Composition vector approach to whole-genome-based prokaryotic phylogeny: Success and foundations

Li, Qiang; Hao, Bailin

doi:10.1016/j.jbiotec.2009.12.015

Cited by 32 publications

(38 citation statements)

References 31 publications

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“…The Proteobacteria metrics showed only a marginal, but significant ( P < 0.05, Wilcoxon test) improvement, which might be due to its diverse and nonmonophyletic nature (Garrity 2005). A disagreement of the inferred tree with the reference taxonomy was also observed with the Proteobacterial CVTree based on translated protein products (Li et al 2010). The best performance improvement due to specific metrics was observed for the phylum Actinobacteria, where the average cophenetic correlation significantly increased from 0.39 to 0.64 ( P = 8.23e−10, Wilcoxon test), whereas the average quartet distance decreased from 0.53 to 0.43 ( P = 2.73e−13, Wilcoxon test).…”

Section: Resultsmentioning

confidence: 98%

“…Various methods have been proposed for the evolutionary comparisons of entire genomes or large genome segments, including alignment-free methods (Burge et al 1992; Karlin and Cardon 1994; Kirzhner et al 2002; Pride et al 2003; Qi et al 2004; Sims et al 2009; Takahashi et al 2009; Li et al 2010) and the alignment-based methods, such as the genome blast distance phylogeny (GBDP) (Henz et al 2005). A direct comparison between genome tree inference methods is lacking, especially with the alignment-based method GBDP.…”

Section: Introductionmentioning

confidence: 99%

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Alignment-Free Genome Tree Inference by Learning Group-Specific Distance Metrics

Patil

McHardy

2013

Genome Biology and Evolution

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Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods—9 alignment-free and 1 alignment-based.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Alignment-Free Genome Tree Inference by Learning Group-Specific Distance Metrics

Patil

McHardy

2013

Genome Biology and Evolution

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“…Prokaryotic phylogenies inferring is still an open problem, and Bergey's taxonomy changes the taxonomy of several species in each new release [42]. For example, the genus Oceanobacillus was part of the phylum Proteobacteria (B12) in Bergey's taxonomy 3.0 [43], but it belonged to phylum Firmicutes (B13) in Bergey's taxonomy 5.0 [29].…”

Section: Resultsmentioning

confidence: 99%

Prokaryotic Phylogenies Inferred from Whole-Genome Sequence and Annotation Data

Cao

Wang

et al. 2013

BioMed Research International

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Phylogenetic trees are used to represent the evolutionary relationship among various groups of species. In this paper, a novel method for inferring prokaryotic phylogenies using multiple genomic information is proposed. The method is called CGCPhy and based on the distance matrix of orthologous gene clusters between whole-genome pairs. CGCPhy comprises four main steps. First, orthologous genes are determined by sequence similarity, genomic function, and genomic structure information. Second, genes involving potential HGT events are eliminated, since such genes are considered to be the highly conserved genes across different species and the genes located on fragments with abnormal genome barcode. Third, we calculate the distance of the orthologous gene clusters between each genome pair in terms of the number of orthologous genes in conserved clusters. Finally, the neighbor-joining method is employed to construct phylogenetic trees across different species. CGCPhy has been examined on different datasets from 617 complete single-chromosome prokaryotic genomes and achieved applicative accuracies on different species sets in agreement with Bergey's taxonomy in quartet topologies. Simulation results show that CGCPhy achieves high average accuracy and has a low standard deviation on different datasets, so it has an applicative potential for phylogenetic analysis.

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“…The CVTrees were constructed for all available prokaryotic genomes using different peptide lengths from K = 3–7. Because the most reliable trees are obtained at K = 5 and 6 (Li et al 2010), we only show the K = 6 data in this report.…”

Section: Methodsmentioning

confidence: 99%

Geographic divergence of “Sulfolobus islandicus” strains assessed by genomic analyses including electronic DNA hybridization confirms they are geovars

Zuo

Hao

Staley

2013

Antonie van Leeuwenhoek

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Ten well-annotated genomes of “Sulfolobus islandicus” strains from different geographic locations have been released at the NCBI database. Whole genome based composition vector trees indicate that these strains show the same branching patterns as originally reported by multi-locus sequence analysis. To determine whether the ten strains meet the criteria for separate species, DNA–DNA hybridization (DDH) was performed in silico. DDH values of strains from the same geographic location, i.e., Iceland, Kamchatka and North America, ranged from 82.4 to 95.4 %, clearly qualifying them as members of the same species. The lowest DDH values found between locations ranged from 75.5 to 76.6 %, which exceed the 70 % DDH threshold for a species thereby indicating they are all members of the same species based on the currently accepted definition. The clear divergences of strains from the different geographic locations are sufficiently great to consider them as separate geovars. “S. islandicus” has not yet been validly named and a type strain has not been deposited in culture collections. We urgently recommend that those who study the organism fulfill the criteria of the International Code of Nomenclature of Bacteria in order to designate a type strain and to identify and deposit related strains of this species to make them available to the broader scientific community.

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Composition vector approach to whole-genome-based prokaryotic phylogeny: Success and foundations

Cited by 32 publications

References 31 publications

Alignment-Free Genome Tree Inference by Learning Group-Specific Distance Metrics

Alignment-Free Genome Tree Inference by Learning Group-Specific Distance Metrics

Prokaryotic Phylogenies Inferred from Whole-Genome Sequence and Annotation Data

Geographic divergence of “Sulfolobus islandicus” strains assessed by genomic analyses including electronic DNA hybridization confirms they are geovars

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