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Epigenetic regulation of miRNA in CLL [ChIP_hsa_miRNA_v3b]

ABSTRACT: Genome-wide profiling of histone 3 lysine 4 trimethylation is used for identification of microRNA transcriptional start sites. Various cell types allow a broad range detection of putative promoters rather independent of tissue specific expression patterns. These data are used for identification of aberrant epigenetic regulation at the respective regions.

ORGANISM(S): Homo sapiens

PROVIDER: GSE33305 | GEO | 2012/05/31

SECONDARY ACCESSION(S): PRJNA156647

REPOSITORIES: GEO

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Epigenetic regulation of miRNA in CLL [ChIP_hsa_miRNA_v2]

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Epigenetic regulation of miRNA in CLL [ChIP_hsa_miRNA_v2]

Project description:Genome-wide profiling of histone 3 lysine 4 trimethylation is used for identification of microRNA transcriptional start sites. Various cell types allow a broad range detection of putative promoters rather independent of tissue specific expression patterns. These data are used for identification of aberrant epigenetic regulation at the respective regions. 5 samples total: 1 primary CLL sample, 1 CLL related cell line -/+ 5-aza-2M-bM-^@M-^Y-deoxycytidine (DAC) treatment (Granta-519), myeloid cell line Kasumi-1 (-/+ 5-aza-2M-bM-^@M-^Y-deoxycytidine treatment); H3K4me3 enriched vs. Input

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Project description:We addressed the lack of experimentally supported transcript annotations in the Rhesus macaque genome by ab initio identification of the transcription start sites (TSSs). We took advantage of histone H3 lysine 4 trimethylation (H3K4me3)'s ability to mark TSSs and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures in the macaque brain. We then integrated the two types of our newly generated data with genomic sequence features and extended a TSS prediction algorithm to ab initio predict and verify 16,833 of previously electronically annotated transcription start sites at 500 bp resolution and predicted ~10,000 new TSSs. We took advantage of histone H3 lysine 4 trimethylation (H3K4me3)M-bM-^@M-^Ys ability to mark transcription start sites (TSSs) and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures. By integrating the ChIP-seq, RNA-seq and small RNA-seq data (previously uploaded to GEO as GSM450615 by our collaborator) with genomic sequence features and extending and improving a state-of-the-art TSS prediction algorithm, we ab initio predicted and verified previously electronically annotated TSSs at a high resolution, and predicted some novel TSSs.

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