Other genomic arrays: Methylation, chIP on chip…

UBio

Training Courses

SNP-arrays and copy number

Genotyping arrays can detect CNVs

Copy numbers from SNP arrays

Illumina SNP arrays: Hybridization to Universal IllumiCode TM

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 arrays

METHYLATION MICROARRAYS

BeadArrays o o o o o o Until 12 samples per chip.

27,578 CpG loci, >14.000 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

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

METHYLATION MICROARRAYS

SOFTWARE Bead Studio  Genome Studio

Methylation module

http://www.illumina.com/pages.ilmn?ID=196 Lumi package (Import, background correction, normalization) Beadarray package (Import, QC) Methylumi (Import, QC ,normalization, differential meth.)

METHYLATION MICROARRAYS

DIFFERENTIAL METHYLATION Bead Studio  Genome Studio

Methylation module

http://www.illumina.com/pages.ilmn?ID=196 Beta values: Hypermethylated β = I methylated /I methylated +I no_methylated 1 0.7

β Hypomethylated 0.3

0

METHYLATION MICROARRAYS

NORMALIZATION 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

METHYLATION MICROARRAYS

DIFFERENTIAL METHYLATION β s Wilcoxon rank-test ( UBio ) Limma (Pomelo) Permutations (Pomelo) FDR<0.05

+ Median β s class A Median β s class B Differentially methylated genes

ChIP on chip

ChIP on Chip

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

ChIP on Chip

Discover protein/DNA interactions!!

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

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

ChIP on Chip software

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)…)

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.

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) UBio training courses: See “Course on Introduction to Sequence Analysis”

Thanks !

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