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RNA-seq experiences and plans LUMC

RNA-seq experiences and plans LUMC. Peter A.C. ’ t Hoen Human Genetics, LUMC. Pipelines. PASSion: a pattern growth algorithm-based pipeline for splice junction detection in paired-end RNA-Seq data Bioinformatics 28:479-86 (2012)

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RNA-seq experiences and plans LUMC

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  1. RNA-seq experiences and plansLUMC Peter A.C. ’t Hoen Human Genetics, LUMC

  2. Pipelines • PASSion: a pattern growth algorithm-based pipeline for splice junction detection in paired-end RNA-Seq data Bioinformatics 28:479-86 (2012) • eMiR: pipeline for mapping, 5p-3p resolution and annotation of miRNAs BMC Genomics 11:716 (2010)

  3. PASSion

  4. PASSion: performance simulated data

  5. eMiR

  6. eMiR

  7. eMiR results LUMC samples

  8. eMiR results LUMC samples (2)

  9. Other studies: Methods • Tag-based: one read per transcript • DeepSAGE  most 3’ CATG • DeepCAGE  5’-end • RNA-Seq: multiple reads per transcript • Whole mRNA sequencing after fragmentation

  10. DeepSAGE – sample preparation PCR enrichment and gel purification (~85bp)

  11. Example gene: Gapd 14542 12555

  12. Example gene: alternative polyadenylation 97 99

  13. Expression profiling in a human cohort • 105 subjects with GWAS and phenotype data • RNA isolated from total blood • Expression profiling by deep-SAGE • 95 passed all QC

  14. Analysis pipeline • Trimming / addition of nucleotides • Genome alignment (Bowtie) • UCSC genome browser .wiggle files for visualization • Annotation (ENSEMBL/Biomart) • Reads summed per gene • OR tagwise analysis

  15. SNPs: sample swaps detected

  16. Gender-specific gene expression male Normalized expression of Y-chr genes female Normalized XIST expression

  17. Contaminated samples detected

  18. Genes associated with BMI • Differential expression analysis 1. In edgeR (designed for count data) 2. In limma (designed for microarray data; voom: mean-variance model) • Gender as confounder

  19. Limma and edgeR reasonably consistent -log10 P-value In red: high expressed genes

  20. Genes associated with BMI (N=9, FDR 0.05)

  21. Allele specific expression detected for some genes

  22. Helicos single molecule sequencing

  23. Example polyA profiling on Helicos Eleonora de Klerk

  24. Oculopharyngeal muscular dystrophy: General switch to shorter 3’-UTRs Eleonora de Klerk

  25. Example RNA-Seq (Helicos) ADAMTS8 ADAMTS15 NOV Peter Henneman

  26. Analysis of pre-mRNA processing pre-mRNA splicing intermediate mRNA mature mRNA Irina Pulyakhina

  27. Pre-mRNA analysis tools • map to both exon-exon junctions and introns; • prioritize intronic alignments; • report multiple alignments; • deal with both low and high coverage; • deal with indels and mismatches; • find novel exons and splice sites; • look for both canonical and non-canonical splice sites

  28. GSNAP Difference between TopHat and GSNAP results: G T A T C G A T T T T T T TG T . . . GSNAP TopHat T T T T T T TG T TopHat alignment: GSNAP alignment: T T T T T T TG C . . . G T A T C G A T T T T T T TG T . . . T T T T T T TG T T T T T T T TG T

  29. pre-splicing Normal (standard) insert size Intermediate (pre+post) post-splicing int int int ex int e-i int e-e ex ex ex e-i ex e-e e-i e-i e-i e-e e-e e-e ? Extremely large insert size int int int ex int e-i int e-e ex ex ex e-i ex e-e e-e e-e e-i e-i e-i e-e

  30. Insert size cut-off

  31. Plans for GEUVADIS • Transcription of repeat sequences such as Macro Satellite Repeats • Study effect on local and global gene expression • Study heterogeneity of transcripts expressed from repeats

  32. FSHD: disease mechanism Lemmers et al. Science 329:1650-3 (2010)

  33. Acknowledgements Rick Jansen Jeroen van Zanten Gerard van Grootheest Brenda Penninx Jan Smit Joukejan Hottenga Gonneke Willemsen Dorret Boomsma Eco de Geus Shoaib Amini Irina Pulyakhina Eleonora de Klerk Henk Buermans Yanju Zhang Kai Ye Jeroen Laros Johan den Dunnen Gertjan van Ommen NTR

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