1. Other genomic arrays: Methylation, chIP on chip… UBio Training Courses

2. SNP-arrays and copy number Genotyping arrays can detect CNVs

3. Copy numbers from SNP arrays

4. Illumina SNP arrays: Hybridization to Universal IllumiCode TM Intensity Copy number Illumina uses the same technology for methylation arrays (bi-sulfited nucleotides are like SNPs)

5. Calculation of aCGH-like ratios Median R CEPHIndividual R cell line (NCI60)

6. Methylation arrays

7. BeadArrays o Until 12 samples per chip. o 27,578 CpG loci, >14.000 genes o 2 beads per locus (methylated/no methylated) o Random distribution (50 mer) o Input: Bisulphyted DNA o Includes probes for the promoter regions of miRNA 110 genes METHYLATION MICROARRAYS Infinium HumanMethylation27 BeadChip

8. Illumina Golden Gate Assay Until 147,456 DNA methylation measures simultaneously. Resolution: 1 CpG Until 96 samples simultaneously GoldenGate Methylation Cancer Panel I 1,505 CpG loci selected from 807 gene Allows custom designs METHYLATION MICROARRAYS

9. SOFTWARE Lumi package (Import, background correction, normalization) Beadarray package (Import, QC) Methylumi (Import, QC,normalization, differential meth.) Bead Studio  Genome Studio Methylation module http://www.illumina.com/pages.ilmn?ID=196 METHYLATION MICROARRAYS

10. DIFFERENTIAL METHYLATION METHYLATION MICROARRAYS Bead Studio  Genome Studio Methylation module http://www.illumina.com/pages.ilmn?ID=196 Beta values: β = I methylated /I methylated +I no_methylated β 1 0 Hypermethylated Hypomethylated 0.7 0.3

11. NORMALIZATION METHYLATION MICROARRAYS Methylumi normalization 1)Calculate medians for Cy3 and Cy5 at high an low betas 2)Cy5 medians adjusted to Cy3 channel (dye bias) 3)Recalculate betas with new intensities

12. DIFFERENTIAL METHYLATION METHYLATION MICROARRAYS Wilcoxon rank-test (UBio) Limma (Pomelo) Permutations (Pomelo) βsβs FDR<0.05 Median β s class A Median β s class B + Differentially methylated genes

13. ChIP on chip

14. ChIP on Chip We thank Chris Glass lab, UCSD, for the original slide

15. Discover protein/DNA interactions!! ChIP on Chip

16. ChIP on Chip software Chip Analytics WORKFLOW I. 1. Pre-normalization. Background substraction: Foreground – background Default: Median blank substraction  Each channel – median negative controls 2. Normalization (dye-byas and interarray normalization) Default : Median dye-byas, median interarray. Recommended: Loess

17. ChIP on Chip software Chip Analytics WORKFLOW II. 3. Error modelling To identify which probes are most representative of binding events: P(X) =P-value of a single probe matching event P(X neighb ) = Positive signals in a probe should be corroborated by the signals of probes that are its genomic neighbors, provided they are close enough P(X neighb ) follows a Gaussian distribution Both the P(X) and the P(Xneighb) values of a probe need to satisfy significance thresholds in order for a probe to be considered as representing a binding event

18. ChIP on Chip software Chip Analytics WORKFLOW III. 4. Segment identification (clusters of enriched probes) 5. Gene identification -Segment, Gene or Probe report (Gene or probe ID, Chr, Start, End, p(X)…) bp

19. CoCas http://www.ciml.univ-mrs.fr/software/cocas/index.html Agilent platform Normalization QC Report Genome Visualization Peak Finder Benoukraf et al. Bioinformatics 2009.

20. UBio training courses: See “Course on Introduction to Sequence Analysis” Weeder: Motif discovery in sequences from co-regulated genes (single specie). WeederH: Motif discovery in sequences from homologous genes. Pscan: Motif discovery in sequences from co-regulated genes (JASPAR,TRANSFAC matrices)

21. http://bioinfo.cnio.es/ Visit UBio web ! Thanks !