and the completion of its nuclear genome sequence, the timing of major speciation events leading to the origin of this fruit fly remain elusive because of the paucity of extensive fossil records and biogeographic data. Use of molecular clocks as an alternative has been fraught with non-clock-like accumulation of nucleotide and amino-acid substitutions. Here we present a novel methodology in which genomic mutation distances are used to overcome these limitations and to make use of all available gene sequence data for constructing a fruit fly molecular time scale. Our analysis of 2977 pairwise sequence comparisons from 176 nuclear genes reveals a long-term fruit fly mutation clock ticking at a rate of 11.1 mutations per kilobase pair per Myr. Genomic mutation clock-based timings of the landmark speciation events leading to the evolution of D. melanogaster show that it shared most recent common ancestry 5.4 MYA with D. simulans, 12.6 MYA with D. erecta+D. orena, 12.8 MYA with D. yakuba+D. teisseri, 35.6 MYA with the takahashii subgroup, 41.3 MYA with the montium subgroup, 44.2 MYA with the ananassae subgroup, 54.9 MYA with the obscura group, 62.2 MYA with the willistoni group, and 62.9 MYA with the subgenus Drosophila. These and other estimates are compatible with those known from limited biogeographic and fossil records. The inferred temporal pattern of fruit fly evolution shows correspondence with the cooling patterns of paleoclimate changes and habitat fragmentation in the Cenozoic.

Animals↗

PubMed2002

Evolutionary distance estimation under heterogeneous substitution pattern among lineages.

Most of the sophisticated methods to estimate evolutionary divergence between DNA sequences assume that the two sequences have evolved with the same pattern of nucleotide substitution after their divergence from their most recent common ancestor (homogeneity assumption). If this assumption is violated, the evolutionary distance estimated will be biased, which may result in biased estimates of divergence times and substitution rates, and may lead to erroneous branching patterns in the inferred phylogenies. Here we present a simple modification for existing distance estimation methods to relax the assumption of the substitution pattern homogeneity among lineages when analyzing DNA and protein sequences. Results from computer simulations and empirical data analyses for human and mouse genes are presented to demonstrate that the proposed modification reduces the estimation bias considerably and that the modified method performs much better than the LogDet methods, which do not require the homogeneity assumption in estimating the number of substitutions per site. We also discuss the relationship of the substitution and mutation rate estimates when the substitution pattern is not the same in the lineages leading to the two sequences compared.

Animals↗