Dataset Information

Core set approach to reduce uncertainty of gene trees.

ABSTRACT:

Background

A genealogy based on gene sequences within a species plays an essential role in the estimation of the character, structure, and evolutionary history of that species. Because intraspecific sequences are more closely related than interspecific ones, detailed information on the evolutionary process may be available by determining all the node sequences of trees and provide insight into functional constraints and adaptations. However, strong evolutionary correlations on a few lineages make this determination difficult as a whole, and the maximum parsimony (MP) method frequently allows a number of topologies with a same total branching length.

Results

Kitazoe et al. developed multidimensional vector-space representation of phylogeny. It converts additivity of evolutionary distances to orthogonality among the vectors expressing branches, and provides a unified index to measure deviations from the orthogoality. In this paper, this index is used to detect and exclude sequences with large deviations from orthogonality, and then selects a maximum subset ("core set") of sequences for which MP generates a single solution. Once the core set tree is formed whose all the node sequences are given, the excluded sequences are found to have basically two phylogenetic positions on this tree, respectively. Fortunately, since multiple substitutions are rare in intra-species sequences, the variance of nucleotide transitions is confined to a small range. By applying the core set approach to 38 partial env sequences of HIV-1 in a single patient and also 198 mitochondrial COI and COII DNA sequences of Anopheles dirus, we demonstrate how consistently this approach constructs the tree.

Conclusion

In the HIV dataset, we confirmed that the obtained core set tree is the unique maximum set for which MP proposes a single tree. In the mosquito data set, the fluctuation of nucleotide transitions caused by the sequences excluded from the core set was very small. We reproduced this core-set tree by simulation based on random process, and applied our approach to many sets of the obtained endpoint sequences. Consequently, the ninety percent of the endpoint sequences was identified as the core sets and the obtained node sequences were perfectly identical to the true ones.

SUBMITTER: Okabayashi T

PROVIDER: S-EPMC1508163 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Core set approach to reduce uncertainty of gene trees.

Okabayashi Takahisa T Kitazoe Yasuhiro Y Kishino Hirohisa H Watabe Teruaki T Nakajima Noriaki N Okuhara Yoshiyasu Y O'Loughlin Samantha S Walton Catherine C

BMC evolutionary biology 20060520

<h4>Background</h4>A genealogy based on gene sequences within a species plays an essential role in the estimation of the character, structure, and evolutionary history of that species. Because intraspecific sequences are more closely related than interspecific ones, detailed information on the evolutionary process may be available by determining all the node sequences of trees and provide insight into functional constraints and adaptations. However, strong evolutionary correlations on a few line ...[more]

PMID: 16712735

Similar Datasets

Project description:BackgroundKinases mTORC1 and AMPK act as energy sensors, controlling nutrient responses and cellular growth. Changes in nutrient levels affect diverse transcriptional networks, making it challenging to identify downstream paths that regulate cellular growth or a switch to development via nutrient variation. The life cycle of Dictyostelium presents an excellent model to study the mTORC1 signaling function for growth and development. Dictyostelium grow as single cells in nutrient-rich media, but, upon nutrient withdrawal, growth ceases and cells enter a program for multi-cell development. While nearly half the genome shows gene expression changes upon nutrient removal, we hypothesized that not all of these genes are required for the switch to program development. Through manipulation of mTORC1 activity alone, without nutrient removal, we focused on a core network of genes that are required for switching between growth and development for regulation of cell fate decisions.ResultsTo identify developmentally essential genes, we sought ways to promote development in the absence of nutrient loss. We first examined the activities of mTORC1 and AMPK in Dictyostelium during phases of rapid growth and starvation-induced development and showed they exhibited reciprocal patterns of regulation under various conditions. Using these as initial readouts, we identified rich media conditions that promoted rapid cell growth but, upon mTORC1 inactivation by rapamycin, led to a growth/development switch. Examination of gene expression during cell fate switching showed that changes in expression of most starvation-regulated genes were not required for developmental induction. Approximately 1000 genes which become downregulated upon rapamycin treatment comprise a cellular growth network involving ribosome biogenesis, protein synthesis, and cell cycle processes. Conversely, the upregulation of ~ 500 genes by rapamycin treatment defines essential signaling pathways for developmental induction, and ~ 135 of their protein products intersect through the well-defined cAMP/PKA network. Many of the rapamycin-induced genes we found are currently unclassified, and mutation analyses of 5 such genes suggest a novel gene class essential for developmental regulation.ConclusionsWe show that manipulating activities of mTORC1/AMPK in the absence of nutrient withdrawal is sufficient for a growth-to-developmental fate switch in Dictyostelium, providing a means to identify transcriptional networks and signaling pathways essential for early development.

Project description:Comparative genomic sequencing is shedding new light on bacterial identification, taxonomy and phylogeny. An in silico assessment of a core gene set necessary for cellular functioning was made to determine a consensus set of genes that would be useful for the identification, taxonomy and phylogeny of the species belonging to the subclass Actinobacteridae which contained two orders Actinomycetales and Bifidobacteriales. The subclass Actinobacteridae comprised about 85% of the actinobacteria families. The following recommended criteria were used to establish a comprehensive gene set; the gene should (i) be long enough to contain phylogenetically useful information, (ii) not be subject to horizontal gene transfer, (iii) be a single copy (iv) have at least two regions sufficiently conserved that allow the design of amplification and sequencing primers and (v) predict whole-genome relationships. We applied these constraints to 50 different Actinobacteridae genomes and made 1,224 pairwise comparisons of the genome conserved regions and gene fragments obtained by using Sequence VARiability Analysis Program (SVARAP), which allow designing the primers. Following a comparative statistical modeling phase, 3 gene fragments were selected, ychF, rpoB, and secY with R2>0.85. Selected sets of broad range primers were tested from the 3 gene fragments and were demonstrated to be useful for amplification and sequencing of 25 species belonging to 9 genera of Actinobacteridae. The intraspecies similarities were 96.3-100% for ychF, 97.8-100% for rpoB and 96.9-100% for secY among 73 strains belonging to 15 species of the subclass Actinobacteridae compare to 99.4-100% for 16S rRNA. The phylogenetic topology obtained from the combined datasets ychF+rpoB+secY was globally similar to that inferred from the 16S rRNA but with higher confidence. It was concluded that multi-locus sequence analysis using core gene set might represent the first consensus and valid approach for investigating the bacterial identification, phylogeny and taxonomy.

Dataset Information

Core set approach to reduce uncertainty of gene trees.

Background

Results

Conclusion

Publications

Core set approach to reduce uncertainty of gene trees.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets