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MIRNAS ARRAYS: DATABASES AND PLATFORMS

MIRNAS ARRAYS: DATABASES AND PLATFORMS. U B io Training Courses. Gonzalo Gómez//ggomez@cnio.es. miRNAs. Firstly detected in C. elegans (V. Ambros, 1993) Mapping to non-coding regions (introns) P ri-miRNA processed by Drosha DICER removes the structural loop

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MIRNAS ARRAYS: DATABASES AND PLATFORMS

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  1. MIRNAS ARRAYS: DATABASES AND PLATFORMS UBio Training Courses Gonzalo Gómez//ggomez@cnio.es

  2. miRNAs • Firstly detected in C. elegans (V. Ambros, 1993) • Mapping to non-coding regions (introns) • Pri-miRNA processed by Drosha • DICER removes the structural loop • mature miRNA: ssRNA, 22 nucleotides • miRNA-RISK complex: mRNAs post-transcriptional inhibition • 33% of human genes are supposed to be regulated by miRNAs.

  3. miRNAs Biological role • Brain development (miR-430) • • Nervous system development(miR-273) • • Pancreatic Langerhans islands development (miR-375) • • Adipocytes development (miR-143) • • Heart development(miR-1) • Inmune response (miR-223, cluster miR-17~92, miR-146a,miR-155…) • • Apoptosis (miR-14)

  4. miRNAs and cancer OncomiRs Oncogene miRNA Tumor formation Tumor suppressor miRNA Upregulation Proliferation Invasion Angiogenesis Cell death • Proliferation • Invasion • Angiogenesis • Cell death Downregulation Esquela-Kerscher & Slack. Nature Reviews Cancer. 2006.

  5. miRNAs Target sites • miRNAs seed region: 5’, nucleotides 2-7(8) • Most gene targets of a given miRNA have only a • single 7 nt matching to that miRNA seed region. • 7-8 nt hundreds of target predictions for each • miRNA family • (~300 conserved targets per miRNA family in vertebrates) • High rate of false positives in predictions. Bartel D. Cell 2009.

  6. miRNAs Target Prediction Algorithms • PREDICTION CRITERIA • miR seed-target complete base-pairing • Interspecies conservation • Number of binding sites for a given 3’UTR in a particular gene • Free energy for the miR-target duplex • Binding site accessibility • miRNA- target secondary structure Other prediction algorithms… Bartel D. Cell 2009. More target prediction tools: http://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software#Inter_molecular_interactions:_MicroRNA:UTR

  7. miRNAs Databases miRBase Version Date miRNAs 1.0 12/02 218 1.1 01/03 262 1.2 04/03 295 1.3 05/03 332 1.4 07/03 345 2.0 07/03 506 2.1 09/03 558 2.2 11/03 593 3.0 01/04 719 3.1 04/04 899 4.0 07/04 1185 5.0 09/04 1345 5.1 12/04 1420 6.0 04/05 1650 7.0 06/05 2909 7.1 10/05 3424 8.0 02/06 3518 8.1 05/06 3963 8.2 07/06 4039 9.0 10/06 4361 9.1 02/07 4449 9.2 05/07 4584 10.0 08/07 5071 10.1 12/07 5395 11.0 04/08 6396 12.0 09/08 8619 13.0 03/09 9539 14.0 09/09 10833 http://www.mirbase.org/ H. Sapiens ~695 miRNAs M. musculus: ~488 miRNAs

  8. miRNAs miRNA nomenclature (miRBase) • mir: immature sequence (hairpin). E.g. hsa-mir-203 • b) miR: mature miRNA sequence. E.g. hsa-miR-203 • - a/b: paralog miRs, difer in 1-2 nucleotides. • E.g. hsa-miR-9a, hsa-miR-9b • - 1-2: Identical miRs, different hairpin. • Ej. hsa-miR-19b-1, hsa-miR-19b-2 • - 5p-3p: mature miR generated from precursor 5´ (or 3) sequence. • E.g. hsa-miR-17-5p • - *: Minor transcript complementary to mature miR. • E.g. hsa-miR-33a*

  9. miRNAs Databases • TarBase • http://microrna.gr/tarbase Contains only those miRNA-target relationships experimentally validated The database of experimentally supported targets: a functional update of TarBase. Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P, Hatzigeorgiou AG., Nucleic Acids Res. 2009 Jan;37(Database issue):D155-8.

  10. miRNAs and disease Databases http://cmbi.bjmu.edu.cn/hmdd http://www.mir2disease.org/ Cancer (Calin and Croce 2006; Ura et al 2008; Stamatopoulos et al 2009…) Cardiovascular disease (Latronico et al. 2007; van Rooij and Olson 2007) Schizophrenia (Hansen, et al. 2007; Perkins et al. 2007) Renal misfunction (Williams 2007) Tourette syndrome (Esau and Monia 2007) Psoriasis (Sonkoly et al. 2007) Muscle disorders (Eisenberg et al. 2007), X fragile syndrome (Fiore and Schratt 2007) Policitemia vera (Bruchova et al. 2007) Diabetes (Williams 2007) Chronic hepatitis (Murakami et al. 2006) AIDS (Hariharan etal. 2005) Obesity (Weiler et al. 2006, Lovis et al. 2008, Xie et al. 2009).

  11. miRNAs Detection Methods

  12. miRNA microarrays Commercial platforms Human, rat, mouse Human, rat, mouse, dog, chimpanzee, etc Human, rat, mouse Human, multispecie

  13. miRNA microarrays Commercial platforms • Agilent recommendations. • No normalization • Normalization 75th percentile • (75th value = 1) Additional G-C pair in the probe-target interaction region stabilizes targeted miRNAs relative to homologous RNAs. Additionally, all probes contain a 5' hairpin (blue), abutting the probe-target region, to increase target and size miRNA specificity

  14. miRNA microarrays Commercial platforms LNA = Locked Nucleic Acid Preprocessing scripts provided by Exiqon Background correction = normexp Normalization = quantiles RG_normexp <- backgroundCorrect(RGfilt_0, method = "normexp", offset=50) MA_norm <- normalizeBetweenArrays(RG_normexp$G, method="quantile") MA_lognorm<-log2(MA_norm) LNA is chemical modification. Ribose ring is "locked" with a methylene bridge connecting the 2’-O atom and the 4’-C atom. LNA makes DNA-miR pairs more stable (higher Tm) when perfect match and 1mismatch hybridizations occurs.

  15. T test, SAM, limma METHOD FWER FDR Pvalue adjustment miRNA microarrays Differential expression analysis 20 normalized arrays 600 miRNAs 2 classes (healthy y tumor) GEPAS Asterias SAM tools… pvalue OK! Differentially expressed miRNAs between classes FWER: Type I Family Wise Error Rate FDR: False Discovery Rate

  16. THANKS! Visit UBio web ! http://bioinfo.cnio.es/

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