Other genomic Other genomic arrays: Methylation, arrays: Methylation,

chIP on chipchIP on chip……UUBBioio Training Courses Training Courses

SNP-arrays and copy SNP-arrays and copy numbernumber

Genotyping arrays can detect CNVsGenotyping arrays can detect CNVs

Copy numbers from SNP arrays

Illumina SNP arrays: Hybridization to Universal IllumiCodeTM

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Illumina uses the same technology for methylation arrays(bi-sulfited nucleotides are like SNPs)

Calculation of aCGH-like ratios

Median R CEPH Individual R cell line(NCI60)

Methylation arraysMethylation arrays

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 MICROARRAYSIn

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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

SOFTWARE

Lumi package (Import, background correction, normalization)Beadarray package (Import, QC)Methylumi (Import, QC ,normalization, differential meth.)

Bead Studio Genome StudioMethylation modulehttp://www.illumina.com/pages.ilmn?ID=196

METHYLATION MICROARRAYS

DIFFERENTIAL METHYLATION

METHYLATION MICROARRAYS

Bead Studio Genome StudioMethylation modulehttp://www.illumina.com/pages.ilmn?ID=196

Beta values:

β= Imethylated/Imethylated+Ino_methylated

β1 0

Hypermethylated Hypomethylated

0.7 0.3

NORMALIZATION

METHYLATION MICROARRAYS

Methylumi normalization

1) Calculate medians for Cy3 and Cy5 at high an low betas2) Cy5 medians adjusted to Cy3 channel (dye bias)3) Recalculate betas with new intensities

DIFFERENTIAL METHYLATION

METHYLATION MICROARRAYS

Wilcoxon rank-test (UBio)Limma (Pomelo)Permutations (Pomelo)

βs

FDR<0.05 Median βs class AMedian βs class B

+

Differentially methylated genes

ChIP on chipChIP on chip

ChIP on Chip

We thank Chris Glass lab, UCSD, for the original slide

Discover protein/DNA interactions!!

ChIP on Chip

ChIP on Chip software

Chip Analytics

WORKFLOW I.

1. Pre-normalization.Background substraction: Foreground – backgroundDefault: Median blank substraction Each channel – median negative controls

2. Normalization (dye-byas and interarray normalization) Default : Median dye-byas, median interarray. Recommended: Loess

ChIP on Chip softwareChip 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(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

ChIP on Chip softwareChip 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

CoCashttp://www.ciml.univ-mrs.fr/software/cocas/index.html

Agilent platform

NormalizationQC ReportGenome VisualizationPeak Finder

Benoukraf et al. Bioinformatics 2009.

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)

http://bioinfo.cnio.es/

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