Dataset Information

Computational identification of operons in microbial genomes.

ABSTRACT: By applying graph representations to biochemical pathways, a new computational pipeline is proposed to find potential operons in microbial genomes. The algorithm relies on the fact that enzyme genes in operons tend to catalyze successive reactions in metabolic pathways. We applied this algorithm to 42 microbial genomes to identify putative operon structures. The predicted operons from Escherichia coli were compared with a selected metabolism-related operon dataset from the RegulonDB database, yielding a prediction sensitivity (89%) and specificity (87%) relative to this dataset. Several examples of detected operons are given and analyzed. Modular gene cluster transfer and operon fusion are observed. A further use of predicted operon data to assign function to putative genes was suggested and, as an example, a previous putative gene (MJ1604) from Methanococcus jannaschii is now annotated as a phosphofructokinase, which was regarded previously as a missing enzyme in this organism. GC content changes in the operon region and nonoperon region were examined. The results reveal a clear GC content transition at the boundaries of putative operons. We looked further into the conservation of operons across genomes. A trp operon alignment is analyzed in depth to show gene loss and rearrangement in different organisms during operon evolution.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC186635 | biostudies-literature | 2002 Aug

REPOSITORIES: biostudies-literature

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Computational identification of operons in microbial genomes.

Zheng Yu Y Szustakowski Joseph D JD Fortnow Lance L Roberts Richard J RJ Kasif Simon S

Genome research 20020801 8

By applying graph representations to biochemical pathways, a new computational pipeline is proposed to find potential operons in microbial genomes. The algorithm relies on the fact that enzyme genes in operons tend to catalyze successive reactions in metabolic pathways. We applied this algorithm to 42 microbial genomes to identify putative operon structures. The predicted operons from Escherichia coli were compared with a selected metabolism-related operon dataset from the RegulonDB database, yi ...[more]

PMID: 12176930

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Project description:BACKGROUND:Although synonymous single nucleotide variants (sSNVs) do not alter the protein sequences, they have been shown to play an important role in human disease. Distinguishing pathogenic sSNVs from neutral ones is challenging because pathogenic sSNVs tend to have low prevalence. Although many methods have been developed for predicting the functional impact of single nucleotide variants, only a few have been specifically designed for identifying pathogenic sSNVs. RESULTS:In this work, we describe a computational model, IDSV (Identification of Deleterious Synonymous Variants), which uses random forest (RF) to detect deleterious sSNVs in human genomes. We systematically investigate a total of 74 multifaceted features across seven categories: splicing, conservation, codon usage, sequence, pre-mRNA folding energy, translation efficiency, and function regions annotation features. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the sequential backward selection method. Based on the optimized 10 features, a RF classifier is developed to identify deleterious sSNVs. The results on benchmark datasets show that IDSV outperforms other state-of-the-art methods in identifying sSNVs that are pathogenic. CONCLUSIONS:We have developed an efficient feature-based prediction approach (IDSV) for deleterious sSNVs by using a wide variety of features. Among all the features, a compact and useful feature subset that has an important implication for identifying deleterious sSNVs is identified. Our results indicate that besides splicing and conservation features, a new translation efficiency feature is also an informative feature for identifying deleterious sSNVs. While the function regions annotation and sequence features are weakly informative, they may have the ability to discriminate deleterious sSNVs from benign ones when combined with other features. The data and source code are available on website http://bioinfo.ahu.edu.cn:8080/IDSV .

Dataset Information

Computational identification of operons in microbial genomes.

Publications

Computational identification of operons in microbial genomes.

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