Dataset Information

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes.

ABSTRACT: BACKGROUND: Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. RESULTS: This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. CONCLUSION: Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

SUBMITTER: Zhu H

PROVIDER: S-EPMC1847833 | biostudies-other | 2007

REPOSITORIES: biostudies-other

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MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes.

Zhu Huaiqiu H Hu Gang-Qing GQ Yang Yi-Fan YF Wang Jin J She Zhen-Su ZS

BMC bioinformatics 20070316

<h4>Background</h4>Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes.<h4>Results</h4>This paper describes a new prokaryotic genefinding algorithm based ...[more]

PMID: 17367537

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Project description:BACKGROUND: The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not. DESCRIPTION: The DarkHorse algorithm has been applied to 955 microbial genome sequences, and the results organized into a web-searchable relational database, called the DarkHorse HGT Candidate Resource http://darkhorse.ucsd.edu. Users can select individual genomes or groups of genomes to screen by LPI score, search for protein functions by descriptive annotation or amino acid sequence similarity, or select proteins with unusual G+C composition in their underlying coding sequences. The search engine reports LPI scores for match partners as well as query sequences, providing the opportunity to explore whether potential HGT donor sequences are phylogenetically typical or atypical within their own genomes. This information can be used to predict whether or not sufficient information is available to build a well-supported phylogenetic tree using the potential donor sequence. CONCLUSION: The DarkHorse HGT Candidate database provides a powerful, flexible set of tools for identifying phylogenetically atypical proteins, allowing researchers to explore both individual HGT events in single genomes, and large-scale HGT patterns among protein families and genome groups. Although the DarkHorse algorithm cannot, by itself, provide definitive proof of horizontal gene transfer, it is a flexible, powerful tool that can be combined with slower, more rigorous methods in situations where these other methods could not otherwise be applied.

Project description:During the long history of biological evolution, genome structures have undergone enormous changes. Nevertheless, some traits or vestiges of the primordial genome (defined as the most primitive nucleic acid genome for life on earth in this paper) may remain in modern genetic systems. It is of great importance to find these traits or vestiges for the study of the origin and evolution of genomes. As the shorter is a sequence, the less probable it would be modified during genome evolution. And if mutated, it would be easier to reappear at the same site or another site. Consequently, the genomic frequencies of very short nucleotide sequences, such as dinucleotides, would have considerable chances to be conserved during billions of years of evolution. Prokaryotic genomes are very diverse and with a wide range of GC content. Therefore, in order to find traits or vestiges of the primordial genome remained in modern genetic systems, we have studied the characteristics of dinucleotide frequencies across bacterial and archaeal genomes. We analyzed the dinucleotide frequency patterns of the whole-genome sequences from more than 1300 prokaryotic species (bacterial and archaeal genomes available as of December 2012). The results show that the frequencies of the dinucleotides AC, AG, CA, CT, GA, GT, TC, and TG are well-conserved across various genomes, while the frequencies of other dinucleotides vary considerably among species. The dinucleotide frequency conservation/variation pattern seems to correlate with the distributions of dinucleotides throughout a genome and across genomes. Further analysis indicates that the phenomenon would be determined by strand symmetry of genomic sequences (the second parity rule) and GC content variations among genomes. We discussed some possible origins of strand symmetry. And we propose that the phenomenon of frequency conservation of some dinucleotides may provide insights into the genomic composition of the primordial genetic system.

Dataset Information

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes.

Publications

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes.

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