Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1021557 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Legionella pneumophila is a gram-negative opportunistic human pathogen that infects and multiplies in a broad range of phagocytic protozoan and mammalian phagocytes. Based on the observation that small regulatory RNAs (sRNAs) play an important role in controlling virulence-related genes in several pathogenic bacteria, we attempted to test the hypothesis that sRNAs play a similar role in L. pneumophila. We used computational prediction followed by experimental verification to identify and characterize sRNAs encoded in the L. pneumophila genome. A 50-mer probe microarray was constructed to test the expression of predicted sRNAs in bacteria grown under a variety of conditions. This strategy successfully identified 22 expressed RNAs, out of which six were confirmed by northern blot and RACE. One of the identified sRNAs is highly expressed when the bacteria enter post exponential phase and computational prediction of its secondary structure reveals a striking similarity to the structure of 6S RNA, a widely distributed prokaryotic sRNA, known to regulate the activity of σ70-containing RNAP. A 70-mer probe microarray was used to identify genes affected by L. pneumophila 6S RNA in stationary phase. The 6S RNA encoded by the ssrS gene positively regulates expression of genes encoding type IVB secretion system effectors, stress response genes such as groES and recA as well as many genes with unknown or hypothetical functions. Deletion of 6S RNA significantly reduced L. pneumophila intracellular multiplication in both protist and mammalian host cells, but had no detectable effect on growth in rich media.
2010-01-01 | GSE19196 | GEO
Project description:MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data. We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20. There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.
2015-04-28 | GSE68305 | GEO
Project description:MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data. We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20. There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets. Small RNA sequencing was done for 10 days old seedlings of Phaseolus vulgaris Cv. Anupam
2015-04-28 | E-GEOD-68305 | biostudies-arrayexpress
Project description:Legionella pneumophila is a gram-negative opportunistic human pathogen that infects and multiplies in a broad range of phagocytic protozoan and mammalian phagocytes. Based on the observation that small regulatory RNAs (sRNAs) play an important role in controlling virulence-related genes in several pathogenic bacteria, we attempted to test the hypothesis that sRNAs play a similar role in L. pneumophila. We used computational prediction followed by experimental verification to identify and characterize sRNAs encoded in the L. pneumophila genome. A 50-mer probe microarray was constructed to test the expression of predicted sRNAs in bacteria grown under a variety of conditions. This strategy successfully identified 22 expressed RNAs, out of which six were confirmed by northern blot and RACE. One of the identified sRNAs is highly expressed when the bacteria enter post exponential phase and computational prediction of its secondary structure reveals a striking similarity to the structure of 6S RNA, a widely distributed prokaryotic sRNA, known to regulate the activity of σ70-containing RNAP. A 70-mer probe microarray was used to identify genes affected by L. pneumophila 6S RNA in stationary phase. The 6S RNA encoded by the ssrS gene positively regulates expression of genes encoding type IVB secretion system effectors, stress response genes such as groES and recA as well as many genes with unknown or hypothetical functions. Deletion of 6S RNA significantly reduced L. pneumophila intracellular multiplication in both protist and mammalian host cells, but had no detectable effect on growth in rich media.
2010-01-01 | GSE19200 | GEO
Project description:We present a computational method for building a regulatory network from global phosphoproteomic and transcription profiling data. To recover the critical missing links between signaling events and transcriptional responses, we relate changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). Genome-wide DNase I hypersensitivity followed by sequencing (DNase-Seq) to measure chromatin accessibility in a cell line derived from the U87MG glioblastoma cell line to express high level of EGFRvIII (U87H; 2 million copies of EGFRvIII per cell) and a control cell line expressing kinase dead EGFRvIII (U87DK; 2 million kinase dead EGFRvIII per cell). A prediction from the computational method, the transcriptional co-regulator p300, was experimentally validated by chromatin immunoprecipitation followed by sequencing (ChIP-Seq).
2013-01-01 | E-GEOD-36900 | biostudies-arrayexpress
Project description:Computational prediction and experimental validation identify functionally conserved lncRNAs from zebrafish to human
| PRJNA1003409 | ENA