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MicroARNs, comparaison, prediction

This presentation by Eric Bonnet from Ghent University/VIB delves into the fascinating world of small RNAs, specifically microRNAs (miRNAs) and their role in gene expression regulation. It covers their biogenesis, functions, and predictive bioinformatics approaches using tiling arrays. The discussion highlights the complexity of non-coding RNAs, the differences between miRNAs and siRNAs, and methods to reduce false predictions in miRNA identification. This thorough exploration aims to shed light on the molecular mechanisms underlying gene regulation and the computational techniques used to study them.

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MicroARNs, comparaison, prediction

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  1. MicroARNs, comparaison, prediction Eric Bonnet Bioinformatics & Evolutionary Genomics Department of Plant Systems Biology Ghent University / VIB Belgium

  2. Small RNAs biogenesis and function

  3. Plan de la presentation • But de la presentation • Biogenese, fonction et prediction des petits ARN non-codants. • Plan de la presentation • 1/ Biogenese et fonction des petits ARN non-codants • 2/ Principes de prediction bioinformatique des microARN

  4. Tiling arrays • Tiling arrays: using probes to analyze expression of complete genomic regions. => transcriptome (a lot) bigger than expected. => many non coding RNAs

  5. Non coding RNAs classes • Degradation products • Ribosomal RNA • Transfer RNA • Small nucleolar RNA • … • microRNA (miRNA) • Short interfering RNA (siRNAs)

  6. Small RNAs • Very short: ~21 nt • Discovered (very) recently (1993) • Regulate expression of protein coding genes at the post-transcriptional level • “Dark matter” of Biology

  7. Small RNA biogenesis

  8. miRNA versus siRNA • miRNA: • Processed from imperfect stem-loop hairpin-like structures. • Encoded by independent loci. • siRNA • Processed from long double stranded RNA sequences. • Regulate the genes from which they originate.

  9. miRNA biogenesis

  10. siRNA biogenesis

  11. miRNA / siRNA functions • Regulation of gene expression [miRNA] • Defense against exogenous sequences (viruses, pathogens, etc..). [siRNA] • Chromatin structure: siRNA mediates silencing of Tranposons, transgenes at the DNA level (methylation). [siRNA]

  12. miRNA function in animals

  13. ???

  14. miRNA function in plants

  15. miRNA gene prediction

  16. miRNA computational prediction = ?

  17. Solution: filtering • Reduce false positives using: • Structural & compositional features • Comparative genomics • Statistical properties

  18. Structural & compositional features MiRNA is part of one continuous helix Unpaired bases miRNA: max 5 & Maximum bulge size: 2 G.U pairs: maximum 5 Unpaired bases miRNA*: max 5 Paired bases : minimum 15 Free energy: maximum -30 Kcal/mol MiRNA GC content & entropy value

  19. Comparative genomics

  20. Permutation test: Compare Free energy value for a miRNA precursor with the value of re-shuffled sequences. Precursor statistical properties

  21. miRNA gene prediction in plants

  22. miRNA genes prediction in animals

  23. What’s the catch ? • => Evidence for miRNA genes not conserved in other organisms (specific). • => Comparative genomics filter can not be used • => Use RNA high-throughput expression data (MPSS, “454”) to check predictions.

  24. miRNA targets prediction

  25. Future

  26. Bibliography Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004 Jan 23;116(2):281-97. Baulcombe D. RNA silencing in plants.Nature. 2004 Sep 16;431(7006):356-63. Bonnet E, Van de Peer Y, Rouze P. The small RNA world of plants.New Phytol. 2006;171(3):451-68.

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