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Identification and analysis of putative promoter motifs in Flavivirus genome.

ABSTRACT: The genus Flavivirus comprises medically significant pathogenic virus; causing several infections in humans worldwide. Flavivirus genomes are 10-11 kb approximately and encode both structural and non structural region. The non structural region plays fundamental role in the stability, regulation and cell cycle of virus. The complete genomes of 26 Flavivirus were used for identification of promoter motifs through in silico approaches. The promoter sequences were encoded in merely 16 viruses and 10 viruses could not encode it. All these in silico identified promoter motifs was confirmed and verified with the known experimental data. This analysis suggests that presence of promoter may play a crucial role in the pattern of gene expression, regulation networks, cell specificity and development. It may also be useful for designing efficient expression vector and target specific delivery system in the gene therapy.

SUBMITTER: Somvanshi P

PROVIDER: S-EPMC2637965 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Identification and analysis of putative promoter motifs in Flavivirus genome.

Somvanshi Pallavi P Singh Vijai V Seth Prahlad Kishore PK

Bioinformation 20081206 4

The genus Flavivirus comprises medically significant pathogenic virus; causing several infections in humans worldwide. Flavivirus genomes are 10-11 kb approximately and encode both structural and non structural region. The non structural region plays fundamental role in the stability, regulation and cell cycle of virus. The complete genomes of 26 Flavivirus were used for identification of promoter motifs through in silico approaches. The promoter sequences were encoded in merely 16 viruses and 1 ...[more]

PMID: 19238240

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Project description:BackgroundWith the advent of microarray technology, it has become feasible to identify virtually all genes in an organism that are induced by developmental or environmental changes. However, relying solely on gene expression data may be of limited value if the aim is to infer the underlying genetic networks. Development of computational methods to combine microarray data with other information sources is therefore necessary. Here we describe one such method.ResultsBy means of our method, previously published Arabidopsis microarray data from cold acclimated plants at six different time points, promoter motif sequence data extracted from approximately 24,000 Arabidopsis promoters and known transcription factor binding sites were combined to construct a putative genetic regulatory interaction network. The inferred network includes both previously characterised and hitherto un-described regulatory interactions between transcription factor (TF) genes and genes that encode other TFs or other proteins. Part of the obtained transcription factor regulatory network is presented here. More detailed information is available in the additional files.ConclusionThe rule-based method described here can be used to infer genetic networks by combining data from microarrays, promoter sequences and known promoter binding sites. This method should in principle be applicable to any biological system. We tested the method on the cold acclimation process in Arabidopsis and could identify a more complex putative genetic regulatory network than previously described. However, it should be noted that information on specific binding sites for individual TFs were in most cases not available. Thus, gene targets for the entire TF gene families were predicted. In addition, the networks were built solely by a bioinformatics approach and experimental verifications will be necessary for their final validation. On the other hand, since our method highlights putative novel interactions, more directed experiments could now be performed.

Project description:BackgroundThe use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures.ResultsWe develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 10(5) gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.ConclusionsThe presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.

Dataset Information

Identification and analysis of putative promoter motifs in Flavivirus genome.

Publications

Identification and analysis of putative promoter motifs in Flavivirus genome.

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