Download Other genomic arrays: Methylation, chIP on chip…

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 IllumiCodeTM
Intensity <-> Copy number
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
Infinium HumanMethylation27 BeadChip
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
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
Hypomethylated
β= Imethylated/Imethylated+Ino_methylated
1
0.7
β
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
βs
Wilcoxon rank-test (UBio)
Limma (Pomelo)
Permutations (Pomelo)
FDR<0.05 +
Median βs class A
Median βs 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(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 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”
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