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

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.

Methylation arrays

METHYLATION MICROARRAYS BeadArrays 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 Unos $225 per sample. Mas info Illumina tutorial. LumiCode permite identificar beads. EXPLICAR CASO-BEAD, extension marca la muestra Infinium HumanMethylation27 BeadChip

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 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.)

METHYLATION MICROARRAYS DIFFERENTIAL METHYLATION Bead Studio  Genome Studio Methylation module http://www.illumina.com/pages.ilmn?ID=196 Beta values: β= Imethylated/Imethylated+Ino_methylated Hypermethylated Hypomethylated 1 0.7 β 0.3

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

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

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 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

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

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)…)

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.

UBio training courses: See “Course on Introduction to Sequence Analysis” Adapted from Philip Groth

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