Bioinformatics History and Introduction - Chagall

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Direct experimental identificationof target genes• Reporter constructs (luciferase, GTP) with the3’ UTR of the predicted target gene: measurechange in intensities after introduction of miRNA• RT-PCR: track changes in mRNA levels afterintroduction of miRNA• Does not directly identify MREs (miRNArecognition elements)– Site-directed mutagenesis studies– Restoration of complementarity by mutating miRNAsequence24 January 2013 Luce Skrabanek
Indirect experimental identification of targetgenes (target “enrichment”)• For miRNAs involved in RNA degradation– Microarrays: differential gene expression with/withoutmiRNA [secondary and non-specific effects]– RNA-Seq: next gen sequencing method of measuringdifferential expression [secondary and non-specific effects]– HITS-CLIP (high-throughput sequencing of RNAs isolatedby crosslinking immunoprecipitation): can identify specificbinding sites for targeted miRNAs [non-functional sites] [Chi et alNature 2009]• For miRNAs involved in translation repression– pSILAC (stable isotope labeling of amino acids in cellculture): differential protein expression levels [secondary and nonspecificeffects] [Baek et al Nature 2008; Selbach et al Nature 2008]24 January 2013 Luce Skrabanek
Page 1 and 2: BioinformaticsHistory and Introduct
Page 3 and 4: Required skills• Biology• Compu
Page 5 and 6: Applications of bioinformatics• M
Page 7 and 8: The scales of knowledge• Historic
Page 9 and 10: Biology of Systems• Huge amounts
Page 11 and 12: 24 January 2013 Luce SkrabanekRavi
Page 13 and 14: Where do we get our data from?• E
Page 15 and 16: Caution!• Errors in the databases
Page 17 and 18: My protein doesn’t begin with an
Page 19 and 20: Some sample error rates• GenBank:
Page 21 and 22: Propagation of errors• The qualit
Page 23 and 24: miRNAs: an introduction• Small RN
Page 25: miRNA3’ UTR24 January 2013 Luce S
Page 29 and 30: Computational prediction of miRNA t
Page 31 and 32: Pipeline to identify miRNA targets
Page 33 and 34: Sequence matching algorithm• Modi
Page 35 and 36: Control sequences• 100 sets of ra
Page 37 and 38: Pipeline to identify mammalian targ
Page 39 and 40: Validation• Luciferase reporter a
Page 41 and 42: 24 January 2013 Luce SkrabanekLewis
Page 43 and 44: Many other programs…• Many prog
Page 45 and 46: Evaluation comparison5 miRNAs vs. S
Page 47 and 48: SVMs: Brief introduction• Positiv
Page 49 and 50: TargetMiner• Used the 289 negativ
Page 51 and 52: Further evaluation• Trained other
Page 53: Important points• This type of an

Bioinformatics History and Introduction - Chagall

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