Dataset Information

Phylogenetic construction of 17 bacterial phyla by new method and carefully selected orthologs.

ABSTRACT: Here, we constructed a phylogenetic tree of 17 bacterial phyla covering eubacteria and archaea by using a new method and 102 carefully selected orthologs from their genomes. One of the serious disturbing factors in phylogeny construction is the existence of out-paralogs that cannot easily be found out and discarded. In our method, out-paralogs are detected and removed by constructing a phylogenetic tree of the genes in question and examining the clustered genes in the tree. We also developed a method for comparing two tree topologies or shapes, ComTree. Applying ComTree to the constructed tree we computed the relative number of orthologs that support a node of the tree. This number is called the Positive Ortholog Ratio (POR), which is conceptually and methodologically different from the frequently used bootstrap value. Our study concretely shows drawbacks of the bootstrap test. Our result of bacterial phylogeny analysis is consistent with previous ones showing that hyperthermophilic bacteria such as Thermotogae and Aquificae diverged earlier than the others in the eubacterial phylogeny studied. It is noted that our results are consistent whether thermophilic archaea or mesophilic archaea is employed for determining the root of the tree. The earliest divergence of hyperthermophilic eubacteria is supported by genes involved in fundamental metabolic processes such as glycolysis, nucleotide and amino acid syntheses.

SUBMITTER: Horiike T

PROVIDER: S-EPMC2648810 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Phylogenetic construction of 17 bacterial phyla by new method and carefully selected orthologs.

Horiike Tokumasa T Miyata Daisuke D Hamada Kazuo K Saruhashi Satoshi S Shinozawa Takao T Kumar Sudhir S Chakraborty Ranajit R Komiyama Tomoyoshi T Tateno Yoshio Y

Gene 20081021 1-2

Here, we constructed a phylogenetic tree of 17 bacterial phyla covering eubacteria and archaea by using a new method and 102 carefully selected orthologs from their genomes. One of the serious disturbing factors in phylogeny construction is the existence of out-paralogs that cannot easily be found out and discarded. In our method, out-paralogs are detected and removed by constructing a phylogenetic tree of the genes in question and examining the clustered genes in the tree. We also developed a m ...[more]

PMID: 19000750

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Project description:BackgroundThe efficiencies of the stop codons TAA, TAG, and TGA in protein synthesis termination are not the same. These variations could allow many genes to be regulated. There are many similar nucleotide trimers found on the second and third reading-frames of a gene. They are called premature stop codons (PSC). Like stop codons, the PSC in bacterial genomes are also highly bias in terms of their quantities and qualities on the genes. Phylogenetically related species often share a similar PSC profile. We want to know whether the selective forces that influence the stop codons and the PSC usage biases in a genome are related. We also wish to know how strong these trimers in a genome are related to the natural history of the bacterium. Knowing these relations may provide better knowledge in the phylogeny of bacteriaResultsA 16SrRNA-alignment tree of 19 well-studied α-, β- and γ-Proteobacteria Type species is used as standard reference for bacterial phylogeny. The genomes of sixty-one bacteria, belonging to the α-, β- and γ-Proteobacteria subphyla, are used for this study. The stop codons and PSC are collectively termed "Translation Stop Signals" (TSS). A gene is represented by nine scalars corresponding to the numbers of counts of TAA, TAG, and TGA on each of the three reading-frames of that gene. "Translation Stop Signals Ratio" (TSSR) is the ratio between the TSS counts. Four types of TSSR are investigated. The TSSR-1, TSSR-2 and TSSR-3 are each a 3-scalar series corresponding respectively to the average ratio of TAA: TAG: TGA on the first, second, and third reading-frames of all genes in a genome. The Genomic-TSSR is a 9-scalar series representing the ratio of distribution of all TSS on the three reading-frames of all genes in a genome. Results show that bacteria grouped by their similarities based on TSSR-1, TSSR-2, or TSSR-3 values could only partially resolve the phylogeny of the species. However, grouping bacteria based on thier Genomic-TSSR values resulted in clusters of bacteria identical to those bacterial clusters of the reference tree. Unlike the 16SrRNA method, the Genomic-TSSR tree is also able to separate closely related species/strains at high resolution. Species and strains separated by the Genomic-TSSR grouping method are often in good agreement with those classified by other taxonomic methods. Correspondence analysis of individual genes shows that most genes in a bacterial genome share a similar TSSR value. However, within a chromosome, the Genic-TSSR values of genes near the replication origin region (Ori) are more similar to each other than those genes near the terminus region (Ter).ConclusionThe translation stop signals on the three reading-frames of the genes on a bacterial genome are interrelated, possibly due to frequent off-frame recombination facilitated by translational-associated recombination (TSR). However, TSR may not occur randomly in a bacterial chromosome. Genes near the Ori region are often highly expressed and a bacterium always maintains multiple copies of Ori. Frequent collisions between DNA- polymerase and RNA-polymerase would create many DNA strand-breaks on the genes; whereas DNA strand-break induced homologues-recombination is more likely to take place between genes with similar sequence. Thus, localized recombination could explain why the TSSR of genes near the Ori region are more similar to each other. The quantity and quality of these TSS in a genome strongly reflect the natural history of a bacterium. We propose that the Genomic- TSSR can be used as a subjective biomarker to represent the phyletic status of a bacterium.

Project description:Protein function prediction has been the most worked upon and the most challenging problem for computational biologists. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions. New un-annotated sequences are being added to the public protein databases (e.g. UniprotKB) at an enormous pace [1]. Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s). Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins [2]. For proteins with well characterised close relatives, it is trivial to infer function. Orphan proteins without discernible sequence relatives present a greater challenge [3]. Here the task of experimental characterisation is blind and becomes unwieldy. It is highly unlikely that all known proteins will ever be completely experimentally characterised [4]. Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. The Dataset was generated by calculating the sequence, sub-sequence, physiochemical, annotation-based features for each 171,212 reviewed proteins using method in [10]. These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. The Dataset contains the Entry and Entry name of each sequence corresponding to UniprotKB Database. This dataset being huge in size (171,212 samples X 9890 features, 1739 classes with multiple values) and equipped with enough number of positive and negative samples of each 1739 class, is good for testing efficiency of any upcoming deep learning models [5]. We divided the full dataset of 171,212 reviewed proteins in the ratio 3:1 to form Train/Test dataset 1; train dataset with 128,409 samples and test dataset with 42,803 samples to facilitate training of a deep learning model. The train and test datasets are stratified to contain good proportion of each 1739 classes. We then prepared a dataset 2 of pathogenic unreviewed proteins of the 9 bacterial phyla each with 9890 features same as train/train dataset of reviewed proteins but without target labels in order to predict their functions using deep learning model proposed in [5].

Dataset Information

Phylogenetic construction of 17 bacterial phyla by new method and carefully selected orthologs.

Publications

Phylogenetic construction of 17 bacterial phyla by new method and carefully selected orthologs.

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