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ICMB @ Geuvadis achievements and contributions

ICMB @ Geuvadis achievements and contributions. Robert Häsler , functional genomics. ICMB achievements mRNA & miRNA sequencing. mRNA 72 samples sequenced miRNA 24/72 samples already sequenced high (rank) similarities of results between sites. ICMB potential contributions (I)

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ICMB @ Geuvadis achievements and contributions

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  1. ICMB @ Geuvadis achievements and contributions Robert Häsler, functional genomics

  2. ICMB achievements mRNA & miRNA sequencing mRNA 72 samples sequenced miRNA 24/72 samples already sequenced high (rank) similarities of results between sites

  3. ICMB potential contributions (I) nTARs workflow (novel transcriptional active region) mapping covered regions known? no nTar yes no BLAT of high quality unmapped reads discard hit sequence QC bad OK linked to known exon/other nTar? no yes discard hit RF + start codon RF UTR, intron no no yes yes nTar / isoform isoform part known elongation? ICMB references: Philip et al 2012 Bioinformatics, Klostermeier et al 2011 BMC Genomics duplication? BLAT + unique flag BLAT blast like alignment tool RF reading frame

  4. ICMB potential contributions (II) detection of splice variation patterns example scenario isoform 1 isoform 2 isoform 3 isoform 4 high GYN GYN NAG NAG GYN GYN mid NAG NAG expression values by cufflinks GYN GYN low NAG NAG very low GYN GYN NAG NAG

  5. ICMB potential contributions (II) detection of splice variation patterns example scenario variant introduced isoform 1 isoform 2 isoform 3 isoform 4 none GYNXYN NAG NAG GYNXYN none NAG NAG expression values by cufflinks GYN XYN low NAG NAG very low GYN XYN NAG NAG ICMB references: Brosch et al 2012 Cell Metab, Häsler et al 2011 Eur J Cell Biol, Kramer et al 2011 Genetics, ElSharawy et al 2009 Human Mutat, Hiller et al 2008 RNA; Szafranski et al 2007 Genome Biol, Hiller et al 2006 Am J Hum Genet, Hiller et al 2004 Nat Genet

  6. ICMB potential contributions (III) linking miRNA & miRNA-targets 2% encoding vs. 60-70% non-coding RNA 10-30% of all genes regulated by miRNAs experimental miR target prediction expensive, slow in silico miR target prediction ~3000 targets/miRNA low/no overlap between different prediction tools functional effects hard to predict

  7. ICMB potential contributions (III) linking miRNA & miRNA-targets how to? extract available information: is there a splice-relevant variant? is the variant associated to modified mRNA expression? TASSDB (Sinah et al, 2012) tandem splice site data base expected outcome: candidates of variants from the 1000 Genomes project with potential functional impact sequence pattern donor/acceptor position conservation nonsense mediated decay ICMB references: Häsler et al 2012 PLoS One, Keller et al 2011 Nat Methods, Schulte et al 2010 NAR, Sinha et al, 2010 BMC Bioinformatics,Hiller et al 2007 NAR

  8. ICMB potential contributions summary team nTARs splice variation patterns linking miRNA to miRNA targets Philip Rosenstiel Stefan Schreiber Robert Häsler Matthias Barann Daniela Esser Markus Schilhabel our position in the analysis pipeline?

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