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 IllumiCodeTM
Illumina uses the same technology for methylation arrays
(bi-sulfited nucleotides are like SNPs)
Intensity <-> Copy number

5. Calculation of aCGH-like ratios
Median R CEPH
Individual R cell line
(NCI60)
Now there exist specific methods to detect CNVs in Affy and Illumina SNP arrays.

6. Methylation arrays

7. METHYLATION MICROARRAYS
BeadArrays
Until 12 samples per chip.
27,578 CpG loci, > genes
2 beads per locus (methylated/no methylated)
Random distribution (50 mer)
Input: Bisulphyted DNA
Includes probes for the promoter regions of miRNA 110 genes
Unos $225 per sample. Mas info Illumina tutorial. LumiCode permite identificar beads. EXPLICAR CASO-BEAD, extension marca la muestra
Infinium HumanMethylation27 BeadChip

8. METHYLATION MICROARRAYS
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

9. METHYLATION MICROARRAYS
SOFTWARE
Bead Studio  Genome Studio
Methylation module
Recientemente Genome Studio para Secuenciador, tambine tiene modulo de metilacion
Lumi package (Import, background correction, normalization)
Beadarray package (Import, QC)
Methylumi (Import, QC ,normalization, differential meth.)

10. METHYLATION MICROARRAYS
DIFFERENTIAL METHYLATION
Bead Studio  Genome Studio
Methylation module
Beta values:
β= Imethylated/Imethylated+Ino_methylated
Hypermethylated
Hypomethylated 1
0.7 β
0.3

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

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

13. ChIP on chip

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

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

16. ChIP on Chip software WORKFLOW I. 1. Pre-normalization.
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 WORKFLOW II. 3. Error modelling Chip Analytics
To identify which probes are most representative of binding events:
P(X)=P-value of a single probe matching event
P(Xneighb)= Positive signals in a probe should be corroborated by the signals of probes that are its genomic neighbors,
provided they are close enough
P(Xneighb) 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 WORKFLOW III.
Chip Analytics
WORKFLOW III.
4. Segment identification (clusters of enriched probes) bp
5. Gene identification
-Segment, Gene or Probe report (Gene or probe ID, Chr, Start, End, p(X)…)

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”
Adapted from Philip Groth

21. Thanks !
Visit UBio web !