Other genomic arrays: Methylation, chIP on chip

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Other genomic arrays Methylation, chIP on chip

• UBio Training Courses

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SNP-arrays and copy number

• Genotyping arrays can detect CNVs

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Copy numbers from SNP arrays
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Illumina SNP arrays Hybridization to Universal
IllumiCodeTM
Illumina uses the same technology for methylation
arrays (bi-sulfited nucleotides are like SNPs)
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Calculation of aCGH-like ratios
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Methylation arrays
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METHYLATION MICROARRAYS

• BeadArrays
• Until 12 samples per chip.
• 27,578 CpG loci, gt14.000 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

Infinium HumanMethylation27 BeadChip
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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

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METHYLATION MICROARRAYS
SOFTWARE
Bead Studio ? Genome Studio Methylation
module http//www.illumina.com/pages.ilmn?ID196
Lumi package (Import, background correction,
normalization) Beadarray package (Import,
QC) Methylumi (Import, QC ,normalization,
differential meth.)
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METHYLATION MICROARRAYS
DIFFERENTIAL METHYLATION
Bead Studio ? Genome Studio Methylation
module http//www.illumina.com/pages.ilmn?ID196
Beta values ß Imethylated/ImethylatedIno_meth
ylated
Hypermethylated
Hypomethylated
ß
0
1
0.7
0.3
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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

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METHYLATION MICROARRAYS
DIFFERENTIAL METHYLATION
Wilcoxon rank-test (UBio) Limma
(Pomelo) Permutations (Pomelo)
ßs
Median ßs class A Median ßs class B
FDRlt0.05

Differentially methylated genes
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ChIP on chip
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ChIP on Chip
We thank Chris Glass lab, UCSD, for the original
slide
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ChIP on Chip
Discover protein/DNA interactions!!
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ChIP on Chip software
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
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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
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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))
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CoCas http//www.ciml.univ-mrs.fr/software/cocas/i
ndex.html
Agilent platform Normalization QC Report Genome
Visualization Peak Finder
Benoukraf et al. Bioinformatics 2009.
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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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